Intrarater and Interrater Reliability of the Quantification of Knee Cartilage MR Relaxation Metrics

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Yablon, Michaela K. Lewis, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4926999/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 Background Magnetic resonance (MR) imaging is often used to study osteoarthritis (OA), as advanced MR imaging methods can provide a quantitative assessment of tissue biochemistry or composition. For example, the magnetic relaxation times T 1ρ (i.e., 1/R 1ρ ) and T 2 (i.e., 1/R 2 ) of water molecules within articular cartilage have been demonstrated to be imaging biomarkers sensitive to the compositional changes associated with early OA. However, the outcome of MR imaging data analysis depends on relaxation data acquisition methods as well as assessor variability if manual segmentation is performed. Therefore, the goal of the current study was to evaluate the intra- and interrater reliability of established imaging protocols for performing quantitative cartilage MR relaxation metrics of the knee joint. Methods Right knee MR images were obtained from five healthy individuals (average age, 24.4 years; 3 females) via a 3.0T MRI scanner equipped with a 16-channel knee T/R coil. A double echo steady state (DESS) sequence was used for anatomical imaging, and the established MAPSS sequences were used for R 1ρ and R 2 mapping. One assessor performed manual segmentations of the knee cartilage on two separate occasions, whereas a second assessor performed segmentations once. Both the R 1ρ and R 2 mean values were then calculated for the tibial, patellar, femoral trochlear, central femoral condylar, and posterior femoral condylar cartilages. Intraclass correlation coefficients [ICC (3,1)] and ICCs (2,1) were used to evaluate intra- and interrater reliability, respectively. The standard error of measurement (SEM) was used to assess absolute reliability. Results The intrarater knee cartilage relaxation metrics demonstrated good to excellent reliability, ranging between 0.88 and 0.99, with SEMs ranging between 0.16 and 0.80. The interrater reliability similarly ranged from 0.79–0.97, with SEMs ranging between 0.27 and 1.10. Conclusions Manual segmentation of specific MR slices and known subregions is highly reliable and repeatable for the quantification of cartilage MR relaxation metrics. This validation paves the way for the large-scale application of this method in prospective trials that longitudinally monitor OA development and progression in the knee joint. knee cartilage reliability quantitative MRI Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Osteoarthritis (OA) is a common joint disorder in middle-aged and elderly people who significantly affects joint functionality, causing disability and increased healthcare utilization. The knee joint is the most commonly affected joint, with an estimated prevalence of 365 million people worldwide, followed by the hip and hand [ 1 ]. Knee OA impacts both tibiofemoral and patellofemoral joints, with patellofemoral OA being highly prevalent [ 2 – 4 ], but the latter is often underrecognized. A radiographic study revealed that among adults aged 50 years and above with knee pain, 40% had combined tibiofemoral and patellofemoral OA, 24% had isolated patellofemoral OA, and 4% had isolated tibiofemoral OA [ 2 ]. The current gold standard for diagnosing OA, in addition to routine clinical examination, is plain X-ray [ 5 ]. This method is safe, cost-effective, and widely available; however, it is known to be insensitive to early OA changes. Since clinical OA represents a late-stage condition with limited opportunities for disease intervention, identifying and characterizing OA in its earlier stages could enhance our understanding of OA mechanisms and aid in the development and evaluation of disease-modifying treatments. In the early stages of OA, articular cartilage fails to synthesize extracellular matrix components, leading to a loss of proteoglycans and collagen, reduced elasticity, and decreased water content [ 6 ]. Quantitative MR imaging provides noninvasive tools for assessing the biochemical composition of articular cartilage, allowing for the characterization of cartilage integrity before irreversible damage occurs. Over the past two decades, studies have shown that water proton relaxation times T 1ρ and T 2 can provide valuable information on the proteoglycan content and collagen orientation within cartilage [ 7 , 8 ]. These relaxation metrics have been reported to be effective in distinguishing between individuals with and without knee OA [ 9 – 12 ]. Cartilage segmentation is a critical factor influencing the quantification of MR relaxation metrics [ 13 ]. Recent advances in artificial intelligence (AI) have significantly improved cartilage segmentation analysis [ 14 – 17 ]. Despite its growing popularity, AI-based segmentation is not yet commercially or readily accessible to all institutions, making manual segmentation valuable due to its inherent benefits. Manual segmentation offers superior accuracy and precision and remains the gold standard for validating and training deep learning models. However, a major downside of manual cartilage segmentation is the potential for variability between different assessors and even within the same assessor over time. The reproducibility of compositional MR relaxation metrics could be compromised by various sources, ranging from data acquisition to postprocessing, as previously documented in the literature [ 18 , 19 ]. With the establishment of standardized compositional MR imaging acquisitions [ 20 ], significant attention should be directed toward data postprocessing. Therefore, the aim of this study was to examine the intra- and interrater reliability of knee cartilage MR relaxation metrics via manual segmentation by end users. Methods Subjects A sample of five healthy individuals volunteered for the reliability analysis (average age, 24.4 ± 1.3 years; 3 females). The exclusion criteria were (1) any preexisting musculoskeletal diseases of the lower extremity, (2) any past surgeries of the lower extremity, and (3) any contraindications to MR imaging, such as the presence of a cardiac pacemaker, metal implants, intraocular foreign body, aneurysm clips, pregnancy, claustrophobia, etc. The study was approved by the University of Michigan Institutional Review Boards (IRB) (approval number: HUM00200085). All procedures followed were in accordance with the Declaration of Helsinki, ensuring that the rights and well-being of participants were protected. Prior to data collection, all participants signed written informed consent forms, acknowledging their understanding of the study and their voluntary participation. MR Imaging Protocols All participants were scanned on the right knee in the supine position via a Philips Ingenia 3.0T wide-bore MRI scanner with a 16-channel knee transmit-receive coil. All the images were acquired in the sagittal plane. The double echo steady state (DESS) sequence was used for anatomical imaging with a field-of-view (FOV) of 140*140*112 mm 3 and an acquired voxel resolution of 0.4*0.5*0.9 mm 3 , which was interpolated to 0.36*0.36*0.63 mm 3 when 3D volumetric images were reconstructed. The number of reconstructed imaging slices was 178, and the scan time was 5:52 minutes. R 1ρ and R 2 mappings were obtained from the established MAPSS sequences [ 19 , 21 ] implemented by the vendor as working-in-progress (WIP) prototypes. The FOV was 140*140*96 mm 3, and the acquired or reconstructed voxel size was 0.44*0.66*4.0 or 0.27*0.27*4.0 mm 3 . For R 1ρ mapping, the spin‒lock durations (TSLs) were 0, 10, 15, 20, 25, 30, 35, and 40 ms, and the spin‒lock strength was 500 Hz. For R 2 mapping, the echo times (TEs) were 0, 18, 36, and 54 ms. The scan duration for R 1ρ or R 2 mapping was 9:13 minutes. MR Imaging Processing Manual cartilage segmentations were performed on the reformatted DESS images via ITK-SNAP 4.0 software [ 22 ]. R 1ρ− and R 2 -weighted images were coregistered, as documented in the literature [ 23 ], with the reformatted DESS images as the reference. The reformatted DESS images had the same slice thickness (4 mm) as the R 1ρ− and R 2 -weighted images. The regions of interest (ROIs) included the articular cartilages of the tibia (T), patella (P), femoral trochlea (TrF), central femoral (cF) condyle, and posterior femoral (pF) condyle (Fig. 1 ). In the sagittal images, the tibial, patellar, and femoral trochlear cartilages were identified by tracing the boundaries on the basis of their anatomical landmarks, slice by slice. All slices that displayed cartilage underwent segmentation, except those with partial cartilage volume averaging at the margins. As the cartilaginous surface of the femoral cartilage is continuous, artificial boundaries had to be defined for separating the ROIs (Fig. 1 ). Only the weight-bearing portion of the femoral cartilage was considered and further classified into the cF and pF regions. The former was separated from the trochlear plane, and the latter was separated in the coronal plane. The anterior boundary was defined as the first image where the continuous trochlear cartilage layer separated into distinct cartilage layers on the femoral condyles (Fig. 2 a). The posterior boundary was defined as 60% of the distance between the anterior boundary and the last image containing the posterior aspects of the femoral condyles, known as “double bulls-eye”, containing both cartilage and bone (Fig. 2 b) [ 24 , 25 ]. These landmarks were used because they could be clearly identified and reproduced in the subjects. While the segmentation was performed in the sagittal images and the landmarks were identified in the coronal images, the boundaries could be defined by using the same landmarks and 3D viewing during segmentation. The signal intensities ( \(\:S\) ) in R 1ρ− or R 2− weighted images were fitted via home-grown IDL 8.9 (Interactive Data Language, Harris Geospatial Solutions, Inc., Broomfield, CO, USA) programming scripts [ 23 ], voxel by voxel, to a simple exponential decay relaxation model, i.e., \(\:S=C*{e}^{-\left({R}_{1\rho\:}\:*\:TSL\right)}\:\) or \(\:\:S=C*{e}^{-\left({R}_{2}\:*\:TE\right)}\) , where \(\:C\) is a fitted constant. The average R 1ρ or R 2 relaxation rate (in units of 1/s) was subsequently calculated from the fits for cartilage in different ROIs of the knee joint. Statistical Analysis Intra- and interrater reliability statistics were calculated for the R 1ρ and R 2 values of knee cartilage. Intrarater reliability was obtained for one rater (ML) who segmented each scan twice on different occasions. Interrater reliability was obtained for two raters (ML and JH), who individually segmented the knee cartilages for each scan. Intraclass correlation coefficients [ICC (3,1)] and ICC (2,1) were used to evaluate intra- and interrater reliability for each ROI for R 1ρ and R 2 , respectively [ 26 ]. The standard error of measurement (SEM) was used to assess absolute reliability. ICCs less than 0.5 indicated poor reliability; 0.5–0.75, moderate reliability; 0.75–0.9, good reliability; and greater than 0.9, excellent reliability [ 27 ]. Bland‒Altman limits of agreement were calculated with an exact 95% confidence interval (CI). The data collected from this study were analyzed via SPSS 28.0. Results Figures 3 and 4 show representative images of R 1 ρ- and R 2- weighted images from one subject at various TSL and TE values, respectively. Tables 1 and 2 provide the R 1 ρ and R 2 relaxation rates in each ROI as estimated from each rater and session. The tibia cartilage presented the greatest R 1 ρ and R 2 relaxation rates, followed by the femoral cartilage (if all subregions were considered together), whereas the patella cartilage presented the lowest rates. The intrarater consistency in the segmented ROI areas of the knee R 1 ρ relaxation rates demonstrated that the ICC values ranged between 0.84 and 0.99, and the results were similar for R 2 values ranging between 0.91 and 0.99. The SEM ranged between 0.22 and 0.57 and between 0.21 and 0.74 for R 1 ρ and R 2 , respectively (Table 1). For interrater consistency, the ICCs for the R 1 ρ values ranged between 0.80 and 0.99, and the R 2 values ranged between 0.75 and 0.99. Finally, the SEM for interrater reliability ranged from 0.34--1.10 and 0.27--0.84 for R 1 ρ and R 2 , respectively (Table 2). For the intrarater analysis, Bland‒Altman agreement revealed a mean difference in relaxation rates of 0.27 s -1 , the limits of agreement were -1.23 and 1.78, and the outer 95% CIs were -0.49 and 0.055 (Figure 5a). For the interrater analysis, the mean difference was 0.81 s -1 , the limits of agreement were -1.50 and 3.13, and the 95% CIs were -1.15 and 0.47 (Figure 5b). Discussion While automatic cartilage segmentation through AI has been reported, manual segmentation remains the gold standard, offering superior accuracy and precision. The goal of this study was to examine the intra- and interrater reliability of knee cartilage MR relaxation metrics via manual cartilage segmentation. Our findings suggested good to excellent intra- and interrater reliability for the quantification of knee MR relaxation metrics, with slightly lower interrater reliability than intrarater reliability. Consistent with the literature [ 14 , 28 ], tibia cartilage presented the greatest R 1ρ and R 2 relaxation rates, followed by femoral cartilage, whereas patella cartilage presented the lowest rates. In our study, reliability was greater in the patellar, trochlear, and central femoral condylar cartilages than in the tibia and posterior femoral condylar cartilages. Since our segmentations were based on anatomical identification, the reproducibility of these landmarks became crucial in determining the boundaries for segmentation. Patellar and femoral trochlear cartilages, which comprise the patellofemoral joint, can be easily visualized on MR images with distinct cartilage-to-bone boundaries (Fig. 1 ). Additionally, the patellar cartilage, followed by the trochlear cartilage, is thicker than the other knee cartilages. Previous studies have suggested that the thicker the cartilage is, the lower the observed variability [ 29 , 30 ], which is consistent with the results of the present study. However, posterior femoral condylar cartilage exhibited the greatest variability, which can be attributed to observer-dependent differences in the determination of the artificial boundaries, both anteriorly and posteriorly. Nevertheless, the overall high intra- and interrater reliability (ICCs ranging between 0.75 and 0.99) suggests that subtle differences in cartilage selection in specific regions do not meaningfully alter the mean R 1ρ and R 2 relaxation rates. Since our cartilage segmentation was reproducible, the mean R 1ρ and R 2 relaxation rates were expected to demonstrate good to excellent intra- and interrater reliability, which is consistent with the literature. In the study by William et al. [ 30 ], the authors reported intrarater ICCs ranging between 0.80 and 0.98 for T 2 mapping in the knee joint, whereas the current study reported intrarater ICCs ranging between 0.91 and 0.99 among the tibiofemoral and patellofemoral joints. Another study by Welsch et al. [ 31 ] reported an average interrater ICC of 0.90 for T 2 mapping in the knee joint, whereas our interrater agreement, though slightly lower, was still considered excellent, with ICCs ranging between 0.75 and 0.99. Collectively, the evidence suggests that manual segmentation of specific MR slices and known subregions is highly reliable. While AI-based segmentation has the advantage of eliminating the manual segmentation burden, its overall reliability and accuracy remain unclear. Norman et al. [ 32 ] utilized a deep learning model to perform automatic segmentation via DESS sequences and reported Dice coefficients ranging from 0.77 to 0.88 for cartilage. Similarly, Gatti and Maly [ 16 ] used knee MR images and reported Dice coefficients ranging from 0.88 to 0.91 for different cartilage compartments, with even better results for healthy cartilage. The Dice coefficient, a statistical tool that measures the agreement of segmentations, with 1 being a perfect match and 0.8 to 0.9 generally considered good and useful [ 13 ]. While the reliability of AI-based segmentation appears to be comparable with that of manual segmentation, it is important to note that AI-based segmentation was validated against manual analysis, which resulted in a doubling of the errors. Importantly, reliability does not necessarily imply accuracy. Since the true cartilage boundaries for the samples in this study are currently unknown, it is unclear how accurately these MR relaxation metrics are reflected. Future studies will need to address this critical gap for both manual and AI-based segmentations to enhance the assessment of degenerative joint diseases. Even though this study is limited by having only two raters and five image sets, the results likely reflect the true reliability of generating knee MR cartilage relaxation metrics from manual segmentations of the respective ROIs. However, it is important to note that the study included only healthy subjects. Individuals with OA may have bone spurs and cartilage defects that could complicate the accurate outlining of the cartilage surface. Additionally, this study did not examine the zonal behavior of cartilage relaxation metrics, which have been shown to differ within cartilage layers due to varying loads [ 31 , 33 ]. Future research should include subjects with OA and assess the zonal behavior of cartilage relaxation metrics to improve our understanding of cartilage health and pathology. Conclusions In conclusion, our analysis results suggest that manually segmenting specific MR slices and known subregions is highly reliable and repeatable for the quantification of MR cartilage relaxation metrics, albeit at the cost of human manpower. Our findings contribute to the existing body of literature and indicate that manual segmentation of knee cartilage demonstrates excellent intra- and interrater reliability, with patellofemoral joint assessment being superior to tibiofemoral joint assessment. Additionally, this validation paves the way for the large-scale application of this method in prospective trials that longitudinally monitor the development and progression of knee osteoarthritis in individuals at risk of further developing the disease. Abbreviations AI: artificial intelligence; MR: magnetic resonance; OA: osteoarthritis Declarations Ethics Approval and Consent to Participate The study was approved by the University of Michigan Institutional Review Boards, and prior to data collection, all the subjects signed a written informed consent form (HUM00200085). Consent for Publication: Not applicable. Availability of Data and Materials The datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request. Competing Interests: The authors declare that they have no competing interests. Funding Funding was obtained from the College of Health Sciences, University of Michigan-Flint. Authors' Contributions TCL and YP secured funding for the study. All the authors were actively involved in subject recruitment and data collection. YP, ML, and JH were instrumental in processing the MR data. TCL conducted the statistical analysis and contributed significantly to the interpretation of the data. Additionally, all authors participated in reviewing the manuscript, providing critical feedback and approval of the final version. Acknowledgments: Not applicable. References Long H, Liu Q, Yin H, Wang K, Diao N, Zhang Y, Lin J, Guo A: Prevalence trends of site-specific osteoarthritis from 1990 to 2019: findings from the Global Burden of Disease Study 2019 . Arthritis Rheumatol 2022, 74 (7):1172-1183. Duncan RC, Hay EM, Saklatvala J, Croft PR: Prevalence of radiographic osteoarthritis--it all depends on your point of view . Rheumatology (Oxford, England) 2006, 45 (6):757-760. McAlindon TE, Snow S, Cooper C, Dieppe PA: Radiographic patterns of osteoarthritis of the knee joint in the community: the importance of the patellofemoral joint . Annals of the rheumatic diseases 1992, 51 (7):844-849. 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J Magn Reson Imaging 2012, 35 (6):1422-1429. Williams A, Qian Y, Chu CR: UTE-T2 ∗ mapping of human articular cartilage in vivo: a repeatability assessment . Osteoarthritis Cartilage 2011, 19 (1):84-88. Welsch GH, Apprich S, Zbyn S, Mamisch TC, Mlynarik V, Scheffler K, Bieri O, Trattnig S: Biochemical (T2, T2* and magnetization transfer ratio) MRI of knee cartilage: feasibility at ultrahigh field (7T) compared with high field (3T) strength . Eur Radiol 2011, 21 (6):1136-1143. Norman B, Pedoia V, Majumdar S: Use of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry . Radiology 2018, 288 (1):177-185. Banjar M, Horiuchi S, Gedeon DN, Yoshioka H: Review of quantitative knee articular cartilage MR imaging . Magn Reson Med Sci 2022, 21 (1):29-40. Tables Table 1. Intra-rater reliability for R 1 ρ and R 2 (1/s) cartilage relaxation rates. Regions of Interest Trial 1 mean (SD) Trial 2 mean (SD) Pearson Correlation (r) ICC (3,1) (95% CI) SEM R 1 ρ (1/s) Central Femoral 28.57 (3.40) 28.71 (3.44) 0.97* 0.98 (0.86 – 0.99)* 0.46 Posterior Femoral 26.29 (1.22) 26.91 (1.18) 0.84 0.91 (0.16 – 0.99)* 0.35 Tibia 27.41 (2.06) 27.29 (1.42) 0.92* 0.92 (0.27 – 0.99)* 0.47 Patella 20.33 (2.27) 20.33 (2.40) 0.99* 0.99 (0.94 – 0.99)* 0.22 Trochlea 26.84 (2.13) 27.09 (3.16) 0.99* 0.95 (0.56 – 0.99)* 0.57 R 2 (1/s) Central Femoral 32.52 (4.94) 32.73 (5.31) 0.99* 0.99 (0.96 – 1.00)* 0.48 Posterior Femoral 29.46 (2.25) 29.83 (2.21) 0.99* 0.99 (0.98 – 1.00)* 0.21 Tibia 34.09 (3.49) 34.69 (3.54) 0.91* 0.95 (0.57 – 0.99)* 0.74 Patella 26.84 (2.42) 27.05 (2.58) 0.99* 0.99 (0.95 – 1.00)* 0.24 Trochlea 32.80 (2.40) 33.26 (2.83) 0.97* 0.97 (0.77 – 0.99)* 0.43 CI: confidence interval; CV: coefficient of variation; ICC: intra-class correlation coefficient; SEM: standard error of measurement. * indicates p<0.05 Table 2. Inter-rater reliability for R 1 ρ and R 2 (1/s) cartilage relaxation rates. Regions of Interest Rater 1 mean (SD) Rater 2 mean (SD) Pearson Correlation (r) ICC (2,1) (95% CI) SEM R 1 ρ (1/s) Central Femoral 28.57 (3.40) 29.82 (4.37) 0.92* 0.93 (0.45 – 0.99)* 0.98 Posterior Femoral 26.29 (1.22) 27.60 (1.90) 0.95* 0.79 (-0.26 – 0.97)* 0.74 Tibia 27.41 (2.06) 27.81 (1.23) 0.93* 0.90 (0.26 – 0.99)* 0.50 Patella 20.33 (2.27) 21.00 (2.54) 0.99* 0.97 (0.07 – 0.99)* 0.34 Trochlea 26.84 (2.13) 27.96 (3.32) 0.80 0.83 (-0.12 – 0.98)* 1.10 R 2 (1/s) Central Femoral 32.52 (4.94) 33.34 (5.43) 0.95* 0.97 (0.79 – 0.99)* 0.84 Posterior Femoral 29.46 (2.25) 30.29 (1.75) 0.93* 0.91 (0.23 – 0.99)* 0.56 Tibia 34.09 (3.49) 34.68 (3.00) 0.98* 0.98 (0.81 – 0.99)* 0.43 Patella 26.84 (2.42) 27.27 (2.75) 0.99* 0.98 (0.81 – 0.99)* 0.27 Trochlea 32.80 (2.40) 33.52 (1.87) 0.75 0.84 (-0.18 – 0.98) 0.83 CI: confidence interval; CV: coefficient of variation; ICC: intra-class correlation coefficient; SEM: standard error of measurement. * indicates p<0.05 Additional Declarations No competing interests reported. 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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-4926999","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":356432430,"identity":"b037d731-28e9-4139-8dcb-1346b759787d","order_by":0,"name":"Tzu-Chieh Liao","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAxElEQVRIiWNgGAWjYBACxgYGxgMJDAxyICYzRICwFgaQFmPitYDAASBOBKkkTgtze++BAw9qbNI3XDvc+LmAwUZ2wwFCDus5l3Ag4Vha7obbic3SMxjSjAlrmZFjcCCB7TBISxszD8PhRMJa5r8Bavl3ON0AouU/EVpm8BgcSGw7nADVcoAILT1AhyX2pRnOBPmFxyDZeCYhLYbtZwwf/vhmI893O/3hZ54KO9k+gloaULgGBJSDgDwRakbBKBgFo2CkAwCztEnSjhb+6AAAAABJRU5ErkJggg==","orcid":"","institution":"University of Michigan-Flint","correspondingAuthor":true,"prefix":"","firstName":"Tzu-Chieh","middleName":"","lastName":"Liao","suffix":""},{"id":356432431,"identity":"8cce6a37-fea4-4945-864e-bb13438a329d","order_by":1,"name":"Yuxi Pang","email":"","orcid":"","institution":"St. Jude Children’s Research Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yuxi","middleName":"","lastName":"Pang","suffix":""},{"id":356432432,"identity":"3e5198b9-5eeb-4855-ac99-20bda448a97e","order_by":2,"name":"Corrie M. Yablon","email":"","orcid":"","institution":"University of Michigan","correspondingAuthor":false,"prefix":"","firstName":"Corrie","middleName":"M.","lastName":"Yablon","suffix":""},{"id":356432433,"identity":"02baa012-65f7-4703-9879-c593d1cf43df","order_by":3,"name":"Michaela K. Lewis","email":"","orcid":"","institution":"University of Michigan-Flint","correspondingAuthor":false,"prefix":"","firstName":"Michaela","middleName":"K.","lastName":"Lewis","suffix":""},{"id":356432434,"identity":"8b348a30-583e-4cc5-ba83-0df453d50d7f","order_by":4,"name":"Jeongmin G. Hyun","email":"","orcid":"","institution":"University of Michigan-Flint","correspondingAuthor":false,"prefix":"","firstName":"Jeongmin","middleName":"G.","lastName":"Hyun","suffix":""}],"badges":[],"createdAt":"2024-08-16 20:39:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4926999/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4926999/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":66675222,"identity":"48896132-5dfe-4d2c-a7a8-88b5426cca41","added_by":"auto","created_at":"2024-10-15 11:05:41","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":8669340,"visible":true,"origin":"","legend":"\u003cp\u003eSagittal images show sample segmentations on the DESS images with a slice thickness of 4 mm. The images A to C are displayed from medial to lateral. The figures show the regions of interest, tibia (T), patella (P), femoral trochlea (TrF), central femoral condyle (cF), posterior femoral condyle (pF).\u003c/p\u003e","description":"","filename":"Figure1.2.png","url":"https://assets-eu.researchsquare.com/files/rs-4926999/v1/8fcc700dd867c8804947e7ab.png"},{"id":66675221,"identity":"3c463839-10a6-48c3-843a-91c0c9424abd","added_by":"auto","created_at":"2024-10-15 11:05:40","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":5298192,"visible":true,"origin":"","legend":"\u003cp\u003eIn the coronal plane, sample reconstructed DESS images depict (A) the FIRST slice included in the femoral condylar region of interest (ROI) where the continuous trochlear cartilage layer separates into distinct cartilage layers on the femoral condyles (arrow); (B) the LAST slice included in the femoral condylar ROI known as “double bulls-eye” containing both cartilage and bone. Central femoral cartilage was defined as the first 60% from A/B while posterior femoral was defined as last 40%.\u003c/p\u003e","description":"","filename":"Figure2.1.png","url":"https://assets-eu.researchsquare.com/files/rs-4926999/v1/c46b637654919020c20cb2a8.png"},{"id":66675219,"identity":"15e05fb8-be76-46be-94ef-30502fe33d19","added_by":"auto","created_at":"2024-10-15 11:05:40","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":2949916,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative samples of R\u003csub\u003e1ρ \u003c/sub\u003eweighted images at various spin-lock durations (TSL). The represented samples are the same anatomical slice as Figure 1C.\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-4926999/v1/8275bc84a6bc4d8758ea57a2.png"},{"id":66675223,"identity":"9a3ea879-9d6b-4c45-bc9f-3b73eda576a7","added_by":"auto","created_at":"2024-10-15 11:05:41","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":3045528,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative samples of R\u003csub\u003e2 \u003c/sub\u003eweighted images at various echo times (TE). The represented samples are the same anatomical slice as Figure 1C.\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-4926999/v1/3e30a159c852aa213c4ece12.png"},{"id":66676108,"identity":"5f54b038-7896-43e0-acf5-0472aa69afd3","added_by":"auto","created_at":"2024-10-15 11:13:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":1805843,"visible":true,"origin":"","legend":"\u003cp\u003eBland-Altman plots comparing (A) intra-rater and (B) inter-rater agreement of cartilage relaxations. Solid line: average difference (bias estimate). Dashed lines: 95% Bland-Altman limits of agreement.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-4926999/v1/41222b0d424f1d24e5556262.png"},{"id":85168393,"identity":"9cf213f5-70c7-4fef-a0ac-0c529b18fe33","added_by":"auto","created_at":"2025-06-23 04:47:02","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":40610564,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4926999/v1/28b7cdce-1de5-403a-92ce-de11ee674e67.pdf"},{"id":66675218,"identity":"78692d78-b06e-454b-b4b6-6384b746d3f5","added_by":"auto","created_at":"2024-10-15 11:05:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":15762,"visible":true,"origin":"","legend":"","description":"","filename":"oldTABLE2.docx","url":"https://assets-eu.researchsquare.com/files/rs-4926999/v1/0cc7835a02c410f7e0417def.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Intrarater and Interrater Reliability of the Quantification of Knee Cartilage MR Relaxation Metrics","fulltext":[{"header":"Introduction","content":"\u003cp\u003eOsteoarthritis (OA) is a common joint disorder in middle-aged and elderly people who significantly affects joint functionality, causing disability and increased healthcare utilization. The knee joint is the most commonly affected joint, with an estimated prevalence of 365\u0026nbsp;million people worldwide, followed by the hip and hand [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Knee OA impacts both tibiofemoral and patellofemoral joints, with patellofemoral OA being highly prevalent [\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], but the latter is often underrecognized. A radiographic study revealed that among adults aged 50 years and above with knee pain, 40% had combined tibiofemoral and patellofemoral OA, 24% had isolated patellofemoral OA, and 4% had isolated tibiofemoral OA [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe current gold standard for diagnosing OA, in addition to routine clinical examination, is plain X-ray [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This method is safe, cost-effective, and widely available; however, it is known to be insensitive to early OA changes. Since clinical OA represents a late-stage condition with limited opportunities for disease intervention, identifying and characterizing OA in its earlier stages could enhance our understanding of OA mechanisms and aid in the development and evaluation of disease-modifying treatments.\u003c/p\u003e \u003cp\u003eIn the early stages of OA, articular cartilage fails to synthesize extracellular matrix components, leading to a loss of proteoglycans and collagen, reduced elasticity, and decreased water content [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Quantitative MR imaging provides noninvasive tools for assessing the biochemical composition of articular cartilage, allowing for the characterization of cartilage integrity before irreversible damage occurs. Over the past two decades, studies have shown that water proton relaxation times T\u003csub\u003e1ρ\u003c/sub\u003e and T\u003csub\u003e2\u003c/sub\u003e can provide valuable information on the proteoglycan content and collagen orientation within cartilage [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. These relaxation metrics have been reported to be effective in distinguishing between individuals with and without knee OA [\u003cspan additionalcitationids=\"CR10 CR11\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eCartilage segmentation is a critical factor influencing the quantification of MR relaxation metrics [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Recent advances in artificial intelligence (AI) have significantly improved cartilage segmentation analysis [\u003cspan additionalcitationids=\"CR15 CR16\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Despite its growing popularity, AI-based segmentation is not yet commercially or readily accessible to all institutions, making manual segmentation valuable due to its inherent benefits. Manual segmentation offers superior accuracy and precision and remains the gold standard for validating and training deep learning models. However, a major downside of manual cartilage segmentation is the potential for variability between different assessors and even within the same assessor over time.\u003c/p\u003e \u003cp\u003eThe reproducibility of compositional MR relaxation metrics could be compromised by various sources, ranging from data acquisition to postprocessing, as previously documented in the literature [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. With the establishment of standardized compositional MR imaging acquisitions [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], significant attention should be directed toward data postprocessing. Therefore, the aim of this study was to examine the intra- and interrater reliability of knee cartilage MR relaxation metrics via manual segmentation by end users.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eSubjects\u003c/h2\u003e \u003cp\u003eA sample of five healthy individuals volunteered for the reliability analysis (average age, 24.4 \u0026plusmn; 1.3 years; 3 females). The exclusion criteria were (1) any preexisting musculoskeletal diseases of the lower extremity, (2) any past surgeries of the lower extremity, and (3) any contraindications to MR imaging, such as the presence of a cardiac pacemaker, metal implants, intraocular foreign body, aneurysm clips, pregnancy, claustrophobia, etc. The study was approved by the University of Michigan Institutional Review Boards (IRB) (approval number: HUM00200085). All procedures followed were in accordance with the Declaration of Helsinki, ensuring that the rights and well-being of participants were protected. Prior to data collection, all participants signed written informed consent forms, acknowledging their understanding of the study and their voluntary participation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eMR Imaging Protocols\u003c/h2\u003e \u003cp\u003eAll participants were scanned on the right knee in the supine position via a Philips Ingenia 3.0T wide-bore MRI scanner with a 16-channel knee transmit-receive coil. All the images were acquired in the sagittal plane. The double echo steady state (DESS) sequence was used for anatomical imaging with a field-of-view (FOV) of 140*140*112 mm\u003csup\u003e3\u003c/sup\u003e and an acquired voxel resolution of 0.4*0.5*0.9 mm\u003csup\u003e3\u003c/sup\u003e, which was interpolated to 0.36*0.36*0.63 mm\u003csup\u003e3\u003c/sup\u003e when 3D volumetric images were reconstructed. The number of reconstructed imaging slices was 178, and the scan time was 5:52 minutes. R\u003csub\u003e1ρ\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e mappings were obtained from the established MAPSS sequences [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] implemented by the vendor as working-in-progress (WIP) prototypes. The FOV was 140*140*96 mm\u003csup\u003e3,\u003c/sup\u003e and the acquired or reconstructed voxel size was 0.44*0.66*4.0 or 0.27*0.27*4.0 mm\u003csup\u003e3\u003c/sup\u003e. For R\u003csub\u003e1ρ\u003c/sub\u003e mapping, the spin‒lock durations (TSLs) were 0, 10, 15, 20, 25, 30, 35, and 40 ms, and the spin‒lock strength was 500 Hz. For R\u003csub\u003e2\u003c/sub\u003e mapping, the echo times (TEs) were 0, 18, 36, and 54 ms. The scan duration for R\u003csub\u003e1ρ\u003c/sub\u003e or R\u003csub\u003e2\u003c/sub\u003e mapping was 9:13 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eMR Imaging Processing\u003c/h2\u003e \u003cp\u003eManual cartilage segmentations were performed on the reformatted DESS images via ITK-SNAP 4.0 software [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. R\u003csub\u003e1ρ\u0026minus;\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e-weighted images were coregistered, as documented in the literature [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], with the reformatted DESS images as the reference. The reformatted DESS images had the same slice thickness (4 mm) as the R\u003csub\u003e1ρ\u0026minus;\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e-weighted images. The regions of interest (ROIs) included the articular cartilages of the tibia (T), patella (P), femoral trochlea (TrF), central femoral (cF) condyle, and posterior femoral (pF) condyle (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In the sagittal images, the tibial, patellar, and femoral trochlear cartilages were identified by tracing the boundaries on the basis of their anatomical landmarks, slice by slice. All slices that displayed cartilage underwent segmentation, except those with partial cartilage volume averaging at the margins.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAs the cartilaginous surface of the femoral cartilage is continuous, artificial boundaries had to be defined for separating the ROIs (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Only the weight-bearing portion of the femoral cartilage was considered and further classified into the cF and pF regions. The former was separated from the trochlear plane, and the latter was separated in the coronal plane. The anterior boundary was defined as the first image where the continuous trochlear cartilage layer separated into distinct cartilage layers on the femoral condyles (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The posterior boundary was defined as 60% of the distance between the anterior boundary and the last image containing the posterior aspects of the femoral condyles, known as \u0026ldquo;double bulls-eye\u0026rdquo;, containing both cartilage and bone (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb) [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. These landmarks were used because they could be clearly identified and reproduced in the subjects. While the segmentation was performed in the sagittal images and the landmarks were identified in the coronal images, the boundaries could be defined by using the same landmarks and 3D viewing during segmentation.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe signal intensities (\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S\\)\u003c/span\u003e\u003c/span\u003e) in R\u003csub\u003e1ρ\u0026minus;\u003c/sub\u003e or R\u003csub\u003e2\u0026minus;\u003c/sub\u003eweighted images were fitted via home-grown IDL 8.9 (Interactive Data Language, Harris Geospatial Solutions, Inc., Broomfield, CO, USA) programming scripts [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], voxel by voxel, to a simple exponential decay relaxation model, i.e., \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:S=C*{e}^{-\\left({R}_{1\\rho\\:}\\:*\\:TSL\\right)}\\:\\)\u003c/span\u003e\u003c/span\u003eor \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:S=C*{e}^{-\\left({R}_{2}\\:*\\:TE\\right)}\\)\u003c/span\u003e\u003c/span\u003e, where \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:C\\)\u003c/span\u003e\u003c/span\u003e is a fitted constant. The average R\u003csub\u003e1ρ\u003c/sub\u003e or R\u003csub\u003e2\u003c/sub\u003e relaxation rate (in units of 1/s) was subsequently calculated from the fits for cartilage in different ROIs of the knee joint.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIntra- and interrater reliability statistics were calculated for the R\u003csub\u003e1ρ\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e values of knee cartilage. Intrarater reliability was obtained for one rater (ML) who segmented each scan twice on different occasions. Interrater reliability was obtained for two raters (ML and JH), who individually segmented the knee cartilages for each scan. Intraclass correlation coefficients [ICC (3,1)] and ICC (2,1) were used to evaluate intra- and interrater reliability for each ROI for R\u003csub\u003e1ρ\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e, respectively [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. The standard error of measurement (SEM) was used to assess absolute reliability. ICCs less than 0.5 indicated poor reliability; 0.5\u0026ndash;0.75, moderate reliability; 0.75\u0026ndash;0.9, good reliability; and greater than 0.9, excellent reliability [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Bland‒Altman limits of agreement were calculated with an exact 95% confidence interval (CI). The data collected from this study were analyzed via SPSS 28.0.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eFigures 3 and 4 show\u0026nbsp;representative\u0026nbsp;images of\u0026nbsp;R\u003csub\u003e1\u003c/sub\u003e\u003csub\u003e\u0026rho;-\u0026nbsp;\u003c/sub\u003eand R\u003csub\u003e2-\u003c/sub\u003eweighted images from one subject at various TSL and TE values, respectively. Tables 1 and 2 provide the\u0026nbsp;R\u003csub\u003e1\u003c/sub\u003e\u003csub\u003e\u0026rho;\u0026nbsp;\u003c/sub\u003eand R\u003csub\u003e2\u0026nbsp;\u003c/sub\u003erelaxation rates in each\u0026nbsp;ROI\u0026nbsp;as estimated from each rater and session.\u0026nbsp;The tibia\u0026nbsp;cartilage\u0026nbsp;presented\u0026nbsp;the greatest\u0026nbsp;R\u003csub\u003e1\u003c/sub\u003e\u003csub\u003e\u0026rho;\u0026nbsp;\u003c/sub\u003eand R\u003csub\u003e2\u0026nbsp;\u003c/sub\u003erelaxation rates,\u0026nbsp;followed by\u0026nbsp;the\u0026nbsp;femoral cartilage (if all subregions\u0026nbsp;were considered\u0026nbsp;together), whereas\u0026nbsp;the\u0026nbsp;patella cartilage\u0026nbsp;presented\u0026nbsp;the lowest rates.\u003c/p\u003e\n\u003cp\u003eThe intrarater\u0026nbsp;consistency in\u0026nbsp;the\u0026nbsp;segmented ROI areas of\u0026nbsp;the\u0026nbsp;knee\u0026nbsp;R\u003csub\u003e1\u003c/sub\u003e\u003csub\u003e\u0026rho;\u003c/sub\u003e relaxation rates demonstrated\u0026nbsp;that the\u0026nbsp;ICC values ranged between 0.84\u0026nbsp;and\u0026nbsp;0.99,\u0026nbsp;and\u0026nbsp;the results were similar for R\u003csub\u003e2\u003c/sub\u003e values ranging between 0.91\u0026nbsp;and\u0026nbsp;0.99. The SEM ranged between 0.22\u0026nbsp;and\u0026nbsp;0.57 and\u0026nbsp;between\u0026nbsp;0.21\u0026nbsp;and\u0026nbsp;0.74 for\u0026nbsp;R\u003csub\u003e1\u003c/sub\u003e\u003csub\u003e\u0026rho;\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e, respectively (Table 1).\u0026nbsp;For interrater\u0026nbsp;consistency, the ICCs for\u0026nbsp;the\u0026nbsp;R\u003csub\u003e1\u003c/sub\u003e\u003csub\u003e\u0026rho;\u003c/sub\u003e values ranged between 0.80\u0026nbsp;and\u0026nbsp;0.99, and\u0026nbsp;the\u0026nbsp;R\u003csub\u003e2\u003c/sub\u003e values\u0026nbsp;ranged between 0.75\u0026nbsp;and\u0026nbsp;0.99.\u0026nbsp;Finally, the SEM for\u0026nbsp;interrater\u0026nbsp;reliability ranged\u0026nbsp;from\u0026nbsp;0.34--1.10 and 0.27--0.84 for\u0026nbsp;R\u003csub\u003e1\u003c/sub\u003e\u003csub\u003e\u0026rho;\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e, respectively (Table 2). For\u0026nbsp;the intrarater\u0026nbsp;analysis,\u0026nbsp;Bland‒Altman\u0026nbsp;agreement\u0026nbsp;revealed\u0026nbsp;a mean difference in relaxation rates of 0.27 s\u003csup\u003e-1\u003c/sup\u003e, the limits of agreement were -1.23 and 1.78,\u0026nbsp;and the\u0026nbsp;outer 95%\u0026nbsp;CIs were\u0026nbsp;-0.49 and 0.055 (Figure 5a).\u0026nbsp;For the interrater\u0026nbsp;analysis,\u0026nbsp;the\u0026nbsp;mean difference was 0.81 s\u003csup\u003e-1\u003c/sup\u003e, the limits of agreement were -1.50 and 3.13, and the 95% CIs were -1.15 and 0.47 (Figure 5b).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eWhile automatic cartilage segmentation through AI has been reported, manual segmentation remains the gold standard, offering superior accuracy and precision. The goal of this study was to examine the intra- and interrater reliability of knee cartilage MR relaxation metrics via manual cartilage segmentation. Our findings suggested good to excellent intra- and interrater reliability for the quantification of knee MR relaxation metrics, with slightly lower interrater reliability than intrarater reliability. Consistent with the literature [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], tibia cartilage presented the greatest R\u003csub\u003e1ρ\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e relaxation rates, followed by femoral cartilage, whereas patella cartilage presented the lowest rates.\u003c/p\u003e \u003cp\u003eIn our study, reliability was greater in the patellar, trochlear, and central femoral condylar cartilages than in the tibia and posterior femoral condylar cartilages. Since our segmentations were based on anatomical identification, the reproducibility of these landmarks became crucial in determining the boundaries for segmentation. Patellar and femoral trochlear cartilages, which comprise the patellofemoral joint, can be easily visualized on MR images with distinct cartilage-to-bone boundaries (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). Additionally, the patellar cartilage, followed by the trochlear cartilage, is thicker than the other knee cartilages. Previous studies have suggested that the thicker the cartilage is, the lower the observed variability [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], which is consistent with the results of the present study. However, posterior femoral condylar cartilage exhibited the greatest variability, which can be attributed to observer-dependent differences in the determination of the artificial boundaries, both anteriorly and posteriorly. Nevertheless, the overall high intra- and interrater reliability (ICCs ranging between 0.75 and 0.99) suggests that subtle differences in cartilage selection in specific regions do not meaningfully alter the mean R\u003csub\u003e1ρ\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e relaxation rates.\u003c/p\u003e \u003cp\u003eSince our cartilage segmentation was reproducible, the mean R\u003csub\u003e1ρ\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e relaxation rates were expected to demonstrate good to excellent intra- and interrater reliability, which is consistent with the literature. In the study by William et al. [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], the authors reported intrarater ICCs ranging between 0.80 and 0.98 for T\u003csub\u003e2\u003c/sub\u003e mapping in the knee joint, whereas the current study reported intrarater ICCs ranging between 0.91 and 0.99 among the tibiofemoral and patellofemoral joints. Another study by Welsch et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] reported an average interrater ICC of 0.90 for T\u003csub\u003e2\u003c/sub\u003e mapping in the knee joint, whereas our interrater agreement, though slightly lower, was still considered excellent, with ICCs ranging between 0.75 and 0.99. Collectively, the evidence suggests that manual segmentation of specific MR slices and known subregions is highly reliable.\u003c/p\u003e \u003cp\u003eWhile AI-based segmentation has the advantage of eliminating the manual segmentation burden, its overall reliability and accuracy remain unclear. Norman et al. [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] utilized a deep learning model to perform automatic segmentation via DESS sequences and reported Dice coefficients ranging from 0.77 to 0.88 for cartilage. Similarly, Gatti and Maly [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] used knee MR images and reported Dice coefficients ranging from 0.88 to 0.91 for different cartilage compartments, with even better results for healthy cartilage. The Dice coefficient, a statistical tool that measures the agreement of segmentations, with 1 being a perfect match and 0.8 to 0.9 generally considered good and useful [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. While the reliability of AI-based segmentation appears to be comparable with that of manual segmentation, it is important to note that AI-based segmentation was validated against manual analysis, which resulted in a doubling of the errors. Importantly, reliability does not necessarily imply accuracy. Since the true cartilage boundaries for the samples in this study are currently unknown, it is unclear how accurately these MR relaxation metrics are reflected. Future studies will need to address this critical gap for both manual and AI-based segmentations to enhance the assessment of degenerative joint diseases.\u003c/p\u003e \u003cp\u003eEven though this study is limited by having only two raters and five image sets, the results likely reflect the true reliability of generating knee MR cartilage relaxation metrics from manual segmentations of the respective ROIs. However, it is important to note that the study included only healthy subjects. Individuals with OA may have bone spurs and cartilage defects that could complicate the accurate outlining of the cartilage surface. Additionally, this study did not examine the zonal behavior of cartilage relaxation metrics, which have been shown to differ within cartilage layers due to varying loads [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Future research should include subjects with OA and assess the zonal behavior of cartilage relaxation metrics to improve our understanding of cartilage health and pathology.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eIn conclusion, our analysis results suggest that manually segmenting specific MR slices and known subregions is highly reliable and repeatable for the quantification of MR cartilage relaxation metrics, albeit at the cost of human manpower. Our findings contribute to the existing body of literature and indicate that manual segmentation of knee cartilage demonstrates excellent intra- and interrater reliability, with patellofemoral joint assessment being superior to tibiofemoral joint assessment. Additionally, this validation paves the way for the large-scale application of this method in prospective trials that longitudinally monitor the development and progression of knee osteoarthritis in individuals at risk of further developing the disease.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eAI: artificial intelligence; MR: magnetic resonance; OA: osteoarthritis\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cem\u003eEthics Approval and Consent to Participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the University of Michigan Institutional Review Boards,\u0026nbsp;and prior to data collection, all\u0026nbsp;the\u0026nbsp;subjects signed a written informed consent\u0026nbsp;form\u0026nbsp;(HUM00200085).\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent for Publication:\u0026nbsp;\u003c/em\u003eNot applicable.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of Data and Materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and/or analyzed during the current study are available from the corresponding author upon reasonable request.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCompeting Interests:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eFunding was\u0026nbsp;obtained\u0026nbsp;from\u0026nbsp;the\u0026nbsp;College of Health Sciences, University of Michigan-Flint.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; Contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eTCL and YP secured funding for the study. All the authors were actively involved in subject recruitment and data collection. YP, ML, and JH were instrumental in processing the MR data. TCL conducted the statistical analysis and contributed significantly to the interpretation of the data. Additionally, all authors participated in reviewing the manuscript, providing critical feedback and approval of the final version.\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAcknowledgments:\u0026nbsp;\u003c/em\u003eNot applicable.\u003cstrong\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLong H, Liu Q, Yin H, Wang K, Diao N, Zhang Y, Lin J, Guo A: \u003cstrong\u003ePrevalence trends of site-specific osteoarthritis from 1990 to 2019: findings from the Global Burden of Disease Study 2019\u003c/strong\u003e. \u003cem\u003eArthritis Rheumatol \u003c/em\u003e2022, \u003cstrong\u003e74\u003c/strong\u003e(7):1172-1183.\u003c/li\u003e\n\u003cli\u003eDuncan RC, Hay EM, Saklatvala J, Croft PR: \u003cstrong\u003ePrevalence of radiographic osteoarthritis--it all depends on your point of view\u003c/strong\u003e. \u003cem\u003eRheumatology (Oxford, England) \u003c/em\u003e2006, \u003cstrong\u003e45\u003c/strong\u003e(6):757-760.\u003c/li\u003e\n\u003cli\u003eMcAlindon TE, Snow S, Cooper C, Dieppe PA: \u003cstrong\u003eRadiographic patterns of osteoarthritis of the knee joint in the community: the importance of the patellofemoral joint\u003c/strong\u003e. \u003cem\u003eAnnals of the rheumatic diseases \u003c/em\u003e1992, \u003cstrong\u003e51\u003c/strong\u003e(7):844-849.\u003c/li\u003e\n\u003cli\u003eSzebenyi B, Hollander AP, Dieppe P, Quilty B, Duddy J, Clarke S, Kirwan JR: \u003cstrong\u003eAssociations between pain, function, and radiographic features in osteoarthritis of the knee\u003c/strong\u003e. \u003cem\u003eArthritis and rheumatism \u003c/em\u003e2006, \u003cstrong\u003e54\u003c/strong\u003e(1):230-235.\u003c/li\u003e\n\u003cli\u003eKellgren JH, Lawrence JS: \u003cstrong\u003eRadiological assessment of osteo-arthrosis\u003c/strong\u003e. \u003cem\u003eAnn Rheum Dis \u003c/em\u003e1957, \u003cstrong\u003e16\u003c/strong\u003e(4):494-502.\u003c/li\u003e\n\u003cli\u003eCucchiarini M, de Girolamo L, Filardo G, Oliveira JM, Orth P, Pape D, Reboul P: \u003cstrong\u003eBasic science of osteoarthritis\u003c/strong\u003e. \u003cem\u003eJ Exp Orthop \u003c/em\u003e2016, \u003cstrong\u003e3\u003c/strong\u003e(1):22.\u003c/li\u003e\n\u003cli\u003eLink TM, Neumann J, Li X: \u003cstrong\u003ePrestructural cartilage assessment using MRI\u003c/strong\u003e. \u003cem\u003eJ Magn Reson Imaging \u003c/em\u003e2017, \u003cstrong\u003e45\u003c/strong\u003e(4):949-965.\u003c/li\u003e\n\u003cli\u003eRegatte RR, Akella SV, Lonner JH, Kneeland JB, Reddy R: \u003cstrong\u003eT1rho relaxation mapping in human osteoarthritis (OA) cartilage: comparison of T1rho with T2\u003c/strong\u003e. \u003cem\u003eJournal of magnetic resonance imaging : JMRI \u003c/em\u003e2006, \u003cstrong\u003e23\u003c/strong\u003e(4):547-553.\u003c/li\u003e\n\u003cli\u003eBaum T, Joseph GB, Arulanandan A, Nardo L, Virayavanich W, Carballido-Gamio J, Nevitt MC, Lynch J, McCulloch CE, Link TM: \u003cstrong\u003eAssociation of magnetic resonance imaging-based knee cartilage T2 measurements and focal knee lesions with knee pain: data from the Osteoarthritis Initiative\u003c/strong\u003e. \u003cem\u003eArthritis Care Res (Hoboken) \u003c/em\u003e2012, \u003cstrong\u003e64\u003c/strong\u003e(2):248-255.\u003c/li\u003e\n\u003cli\u003eJoseph GB, Baum T, Alizai H, Carballido-Gamio J, Nardo L, Virayavanich W, Lynch JA, Nevitt MC, McCulloch CE, Majumdar S\u003cem\u003e \u003c/em\u003eet al: \u003cstrong\u003eBaseline mean and heterogeneity of MR cartilage T2 are associated with morphologic degeneration of cartilage, meniscus, and bone marrow over 3 years--data from the Osteoarthritis Initiative\u003c/strong\u003e. \u003cem\u003eOsteoarthritis Cartilage \u003c/em\u003e2012, \u003cstrong\u003e20\u003c/strong\u003e(7):727-735.\u003c/li\u003e\n\u003cli\u003eLi X, Benjamin Ma C, Link TM, Castillo DD, Blumenkrantz G, Lozano J, Carballido-Gamio J, Ries M, Majumdar S: \u003cstrong\u003eIn vivo T(1rho) and T(2) mapping of articular cartilage in osteoarthritis of the knee using 3 T MRI\u003c/strong\u003e. \u003cem\u003eOsteoarthritis and cartilage \u003c/em\u003e2007, \u003cstrong\u003e15\u003c/strong\u003e(7):789-797.\u003c/li\u003e\n\u003cli\u003eAtkinson HF, Birmingham TB, Moyer RF, Yacoub D, Kanko LE, Bryant DM, Thiessen JD, Thompson RT: \u003cstrong\u003eMRI T2 and T1\u0026rho; 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mapping of human articular cartilage in vivo: a repeatability assessment\u003c/strong\u003e. \u003cem\u003eOsteoarthritis Cartilage \u003c/em\u003e2011, \u003cstrong\u003e19\u003c/strong\u003e(1):84-88.\u003c/li\u003e\n\u003cli\u003eWelsch GH, Apprich S, Zbyn S, Mamisch TC, Mlynarik V, Scheffler K, Bieri O, Trattnig S: \u003cstrong\u003eBiochemical (T2, T2* and magnetization transfer ratio) MRI of knee cartilage: feasibility at ultrahigh field (7T) compared with high field (3T) strength\u003c/strong\u003e. \u003cem\u003eEur Radiol \u003c/em\u003e2011, \u003cstrong\u003e21\u003c/strong\u003e(6):1136-1143.\u003c/li\u003e\n\u003cli\u003eNorman B, Pedoia V, Majumdar S: \u003cstrong\u003eUse of 2D U-Net convolutional neural networks for automated cartilage and meniscus segmentation of knee MR imaging data to determine relaxometry and morphometry\u003c/strong\u003e. \u003cem\u003eRadiology \u003c/em\u003e2018, \u003cstrong\u003e288\u003c/strong\u003e(1):177-185.\u003c/li\u003e\n\u003cli\u003eBanjar M, Horiuchi S, Gedeon DN, Yoshioka H: \u003cstrong\u003eReview of quantitative knee articular cartilage MR imaging\u003c/strong\u003e. \u003cem\u003eMagn Reson Med Sci \u003c/em\u003e2022, \u003cstrong\u003e21\u003c/strong\u003e(1):29-40.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Intra-rater reliability for R\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026rho;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and R\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e(1/s) cartilage relaxation rates.\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"804\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegions of Interest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrial 1\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003emean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\"\u003e\n \u003cp\u003e\u003cstrong\u003eTrial 2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003emean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson Correlation (r)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC (3,1)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csub\u003e1\u003c/sub\u003e\u003c/em\u003e\u003cem\u003e\u003csub\u003e\u0026rho;\u003c/sub\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;(1/s)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Central Femoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e28.57 (3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e28.71 (3.44)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.97*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.98 (0.86 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.46\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Posterior Femoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e26.29 (1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e26.91 (1.18)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.91 (0.16 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.35\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Tibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e27.41 (2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e27.29 (1.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.92*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.92 (0.27 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.47\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Patella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e20.33 (2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e20.33 (2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.99*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.94 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Trochlea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e26.84 (2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e27.09 (3.16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.99*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.56 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003cem\u003e\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e\u003cem\u003e(1/s)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Central Femoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e32.52 (4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e32.73 (5.31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.99*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.96 \u0026ndash; 1.00)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.48\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Posterior Femoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e29.46 (2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e29.83 (2.21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.99*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.98 \u0026ndash; 1.00)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Tibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e34.09 (3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e34.69 (3.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.91*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.95 (0.57 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Patella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e26.84 (2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e27.05 (2.58)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.99*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.99 (0.95 \u0026ndash; 1.00)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Trochlea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e32.80 (2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e33.26 (2.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.97*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.77 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCI: confidence interval; CV: coefficient of variation; ICC: intra-class correlation coefficient; SEM: standard error of measurement. * indicates p\u0026lt;0.05\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 2. Inter-rater reliability for R\u003csub\u003e1\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026rho;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;and R\u003csub\u003e2\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e\u003csub\u003e\u0026nbsp;\u003c/sub\u003e\u003c/strong\u003e\u003cstrong\u003e(1/s)\u003csub\u003e\u0026nbsp;\u003c/sub\u003ecartilage relaxation rates.\u0026nbsp;\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"804\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRegions of Interest\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRater 1\u0026nbsp;\u003c/strong\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e\n \u003cp\u003emean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\"\u003e\n \u003cp\u003e\u003cstrong\u003eRater 2\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003emean (SD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\"\u003e\n \u003cp\u003e\u003cstrong\u003ePearson Correlation (r)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\"\u003e\n \u003cp\u003e\u003cstrong\u003eICC (2,1)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\"\u003e\n \u003cp\u003e\u003cstrong\u003eSEM\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003csub\u003e1\u003c/sub\u003e\u003c/em\u003e\u003cem\u003e\u003csub\u003e\u0026rho;\u003c/sub\u003e\u003c/em\u003e\u003cem\u003e\u0026nbsp;(1/s)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Central Femoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e28.57 (3.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e29.82 (4.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.92*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.93 (0.45 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.98\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Posterior Femoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e26.29 (1.22)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e27.60 (1.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.95*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.79 (-0.26 \u0026ndash; 0.97)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.74\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Tibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e27.41 (2.06)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e27.81 (1.23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.93*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.90 (0.26 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Patella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e20.33 (2.27)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e21.00 (2.54)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.99*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.07 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.34\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Trochlea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e26.84 (2.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e27.96 (3.32)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.80\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.83 (-0.12 \u0026ndash; 0.98)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e1.10\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003eR\u003c/em\u003e\u003cem\u003e\u003csub\u003e2\u0026nbsp;\u003c/sub\u003e\u003c/em\u003e\u003cem\u003e(1/s)\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e\u003cem\u003e\u0026nbsp;\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Central Femoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e32.52 (4.94)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e33.34 (5.43)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.95*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.97 (0.79 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.84\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;Posterior Femoral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e29.46 (2.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e30.29 (1.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.93*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.91 (0.23 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Tibia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e34.09 (3.49)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e34.68 (3.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.98*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.98 (0.81 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Patella\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e26.84 (2.42)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e27.27 (2.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.99*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.98 (0.81 \u0026ndash; 0.99)*\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.27\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd width=\"23.134328358208954%\" valign=\"top\"\u003e\n \u003cp\u003e\u0026nbsp; \u0026nbsp; Trochlea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e32.80 (2.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e33.52 (1.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"14.925373134328359%\" valign=\"top\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"22.388059701492537%\" valign=\"top\"\u003e\n \u003cp\u003e0.84 (-0.18 \u0026ndash; 0.98)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd width=\"9.701492537313433%\" valign=\"top\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eCI: confidence interval; CV: coefficient of variation; ICC: intra-class correlation coefficient; SEM: standard error of measurement. * indicates p\u0026lt;0.05\u003c/p\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 cartilage, reliability, quantitative MRI","lastPublishedDoi":"10.21203/rs.3.rs-4926999/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4926999/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMagnetic resonance (MR) imaging is often used to study osteoarthritis (OA), as advanced MR imaging methods can provide a quantitative assessment of tissue biochemistry or composition. For example, the magnetic relaxation times T\u003csub\u003e1ρ\u003c/sub\u003e (i.e., 1/R\u003csub\u003e1ρ\u003c/sub\u003e) and T\u003csub\u003e2\u003c/sub\u003e (i.e., 1/R\u003csub\u003e2\u003c/sub\u003e) of water molecules within articular cartilage have been demonstrated to be imaging biomarkers sensitive to the compositional changes associated with early OA. However, the outcome of MR imaging data analysis depends on relaxation data acquisition methods as well as assessor variability if manual segmentation is performed. Therefore, the goal of the current study was to evaluate the intra- and interrater reliability of established imaging protocols for performing quantitative cartilage MR relaxation metrics of the knee joint.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eRight knee MR images were obtained from five healthy individuals (average age, 24.4 years; 3 females) via a 3.0T MRI scanner equipped with a 16-channel knee T/R coil. A double echo steady state (DESS) sequence was used for anatomical imaging, and the established MAPSS sequences were used for R\u003csub\u003e1ρ\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e mapping. One assessor performed manual segmentations of the knee cartilage on two separate occasions, whereas a second assessor performed segmentations once. Both the R\u003csub\u003e1ρ\u003c/sub\u003e and R\u003csub\u003e2\u003c/sub\u003e mean values were then calculated for the tibial, patellar, femoral trochlear, central femoral condylar, and posterior femoral condylar cartilages. Intraclass correlation coefficients [ICC (3,1)] and ICCs (2,1) were used to evaluate intra- and interrater reliability, respectively. The standard error of measurement (SEM) was used to assess absolute reliability.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe intrarater knee cartilage relaxation metrics demonstrated good to excellent reliability, ranging between 0.88 and 0.99, with SEMs ranging between 0.16 and 0.80. The interrater reliability similarly ranged from 0.79\u0026ndash;0.97, with SEMs ranging between 0.27 and 1.10.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eManual segmentation of specific MR slices and known subregions is highly reliable and repeatable for the quantification of cartilage MR relaxation metrics. This validation paves the way for the large-scale application of this method in prospective trials that longitudinally monitor OA development and progression in the knee joint.\u003c/p\u003e","manuscriptTitle":"Intrarater and Interrater Reliability of the Quantification of Knee Cartilage MR Relaxation Metrics","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-10-15 11:05:35","doi":"10.21203/rs.3.rs-4926999/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":"ffefdf5b-1eb6-4a8b-880a-59c659a1c4a2","owner":[],"postedDate":"October 15th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-23T04:38:11+00:00","versionOfRecord":[],"versionCreatedAt":"2024-10-15 11:05:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4926999","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4926999","identity":"rs-4926999","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}