Merged CT and MRI imaging of ACL footprints A novel in vitro technique for individualized footprint analysis

Merged CT and MRI imaging of ACL footprints A novel in vitro technique for individualized footprint analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Merged CT and MRI imaging of ACL footprints A novel in vitro technique for individualized footprint analysis Thomas Tampere, Hannes Vermue, Sam Pintelon, Nele Arnout, Thomas Luyckx, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6433199/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Purpose: In contrast to knee arthroplasty, use of robotics in sports medicine surgery is a great void. Currently used robotic interfaces in arthroplasty are based on preoperative computed-tomography (CT) scans or on image-free systems. To optimize anterior cruciate ligament (ACL) reconstruction to a more patient specific approach, aiming to improve clinical outcomes, implementation of computer assisted robotic surgery might be the next step to individualized ACL reconstruction. As sports surgery is most often soft tissue surgery, it might be beneficial to incorporate magnetic resonance imaging (MRI) imaging into preoperative planning, intra-operative guidance, and perioperative kinematic analysis. In a first step, goal of this study was to evaluate the in-vitro feasibility and precision of merging MRI and full leg CT images with patient specific kinematic data to analyse patient specific inter-footprint behaviour of the ACL. Methods: CT and (low- and high-quality) MRI scans were acquired from 20 cadaveric lower limbs and scans were subsequently rendered in 3D. Kinematic data were obtained during testing on a passive knee rig. On 4 knees, marker sets were applied prior to scanning. Surface matching using the iterative closest point (ICP) and marker-based matching of CT, MRI and kinematic data was acquired and matching accuracy of the combined models were evaluated with statistical inter-point deviation. Results: Matching low-quality and high-quality MRI scans on the full-leg CT images (with applied kinematics), the absolute mean difference between matching distances was found to be significantly differing (1.15 vs 0.89 mm / p=0.0000052) in favour of high-quality MRI scans. Furthermore, comparison of the percentage within 1 mm (48.33% vs 66.45%) and 2 mm (87.95% and 97.30%) showed both significant differences (p=0.0000152 and p=0.0014237). Adding markers significantly increased the precision of CT-MRI matching (p=0.0000001, 0.89 mm vs 0.62 mm). The percentage of CT-high quality MRI point distances within 1 mm and 2 mm was also significantly different with respective p-values of 0.0000152 and 0.0014237. Trimming the femur to the metaphysis also increased matching accuracy (p=0.00000). Conclusion: Merged CT and MRI imaging with incorporation of patient specific data might be a valuable first step towards a more individualized perioperative ACL footprint analysis and planning to achieve a more tailored and personalized (robotic assisted) ACL reconstruction in the future. Level of evidence: V knee anterior cruciate ligament reconstruction robotics imaging Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1. Introduction Intra-articular anterior cruciate ligament (ACL) reconstruction has evolved considerably over the past decades, due to expanding insights into ACL anatomy, biomechanics, and improvement of surgical techniques [ 2 , 11 , 20 , 29 ]. Evolution should be equal to improvement, but until today we are still struggling with unsatisfying outcome data as only 50–65% of athletes return to their pre-injury level of sports, reoperation rates up to 18.9–26.7%, and need for revision surgery in 3–14% [ 1 , 5 , 10 ]. These unsatisfying numbers led to an on-going search to improve clinical outcome, with today’s focus on anterolateral knee stability and pre- and postoperative neuromuscular control [ 5 , 8 , 32 , 33 ]. One must recognize however that, unless the shift of focus, there is still no consensus about optimal patient-specific tunnel placement for intra-articular ACL reconstruction. Anatomic reconstruction has been recommended as the method of choice to equal physiologic joint biomechanics in symptomatic knee instability. The concept of patient-specific anatomical ACL reconstruction is until today not clearly defined; tibial tunnel positioning is still generalized and based on anatomical (meniscal) landmarks and on the femoral side there has been an evolution from over-the-top fixation to a transtibial single-bundle high-in-the-notch to more horizontal transportal single and double femoral tunnel drilling to reconstruct the anteromedial (AM) and/or posterolateral (PL) bundle(s) [ 9 , 16 , 18 ]. It is known that imprecise tunnel placement in ACL reconstruction may cause graft impingement and graft stretching, resulting in persistent instability, failed restoration of normal knee kinematics and subsequent graft failure [ 31 , 36 ]. A recent systematic review of Vermeijden et al. showed that still up to 22% of failures is due to technical errors, with the majority caused by femoral tunnel malpositioning (63%), followed by tibial tunnel malpositioning in 7% [ 36 ]. The current trend is to aim for a mid-bundle position, which is propagated as isometric/anatomic; several methods have been advocated to achieve this position, including bony/soft tissue landmarks, intraoperative fluoroscopy with generalized reference values and computer-assisted navigation systems [ 16 , 25 , 30 ]. Due to the high variability of the ACL footprint centre locations, current techniques may fail in restoring native anatomy [ 12 ]. In contrast to the recent revolution in knee arthroplasty (KA) with introduction of robotic systems to enhance precision and achieve patient-specific implant positioning, navigation and computer-assisted-surgery in sports medicine are banned to the past due to inconclusive results concerning added value of these techniques [ 7 , 13 , 14 , 15 , 22 , 26 , 27 , 28 , 42 , 43 , 44 , 45 ]. In robotic KA, promising results are emerging, based on 2 different techniques, with preoperative planning grounded on computed tomography (CT) scanning and intra-operative image-free techniques (bone morphing) [ 17 , 37 , 41 ]. In contrast to robotic KA, preoperative planning, and intra-operative guidance in arthroscopic soft tissue reconstruction, however, should be based on detailed and personalized imaging of soft tissue insertions and individualized kinematic analysis. CT and magnetic resonance imaging (MRI) are todays used techniques for diagnostic purposes and preoperative planning. CT allows visualisation of distinct bone contours but lacks in imaging of soft tissues, whereas MRI can perform detailed soft tissue imaging but without highly detailed bony contours; Innocenti et al. recently showed that even segmental ACL footprint identification is reliable on MRI [ 19 ]. Preceded by robotic systems in knee arthroplasty, a future (r)evolution seems to lie in the use of robotics and artificial intelligence in sports surgery to reach isometric/anatomic tunnel placement to enhance graft ingrowth and maturation. A first step in this direction appears to be the introduction of augmented reality with intraoperative overlay of anatomical reference points and patient specific anatomy. To achieve patient-specific tunnel placement, this in-vitro study was conducted to evaluate, in a first step towards intra-operative computer-assisted guidance, the feasibility and accuracy of merging 2D CT and MRI data to a tailored 3D kinematic model enabling detailed perioperative analysis of individual footprint morphology and inter-footprint accordance during range of motion. 2. Materials and methods 2.1. Specimen preparation Twenty paired lower limbs (20 femurs / 20 tibias) were obtained from 6 male and 4 female fresh frozen human donors (mean age of 74, range ± 8.2), after approval of the study protocol by the local ethics committee of the Ghent University Hospital (EC/2014/0847). Cadavers were stored at -22°C prior to the experiment. The upper and lower body were separated at the level of the pelvis prior to helical CT scanning. For MRI scanning, left and right lower limbs were separated at the level of the symphysis pubis and the sacral bone. Inclusion criteria were verified on the MRI scans; only knees without evidence of prior injury or surgery and without significant osteoarthritis or ligamentous injuries were eligible for inclusion. 2.2. Imaging and 3D reconstruction Prior to imaging, on both the femur and tibia of 4 knees, a set of 4 optical ball markers (in-house 3D printed) coated with reflective ink, were firmly mounted into the cortical bone using unicortical bolts. Bony anatomy of the frozen specimens was visualized by a helical CT scan (Somatom®, Definition Flash, Siemens Medical Systems, Erlangen, Germany). Each specimen was scanned in full extension. Slice thickness was 0.5 mm, the image matrix was 512x512 pixels and the pixel size was 0.625 mm. To accurately visualize soft tissue structures, an additional 3T MRI scan of each knee was performed (Siemens Trio Tim®, Erlangen, Germany) with a standard knee coil after thawing. 12 MRI scans were high quality (high resolution – voxel size 0.52734375 x 0.52734375mm, slice thickness 0.6mm, 210mA, 120kV), 8 low in quality (low resolution – voxel size 0.87890625 x 0.87890625mm, slice thickness 0.6mm, 210mA, 120kV). The obtained CT and MRI images were then imported into Mimics 14.12® three-dimensional visualization software (Materialise N.V., Heverlee, Belgium) allowing to process 2D (CT and MRI) into 3D. 3D reconstructions were calculated from the selected masks of the native ACL, the distal femoral shaft, condyles, and the tibia plateau on MRI. The femoral and tibial footprints were respectively identified as the intersection between the native ACL and the medial wall of the lateral femoral condyle and the tibia. 2.3. Cutting and alignment guides To mount the specimens into the knee rig along their functional axis, cutting and alignment guides were designed, based on the CT 3D reconstructions. Anatomical landmarks (femoral hip centre, femoral knee centre, tibial knee centre and tibial ankle centre) were identified using 3-Matic® (Materialise N.V., Heverlee, Belgium) to determine the mechanical axes of the tibia and femur. It was paramount to reconstruct these axes to position both bones by means of the guides to assure that the applied loads were aligned with the mechanical axes. Coordinates of those landmarks and the 3D models were then imported into MATLAB® (Mathworks, Natick MA, USA) allowing customization of the tibial guides. Using SolidWorks® (Dassault Systèmes, ‘s Hertogenbosch, The Netherlands), guides were finished, and 3D printed (Ultimaker® 2, Geldermalsen, The Netherlands) [ 39 ]. 2.4. Knee rig Prior to mounting, specimens were thawed for 24h at a room temperature of 20°C and prepared by resecting the skin and subcutaneous fat. Meticulous prevention of drying out of the specimen was done throughout the experiment by using wet drapes. The tibia was severed at the level of the previously determined length for the guides and all soft tissues were cleared from the bone 5cm proximal to this level. The tibia was then rigidly fixed in a cylindrical container with a polyurethane resin using the alignment guides, which ensured a perfect alignment and fit of the tibia inside the container. Specimens were mounted in a custom-made automated knee rig for controlling flexion and extension with different loading conditions (varus/valgus, anterior/posterior, internal/external rotation). The set-up consisted of a framework in which the hemi-pelvis was rigidly fixed in neutral position by three screws (diameter 7 mm) in between 2 wooden bars. The container on the tibial side was slotted into the rotational torque engine, allowing application of rotational movement. The engine was mounted on a bar, allowing application of varus/valgus loads. In a final step, the femur diaphysis was attached in neutral position to the lever arm of the engine by two headless screws through the quadriceps muscle. (Fig. 1) 2.5. Motion capturing Through small incisions in the vastus medialis of the quadriceps two pins were drilled into the femoral shaft on a set distance to apply a Y-shaped marker set. The same procedure was used to fix the T-shaped marker set to the medial side of the proximal tibial shaft. Each marker set consisted of three makers mounted on an asymmetrical metal frame, allowing orientation of the specimen in 3D space. Four OptiTrack® cameras (NaturalPoint Inc, Corvallis OR, USA) were set up around the testing rig in a fashion that all markers were seen by all cameras during the entire range of motion (0-125°). Calibration was done using a calibration stick to identify 3D space of testing. A point cloud of at least one thousand points was necessary for a successful calibration. After successful calibration, fixed position of the cameras was meticulously assured. Precision of kinematic data, using this setup, was confirmed in previous work (deviation < 1.5° and < 1.5mm) [ 38 ]. (Fig. 2) 2.6. Testing sequence Testing sequence consisted of increasing degrees of flexion, with /without application of an anterior load to the tibia with increments of 3kg (0kg – 12kg) and with/without increasing internal/external tibial torque from 0Nm to 4Nm with increments of 1Nm. Furthermore, varus/valgus loads were applied with the same weight increments. Kinematic data were obtained in full extension, 30° of flexion, 60° of flexion, 90° degrees of flexion and at full flexion position for every condition. 2.7. Bone registration and matching of CT/MRI After completing all testing sequences, registration of the femur and tibia was done to determine both structures in 3D space. Marker sets remained in place during registration, so the exact position and orientation of the marker set relative to the bone could be determined. Therefore, the specimens were completely stripped from all soft tissues. Registration was done with a calibrated stylus with three reflective markers, by pointing in close contact as much of the bare bone surface as possible. After establishment of the point-cloud, it was matched to the 3D CT model of the bone by means of the ICP (iterative closest point) algorithm using MATLAB® software [ 3 ]. The ICP algorithm as proposed by Besl et al is an iterative descent procedure, which seeks to minimize the sum of the square distance between all points in a source and their closest points in a target model. This algorithm also provides a solution to various free-form surface matching problems and has been extensively used as an optimizing technique for rigid model-based registration in the medical field. This matching algorithm was then used to match the MRI based 3D model of the femur, tibia, and ACL footprints to the CT model with kinematic data, allowing visualisation of the ACL footprints during the range of motion with or without application of external loads. (Fig. 4) 2.8. Evaluation of matching accuracy To evaluate the accuracy of matching between the CT- and MRI-based models, the (mean) distance between CT and MRI surface vertices was measured. To improve accuracy, CT and MRI scans were cut at the same level based on bony landmarks and the distance between the surfaces was recalculated. For comparison, landmark-based matching was executed using a set of fiducial ball markers; landmark-based matching, generally known as a gold standard matching method for verifying accuracy, was executed. The landmarks (center points) of 4 CT-derived ball models were matched to their corresponding MRI-derived ball models; accordingly, 4 groups were analyzed for matching accuracy: CT and low-quality MRI, CT and high-quality MRI, CT and high-quality MRI based on applied marker sets and between trimmed CT and MRI. 2.9 Statistical analysis Statistical difference between matching of CT with low-quality and high-quality MRI and between the surface-to-surface method and the landmark-based matching (marker sets) was analyzed in terms of investigated matching deviations. Statistical analysis was performed using a student’s T-test. The level of significance (p) was set to 0.05. 4. Results 4.1. CT and MRI matching 4.1.1 Matching of CT and low-quality MRI In this subset of 8 knees (4 specimens), CT and low-quality MRI of 8 femurs and 8 tibias were matched according to the protocol. The mean distance between CT and MRI 3D reconstructions was 1.15 mm (STD ± 0.66 mm / minimum difference 0.05 mm / maximum 5.81 mm). 48.33% of matched points was found to be within 1 mm; 87.95% within 2 mm. Detailed values are given in table 1. Table 1 Values of CT and low-resolution MRI matching of 4 specimens. Mean distance between both scans is given with standard deviation, minimum and maximum values. Percentage of these values within 1 mm and 2 mm are given on the right. Specimen Side Bone Mean [mm] STD [mm] Minimum [mm] Maximum [mm] % within 1 mm % within 2 mm 1 R Femur 1,05 0,51 0,04 4,68 52,39% 94.54% R Tibia 0,95 0,45 0,08 2,95 61,42% 97.17% L Femur 1,12 0,49 0,07 3,62 44,55% 94.41% L Tibia 1,01 0,48 0,07 4,10 55,62% 55.62% 2 R Femur 1,32 0,65 0,05 4,51 36,22% 85.07% R Tibia 1,35 0,68 0,08 4,09 35,17% 83.14% L Femur 1,33 0,71 0,03 4,20 36,85% 83.33% L Tibia 1,14 0,57 0,06 4,71 47,45% 92.02% 3 R Femur 1,28 1,29 0,09 16,30 46,30% 89.90% R Tibia 1,33 0,96 0,12 19,84 38,03% 87.28% L Femur 1,14 0,66 0,02 3,96 49,06% 88.51% L Tibia 0,96 0,57 0,05 3,43 60,67% 93.47% 4 R Femur 1,17 0,73 0,03 4,08 51,04% 86.32% R Tibia 1,10 0,59 0,05 3,71 49,78% 91.32% L Femur 0,98 0,60 0,03 4,53 58,68% 92.85% L Tibia 1,10 0,58 0,01 4,31 50,09% 92.19% 1,15 0,66 0,05 5,81 48,33% 87.95% (R): right – (L): left – (mm): millimetre – (STD): standard deviation. 4.1.2 Matching of CT and high-quality MRI 4 specimens (8 knees) underwent high-quality MRI with an absolute mean difference of 0.89 mm (STD ± 0.46 mm / minimum difference 0.04 mm / maximum 3.94 mm) when compared to the CT images. 66.45% of matched points lies within 1mm difference and up to 97.30% within 2mm. Values for each knee are given in table 2. Table 2 Values of CT and high-resolution MRI matching of 4 specimens. Mean distance between both scans is given with standard deviation, minimum and maximum values. Percentage of these values within 1 mm and 2 mm are given on the right. Specimen Side Bone Mean [mm] STD [mm] Minimum [mm] Maximum [mm] % within 1 mm % within 2 mm 1 R Femur 0,95 0,54 0,04 5,20 62,07% 96.95% R Tibia 0,99 0,52 0,04 5,19 61,03% 95.58% L Femur 0,95 0,40 0,03 3,51 59,50% 98.35% L Tibia 0,85 0,49 0,02 5,05 71,90% 96.84% 2 R Femur 0,98 0,48 0,04 3,51 58,98% 96.42% R Tibia 0,81 0,39 0,03 3,35 73,64% 98.85% L Femur 1,05 0,49 0,04 3,24 52,04% 95.66% L Tibia 1,14 0,51 0,03 3,24 41,12% 94.75% 3 R Femur 0,92 0,41 0,05 3,65 63,39% 98.48% R Tibia 0,82 0,45 0,06 4,59 74,38% 97.75% L Femur 0,83 0,39 0,04 2,93 71,57% 98.78% L Tibia 0,86 0,44 0,03 3,59 69,94% 97.62% 4 R Femur 0,77 0,33 0,04 2,74 78,70% 99.62% R Tibia 0,94 0,53 0,06 4,13 62,16% 96.83% L Femur 0,69 0,49 0,01 4,51 81,40% 97.50% L Tibia 0,70 0,56 0,03 4,62 81,46% 96.75% 0,89 0,46 0,04 3,94 66,45% 97.30% (R): right – (L): left – (mm): millimetre – (STD): standard deviation. 4.1.3 Matching of CT and high-quality MRI with markers Before scanning, reflective markers were drilled into both the femur and tibia of 4 knees. Matching of the CT and high-quality MRI was done, based on matching of the respective markers. An absolute mean difference was seen of 0.62 mm (STD ± 0.45 mm / minimum difference 0.01 mm / maximum 4.27 mm). 84.64% of points was found within a range of 1 mm and 98.43% was found within 2 mm. Mean absolute difference between markers on the CT and MRI images was 0.27 mm (STD ± 0.13). (table 3) Table 3 Values of CT and high-resolution MRI matching of 4 specimens, based on markers. Mean distance between both scans is given with standard deviation, minimum and maximum values. Percentage of these values within 1 mm and 2 mm are represented. On the right, the absolute distance between matched markers was given. Difference between CT and MRI Difference of Markers Specimen Side Bone Mean [mm] STD [mm] Minimum [mm] Maximum [mm] % Within 1 mm % within 2 mm Mean [mm] STD [mm] 1 R Femur 0,69 0,59 0,01 6,71 81,92% 97.02% 0,28 0,08 R Tibia 0,62 0,41 0,01 3,29 82,72% 99.14% 0,32 0,24 L Femur 0,60 0,37 0,00 2,53 86,56% 99.70% 0,40 0,15 L Tibia 0,68 0,45 0,03 3,72 79,63% 98.73% 0,42 0,20 2 R Femur 0,61 0,43 0,02 5,23 86,78% 98.58% 0,15 0,05 R Tibia 0,62 0,44 0,01 3,70 85,16% 98.50% 0,14 0,07 L Femur 0,58 0,43 0,01 5,09 88,06% 98.27% 0,12 0,02 L Tibia 0,59 0,49 0,01 3,92 86,27% 97.53% 0,31 0,21 0,62 0,45 0,01 4,27 84,64% 98.43% 0,27 0,13 (R): right – (L): left – (mm): millimetre – (STD): standard deviation. 4.1.4 Comparison between CT matching with low, high quality and marker-based MRI After matching low quality and high-quality MRI scans on the CT images, the absolute mean difference was found to be significantly differing (1.15 vs 0.89 mm / p = 0.0000052). Furthermore, comparison of the percentage within 1 mm (48.33% vs 66.45%) and 2 mm (87.95% and 97.30%) showed both significant differences (p = 0.0000152 and p = 0.0014237). Adding markers significantly increased the precision of CT-MRI matching (p = 0.0000001, 0.89 mm vs 0.62 mm). The percentage of CT-high quality MRI point distances within 1mm and 2mm was also significantly different with respective p-values of 0.0000152 and 0.0014237. 4.1.5 Comparison between non-trimmed and trimmed MRI images for CT-MRI matching After trimming the diaphyseal part of the MRI, CT-MRI matching was repeated. Absolute mean differences significantly (p = 0.000000) dropped from 1.14 mm (non-trimmed / STD ± 0.92 / minimum 0.03 mm / maximum 11.00 mm) to 0.89 mm (trimmed / STD ± 0.46 / minimum 0.04 mm / maximum 3.94 mm). There was a significant increase of CT-MRI matching within 1 mm (55.07% vs 66.45%, p = 0.000000) and 2 mm (90.60% vs 97.30%, p = 0.000000). (table 4) Table 4 Values of CT and MRI matching of 4 specimens, before and after trimming the MRI images to the metaphyseal part. Mean distance between both scans is given with standard deviation, minimum and maximum values. Percentage of these values within 1 mm and 2 mm are represented. On the right, p-values are given after comparing both situations. Level of significance was set at p < 0.05. Specimen Side Bone Trim Mean [mm] STD [mm] Minimum [mm] Maximum [mm] % Within 1 mm % Within 2 mm p-value 1 R Femur Yes 0,95 0,54 0,04 5,20 62,07% 96,95% 0,0000000 No 1,02 0,58 0,02 9,29 57,67% 95,28% R Tibia Yes 0,99 0,52 0,04 5,19 61,03% 95,58% 0,0000000 No 1,11 0,60 0,02 9,32 50,18% 93,47% L Femur Yes 0,95 0,40 0,03 3,51 59,50% 98,35% 0,0000000 No 1,34 1,56 0,06 15,92 49,20% 91,43% L Tibia Yes 0,85 0,49 0,02 5,05 71,90% 96,84% 0,0000000 No 0,94 0,56 0,06 9,87 65,29% 95,61% 2 R Femur Yes 0,98 0,48 0,04 3,51 58,98% 96,42% 0,0000000 No 1,20 0,69 0,02 8,83 48,34% 86,32% R Tibia Yes 0,81 0,39 0,03 3,35 73,64% 98,85% 0,0000000 No 1,15 0,73 0,03 7,00 53,95% 86,37% L Femur Yes 1,05 0,49 0,04 3,24 52,04% 95,66% 0,0000000 No 1,32 0,71 0,04 5,21 39,55% 82,37% L Tibia Yes 1,14 0,51 0,03 3,24 41,12% 94,75% 0,0000000 No 1,23 0,77 0,10 6,72 48,29% 85,50% 3 R Femur Yes 0,92 0,41 0,05 3,65 63,39% 98,48% 0,0000000 No 1,04 0,54 0,02 8,51 54,50% 94,74% R Tibia Yes 0,82 0,45 0,06 4,59 74,38% 97,75% 0,0000000 No 1,41 1,87 0,03 15,68 57,64% 88,29% L Femur Yes 0,83 0,39 0,04 2,93 71,57% 98,78% 0,0000000 No 1,13 1,42 0,02 15,18 62,65% 93,91% L Tibia Yes 0,86 0,44 0,03 3,59 69,94% 97,62% 0,0000000 No 1,32 1,76 0,02 16,29 58,97% 89,72% 4 R Femur Yes 0,77 0,33 0,04 2,74 78,70% 99,62% 0,0000000 No 1,03 0,79 0,02 15,37 59,97% 93,79% R Tibia Yes 0,94 0,53 0,06 4,13 62,16% 96,83% 0,0000000 No 1,33 0,83 0,04 15,01 38,69% 84,75% L Femur Yes 0,69 0,49 0,01 4,51 81,40% 97,50% 0,0000000 No 0,97 0,66 0,03 9,81 61,65% 91,78% L Tibia Yes 0,70 0,56 0,03 4,62 81,46% 96,75% 0,0000000 No 0,77 0,58 0,02 7,98 74,66% 96,31% Yes 0,89 0,46 0,04 3,94 66,45% 97,30% No 1,14 0,92 0,03 11,00 55,07% 90,60% (R): right – (L): left – (mm): millimetre – (STD): standard deviation. 4.2 Kinematic analysis Following CT-MRI matching, the ACL and posterior cruciate ligament (PCL) were visualized and projected on the CT scans (Fig. 2) with application of the kinematic data, received from the OptiTrack® system, allowing for evaluation of relative individual footprint behaviour during flexion-extension cycles, internal and external torque manoeuvres and during varus/valgus loading. (Fig. 3) Discussion The main findings of this study highlight the high precision of patient-specific CT and MRI merging, allowing detailed visualization and assessment of kinematic behavior of cruciate ligament footprints. It is further noted that optimal matching is achieved, based on high-quality MRI with rigidly fixed markers, with distances between both scans within 1 mm for up to 84.64% and within 2 mm for up to 98.43% of measurements after fusion. This is one of the first studies with foremost the largest series substantiating the feasibility of precise matching of soft tissue MRI images with acquired full leg CT scans and corresponding kinematic data. Today’s used robotic systems in arthroplasty are either based on image-free systems or on preoperative CT-scan analysis of bony contours and reference points [ 17 , 37 , 41 ]. For extrapolation of these techniques to sports surgery, it might however be more expedient to incorporate patient specific soft tissue anatomy into preoperative planning and real-time guidance during arthroscopic ligament reconstruction. Attempts in the past to implement navigation and, in extension, computer assistance in ACL surgery could not, despite the increased precision of tunnel placement, provide added value in terms of clinical outcomes to date [ 7 , 13 , 14 , 15 , 22 , 26 , 27 , 28 , 42 , 43 , 44 , 45 ]. Augmented reality overlay techniques are currently making their way into arthroplasty but may also find their entrance in arthroscopic soft tissue reconstruction in the future [ 22 ]. However, to extract added value from this, the identification of patient-specific anatomy and kinematics is the first step in a more tailored reconstruction, necessitating an individualized imaging modality that embraces high precision with practical implementation in the clinical setting. According to Victor et al., bony landmarks around the knee can accurately be identified on CT scan in a reproducible manner with low intra- and inter-observer variability [ 40 ]. Furthermore, Van den Broeck et al. described in 2014 in a technical note, their segmentation accuracy of long bones, using CT, MRI, and optical scanning. They found that both CT an MRI imaging is accurate for 3D bone reconstructions within 0.5 mm. On average, CT segmentation resulted in a slight overestimation compared to the actual dimensions of the bone whereas MRI segmentation induced a small underestimation of the bone’s geometry [ 35 ]. In terms of accuracy, Campanelli et al showed in 2019 that morphological errors associated with 3D bone models of the femur and tibia generated from CT scans must be considered. They also showed that CT bone models slightly overestimate the bone morphology while the MRI bone models substantially underestimate it. Furthermore, it was shown that the popular ICP registration method, as used in this study, underestimates the morphological errors when compared to registration with fiducial markers; this is in line with the findings in our study, as the use of markers improved accuracy of matching. It was concluded that CT-based bone models could best be used for applications requiring submillimetre accuracy, while in MRI-based bone models, 1 mm of (in)accuracy must be accepted [ 6 ]. To visualize soft tissues structures, such as ligaments, MRI is favoured. Innocenti et al. described a reproducible and accurate method with general variability of less than 1mm to identify femoral and tibial ACL footprints on MRI [ 19 ]. In 2019, Raposo et al., were one of the first describing their in-vitro methodology for video-based computer navigation in knee arthroscopy for patient-specific ACL reconstruction with the use of preoperative planning based on MRI images, however no kinematic analysis was incorporated [ 28 ]. In 2008, Lee et al. published as one of the first on specific measurements of anatomical accuracy of 3D model matching, when a 3D CT-derived model is matched to a 3D MRI-derived model, in terms of both practical anatomical aspects and the numerical error-distance measurement of matching deviation. Besides the 2D contour-based measurements, also a global 3D contour-based measurement of matching deviation was performed with both surface-to-surface matching (using ICP) and marker-based matching (3 markers). An average difference was seen of 0.7 mm (STD ± 0.1) and 1.1 mm (STD ± 0.3). This contrasts with our findings as it was shown on our large series that marker-based matching significantly decreased matching differences from 0.89 to 0.62 mm (p = 0.0000001). A possible explanation might be that accuracy increased in our study by using an extra marker on both the femur and the tibia. Furthermore, Lee et al. increased their matching accuracy by analysing differences at the level of the middle femoral shaft, compared to local 2D contour-based measurements of matching deviation in the femoral condyle (both marker-based) whereas in our study it was shown that accuracy increased after trimming the femoral diaphysis [ 24 ]. Since the first description of application of augmented reality (AR) to orthopaedic surgery by Blackwell et al. in 1998 with prediction that this technology would become commonplace in orthopaedics, to date, AR still has very few applications in orthopaedic practice [ 4 ]. In the recent review of Laverdière et al, only four publications were identified describing application of AR in sports medicine and arthroscopy, with major focus on educational purposes. The use of AR in this field is still in its infancy, but more recent publications confirm the potential of this technology to radically improve surgical practice [ 22 ]. The findings of this study may contribute to further development of overlay technologies with intra-operative visualization of patient-specific anatomy and kinematics toward improved surgical outcomes. In a Cochrane review on computer-assisted surgery for knee ligament reconstruction in 2014, Eggerding et al. were unable to refute a favourable effect of computer assisted surgery for cruciate ligament reconstructions of the knee comparted with conventional reconstructions, neither that there was an improvement in outcome. Only five studies were included in this review with a large variety in used computer-assisted techniques [ 13 ]. The same conclusions were drawn in the earlier meta-analysis and systematic review of randomized controlled trials by Cheng et al [ 7 ]. A later historical review of navigation systems in ACL surgery by Zaffagnini et al. summarized the use of computer assisted surgery as technical assistance for tunnel placement and for kinematic evaluation of the ACL, showing a wide range of applications with inconclusive findings regarding superiority of one system over another and the remark that navigations systems remain invasive with added potential risk [ 44 ]. The most recent systematic review of Yavari et al. confirmed again that clinical outcomes do not differ between technology-assisted surgery and conventional surgery, beside the fact that these technologies are more expensive and time consuming. It was confirmed that tunnels can be more accurately located in radiologically ideal places by using technology, but as stated in our current study, anatomical placement is still undetermined because of variability and inaccuracy of the evaluation systems utilized [ 42 ]. The results of this study should be interpreted in the light of some limitations. A first limitation is the relatively high mean age of the subjects, however, factors influencing segmentation quality and normal kinematics such as previous knee surgery, signs of trauma, end-stage osteoarthritis and ligamentous injuries were excluded and verified on MRI. A second limitation is the relatively small number of specimens, although, to the best of our knowledge, it constitutes the largest series in literature. Third, the most significant limitation lies in the potential human bias in the manual, labour-intensive segmentation process. To our knowledge, automated soft tissue identification and segmentation is not available, but might be necessary as this study protocol signifies a relatively challenging extrapolation to the clinical field. Furthermore, extrapolation of this method and finding to clinical practice might be challenging. Marker based motion capture analysis is a common approach to conduct 3D motion analytics. In this study, we used firmly fixed markers, anchored in the femur and tibia. In vivo analysis, however, necessitates attachment of markers to the skin of human subjects on top of bony landmarks. Because the skin with the attached markers is moving relative to the anatomical landmarks during motion, measurement errors, the so-called soft tissue artifact (STA), are unavoidable. Algorithms were although developed to limit errors due to soft tissue motion and increase the measurement accuracy [ 21 , 23 , 34 ]. To conclude, it can be stated that merged CT and MRI imaging with incorporation of patient specific data might be a valuable first step towards a more individualized perioperative ACL footprint analysis and planning to achieve a more tailored and personalized (robotic assisted) ACL reconstruction in the future. Abbreviations CT: Computed Tomography ACL: Anterior Cruciate Ligament MRI: Magnetic Resonance Imaging 3D: Three Dimensional ICP: Iterative Closest Point Mm: Millimetre AM: Anteromedial PL: posterolateral KA: Knee Arthroplasty mA: Milliampere kV: Kilovolt Kg: kilogram Nm: Newton metre R: Right L: Left STD: Standard Deviation Declarations Conflict of interest: no conflict of interest Funding: no funding was received for this study Ethical approval: Ghent University Hospital (EC/2014/0847) Informed consent: not applicable Acknowledgements: / Authors’ contribution: TT, VH, PS, CA have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data. TT, VH, PS, AN, LT, VJ, CA have been involved in drafting the manuscript or revising it critically for important intellectual content. TT, VH, PS, AN, LT, VJ, CA have given final approval of the version to be published. TT, VH, PS, AN, LT, VJ, CA agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. Social Media Handles * Instagram - tamperethomas * Twitter - @ttampere * Facebook - Thomas Tampere * LinkedIn - Thomas Tampere References Ardern CL, Taylor NF, Feller JA, Webster KE (2014) Fifty-five per cent return to competitive sport following anterior cruciate ligament reconstruction: an updated systematic review and meta-analysis including aspects of physical functioning and contextual factors. Br J Sports Med 48:1543–1552 Baker HP, Bowen E, Sheean A, Bedi A (2023) New considerations in ACL surgery: when is anatomic reconstruction not enough? J Bone Joint Surg Am 105(13):1026–1035 Besl PJ, McKay ND (1992) A method for registration of 3-D shapes. 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A systematic review and a window on future possibilities. Bone Joint J 101:1479–1488 Leardini A, Chiari L, Della Croce U, Cappozzo A (2005) Human movement analysis using stereophotogrammetry: part 3. Soft tissue artifact assessment and compensation. Gait Posture 21:212–225 Lee YS, Seon JK, Shin VI, Kim G, Jeon M (2008) Anatomical evaluation of CT-MRI combined femoral model. Biomed Eng Online. 10.1186/1475-925X-7-6 Oshima T, Nakase J, Ohashi Y, Shimozaki K, Asai K, Tsuchiya H (2020) Intraoperative fluoroscopy shows better agreement and interchangeability in tibial tunnel location during single bundle anterior cruciate ligament reconstruction with postoperative three-dimensional computed tomography compared with an intraoperative image-free navigation system. Knee 27(3):809–816 Park SH, Moon SW, Lee BH, Park S, Kim Y, Lee D, Lim S, Wang JH (2016) Arthroscopically blind anatomical anterior cruciate ligament reconstruction using only navigation guidance: a cadaveric study. Knee 23:813–819 Plaweski S, Schlatterer B, Saragaglia D (2015) The role of computer assisted navigation in revision surgery for failed anterior cruciate ligament reconstruction of the knee: a continuous series of 52 cases. Orthopaedics & Traumatology: Surgery & Research 101:S227-S231 Raposo C, Barreto J, Sousa C, Ribeiro L, Melo R, Oliveira JP, Marques P, Fonseca F, Barrett D (2019) Video-based computer navigation in knee arthroscopy for patient-specific ACL reconstruction. Int J Comput Assist Radiol Surg 14:1529–1539 Runer A, Keeling L, Wagala N, Nugraha H, Özbek EA, Hughes JD, Musahl V (2023) Current trends in graft choice for anterior cruciate ligament reconstruction – part I: anatomy, biomechanics, graft incorporation and fixation. J Exp Orthop 10(1):37–47 Seo S, Kim C, Lee C, Park D, Kwon Y, Kim O, Kim C (2020) Intraoperative fluoroscopy reduces the variability in femoral tunnel placement during single-bundle anterior cruciate ligament reconstruction. Knee Surg Sports Traumatol Arthrosc 28(2):629–636 Shen X, Qin Y, Zuo J, Liu T, Xiao J (2021) A systematic review of risk factors for anterior cruciate ligament reconstruction failure. Int J Sports Med 42(8):682–693 Sonnery-Cottet B, Dutra Vieira T, Ouanezar H (2019) Anterolateral ligament of the knee: diagnosis indications, technique, outcomes. Arthroscopy 35(2):302–303 Sonnery-Cottet B, Saithna A, Quelard B, Daggett M, Borade A, Ouanezar Hervé, Thaunat M, Blakeney WG (2019) Arthrogenic muscle inhibition after ACL reconstruction: a scoping review of the efficacy of interventions. Br J Sports Med 53(5):289–298 Suzuki Y, Inoue T, Nomura T (2018) A simple algorithm for assimilating marker-based motion capture data during periodic human movement into models of multi-rigid-body systems. Front Bioeng Biotechnol. 10.2289/fbioe.2018.00141 Van den Broeck J, Vereecke E, Wirix-Speetjens R, Vander Sloten J (2014) Segmentation accuracy of long bones. Med Eng Phys 36:949–953 Vermeijden HD, Yang AX, van der List JP, DiFelice GS, Rademaker MV, Kerkhoffs GM (2020) Trauma and femoral tunnel position are the most common failure modes of anterior cruciate ligament reconstruction: a systematic review. Knee Surg Sports Traumatol Arthrosc 28(11):3666–3675 Vermue H, Batailler C, Monk P, Haddad F, Luyckx T, Lustig S (2023) The evolution of robotic systems for total knee arthroplasty, each system must be assessed for its own value: a systematic review of clinical evidence and meta-analysis. Arth Orthop Trauma Surg 143(6):3369–3381 Verstraete M, Arnout N, De Baets P, Vancouillie T, Van Hoof T, Victor J (2017) Real-time in vitro evaluation of knee kinematics using enriched CT data. Orthop Proc;99-B:48 Verstraete M, Willemot L, Van Onsem S, Stevens C, Arnout N, Victor J (2016) 3D printed guides for controlled alignment in biomechanics tests. J Biomech 49(3):484–487 Victor J, Van Doninck D, Labey L, Innocenti B, Parizel P, Bellemans J (2009) How precise can bony landmarks be determined on a CT scan of the knee? Knee 16(5):358–365 Yang HY, Seon JK (2023) The landscape of surgical robotics in orthopedics surgery. Biomed Eng Lett 13(4):537–542 Yavari E, Moosa S, Cohen D, Cantu-Morales D, Nagai K, de Hoshino Y SA D (2023) Technology-assisted anterior cruciate ligament reconstruction improves tunnel placement but leads to no change in clinical outcomes: a systematic review and meta-analysis. Knee Surg Sports Traumatol Arthrosc 31:4299–4311 Young SW, Safran MR, Clatworthy M (2013) Applications of computer navigation in sports medicine knee surgery: an evidence-based review. Curr Rev Musculoskelet Med 6:150–157 Zaffagnini S, Urrizola F, Signorelli C, Grassi A, Di Sarsina TR, Lucidi GA, Marcheggiani Muccioli GM, Bonanzinga T, Marcacci M (2016) Current use of navigation in ACL surgery: a historical review. Knee Surg Sports Traumatol Arthrosc 24:3396–3409 Zhu W, Lu W, Han Y, Hui S, Ou Y, Peng L, Fen W, Wang D, Zhang L, Zeng Y (2013) Application of a computerized navigation technique to assist arthroscopic anterior cruciate ligament reconstruction. Int Orthop (SICOT) 37:233–238 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6433199","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":454100438,"identity":"181c15b6-1832-447f-b050-40c5e4281c57","order_by":0,"name":"Thomas Tampere","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAuklEQVRIiWNgGAWjYDACCTBpQ4IOHoiWNAmStRwmQYu9dPPRDT/+nK/jn3b24OOKCgZ5/gZCtsgcS7vZ23ZbQuJ2XrLhmTMMhjMOEHRYjtkN3obbEgy3c8wkG9sYEgwIOQyk5eafP+ck5G/nmP9s/Eeklts8bAckDIC2MDY2EKPlRlrabdm2ZMmNQL9INhyTIOwX9hnJx26++WPHL3c79+DHhhobwiGGbCGIICF+YFpGwSgYBaNgFGACABuKPcjAurNTAAAAAElFTkSuQmCC","orcid":"","institution":"Ghent University Hospital - Ghent University","correspondingAuthor":true,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Tampere","suffix":""},{"id":454100439,"identity":"066bfa60-1652-4318-9846-258e645158bf","order_by":1,"name":"Hannes Vermue","email":"","orcid":"","institution":"Ghent University Hospital - Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Hannes","middleName":"","lastName":"Vermue","suffix":""},{"id":454100444,"identity":"0b63a251-5634-40f0-b9b9-4f9074fab66c","order_by":2,"name":"Sam Pintelon","email":"","orcid":"","institution":"Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Sam","middleName":"","lastName":"Pintelon","suffix":""},{"id":454100445,"identity":"a8715ceb-dff1-493d-8e8d-e19b8a978a14","order_by":3,"name":"Nele Arnout","email":"","orcid":"","institution":"Ghent University Hospital - Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Nele","middleName":"","lastName":"Arnout","suffix":""},{"id":454100446,"identity":"45a429e3-436b-4896-9e4a-9dea4ad9e55e","order_by":4,"name":"Thomas Luyckx","email":"","orcid":"","institution":"AZ Delta","correspondingAuthor":false,"prefix":"","firstName":"Thomas","middleName":"","lastName":"Luyckx","suffix":""},{"id":454100447,"identity":"f3659cfd-0475-4df8-bb81-037b228b9a95","order_by":5,"name":"Jan Victor","email":"","orcid":"","institution":"Ghent University Hospital - Ghent University","correspondingAuthor":false,"prefix":"","firstName":"Jan","middleName":"","lastName":"Victor","suffix":""},{"id":454100448,"identity":"bde0fd26-7e63-46f9-9aa6-9968c469e3ae","order_by":6,"name":"Amelie -Chevalier","email":"","orcid":"","institution":"Antwerp University","correspondingAuthor":false,"prefix":"","firstName":"Amelie","middleName":"","lastName":"-Chevalier","suffix":""}],"badges":[],"createdAt":"2025-04-12 08:23:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6433199/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6433199/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":82711184,"identity":"09ae5c30-e04d-4c95-901a-690622b0b222","added_by":"auto","created_at":"2025-05-14 11:32:14","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":47214,"visible":true,"origin":"","legend":"\u003cp\u003e3D model of the custom-made automated knee rig with tibial engine to control rotation and femoral engine to control flexion-extension. Varus-valgus is applied by adding weights to respectively the medial and lateral side of the tibial anchoring point.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6433199/v1/27fdd715d81853c4ce076519.jpg"},{"id":82712991,"identity":"55916f6e-6a05-4049-9ded-369ad3760685","added_by":"auto","created_at":"2025-05-14 11:48:14","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":58562,"visible":true,"origin":"","legend":"\u003cp\u003ePicture of a mounted specimen into the knee rig with the OptiTrack\u003csup\u003e®\u003c/sup\u003e system. The hemipelvis is fixed in between 2 wooden bars, the tibia is seated into a rotational torque engine. The femur is attached to a lever arm to allow controlled flexion-extension.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6433199/v1/2147d7fafe0c58e1f76f4913.jpg"},{"id":82711186,"identity":"56c09d7b-7799-4800-b269-2dae152ab7fd","added_by":"auto","created_at":"2025-05-14 11:32:14","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":72726,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4: Representation of MRI and CT scan with markers and merged images. Representation of matching high/low quality MRI with CT scan images.\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6433199/v1/3438f5c4600dbf0d3cca3c34.jpg"},{"id":82712447,"identity":"5e511301-aa74-4969-9cb3-35cef6de52e6","added_by":"auto","created_at":"2025-05-14 11:40:14","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":74265,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 2: 3D images are given with projection of MRI image of the distal femur, proximal tibia, ACL, and PCL on the 3D CT images.\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6433199/v1/45292a9341bc96fb25cc3fe6.jpg"},{"id":82711188,"identity":"7fe81b61-c326-432a-b451-32b5fe232f37","added_by":"auto","created_at":"2025-05-14 11:32:14","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":59088,"visible":true,"origin":"","legend":"\u003cp\u003eFigure 4: 3D representation of merged CT-MRI scan during range of motion from 0-120° of flexion with visualization of merged ACL and PCL femoral and tibial footprints.\u003c/p\u003e","description":"","filename":"6.jpg","url":"https://assets-eu.researchsquare.com/files/rs-6433199/v1/a12a701afa320fabd63892e6.jpg"},{"id":100373306,"identity":"d218c049-9b0d-434a-8ea3-c6b8326b532e","added_by":"auto","created_at":"2026-01-16 08:14:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1741272,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6433199/v1/2b47e5a4-4fcf-4ad8-991a-6fae0dd86da7.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Merged CT and MRI imaging of ACL footprints A novel in vitro technique for individualized footprint analysis","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIntra-articular anterior cruciate ligament (ACL) reconstruction has evolved considerably over the past decades, due to expanding insights into ACL anatomy, biomechanics, and improvement of surgical techniques [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Evolution should be equal to improvement, but until today we are still struggling with unsatisfying outcome data as only 50\u0026ndash;65% of athletes return to their pre-injury level of sports, reoperation rates up to 18.9\u0026ndash;26.7%, and need for revision surgery in 3\u0026ndash;14% [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese unsatisfying numbers led to an on-going search to improve clinical outcome, with today\u0026rsquo;s focus on anterolateral knee stability and pre- and postoperative neuromuscular control [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. One must recognize however that, unless the shift of focus, there is still no consensus about optimal patient-specific tunnel placement for intra-articular ACL reconstruction. Anatomic reconstruction has been recommended as the method of choice to equal physiologic joint biomechanics in symptomatic knee instability. The concept of patient-specific anatomical ACL reconstruction is until today not clearly defined; tibial tunnel positioning is still generalized and based on anatomical (meniscal) landmarks and on the femoral side there has been an evolution from over-the-top fixation to a transtibial single-bundle high-in-the-notch to more horizontal transportal single and double femoral tunnel drilling to reconstruct the anteromedial (AM) and/or posterolateral (PL) bundle(s) [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is known that imprecise tunnel placement in ACL reconstruction may cause graft impingement and graft stretching, resulting in persistent instability, failed restoration of normal knee kinematics and subsequent graft failure [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. A recent systematic review of Vermeijden et al. showed that still up to 22% of failures is due to technical errors, with the majority caused by femoral tunnel malpositioning (63%), followed by tibial tunnel malpositioning in 7% [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. The current trend is to aim for a mid-bundle position, which is propagated as isometric/anatomic; several methods have been advocated to achieve this position, including bony/soft tissue landmarks, intraoperative fluoroscopy with generalized reference values and computer-assisted navigation systems [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Due to the high variability of the ACL footprint centre locations, current techniques may fail in restoring native anatomy [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In contrast to the recent revolution in knee arthroplasty (KA) with introduction of robotic systems to enhance precision and achieve patient-specific implant positioning, navigation and computer-assisted-surgery in sports medicine are banned to the past due to inconclusive results concerning added value of these techniques [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn robotic KA, promising results are emerging, based on 2 different techniques, with preoperative planning grounded on computed tomography (CT) scanning and intra-operative image-free techniques (bone morphing) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. In contrast to robotic KA, preoperative planning, and intra-operative guidance in arthroscopic soft tissue reconstruction, however, should be based on detailed and personalized imaging of soft tissue insertions and individualized kinematic analysis. CT and magnetic resonance imaging (MRI) are todays used techniques for diagnostic purposes and preoperative planning. CT allows visualisation of distinct bone contours but lacks in imaging of soft tissues, whereas MRI can perform detailed soft tissue imaging but without highly detailed bony contours; Innocenti et al. recently showed that even segmental ACL footprint identification is reliable on MRI [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Preceded by robotic systems in knee arthroplasty, a future (r)evolution seems to lie in the use of robotics and artificial intelligence in sports surgery to reach isometric/anatomic tunnel placement to enhance graft ingrowth and maturation. A first step in this direction appears to be the introduction of augmented reality with intraoperative overlay of anatomical reference points and patient specific anatomy.\u003c/p\u003e \u003cp\u003eTo achieve patient-specific tunnel placement, this in-vitro study was conducted to evaluate, in a first step towards intra-operative computer-assisted guidance, the feasibility and accuracy of merging 2D CT and MRI data to a tailored 3D kinematic model enabling detailed perioperative analysis of individual footprint morphology and inter-footprint accordance during range of motion.\u003c/p\u003e"},{"header":"2. Materials and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Specimen preparation\u003c/h2\u003e \u003cp\u003eTwenty paired lower limbs (20 femurs / 20 tibias) were obtained from 6 male and 4 female fresh frozen human donors (mean age of 74, range\u0026thinsp;\u0026plusmn;\u0026thinsp;8.2), after approval of the study protocol by the local ethics committee of the Ghent University Hospital (EC/2014/0847). Cadavers were stored at -22\u0026deg;C prior to the experiment. The upper and lower body were separated at the level of the pelvis prior to helical CT scanning. For MRI scanning, left and right lower limbs were separated at the level of the symphysis pubis and the sacral bone. Inclusion criteria were verified on the MRI scans; only knees without evidence of prior injury or surgery and without significant osteoarthritis or ligamentous injuries were eligible for inclusion.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Imaging and 3D reconstruction\u003c/h2\u003e \u003cp\u003ePrior to imaging, on both the femur and tibia of 4 knees, a set of 4 optical ball markers (in-house 3D printed) coated with reflective ink, were firmly mounted into the cortical bone using unicortical bolts. Bony anatomy of the frozen specimens was visualized by a helical CT scan (Somatom\u0026reg;, Definition Flash, Siemens Medical Systems, Erlangen, Germany). Each specimen was scanned in full extension. Slice thickness was 0.5 mm, the image matrix was 512x512 pixels and the pixel size was 0.625 mm. To accurately visualize soft tissue structures, an additional 3T MRI scan of each knee was performed (Siemens Trio Tim\u0026reg;, Erlangen, Germany) with a standard knee coil after thawing. 12 MRI scans were high quality (high resolution \u0026ndash; voxel size 0.52734375 x 0.52734375mm, slice thickness 0.6mm, 210mA, 120kV), 8 low in quality (low resolution \u0026ndash; voxel size 0.87890625 x 0.87890625mm, slice thickness 0.6mm, 210mA, 120kV). The obtained CT and MRI images were then imported into Mimics 14.12\u0026reg; three-dimensional visualization software (Materialise N.V., Heverlee, Belgium) allowing to process 2D (CT and MRI) into 3D. 3D reconstructions were calculated from the selected masks of the native ACL, the distal femoral shaft, condyles, and the tibia plateau on MRI. The femoral and tibial footprints were respectively identified as the intersection between the native ACL and the medial wall of the lateral femoral condyle and the tibia.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Cutting and alignment guides\u003c/h2\u003e \u003cp\u003eTo mount the specimens into the knee rig along their functional axis, cutting and alignment guides were designed, based on the CT 3D reconstructions. Anatomical landmarks (femoral hip centre, femoral knee centre, tibial knee centre and tibial ankle centre) were identified using 3-Matic\u0026reg; (Materialise N.V., Heverlee, Belgium) to determine the mechanical axes of the tibia and femur. It was paramount to reconstruct these axes to position both bones by means of the guides to assure that the applied loads were aligned with the mechanical axes. Coordinates of those landmarks and the 3D models were then imported into MATLAB\u0026reg; (Mathworks, Natick MA, USA) allowing customization of the tibial guides. Using SolidWorks\u0026reg; (Dassault Syst\u0026egrave;mes, \u0026lsquo;s Hertogenbosch, The Netherlands), guides were finished, and 3D printed (Ultimaker\u0026reg; 2, Geldermalsen, The Netherlands) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Knee rig\u003c/h2\u003e \u003cp\u003ePrior to mounting, specimens were thawed for 24h at a room temperature of 20\u0026deg;C and prepared by resecting the skin and subcutaneous fat. Meticulous prevention of drying out of the specimen was done throughout the experiment by using wet drapes. The tibia was severed at the level of the previously determined length for the guides and all soft tissues were cleared from the bone 5cm proximal to this level. The tibia was then rigidly fixed in a cylindrical container with a polyurethane resin using the alignment guides, which ensured a perfect alignment and fit of the tibia inside the container. Specimens were mounted in a custom-made automated knee rig for controlling flexion and extension with different loading conditions (varus/valgus, anterior/posterior, internal/external rotation). The set-up consisted of a framework in which the hemi-pelvis was rigidly fixed in neutral position by three screws (diameter 7 mm) in between 2 wooden bars. The container on the tibial side was slotted into the rotational torque engine, allowing application of rotational movement. The engine was mounted on a bar, allowing application of varus/valgus loads. In a final step, the femur diaphysis was attached in neutral position to the lever arm of the engine by two headless screws through the quadriceps muscle. (Fig.\u0026nbsp;1)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5. Motion capturing\u003c/h2\u003e \u003cp\u003eThrough small incisions in the vastus medialis of the quadriceps two pins were drilled into the femoral shaft on a set distance to apply a Y-shaped marker set. The same procedure was used to fix the T-shaped marker set to the medial side of the proximal tibial shaft. Each marker set consisted of three makers mounted on an asymmetrical metal frame, allowing orientation of the specimen in 3D space. Four OptiTrack\u0026reg; cameras (NaturalPoint Inc, Corvallis OR, USA) were set up around the testing rig in a fashion that all markers were seen by all cameras during the entire range of motion (0-125\u0026deg;). Calibration was done using a calibration stick to identify 3D space of testing. A point cloud of at least one thousand points was necessary for a successful calibration. After successful calibration, fixed position of the cameras was meticulously assured. Precision of kinematic data, using this setup, was confirmed in previous work (deviation\u0026thinsp;\u0026lt;\u0026thinsp;1.5\u0026deg; and \u0026lt;\u0026thinsp;1.5mm) [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. (Fig.\u0026nbsp;2)\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6. Testing sequence\u003c/h2\u003e \u003cp\u003eTesting sequence consisted of increasing degrees of flexion, with /without application of an anterior load to the tibia with increments of 3kg (0kg \u0026ndash; 12kg) and with/without increasing internal/external tibial torque from 0Nm to 4Nm with increments of 1Nm. Furthermore, varus/valgus loads were applied with the same weight increments. Kinematic data were obtained in full extension, 30\u0026deg; of flexion, 60\u0026deg; of flexion, 90\u0026deg; degrees of flexion and at full flexion position for every condition.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.7. Bone registration and matching of CT/MRI\u003c/h2\u003e \u003cp\u003eAfter completing all testing sequences, registration of the femur and tibia was done to determine both structures in 3D space. Marker sets remained in place during registration, so the exact position and orientation of the marker set relative to the bone could be determined. Therefore, the specimens were completely stripped from all soft tissues. Registration was done with a calibrated stylus with three reflective markers, by pointing in close contact as much of the bare bone surface as possible. After establishment of the point-cloud, it was matched to the 3D CT model of the bone by means of the ICP (iterative closest point) algorithm using MATLAB\u0026reg; software [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The ICP algorithm as proposed by Besl et al is an iterative descent procedure, which seeks to minimize the sum of the square distance between all points in a source and their closest points in a target model. This algorithm also provides a solution to various free-form surface matching problems and has been extensively used as an optimizing technique for rigid model-based registration in the medical field. This matching algorithm was then used to match the MRI based 3D model of the femur, tibia, and ACL footprints to the CT model with kinematic data, allowing visualisation of the ACL footprints during the range of motion with or without application of external loads. (Fig.\u0026nbsp;4)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.8. Evaluation of matching accuracy\u003c/h2\u003e \u003cp\u003eTo evaluate the accuracy of matching between the CT- and MRI-based models, the (mean) distance between CT and MRI surface vertices was measured. To improve accuracy, CT and MRI scans were cut at the same level based on bony landmarks and the distance between the surfaces was recalculated. For comparison, landmark-based matching was executed using a set of fiducial ball markers; landmark-based matching, generally known as a gold standard matching method for verifying accuracy, was executed. The landmarks (center points) of 4 CT-derived ball models were matched to their corresponding MRI-derived ball models; accordingly, 4 groups were analyzed for matching accuracy: CT and low-quality MRI, CT and high-quality MRI, CT and high-quality MRI based on applied marker sets and between trimmed CT and MRI.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Statistical analysis\u003c/h2\u003e \u003cp\u003eStatistical difference between matching of CT with low-quality and high-quality MRI and between the surface-to-surface method and the landmark-based matching (marker sets) was analyzed in terms of investigated matching deviations. Statistical analysis was performed using a student\u0026rsquo;s T-test. The level of significance (p) was set to 0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.1. CT and MRI matching\u003c/h2\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e4.1.1 Matching of CT and low-quality MRI\u003c/h2\u003e \u003cp\u003eIn this subset of 8 knees (4 specimens), CT and low-quality MRI of 8 femurs and 8 tibias were matched according to the protocol. The mean distance between CT and MRI 3D reconstructions was 1.15 mm (STD ± 0.66 mm / minimum difference 0.05 mm / maximum 5.81 mm). 48.33% of matched points was found to be within 1 mm; 87.95% within 2 mm. Detailed values are given in table 1.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues of CT and low-resolution MRI matching of 4 specimens. Mean distance between both scans is given with standard deviation, minimum and maximum values. Percentage of these values within 1 mm and 2 mm are given on the right.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecimen\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSide\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTD [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMaximum [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% within 1 mm\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e% within 2 mm\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52,39%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94.54%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61,42%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97.17%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e44,55%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94.41%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,07\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e55,62%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e55.62%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36,22%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e85.07%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e35,17%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e83.14%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e36,85%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e83.33%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e47,45%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e92.02%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e16,30\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e46,30%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e89.90%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19,84\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e38,03%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e87.28%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49,06%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e88.51%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,96\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,57\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e60,67%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e93.47%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,17\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,08\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e51,04%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e86.32%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e49,78%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e91.32%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58,68%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e92.85%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e50,09%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e92.19%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e1,15\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,66\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,05\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e5,81\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e48,33%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e87.95%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e(R): right – (L): left – (mm): millimetre – (STD): standard deviation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section3\"\u003e \u003ch2\u003e4.1.2 Matching of CT and high-quality MRI\u003c/h2\u003e \u003cp\u003e4 specimens (8 knees) underwent high-quality MRI with an absolute mean difference of 0.89 mm (STD ± 0.46 mm / minimum difference 0.04 mm / maximum 3.94 mm) when compared to the CT images. 66.45% of matched points lies within 1mm difference and up to 97.30% within 2mm. Values for each knee are given in table 2.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues of CT and high-resolution MRI matching of 4 specimens. Mean distance between both scans is given with standard deviation, minimum and maximum values. Percentage of these values within 1 mm and 2 mm are given on the right.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecimen\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSide\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTD [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMaximum [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% within 1 mm\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e% within 2 mm\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62,07%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96.95%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e61,03%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95.58%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e59,50%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98.35%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71,90%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96.84%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e58,98%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96.42%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73,64%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98.85%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e52,04%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e95.66%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1,14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e41,12%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e94.75%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63,39%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98.48%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e74,38%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97.75%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e71,57%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98.78%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e69,94%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97.62%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e78,70%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e99.62%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e62,16%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96.83%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81,40%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97.50%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e4,62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81,46%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e96.75%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e3,94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e66,45%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e97.30%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e(R): right – (L): left – (mm): millimetre – (STD): standard deviation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section3\"\u003e \u003ch2\u003e4.1.3 Matching of CT and high-quality MRI with markers\u003c/h2\u003e \u003cp\u003eBefore scanning, reflective markers were drilled into both the femur and tibia of 4 knees. Matching of the CT and high-quality MRI was done, based on matching of the respective markers. An absolute mean difference was seen of 0.62 mm (STD ± 0.45 mm / minimum difference 0.01 mm / maximum 4.27 mm). 84.64% of points was found within a range of 1 mm and 98.43% was found within 2 mm. Mean absolute difference between markers on the CT and MRI images was 0.27 mm (STD ± 0.13). (table 3)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues of CT and high-resolution MRI matching of 4 specimens, based on markers. Mean distance between both scans is given with standard deviation, minimum and maximum values. Percentage of these values within 1 mm and 2 mm are represented. On the right, the absolute distance between matched markers was given.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colspan=\"5\" nameend=\"c9\" namest=\"c5\"\u003e \u003cp\u003eDifference between\u003c/p\u003e \u003cp\u003eCT and MRI\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eDifference\u003c/p\u003e \u003cp\u003eof Markers\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecimen\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSide\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMean [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSTD [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMinimum [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMaximum [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e% Within\u003c/p\u003e \u003cp\u003e1 mm\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e% within 2 mm\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eMean [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eSTD [mm]\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e6,71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81,92%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97.02%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,28\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,08\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82,72%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e99.14%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,24\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e2,53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86,56%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e99.70%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,15\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79,63%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98.73%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,20\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,61\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86,78%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98.58%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e85,16%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98.50%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,07\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,43\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e5,09\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e88,06%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e98.27%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,12\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e3,92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e86,27%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e97.53%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e0,31\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e0,21\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e0,62\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,45\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e4,27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e84,64%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e98.43%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e0,27\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e\u003cb\u003e0,13\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e(R): right – (L): left – (mm): millimetre – (STD): standard deviation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e4.1.4 Comparison between CT matching with low, high quality and marker-based MRI\u003c/h2\u003e \u003cp\u003eAfter matching low quality and high-quality MRI scans on the CT images, the absolute mean difference was found to be significantly differing (1.15 vs 0.89 mm / p = 0.0000052). Furthermore, comparison of the percentage within 1 mm (48.33% vs 66.45%) and 2 mm (87.95% and 97.30%) showed both significant differences (p = 0.0000152 and p = 0.0014237). Adding markers significantly increased the precision of CT-MRI matching (p = 0.0000001, 0.89 mm vs 0.62 mm). The percentage of CT-high quality MRI point distances within 1mm and 2mm was also significantly different with respective p-values of 0.0000152 and 0.0014237.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e4.1.5 Comparison between non-trimmed and trimmed MRI images for CT-MRI matching\u003c/h2\u003e \u003cp\u003eAfter trimming the diaphyseal part of the MRI, CT-MRI matching was repeated. Absolute mean differences significantly (p = 0.000000) dropped from 1.14 mm (non-trimmed / STD ± 0.92 / minimum 0.03 mm / maximum 11.00 mm) to 0.89 mm (trimmed / STD ± 0.46 / minimum 0.04 mm / maximum 3.94 mm). There was a significant increase of CT-MRI matching within 1 mm (55.07% vs 66.45%, p = 0.000000) and 2 mm (90.60% vs 97.30%, p = 0.000000). (table 4)\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\u003cdiv class=\"gridtable\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eValues of CT and MRI matching of 4 specimens, before and after trimming the MRI images to the metaphyseal part. Mean distance between both scans is given with standard deviation, minimum and maximum values. Percentage of these values within 1 mm and 2 mm are represented. On the right, p-values are given after comparing both situations. Level of significance was set at p \u0026lt; 0.05.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e\u003ccolgroup cols=\"11\"\u003e\u003c/colgroup\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecimen\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSide\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTrim\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMean [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSTD [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMinimum [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMaximum [mm]\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e% Within 1 mm\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003e% Within 2 mm\u003c/p\u003e \u003c/th\u003e\u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003ep-value\u003c/p\u003e \u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62,07%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96,95%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9,29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57,67%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95,28%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,99\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,52\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,19\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61,03%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95,58%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,11\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,60\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9,32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e50,18%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e93,47%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,95\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,40\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e59,50%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98,35%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,34\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15,92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e49,20%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e91,43%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,85\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71,90%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96,84%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9,87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e65,29%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95,61%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,48\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58,98%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96,42%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,20\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48,34%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e86,32%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,35\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e73,64%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98,85%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,15\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,73\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,00\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e53,95%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e86,37%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e52,04%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e95,66%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,71\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e5,21\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e39,55%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e82,37%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,14\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,24\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e41,12%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94,75%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,23\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,10\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6,72\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e48,29%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e85,50%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,92\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,05\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,65\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e63,39%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98,48%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,54\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e54,50%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e94,74%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,82\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,45\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74,38%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97,75%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,41\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,87\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15,68\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e57,64%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e88,29%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,39\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,93\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e71,57%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e98,78%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,42\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15,18\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62,65%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e93,91%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,86\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,44\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3,59\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e69,94%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97,62%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,32\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1,76\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e16,29\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58,97%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e89,72%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2,74\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e78,70%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e99,62%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,79\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15,37\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e59,97%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e93,79%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eR\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,94\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,53\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,06\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,13\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e62,16%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96,83%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1,33\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,83\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,04\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e15,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e38,69%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e84,75%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFemur\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,69\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,49\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,01\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,51\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81,40%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e97,50%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,97\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,66\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e9,81\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e61,65%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e91,78%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eL\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTibia\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,70\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,56\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,03\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e4,62\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e81,46%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96,75%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c11\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0,0000000\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0,77\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0,58\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0,02\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e7,98\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e74,66%\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e96,31%\u003c/p\u003e \u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eYes\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e0,89\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,46\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,04\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e3,94\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e66,45%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e97,30%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eNo\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e1,14\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e0,92\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003e0,03\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003e11,00\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003e55,07%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e\u003cb\u003e90,60%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e\u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/table\u003e\u003c/div\u003e \u003cp\u003e\u003c/p\u003e \u003cp\u003e(R): right – (L): left – (mm): millimetre – (STD): standard deviation.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Kinematic analysis\u003c/h2\u003e \u003cp\u003eFollowing CT-MRI matching, the ACL and posterior cruciate ligament (PCL) were visualized and projected on the CT scans (Fig.\u0026nbsp;2) with application of the kinematic data, received from the OptiTrack® system, allowing for evaluation of relative individual footprint behaviour during flexion-extension cycles, internal and external torque manoeuvres and during varus/valgus loading. (Fig.\u0026nbsp;3)\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe main findings of this study highlight the high precision of patient-specific CT and MRI merging, allowing detailed visualization and assessment of kinematic behavior of cruciate ligament footprints. It is further noted that optimal matching is achieved, based on high-quality MRI with rigidly fixed markers, with distances between both scans within 1 mm for up to 84.64% and within 2 mm for up to 98.43% of measurements after fusion. This is one of the first studies with foremost the largest series substantiating the feasibility of precise matching of soft tissue MRI images with acquired full leg CT scans and corresponding kinematic data.\u003c/p\u003e\u003cp\u003eToday’s used robotic systems in arthroplasty are either based on image-free systems or on preoperative CT-scan analysis of bony contours and reference points [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]. For extrapolation of these techniques to sports surgery, it might however be more expedient to incorporate patient specific soft tissue anatomy into preoperative planning and real-time guidance during arthroscopic ligament reconstruction. Attempts in the past to implement navigation and, in extension, computer assistance in ACL surgery could not, despite the increased precision of tunnel placement, provide added value in terms of clinical outcomes to date [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. Augmented reality overlay techniques are currently making their way into arthroplasty but may also find their entrance in arthroscopic soft tissue reconstruction in the future [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. However, to extract added value from this, the identification of patient-specific anatomy and kinematics is the first step in a more tailored reconstruction, necessitating an individualized imaging modality that embraces high precision with practical implementation in the clinical setting.\u003c/p\u003e\u003cp\u003eAccording to Victor et al., bony landmarks around the knee can accurately be identified on CT scan in a reproducible manner with low intra- and inter-observer variability [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Furthermore, Van den Broeck et al. described in 2014 in a technical note, their segmentation accuracy of long bones, using CT, MRI, and optical scanning. They found that both CT an MRI imaging is accurate for 3D bone reconstructions within 0.5 mm. On average, CT segmentation resulted in a slight overestimation compared to the actual dimensions of the bone whereas MRI segmentation induced a small underestimation of the bone’s geometry [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In terms of accuracy, Campanelli et al showed in 2019 that morphological errors associated with 3D bone models of the femur and tibia generated from CT scans must be considered. They also showed that CT bone models slightly overestimate the bone morphology while the MRI bone models substantially underestimate it. Furthermore, it was shown that the popular ICP registration method, as used in this study, underestimates the morphological errors when compared to registration with fiducial markers; this is in line with the findings in our study, as the use of markers improved accuracy of matching. It was concluded that CT-based bone models could best be used for applications requiring submillimetre accuracy, while in MRI-based bone models, 1 mm of (in)accuracy must be accepted [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. To visualize soft tissues structures, such as ligaments, MRI is favoured. Innocenti et al. described a reproducible and accurate method with general variability of less than 1mm to identify femoral and tibial ACL footprints on MRI [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In 2019, Raposo et al., were one of the first describing their in-vitro methodology for video-based computer navigation in knee arthroscopy for patient-specific ACL reconstruction with the use of preoperative planning based on MRI images, however no kinematic analysis was incorporated [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eIn 2008, Lee et al. published as one of the first on specific measurements of anatomical accuracy of 3D model matching, when a 3D CT-derived model is matched to a 3D MRI-derived model, in terms of both practical anatomical aspects and the numerical error-distance measurement of matching deviation. Besides the 2D contour-based measurements, also a global 3D contour-based measurement of matching deviation was performed with both surface-to-surface matching (using ICP) and marker-based matching (3 markers). An average difference was seen of 0.7 mm (STD ± 0.1) and 1.1 mm (STD ± 0.3). This contrasts with our findings as it was shown on our large series that marker-based matching significantly decreased matching differences from 0.89 to 0.62 mm (p = 0.0000001). A possible explanation might be that accuracy increased in our study by using an extra marker on both the femur and the tibia. Furthermore, Lee et al. increased their matching accuracy by analysing differences at the level of the middle femoral shaft, compared to local 2D contour-based measurements of matching deviation in the femoral condyle (both marker-based) whereas in our study it was shown that accuracy increased after trimming the femoral diaphysis [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eSince the first description of application of augmented reality (AR) to orthopaedic surgery by Blackwell et al. in 1998 with prediction that this technology would become commonplace in orthopaedics, to date, AR still has very few applications in orthopaedic practice [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In the recent review of Laverdière et al, only four publications were identified describing application of AR in sports medicine and arthroscopy, with major focus on educational purposes. The use of AR in this field is still in its infancy, but more recent publications confirm the potential of this technology to radically improve surgical practice [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. The findings of this study may contribute to further development of overlay technologies with intra-operative visualization of patient-specific anatomy and kinematics toward improved surgical outcomes.\u003c/p\u003e\u003cp\u003eIn a Cochrane review on computer-assisted surgery for knee ligament reconstruction in 2014, Eggerding et al. were unable to refute a favourable effect of computer assisted surgery for cruciate ligament reconstructions of the knee comparted with conventional reconstructions, neither that there was an improvement in outcome. Only five studies were included in this review with a large variety in used computer-assisted techniques [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. The same conclusions were drawn in the earlier meta-analysis and systematic review of randomized controlled trials by Cheng et al [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. A later historical review of navigation systems in ACL surgery by Zaffagnini et al. summarized the use of computer assisted surgery as technical assistance for tunnel placement and for kinematic evaluation of the ACL, showing a wide range of applications with inconclusive findings regarding superiority of one system over another and the remark that navigations systems remain invasive with added potential risk [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. The most recent systematic review of Yavari et al. confirmed again that clinical outcomes do not differ between technology-assisted surgery and conventional surgery, beside the fact that these technologies are more expensive and time consuming. It was confirmed that tunnels can be more accurately located in radiologically ideal places by using technology, but as stated in our current study, anatomical placement is still undetermined because of variability and inaccuracy of the evaluation systems utilized [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eThe results of this study should be interpreted in the light of some limitations. A first limitation is the relatively high mean age of the subjects, however, factors influencing segmentation quality and normal kinematics such as previous knee surgery, signs of trauma, end-stage osteoarthritis and ligamentous injuries were excluded and verified on MRI. A second limitation is the relatively small number of specimens, although, to the best of our knowledge, it constitutes the largest series in literature. Third, the most significant limitation lies in the potential human bias in the manual, labour-intensive segmentation process. To our knowledge, automated soft tissue identification and segmentation is not available, but might be necessary as this study protocol signifies a relatively challenging extrapolation to the clinical field. Furthermore, extrapolation of this method and finding to clinical practice might be challenging. Marker based motion capture analysis is a common approach to conduct 3D motion analytics. In this study, we used firmly fixed markers, anchored in the femur and tibia. In vivo analysis, however, necessitates attachment of markers to the skin of human subjects on top of bony landmarks. Because the skin with the attached markers is moving relative to the anatomical landmarks during motion, measurement errors, the so-called soft tissue artifact (STA), are unavoidable. Algorithms were although developed to limit errors due to soft tissue motion and increase the measurement accuracy [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTo conclude, it can be stated that merged CT and MRI imaging with incorporation of patient specific data might be a valuable first step towards a more individualized perioperative ACL footprint analysis and planning to achieve a more tailored and personalized (robotic assisted) ACL reconstruction in the future.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cul\u003e\n \u003cli\u003eCT: Computed Tomography\u003c/li\u003e\n \u003cli\u003eACL: Anterior Cruciate Ligament\u003c/li\u003e\n \u003cli\u003eMRI: Magnetic Resonance Imaging\u003c/li\u003e\n \u003cli\u003e3D: Three Dimensional\u003c/li\u003e\n \u003cli\u003eICP: Iterative Closest Point\u003c/li\u003e\n \u003cli\u003eMm: Millimetre\u003c/li\u003e\n \u003cli\u003eAM: Anteromedial\u003c/li\u003e\n \u003cli\u003ePL: posterolateral\u003c/li\u003e\n \u003cli\u003eKA: Knee Arthroplasty\u003c/li\u003e\n \u003cli\u003emA: Milliampere\u003c/li\u003e\n \u003cli\u003ekV: Kilovolt\u003c/li\u003e\n \u003cli\u003eKg: kilogram\u003c/li\u003e\n \u003cli\u003eNm: Newton metre\u003c/li\u003e\n \u003cli\u003eR: Right\u003c/li\u003e\n \u003cli\u003eL: Left\u003c/li\u003e\n \u003cli\u003eSTD: Standard Deviation\u003c/li\u003e\n\u003c/ul\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eConflict of interest:\u0026nbsp;\u003c/strong\u003eno conflict of interest\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u0026nbsp;\u003c/strong\u003eno funding was received for this study\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval:\u0026nbsp;\u003c/strong\u003eGhent University Hospital (EC/2014/0847)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eInformed consent:\u0026nbsp;\u003c/strong\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u0026nbsp;\u003c/strong\u003e/\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contribution:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTT, VH, PS, CA\u0026nbsp;have made substantial contributions to conception and design, or acquisition of data, or analysis and interpretation of data.\u003c/p\u003e\n\u003cp\u003eTT, VH, PS, AN, LT, VJ, CA\u0026nbsp;have been involved in drafting the manuscript or revising it critically for important intellectual content.\u003c/p\u003e\n\u003cp\u003eTT, VH, PS, AN, LT, VJ, CA\u0026nbsp;have given final approval of the version to be published.\u003c/p\u003e\n\u003cp\u003eTT, VH, PS, AN, LT, VJ, CA agree to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSocial Media Handles\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e*\u0026nbsp;\u003c/strong\u003eInstagram - tamperethomas\u003c/p\u003e\n\u003cp\u003e* Twitter - @ttampere\u003c/p\u003e\n\u003cp\u003e* Facebook - Thomas Tampere\u003c/p\u003e\n\u003cp\u003e* LinkedIn - Thomas Tampere\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eArdern CL, Taylor NF, Feller JA, Webster KE (2014) Fifty-five per cent return to competitive sport following anterior cruciate ligament reconstruction: an updated systematic review and meta-analysis including aspects of physical functioning and contextual factors. Br J Sports Med 48:1543\u0026ndash;1552\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBaker HP, Bowen E, Sheean A, Bedi A (2023) New considerations in ACL surgery: when is anatomic reconstruction not enough? J Bone Joint Surg Am 105(13):1026\u0026ndash;1035\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBesl PJ, McKay ND (1992) A method for registration of 3-D shapes. IEEE Trans Pattern Anal Mach Intell 14:239\u0026ndash;256\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlackwell M, Morgan F, DiGioia A (1998) Augmented reality and its future in orthopaedics. Clin Orthop Relat Res 354:111\u0026ndash;122\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBorque KA, Jones M, Laughlin MS, Balendra G, Willinger L, Pinhiero VH, Williams A (2022) Effect of lateral extra-articular tenodesis on the rate of revision anterior cruciate ligament reconstruction in elite athletes. 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Int Orthop (SICOT) 37:233\u0026ndash;238\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"knee, anterior cruciate ligament, reconstruction, robotics, imaging","lastPublishedDoi":"10.21203/rs.3.rs-6433199/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6433199/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003ePurpose: \u003c/em\u003eIn contrast to knee arthroplasty, use of robotics in sports medicine surgery is a great void. Currently used robotic interfaces in arthroplasty are based on preoperative computed-tomography (CT) scans or on image-free systems. To optimize anterior cruciate ligament (ACL) reconstruction to a more patient specific approach, aiming to improve clinical outcomes, implementation of computer assisted robotic surgery might be the next step to individualized ACL reconstruction. As sports surgery is most often soft tissue surgery, it might be beneficial to incorporate magnetic resonance imaging (MRI) imaging into preoperative planning, intra-operative guidance, and perioperative kinematic analysis. In a first step, goal of this study was to evaluate the in-vitro feasibility and precision of merging MRI and full leg CT images with patient specific kinematic data to analyse patient specific inter-footprint behaviour of the ACL.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eMethods: \u003c/em\u003eCT and (low- and high-quality) MRI scans were acquired from 20 cadaveric lower limbs and scans were subsequently rendered in 3D. Kinematic data were obtained during testing on a passive knee rig. On 4 knees, marker sets were applied prior to scanning. Surface matching using the iterative closest point (ICP) and marker-based matching of CT, MRI and kinematic data was acquired and matching accuracy of the combined models were evaluated with statistical inter-point deviation.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eResults: \u003c/em\u003eMatching low-quality and high-quality MRI scans on the full-leg CT images (with applied kinematics), the absolute mean difference between matching distances was found to be significantly differing (1.15 vs 0.89 mm / p=0.0000052) in favour of high-quality MRI scans. Furthermore, comparison of the percentage within 1 mm (48.33% vs 66.45%) and 2 mm (87.95% and 97.30%) showed both significant differences (p=0.0000152 and p=0.0014237). Adding markers significantly increased the precision of CT-MRI matching (p=0.0000001, 0.89 mm vs 0.62 mm). The percentage of CT-high quality MRI point distances within 1 mm and 2 mm was also significantly different with respective p-values of 0.0000152 and 0.0014237. Trimming the femur to the metaphysis also increased matching accuracy (p=0.00000).\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConclusion:\u003c/em\u003e Merged CT and MRI imaging with incorporation of patient specific data might be a valuable first step towards a more individualized perioperative ACL footprint analysis and planning to achieve a more tailored and personalized (robotic assisted) ACL reconstruction in the future.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLevel of evidence:\u003c/strong\u003e V\u003c/p\u003e","manuscriptTitle":"Merged CT and MRI imaging of ACL footprints A novel in vitro technique for individualized footprint analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-14 11:32:09","doi":"10.21203/rs.3.rs-6433199/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d8e2e11-590c-448d-85e6-cc248d52b884","owner":[],"postedDate":"May 14th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-01-15T08:40:09+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-14 11:32:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6433199","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6433199","identity":"rs-6433199","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}