CT and MRI-visible canine brain avatar via three-dimensional digital anatomy printing in aiding neurosurgical training and biopsy planning – proof of concept study

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Roney, Rajnish Gupta, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6779582/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. Advancements in 3D printing technology have propelled its utility in human and veterinary medicine for teaching, surgical planning, and procedural practice. Complex clinical workflows and a lack of realistic training models have prevented widespread clinical implementation of intracranial interventions in veterinary medicine. Currently, many phantoms produced for medical settings are limited in their anatomical representations or compatible imaging modalities. Computer-aided design bridges the gap between a patient’s imaging data, segmenting individual regions of interest, and selecting materials to produce the same imaging features. Digital Anatomy printer with polyjet systems allows for the use of multiple materials to accomplish these designs and produce a usable patient-specific physical avatar that behaves like the original patient. Purpose. This study provides a ready-to-use guideline and workflow system of utilizing the Digital Anatomy printer to develop a physical canine brain avatar based on a specific patient’s imaging data that accurately depicts patient anatomy in CT and MRI scans, which can ultimately be used for next-generation procedural planning and practice. Methods. MRI and CT data from a specific patient with a brain tumor were initially utilized to generate a computer-aided design for an avatar to mimic the original anatomy. Tissues of interest were segmented, and printing materials were selected to mimic the desired imaging properties. Avatars were produced using a 3D polyjet system and underwent CT and MRI scans for evaluation. Avatar material intensity/Hounsfield Units, dimensions, and selected biopsy track were directly compared to the patient data for similarity. Results. Our avatars showed the desired signal in both CT and MRI, with similar proportional material and tissue intensities across the imaging spectrum and minimal dimensional variation between the avatars and the patient. Conclusion. This study established guidelines for canine brain avatar manufacture with DAP and validated the workflow of converting patient-specific clinical imaging to a 3D printed avatar that is anatomically accurate and visible in CT and MRI, producing a unique model for clinicians. Dog Phantom 3D Biopsy Neurosurgery Teaching model Glioma Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Background Three-dimensional (3D) printing has become an increasingly valuable tool in both human and veterinary medicine, offering new opportunities for patient-specific modeling, surgical planning, and procedural training [ 1 , 2 ]. In veterinary neurosurgical practice, intracranial procedures such as tumor biopsy and resection are in their infancy due to their technical difficulty and need for multiple anesthetic events to plan and perform the procedure with currently available systems [ 3 ]. Furthermore, there is a lack of interactive teaching tools that allow for effective clinician training. Currently, cadavers are commonly used for procedure practice; however, they lack the patient’s specific pathology, significantly reducing the realism of training for specific intracranial procedures. Here, we design, manufacture, and validate a computed tomography (CT) and Magnetic Resonance Imaging (MRI)-visible canine brain avatar using Digital Anatomy ® printer (DAP) based on imaging studies of a real canine patient affected with a malignant brain tumor called glioma. Although this work applies DAP in the context of intracranial procedures, this established workflow can be used to simulate a broad range of clinical problems, allowing for widespread adoption and next-generation training opportunities. PolyJet 3D printing PolyJet technology is a high-resolution 3D printing method that deposits microscopic droplets of photopolymer resin, which are cured layer by layer using ultraviolet (UV) light [ 4 ]. Unlike extrusion-based techniques, PolyJet enables the simultaneous deposition of multiple materials with distinct mechanical, visual, and imaging properties [ 5 ]. This multi-material capability allows for the fabrication of anatomically realistic models with precise control over material placement, making them well-suited for replicating complex anatomical features, combining soft tissue, bones, and fluid reservoirs at once [ 6 ]. Furthermore, specific PolyJet resins can be selected or customized to exhibit signal properties visible on CT and MRI scans. These combined features position PolyJet as an ideal platform for producing patient-specific phantoms that deliver both morphological and structural fidelity as well as radiological realism, allowing for patient-specific procedure planning and teaching. 3D printed phantoms Phantoms are physical models that replicate anatomical structures and tissue characteristics for imaging validation, calibration, and training. In CT and MRI, they provide a consistent and repeatable method to evaluate contrast resolution, spatial accuracy, and signal intensity profiles across different imaging parameters [ 7 – 9 ]. These phantoms are constructed using various materials, are usually designed for a single imaging modality, and for a specific region of interest or specific target tissue. They proved to be useful in protocols that require repeated imaging studies for planning and procedure performance, especially in neurosurgery [ 10 ]. In human medicine, 3D-printed phantoms have relatively recently emerged for clinical simulation, surgical training, and radiological calibration [ 11 ]. In the current literature, several models derived from patient imaging datasets were fabricated using single or multi-material 3D printing, with photopolymers screened to mimic specific tissue properties. However, more work is still needed [ 12 ]. Applications range from brain phantoms used in neurosurgical planning to liver models used for surgical training [ 13 – 15 ]. In veterinary medicine, the leveraging of 3D-printed phantoms has just begun [ 16 ]. Most veterinary phantoms to date have focused on rigid, bone-based models for orthopedic planning or teaching anatomy [ 17 ]. High-fidelity phantoms that simultaneously replicate multiple tissue types (e.g., bone, soft tissue, fat, fluids) and patient-specific anatomy with multimodality compatibility currently do not exist within veterinary medicine, despite their potential value for preclinical training and translational research. This void highlights the potential for digital anatomy printing of imaging-compatible phantoms in veterinary medicine, with the potential to be carried over to human applications. Purpose Here we design, manufacture, characterize, and test a CT– and MRI-visible canine brain avatar with a Digital Anatomy ® printer. This proof-of-concept study provides a ready-to-use guideline and workflow system of utilizing DAP to develop a physical canine head avatar (containing all pertinent structures such as skin, muscles, skull, brain and the brain tumor inside) based on imaging data of a real canine patient that accurately depicts the external and internal anatomy in CT and MRI scans. This can ultimately be used for advanced procedural planning and practice. Materials and Methods Workflow overview This study established a multi-phase process involving imaging acquisition of a canine patient with a brain tumor, segmentation, 3D-modeling, fabrication using PolyJet 3D printing, and final evaluation. A schematic overview of the workflow is provided in Fig. 1 , outlining the progression from patient imaging to a fully fabricated, CT and MRI-visible phantom or avatar reflecting the head anatomy. CT and MRI scanning The patient’s imaging was performed using a 16-slice Brightspeed CT scanner (GE Healthcare, Waukesha, WI) and a 1.5T Signa v9.1 MRI machine (GE Healthcare, Waukesha, WI) with standard T1-weighted pre-contrast spin echo, T2-weighted fast spin echo, and T1-weighted post-contrast spin echo sequences. As a baseline, the patient’s brain tumor was faintly identifiable on the CT scan as a hypoattenuating (dark) region, and easily identifiable mass on T2-weighted MRI sequence as a well-defined hyperintense (bright) mass that was mildly hypointense on T1-weighted images. The mass did not show contrast enhancement, and the remaining included structures of the head were normal. The described imaging features were consistent with the ultimately diagnosed glioma and served as the ground truth for the desired imaging characteristics of our avatar. Additional sequences were performed; however, they were not incorporated into phantom development and therefore not described in this study. The phantoms were imaged using a 64-slice Revolution Maxima CT scanner (GE Healthcare, Waukesha, WI) with standard protocols including bone and soft tissue algorithms. MRI studies were conducted using a 1.5T Signa Explorer v25.1 scanner (GE Healthcare, Waukesha, WI) with T1-weighted and T2-weighted 3D isovolumetric fast spin echo sequences (Cube). Multiplanar reconstructions of the 3D CT scans, and MRI datasets were available for review. Relevant imaging parameters for the aforementioned sequences are summarized in Table 1. Table 1 . Summary of imaging settings for canine patient and 3D printed avatars in both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). FOV – Field of view; MA – milliamperes; KVP – kilovoltage peak; TR – relaxation time; TE – echo time; AC – Acquisition count. Canine Patient Computed Tomography Settings (CT) Sequences Slice thickness FOV MA KVP Gantry angle Pre detail 2.5mm 185mm 220 120 0 Bone 1.25mm 185mm 220 120 0 Avatar V2 Sequences Bone CT full 1.25mm 188mm 220 120 0 Detail CT full 2.5mm 188mm 220 120 0 Avatar V3 Sequences Detail 2.5mm 200mm 220 120 0 Bone 1.25mm 200mm 220 120 0 Canine Patient Magnetic Resonance Imaging (MRI) Settings Sequences Slice thickness TR TE AC FOV Ax T1 SE 3 mm 467 23 1 140 mm X T2 FSE 3 mm 5250 88.6 1 140 mm T2 Flair FSE 3 mm 8002 130 1 140 mm AX T1 + C SE 3 mm 350 14 1 140 mm Avatar V2 MRI T2 cube 1mm 2502 85.7 2 180mm MRI T1 cube 1mm 502 12.8 2 180mm 3D Modelling CT and MRI were performed on a Boxer breed canine patient, ultimately diagnosed with a glioma (a malignant type of brain tumor), obtained from the University of Pennsylvania’s School of Veterinary Medicine. This dataset served as the anatomical foundation for physical brain avatar development. Segmentation of bone, brain, skin, soft tissue, temporalis muscles, tumors, vessels, and ventricular system was performed with Axial 3D’s proprietary algorithm. Deviating from the patient’s anatomy, a defect in the right calvarium (skull) was intentionally implemented to allow a region for procedural practice, such as biopsy training, that did not require bone drilling. Stratification into these components allowed for distinct material assignments during the 3D printing phase, facilitating anatomical realism through imaging gradients across tissue spectra and dimensional accuracy in the final phantom (Fig. 2 ). 3D Printing The finalized segments were combined and fabricated using the J850 Digital Anatomy ® PolyJet 3D printer, which uses high-resolution multimaterial deposition of photopolymers [ 5 ]. Each aforementioned anatomical region was assigned a distinct combination of photopolymer resins to closely mimic the imaging appearance of the corresponding biological tissues and simultaneously allow for procedure training post-imaging. Based on desired imaging features, multiple photopolymers were incorporated, including Agilus30™Clear, TissueMatrix Ⓡ , GelMatrix Ⓡ , BoneMatrix Ⓡ , RadioMatrix™, and Vero Ⓡ family. These resins were assigned based on Digital Anatomy Presets (DAP), allowing for tissue-specific mimicking of CT and MRI signal spectra. The print was performed in High Mix mode with a Matte model finish, and print resolution was set to a layer thickness of 27 microns to preserve fine surface detail. A full mapping of anatomical regions to DAP presets and material compositions is presented in Table 2. Table 2 . Description of Multimaterial Composition by Anatomical Region. Anatomy DAP Preset Material Composition - V1 Material Composition –V2- MRI Material Composition – V3- CT Soft Tissue Subcutaneous Fat - Soft Coated Highly Contractile TissueMatrix GelMatrix Agilus30Clear GelMatrix TissueMatrix TissueMatrix VeroPureWhite Skin Digital Material - Soft_DM_400 Agilus30Clear Tissue Matrix Agilus30Clear TissueMatrix Agilus Bone Musculoskeletal - Skull - Dense Musculoskeletal - Vertebra - Porous BoneMatrix VeroPureWhite VeroClear SUP706 BoneMatrix VeroPureWhite SUP706 RadioMatrix Brain General Anatomy - Liver - highly contractile Agilus30Clear TissueMatrix GelMatrix Agilus30Clear TissueMatrix TissueMatrix VeroPureWhite Temporalis General Anatomy – Dense connective Tissues - Soft Agilus30Clear VeroPureWhite Agilus30Clear VeroPureWhite TissueMatrix VeroPureWhite Tumor DAC Material Created TissueMatrix VeroMagenta VeroPureWhite Agilus30Clear VeroPureWhite TissueMatrix VeroPureWhite Ventricles Blood Vessel - Vessel Wall - Moderately Compliant VeroMagenta Agilus30Clear GelMatrix Agilus30Clear VeroMagenta TissueMatrix VeroPureWhite Supported structures were removed mechanically and chemically to conclude the manufacturing process. To achieve quality imaging accuracy, multiple phantom versions were fabricated and evaluated through repeated CT and MRI imaging, and subsequent iterative adjustments in material assignment. This process led to the creation of two CT-optimized avatars (V2 and V3) and one MRI-optimized avatar (V2). The V2 model incorporated fillable compartments within the ventricular system and tumor, which contained water and a heavy cream and water mixture, respectively, for testing purposes. In addition to these development iterations, an unpainted, raw 3D print was included to provide a clear view of the material composition as printed, and a hand-painted version was produced to replicate the external anatomical appearance of the real patient. All outputs are shown in Fig. 3 . Image analysis Imaging studies were reviewed using an open source DICOM viewer (Horos, Annapolis, MD; Version 3). Subjective qualitative comparisons of the appearance of anatomical regions between the patient’s images and the phantoms were recorded (anatomic accuracy, such as perceivable signal and tissue gradient, relative pixel intensities). Next, quantitative analyses were performed. Pixel intensity for MRI studies and Hounsfield Units (HU) for CT studies were measured using fixed-sized regions of interest placed on different anatomical structures, and linear measurements across specific regions were measured using built-in measurement tools of the DICOM viewer. Histogram representations of linear intensity profiles were obtained across various selected exemplary tracts from the surface of the skin to the tumor, using an open-source image analysis program, FIJI [ 18 ]. For the quantitative analysis, selected ROIs between phantoms were compared directly. Single discrete measurements of specific structures were compared as a percentage variation from the original patient. Linear histogram analysis was directly compared to each other. Results Qualitative analysis The 3D-printed canine brain avatar accurately replicated the morphology of the patient’s external features. The model maintained a smooth surface finish and well-defined boundaries, closely resembling the input-derived geometry. There was minimal perceivable shrinkage or surface irregularities upon initial inspection. These findings were confirmed when comparing the external anatomy of the patient and the avatar on 3D volume reconstructions of the CT scans (Fig. 4 ). Initial evaluation of the CT and MRI sequences for two iterations of phantoms showed high levels of signal output for both modalities. Some tissues had notable inequivalences, such as a discontinuity within the right side of the calvarium, which was purposely implemented in the avatar for biopsy training purposes, and therefore expected (Fig. 6 ). Unlike the patient, a majority of the anatomic structures are replaced with the material used to mimic fat instead of the more abundant muscle tissue in the body. Compared to the patient’s original images, the phantom’s CT scans produced multiple tissue types that were easily distinguishable from one another using a standard soft tissue window (Fig. 4 ). Subjectively, the relative X-ray attenuation of each tissue type appeared to be in a similar range to the relative intensities on the patient’s images. MRI of the phantom showed high signal output for multiple material types on T1-weighted imaging. However, there was minimal signal on the T2-weighted series, and therefore, it was not included in further analysis. Specifically, the material used for muscle created an undesired signal void on T1-weighted images; however, the remaining materials used for soft tissue and fluids produced adequate signal intensities to register using our sequence settings (see Table 1). On the T1-weighted series, each tissue type was easily distinguishable from each other, and many tissues showed the same intensity relationships to each other as the original patient. Additional MRI pulse sequences were obtained (proton density, 3D-LAVA, 3D-FIESTA) and showed great tissue differentiation within the avatar; however, since these sequences were not performed on the original patient, they were not included in further analysis (Fig. 5 ). Quantitative analysis Specific tissue types, such as bone, fluid, and fat, have well-described X-ray attenuation (HU) ranges for CT imaging. The HU values of the corresponding tissue types for our CT phantoms, V2 and V3, are displayed in Table 3 with example region of interest (ROI) measurements in Fig. 6 . Table 3. Hounsfield units and intensity for selected regions of interest (ROIs) across imaging studies. Tissue Patient CT V2 CT V3 CT Patient MRI V2 MRI Muscle 43 83 83 125 42 Ventricle -14 3 -12 1069 1321 White 0 -30 58 447 730 Tumor -3 2 43 791 1092 Bone 1389 93 633 65 51 Background -955 -939 -996 44 45 V2 materials produced HU values across the spectrum, predominantly including fat, fluid, and soft tissues. The selected materials for each anatomic structure did not align with the patient’s results. Specifically, the material used for the brain was hypoattenuating (darker) compared to the patient’s brain, in the range of fat instead of soft tissue. Also, similar HU values were produced by the materials used for the ventricles (water) and tumor (heavy cream and water), unlike the patient’s CT scan, where these two tissues can be differentiated. None of the materials used produced signals quantitatively analogous to bone. The third version of the phantom had materials that mimicked similar tissues as the second version, with a new, accurate analog for bone. Materials used for the ventricles and bones matched the HU values of the original patient, with remaining materials producing appropriate relative intensities to each other. The V2 avatar was optimized for MRI testing (Table 2). However, it is important to note that, unlike standard CT HU values, MRI signal intensities cannot be directly compared, as they are influenced by the MRI acquisition parameters. In addition, the lack of a control tissue to normalize MRI signal intensity measurements precluded complete comparative quantitative analysis. However, the V2 phantom T1-weighted MRI produced similar intensities as the patient’s T2-weighted MRI, with similar proportional intensity spectra between tissue types within each independent scan. Dimensional analysis Figure 7 shows the selected measurements conducted for the optimized phantom model. Since the avatars were designed using the patient’s CT scan, only measurements from the avatars' CTs were compared. Table 4 shows the recorded linear measurements of the original scans and the V2 and V3 phantoms, showing that all selected measurements were within 5% variation of the original patient’s dimensions. Table 4. Comparative dimensions. Length (in centimeters) of selected measurements for all CT studies. DV – dorsoventral (whole head); ML – mediolateral (whole head); BDV – Brain (only) dorsoventral; BO – Brain (only) oblique. Direction Patient CT V2 CT V2% variation V3 CT V3% variation DV 11.87 11.56 -2.61 12.17 2.53 ML 12.9 13.35 3.49 13.47 4.42 BDV 4.41 4.4 -0.23 4.30 -2.49 BO 7.48 7.49 0.13 7.93 6.02 Histogram representations of linear intensity profiles along a path from the surface of the head to the deep tumor margin were compared between the actual patient’s CT and V3 avatar (Fig. 7 ). Peaks at distances *80*, *150*, and *290* correlate with the muscle-bone, bone-brain, and brain-ventricle interfaces. The V2 phantom had artifactual peaks at each material transition due to the boundary materials used to separate the phantom tissue types (not pictured in Fig. 7 ). The peaks associated with the interface entering the tumor at the end of this trajectory are indistinct, as is true in the patient’s CT scan. Discussion This study presents a complete end-to-end workflow for the creation of a high-resolution, patient-specific canine brain avatar using CT and MRI data, segmentation, multi-material 3D printing, and imaging validation. The successful production of these avatars validates this step-by-step process, with anticipated feasible improvements for each step in the future. This is particularly relevant for preoperative planning in procedures such as brain biopsies or surgeries, where multiple imaging sessions are often required to place anatomic markers for the imaging-to-patient registration used during these procedures. A physical patient-specific avatar may allow clinicians to simulate and select optimal interventional paths, minimizing the risks and costs associated with general anesthesia required to execute this step with the real patient [ 19 ]. The combination of advanced segmentation and multi-material PolyJet printing enabled high anatomical realism, with clear differentiation of each tissue. Through multiple iterations, refinements to these two steps improved minor surface irregularities, resulting in an externally perceivably similar avatar. Material assignments based on anatomical presets from the Digital Anatomy printer helped replicate expected imaging contrast, produced adequate signal, and proportional intensities. However, many improvements can be made in the future. The material used for brain tissue in the CT V2 phantom was more hyperattenuating than desired, resulting in an incorrect relative intensity among the other structures. Additionally, this version produced minimal signal in T2-weighted MRI sequences, and the material used for muscle in the T1-weighted sequences was not visible. Further testing is required to optimize material selection to emulate T2-weighted MRI imaging features, to improve these drawbacks. The phantom preserved the spatial alignment of key anatomical landmarks, closely matching the geometry observed in the original MRI and CT scans. This level of fidelity is essential for validating neuroanatomical relationships and enables potential use in image-guided navigation and biopsy training [ 20 ]. Our qualitative and quantitative analysis supports these models to produce high spatial accuracy, including simulated paths that could represent procedures such as brain biopsies. However, although the printer is accurate to 27 microns, variable shrinking and deformation during post-processing will inherently introduce pseudorandom differences between the designed and manufactured phantoms. Also, a longitudinal analysis should be performed to assess the durability of these materials and to determine if structural changes occur over time. Overall, this form of validation lays the groundwork for future studies that may incorporate full 3D registration, trajectory planning metrics, and measurement error analysis. Here, we established a workflow that is adaptable to other anatomical regions and species. Similar 3D printing approaches have been applied to the spine, liver, and appendicular skeleton for human medical training and CT quality assurance [ 7 , 21 ]. Our results support the feasibility of extending this multi-material and multimodality approach to veterinary models of other organ systems. However, expanding this work will require additional validation across different anatomical regions, as well as greater automation in segmentation and print preparation. Conclusion This study demonstrates the feasibility of converting patient-specific clinical imaging to a 3D printed phantom that is visible and anatomically accurate on CT and MRI. Despite a few imperfections with sub-optimal material selection, these models successfully replicated key anatomical structures and imaging characteristics of a real canine patient, offering a platform for surgical planning and veterinary training. Overall, this workflow presents a meaningful advance in versatile phantom development and clinical procedure planning. Future work should aim to automate segmentation, improve overall time and cost efficiency, and perfect material selection to mimic all desired HU/intensities. Ultimately, these phantoms can serve for procedure planning and practice models for patient-specific procedures. Declarations Ethics approval and consent to participate This work involved non-experimental animals with consent granted by owners for future use of data for research purposes. Due to the retrospective nature of data usage, ethical approval from a formal committee was not required. Consent for publication All included patient images and company references were approved prior to publication. Availability of data and materials Data used in this study are available from the corresponding author upon reasonable request. Competing interests Authors declare no potential competing interests with respect to the research and publication of this article. Authors’ contributions W.K.P. conceived and designed the study. W.K.P., R.K., and A.N.B. performed experiments, analyzed data, and wrote the manuscript. J.N.R., R.G, N.A., W. M., M. D., R. M assisted with sample processing and testing. W.K.P., M.D., W.M., N.A., and R.M. provided critical feedback and oversaw the research program. All authors listed reviewed the manuscript and provided feedback with writing and revisions. Acknowledgements We would like to acknowledge Stratasys and Axial 3D company representatives for technical support. We want to thank Susan Kmetz, who permitted the usage of CT and MRI imaging of her beloved dog Charlotte for the purpose of this study. References Martelli, N., Serrano, C., van den Brink, H., Pineau, J., Prognon, P., Borget, I., & El Batti, S. (2016). Advantages and disadvantages of 3-dimensional printing in surgery: A systematic review. Surgery, 159(6), 1485–1500. https://doi.org/10.1016/J.SURG.2015.12.017 Altwal, J., Altwal, J., Wilson, C. H., & Griffon, D. J. (n.d.). Applications of 3-dimensional printing in small-animal surgery: A review of current practices. John Wiley & Sons, Ltd . https://onlinelibrary.wiley.com/doi/epdf/10.1111/vsu.13739 Marquez-Grados, F., Vettorato, E., & Corletto, F. (2020). Sevoflurane with opioid or dexmedetomidine infusions in dogs undergoing intracranial surgery: a retrospective observational study. Journal of veterinary science, 21(1), e8. https://doi.org/10.4142/jvs.2020.21.e8 Zhu, Y., Guo, S., Ravichandran, D., Ramanathan, A., Sobczak, M. T., Sacco, A., Patil, D., Thummalapalli, S. V., Pulido, T. V., Lancaster, J. N., Yi, J., Cornella, J. L., Lott, D. G., Chen, X., Mei, X., Zhang, Y. S., Wang, L., Wang, X., Zhao, Y., … Song, K. (2024). 3D‐Printed Polymeric Biomaterials for Health Applications. Advanced Healthcare Materials. https://doi.org/10.1002/adhm.202402571 Patpatiya, P., Chaudhary, K. C., Shastri, A., & Sharma, S. (2022). A review on polyjet 3D printing of polymers and multi-material structures. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 236(14), 7899–7926. https://doi.org/10.1177/09544062221079506 Ruiz, O. G., & Dhaher, Y. Y. (2021). Multi-color and Multi-Material 3D Printing of Knee Joint models. 7(1), 12. https://doi.org/10.1186/S41205-021-00100-0 Filippou, V., & Tsoumpas, C. (2018). Recent advances on the development of phantoms using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound. Medical Physics, 45(9). https://doi.org/10.1002/mp.13058 Qiu, J., Hou, K., Dyer, B. A., Chen, J. C., Shi, L., Sun, Y., Xu, L., Zhao, H., Li, Z., Chen, T., Li, M., Zhang, F., Zhang, H., & Rong, Y. (2021). Constructing customized multimodal phantoms through 3D printing: a preliminary evaluation. Frontiers in Physics, 9. https://doi.org/10.3389/fphy.2021.605630 Hatamikia, S., Gulyas, I., Birkfellner, W., Kronreif, G., Unger, A., Oberoi, G., Lorenz, A., Unger, E., Kettenbach, J., Figl, M., Patsch, J., Strassl, A., Georg, D., & Renner, A. (2022). Realistic 3D printed CT imaging tumor phantoms for validation of image processing algorithms. Physica Medica, 105, 102512. https://doi.org/10.1016/j.ejmp.2022.102512 Mitsouras, D., Lee, T. C., Liacouras, P., Ionita, C. N., Pietilla, T., Maier, S. E., & Mulkern, R. V. (2017). Three-dimensional printing of MRI-visible phantoms and MR image-guided therapy simulation: 3D Printing of MRI-Visible Phantoms. Magnetic Resonance in Medicine, 77(2). https://doi.org/10.1002/MRM.26617 Higgins, M., Leung, S., & Radacsi, N. (2022). 3D Printing Surgical Phantoms and their Role in the Visualization of Medical Procedures. Annals of 3D Printed Medicine, 6, 100057. https://doi.org/10.1016/j.stlm.2022.100057 Bücking, T. M., Hill, E. R., Robertson, J., Maneas, E., Plumb, A., & Nikitichev, D. I. (2017). From medical imaging data to 3D printed anatomical models. PLOS ONE, 12(5). https://doi.org/10.1371/JOURNAL.PONE.0178540 Ramirez, M. J. E., Pena, I. J. R., Castillo, R. E. B., Sufianov, A., Goncharov, E. N., Sánchez, J. A. S., Colome-Hidalgo, M., Nurmukhametov, R. M., Céspedes, J. R. C., & Montemurro, N. (2023). Development of a 3D Printed Brain Model with Vasculature for Neurosurgical Procedure Visualisation and Training. Advances in Cardiovascular Diseases, 11(2), 330. https://doi.org/10.3390/biomedicines11020330 Coles-Black, J., Bolton, D. M., & Chuen, J. (2021). Accessing 3D Printed Vascular Phantoms for Procedural Simulation. Frontiers in Surgery, 7, 626212. https://doi.org/10.3389/FSURG.2020.626212 Rossi, T., Williams, A., & Sun, Z. (2023). Three-Dimensional Printed Liver Models for Surgical Planning and Intraoperative Guidance of Liver Cancer Resection: A Systematic Review. Applied Sciences. https://doi.org/10.3390/app131910757 Rottoo, S., Frangella, L., Bazalova‐Carter, M., & Masella, O. (2024). Development of a 3D-printed canine head phantom for veterinary radiotherapy. https://doi.org/10.48550/arxiv.2409.19694 Lee, H.-R., Adam, G. O., Adam, G. O., Yang, D. K., Tungalag, T., Lee, S.-J., Kim, J.-S., Kang, H.-S., Kim, S.-J., & Kim, N.-S. (2020). An Easy and Economical Way to Produce a Three-Dimensional Bone Phantom in a Dog with Antebrachial Deformities. Open Access Journal, 10(9), 1445. https://doi.org/10.3390/ANI10091445 Schindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., & Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676–682. https://doi.org/10.1038/nmeth.2019 Meyer-Szary, J., Souza Luis, M., Mikulski, S., Patel, A., Schulz, F., Tretiakow, D., Fercho, J., Jaguszewska, K., Frankiewicz, M., Pawłowska, E., Targoński, R., Szarpak, Ł., Dądela, K., Sabiniewicz, R., & Kwiatkowska, J. (2022). The Role of 3D Printing in Planning Complex Medical Procedures and Training of Medical Professionals—Cross-Sectional Multispecialty Review. International Journal of Environmental Research and Public Health, 19(6), 3331. https://doi.org/10.3390/ijerph19063331 Lasso, A., Heffter, T., Rankin, A., Pinter, C., Ungi, T., & Fichtinger, G. (2014). PLUS: open-source toolkit for ultrasound-guided intervention systems. IEEE Transactions on Biomedical Engineering, 61(10), 2527–2537. https://doi.org/10.1109/TBME.2014.2322864. Leng S, McGee K, Morris J, Alexander A, Kuhlmann J, Vrieze T, et al. Anatomic modeling using 3D printing: quality assurance and optimization. 3D Print Med . 2017;3:6. https://doi.org/10.1186/s41205-017-0014-3 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. 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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-6779582","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":464085394,"identity":"7053454a-5335-4c03-b8de-ce855dc6ded9","order_by":0,"name":"Robb Kessel","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Robb","middleName":"","lastName":"Kessel","suffix":""},{"id":464085395,"identity":"81a758b1-cd90-425c-be1b-6f0e5c097077","order_by":1,"name":"Akshaya Nidhi Bhati","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Akshaya","middleName":"Nidhi","lastName":"Bhati","suffix":""},{"id":464085396,"identity":"c053b1fd-70af-4a26-968e-69b318d64700","order_by":2,"name":"Jack N. Roney","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Jack","middleName":"N.","lastName":"Roney","suffix":""},{"id":464085397,"identity":"9aef11e4-4f36-4a0c-8750-c29b6acc9c54","order_by":3,"name":"Rajnish Gupta","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Rajnish","middleName":"","lastName":"Gupta","suffix":""},{"id":464085398,"identity":"6b0e617d-6675-44be-837f-673f7c41487c","order_by":4,"name":"Nduka Amankulor","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Nduka","middleName":"","lastName":"Amankulor","suffix":""},{"id":464085399,"identity":"10a7d679-e95d-478a-adc6-280023786944","order_by":5,"name":"Wilfried Mai","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Wilfried","middleName":"","lastName":"Mai","suffix":""},{"id":464085400,"identity":"26a59c46-3bc0-4baf-b9ff-9c3b858cb1f4","order_by":6,"name":"Mallesham Dasari","email":"","orcid":"","institution":"Northeastern University","correspondingAuthor":false,"prefix":"","firstName":"Mallesham","middleName":"","lastName":"Dasari","suffix":""},{"id":464085401,"identity":"c59a31ba-c3e2-4224-a483-a8c5689aa1ba","order_by":7,"name":"Rahul Mangharam","email":"","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":false,"prefix":"","firstName":"Rahul","middleName":"","lastName":"Mangharam","suffix":""},{"id":464085402,"identity":"7e02a0cd-8cf4-48db-b902-9ac987360ad8","order_by":8,"name":"Wojciech K. Panek","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAqElEQVRIiWNgGAWjYDACHgbGBzC2BLFamA1I1sIGV0mcFoMzZ49V8+6wyTNnYD54m4coLWf70m7znkkrtmxgS7YmSovZeR6z27xthxM3HOAxkyZaSzFv23+gFv5vRGo522PGzNt2AGQLG3Fa7M+cS5aceyY5ccNhNmPLOcRokezJPfjh7Q67xA3Hmx/eeEOMFlDEMDA2AGlm4pQjaxkFo2AUjIJRgAsAACjlMMeQhGF5AAAAAElFTkSuQmCC","orcid":"","institution":"University of Pennsylvania","correspondingAuthor":true,"prefix":"","firstName":"Wojciech","middleName":"K.","lastName":"Panek","suffix":""}],"badges":[],"createdAt":"2025-05-29 22:23:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6779582/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6779582/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":83816335,"identity":"7f5d0696-4812-492b-b011-5a8765ee97a3","added_by":"auto","created_at":"2025-06-03 07:50:25","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":97077,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram showing the full fabrication workflow for the canine brain avatar. Image acquisition, segmentation and CAD modeling, material assignment and slicing, multimaterial PolyJet 3D printing, post-processing, and final imaging validation.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6779582/v1/a288960ab189f9bfadaab949.png"},{"id":83816324,"identity":"2c49c031-f670-471d-acc9-f34b6f9867d2","added_by":"auto","created_at":"2025-06-03 07:50:24","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":490889,"visible":true,"origin":"","legend":"\u003cp\u003e(A) 3D segmentation of the Boxer breed canine brain from high-resolution CT data, performed via Axial3D's proprietary workflow. (B)The tumor mass (highlighted) is separated from the surrounding brain parenchyma for distinct material assignment during printing.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6779582/v1/d0e1946f15d352de410478a5.png"},{"id":83817904,"identity":"dfaacd24-3462-4e67-ac00-203094d80145","added_by":"auto","created_at":"2025-06-03 08:06:24","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1056294,"visible":true,"origin":"","legend":"\u003cp\u003eAvatar Versions and Printed Outputs Compared to Real Patient Reference. (A) Real canine patient. (B) Painted version of the 3D-printed canine avatar. (C) Version 3 Avatar: Final unpainted 3D-printed avatar with embedded external tubing for fluid filling, incorporating refined material assignments, and anatomical accuracy. (D) Version 3 Avatar with fiducial markers (small donut-shaped reference markers) placed on its surface for future image-to-phantom registration and biopsy training.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6779582/v1/aca215a57b9200fb3a095d8c.png"},{"id":83816327,"identity":"bb9e4ffb-88ec-4505-bc5b-f199621d6391","added_by":"auto","created_at":"2025-06-03 07:50:24","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":545376,"visible":true,"origin":"","legend":"\u003cp\u003eExternal 3D reconstruction and qualitative comparison. External CT-based 3D volumetric reconstructions from the patient (A) and avatar V3 (B). Exemplary CT slices used for qualitative analysis between the patient (a) and avatar V3 (b).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-6779582/v1/ba2389131b8355ac3f7c740e.png"},{"id":83816332,"identity":"a22f829a-7d81-45a1-be59-a644b4eee1b8","added_by":"auto","created_at":"2025-06-03 07:50:25","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":360653,"visible":true,"origin":"","legend":"\u003cp\u003eRepresentative slices of the patient’s T2-weighted MRI (A) and the V2 phantom T1-weighted MRI (B). The red outline represents the tumor area. Additional examples of proton density (C), 3D-LAVA (D), and 3D-FIESTA (E) slices showing high signal output with adequate material differentiation.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-6779582/v1/25a426eb4092447df7763a2a.png"},{"id":83816330,"identity":"c9bbd2fe-0755-4cf4-9c5d-4b2943633eca","added_by":"auto","created_at":"2025-06-03 07:50:24","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":290518,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A)\u003c/strong\u003e Regions of Interest (ROIs) measuring CT Hounsfield Units and intensity for regions labeled 1-6: muscle, ventricles, white matter, tumor, bone, and background noise, respectively. \u003cstrong\u003e(B)\u003c/strong\u003eSelected measurements comparing CT scans, including dorsoventral (head, 1), mediolateral (2), dorsoventral (brain, 3), and oblique (4).\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-6779582/v1/7dbb055066f298d588139efb.png"},{"id":83816339,"identity":"03fb7379-5c6e-47b1-b550-2dac58dc751f","added_by":"auto","created_at":"2025-06-03 07:50:25","extension":"png","order_by":7,"title":"Figure 7","display":"","copyAsset":false,"role":"figure","size":254253,"visible":true,"origin":"","legend":"\u003cp\u003eCT slice of the patient (A) and avatar V3 (B) used for linear histogram analysis of a tract (yellow line) extending from the skin to the deep margin of the tumor. Associated histogram plots for the patient path (C) and Avatar (D) with pixel intensity graphed as a function of distance.\u003c/p\u003e","description":"","filename":"7.png","url":"https://assets-eu.researchsquare.com/files/rs-6779582/v1/3102dbe0fe3267914b81d484.png"},{"id":95802781,"identity":"f8a6704b-6005-4a9b-909a-adad9180c036","added_by":"auto","created_at":"2025-11-13 08:28:30","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":4827696,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6779582/v1/bd58fb9d-dc43-4cb9-8f16-b32089a9f1d5.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"CT and MRI-visible canine brain avatar via three-dimensional digital anatomy printing in aiding neurosurgical training and biopsy planning – proof of concept study","fulltext":[{"header":"Background","content":"\u003cp\u003eThree-dimensional (3D) printing has become an increasingly valuable tool in both human and veterinary medicine, offering new opportunities for patient-specific modeling, surgical planning, and procedural training [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In veterinary neurosurgical practice, intracranial procedures such as tumor biopsy and resection are in their infancy due to their technical difficulty and need for multiple anesthetic events to plan and perform the procedure with currently available systems [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Furthermore, there is a lack of interactive teaching tools that allow for effective clinician training. Currently, cadavers are commonly used for procedure practice; however, they lack the patient\u0026rsquo;s specific pathology, significantly reducing the realism of training for specific intracranial procedures. Here, we design, manufacture, and validate a computed tomography (CT) and Magnetic Resonance Imaging (MRI)-visible canine brain avatar using Digital Anatomy\u003csup\u003e\u0026reg;\u003c/sup\u003e printer (DAP) based on imaging studies of a real canine patient affected with a malignant brain tumor called glioma. Although this work applies DAP in the context of intracranial procedures, this established workflow can be used to simulate a broad range of clinical problems, allowing for widespread adoption and next-generation training opportunities.\u003c/p\u003e\n\u003ch3\u003ePolyJet 3D printing\u003c/h3\u003e\n\u003cp\u003ePolyJet technology is a high-resolution 3D printing method that deposits microscopic droplets of photopolymer resin, which are cured layer by layer using ultraviolet (UV) light [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Unlike extrusion-based techniques, PolyJet enables the simultaneous deposition of multiple materials with distinct mechanical, visual, and imaging properties [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. This multi-material capability allows for the fabrication of anatomically realistic models with precise control over material placement, making them well-suited for replicating complex anatomical features, combining soft tissue, bones, and fluid reservoirs at once [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Furthermore, specific PolyJet resins can be selected or customized to exhibit signal properties visible on CT and MRI scans. These combined features position PolyJet as an ideal platform for producing patient-specific phantoms that deliver both morphological and structural fidelity as well as radiological realism, allowing for patient-specific procedure planning and teaching.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e3D printed phantoms\u003c/h2\u003e \u003cp\u003ePhantoms are physical models that replicate anatomical structures and tissue characteristics for imaging validation, calibration, and training. In CT and MRI, they provide a consistent and repeatable method to evaluate contrast resolution, spatial accuracy, and signal intensity profiles across different imaging parameters [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. These phantoms are constructed using various materials, are usually designed for a single imaging modality, and for a specific region of interest or specific target tissue. They proved to be useful in protocols that require repeated imaging studies for planning and procedure performance, especially in neurosurgery [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. In human medicine, 3D-printed phantoms have relatively recently emerged for clinical simulation, surgical training, and radiological calibration [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. In the current literature, several models derived from patient imaging datasets were fabricated using single or multi-material 3D printing, with photopolymers screened to mimic specific tissue properties. However, more work is still needed [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Applications range from brain phantoms used in neurosurgical planning to liver models used for surgical training [\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. In veterinary medicine, the leveraging of 3D-printed phantoms has just begun [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Most veterinary phantoms to date have focused on rigid, bone-based models for orthopedic planning or teaching anatomy [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. High-fidelity phantoms that simultaneously replicate multiple tissue types (e.g., bone, soft tissue, fat, fluids) and patient-specific anatomy with multimodality compatibility currently do not exist within veterinary medicine, despite their potential value for preclinical training and translational research. This void highlights the potential for digital anatomy printing of imaging-compatible phantoms in veterinary medicine, with the potential to be carried over to human applications.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePurpose\u003c/h3\u003e\n\u003cp\u003eHere we design, manufacture, characterize, and test a CT\u0026ndash; and MRI-visible canine brain avatar with a Digital Anatomy\u003csup\u003e\u0026reg;\u003c/sup\u003e printer. This proof-of-concept study provides a ready-to-use guideline and workflow system of utilizing DAP to develop a physical canine head avatar (containing all pertinent structures such as skin, muscles, skull, brain and the brain tumor inside) based on imaging data of a real canine patient that accurately depicts the external and internal anatomy in CT and MRI scans. This can ultimately be used for advanced procedural planning and practice.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eWorkflow overview\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThis study established a multi-phase process involving imaging acquisition of a canine patient with a brain tumor, segmentation, 3D-modeling, fabrication using PolyJet 3D printing, and final evaluation. A schematic overview of the workflow is provided in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, outlining the progression from patient imaging to a fully fabricated, CT and MRI-visible phantom or avatar reflecting the head anatomy.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eCT and MRI scanning\u003c/h3\u003e\n\u003cp\u003eThe patient\u0026rsquo;s imaging was performed using a 16-slice Brightspeed CT scanner (GE Healthcare, Waukesha, WI) and a 1.5T Signa v9.1 MRI machine (GE Healthcare, Waukesha, WI) with standard T1-weighted pre-contrast spin echo, T2-weighted fast spin echo, and T1-weighted post-contrast spin echo sequences. As a baseline, the patient\u0026rsquo;s brain tumor was faintly identifiable on the CT scan as a hypoattenuating (dark) region, and easily identifiable mass on T2-weighted MRI sequence as a well-defined hyperintense (bright) mass that was mildly hypointense on T1-weighted images. The mass did not show contrast enhancement, and the remaining included structures of the head were normal. The described imaging features were consistent with the ultimately diagnosed glioma and served as the ground truth for the desired imaging characteristics of our avatar. Additional sequences were performed; however, they were not incorporated into phantom development and therefore not described in this study. The phantoms were imaged using a 64-slice Revolution Maxima CT scanner (GE Healthcare, Waukesha, WI) with standard protocols including bone and soft tissue algorithms. MRI studies were conducted using a 1.5T Signa Explorer v25.1 scanner (GE Healthcare, Waukesha, WI) with T1-weighted and T2-weighted 3D isovolumetric fast spin echo sequences (Cube). Multiplanar reconstructions of the 3D CT scans, and MRI datasets were available for review. Relevant imaging parameters for the aforementioned sequences are summarized in Table\u0026nbsp;1.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 1\u003c/strong\u003e. Summary of imaging settings for canine patient and 3D printed avatars in both Computed Tomography (CT) and Magnetic Resonance Imaging (MRI). FOV \u0026ndash; Field of view; MA \u0026ndash; milliamperes; KVP \u0026ndash; kilovoltage peak; TR \u0026ndash; relaxation time; TE \u0026ndash; echo time; AC \u0026ndash; Acquisition count.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eCanine Patient Computed Tomography Settings (CT)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSequences\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSlice thickness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFOV\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKVP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGantry angle\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePre detail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e185mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvatar V2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSequences\u003c/b\u003e\u003c/p\u003e \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone CT full\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetail CT full\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e188mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvatar V3\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSequences\u003c/b\u003e\u003c/p\u003e \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\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDetail\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.5mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.25mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e200mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCanine Patient Magnetic Resonance Imaging (MRI) Settings\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSequences\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003eSlice thickness\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eTR\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eTE\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eAC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eFOV\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAx T1 SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eX T2 FSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e88.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eT2 Flair FSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAX T1\u0026thinsp;+\u0026thinsp;C SE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3 mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e140 mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvatar V2\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI T2 cube\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e85.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e180mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRI T1 cube\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e180mm\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3D Modelling\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eCT and MRI were performed on a Boxer breed canine patient, ultimately diagnosed with a glioma (a malignant type of brain tumor), obtained from the University of Pennsylvania\u0026rsquo;s School of Veterinary Medicine. This dataset served as the anatomical foundation for physical brain avatar development. Segmentation of bone, brain, skin, soft tissue, temporalis muscles, tumors, vessels, and ventricular system was performed with Axial 3D\u0026rsquo;s proprietary algorithm. Deviating from the patient\u0026rsquo;s anatomy, a defect in the right calvarium (skull) was intentionally implemented to allow a region for procedural practice, such as biopsy training, that did not require bone drilling. Stratification into these components allowed for distinct material assignments during the 3D printing phase, facilitating anatomical realism through imaging gradients across tissue spectra and dimensional accuracy in the final phantom (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003e3D Printing\u003c/h3\u003e\n\u003cp\u003eThe finalized segments were combined and fabricated using the J850 Digital Anatomy\u003csup\u003e\u0026reg;\u003c/sup\u003e PolyJet 3D printer, which uses high-resolution multimaterial deposition of photopolymers [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Each aforementioned anatomical region was assigned a distinct combination of photopolymer resins to closely mimic the imaging appearance of the corresponding biological tissues and simultaneously allow for procedure training post-imaging. Based on desired imaging features, multiple photopolymers were incorporated, including Agilus30\u0026trade;Clear, TissueMatrix\u003csup\u003eⓇ\u003c/sup\u003e, GelMatrix\u003csup\u003eⓇ\u003c/sup\u003e, BoneMatrix\u003csup\u003eⓇ\u003c/sup\u003e, RadioMatrix\u0026trade;, and Vero\u003csup\u003eⓇ\u003c/sup\u003efamily. These resins were assigned based on Digital Anatomy Presets (DAP), allowing for tissue-specific mimicking of CT and MRI signal spectra. The print was performed in High Mix mode with a Matte model finish, and print resolution was set to a layer thickness of 27 microns to preserve fine surface detail. A full mapping of anatomical regions to DAP presets and material compositions is presented in Table\u0026nbsp;2.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 2\u003c/strong\u003e. Description of Multimaterial Composition by Anatomical Region.\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabb\" border=\"1\"\u003e \u003ccolgroup cols=\"5\"\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=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnatomy\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAP Preset\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMaterial Composition - V1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMaterial Composition\u003c/p\u003e \u003cp\u003e\u0026ndash;V2- MRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMaterial Composition\u003c/p\u003e \u003cp\u003e\u0026ndash; V3- CT\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoft Tissue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSubcutaneous Fat - Soft Coated Highly Contractile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003cp\u003eGelMatrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eGelMatrix\u003c/p\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSkin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Material - Soft_DM_400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eTissue Matrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAgilus\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMusculoskeletal - Skull - Dense\u003c/p\u003e \u003cp\u003eMusculoskeletal - Vertebra - Porous\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBoneMatrix\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003cp\u003eVeroClear\u003c/p\u003e \u003cp\u003eSUP706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBoneMatrix\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003cp\u003eSUP706\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eRadioMatrix\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrain\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral Anatomy - Liver - highly contractile\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003cp\u003eGelMatrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTemporalis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGeneral Anatomy \u0026ndash; Dense connective Tissues - Soft\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDAC Material Created\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003cp\u003eVeroMagenta\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentricles\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBlood Vessel - Vessel Wall - Moderately Compliant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVeroMagenta\u003c/p\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eGelMatrix\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAgilus30Clear\u003c/p\u003e \u003cp\u003eVeroMagenta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTissueMatrix\u003c/p\u003e \u003cp\u003eVeroPureWhite\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSupported structures were removed mechanically and chemically to conclude the manufacturing process. To achieve quality imaging accuracy, multiple phantom versions were fabricated and evaluated through repeated CT and MRI imaging, and subsequent iterative adjustments in material assignment. This process led to the creation of two CT-optimized avatars (V2 and V3) and one MRI-optimized avatar (V2). The V2 model incorporated fillable compartments within the ventricular system and tumor, which contained water and a heavy cream and water mixture, respectively, for testing purposes. In addition to these development iterations, an unpainted, raw 3D print was included to provide a clear view of the material composition as printed, and a hand-painted version was produced to replicate the external anatomical appearance of the real patient. All outputs are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e\n\u003ch3\u003eImage analysis\u003c/h3\u003e\n\u003cp\u003eImaging studies were reviewed using an open source DICOM viewer (Horos, Annapolis, MD; Version 3).\u003c/p\u003e \u003cp\u003eSubjective qualitative comparisons of the appearance of anatomical regions between the patient\u0026rsquo;s images and the phantoms were recorded (anatomic accuracy, such as perceivable signal and tissue gradient, relative pixel intensities). Next, quantitative analyses were performed. Pixel intensity for MRI studies and Hounsfield Units (HU) for CT studies were measured using fixed-sized regions of interest placed on different anatomical structures, and linear measurements across specific regions were measured using built-in measurement tools of the DICOM viewer. Histogram representations of linear intensity profiles were obtained across various selected exemplary tracts from the surface of the skin to the tumor, using an open-source image analysis program, FIJI [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. For the quantitative analysis, selected ROIs between phantoms were compared directly. Single discrete measurements of specific structures were compared as a percentage variation from the original patient. Linear histogram analysis was directly compared to each other.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eQualitative analysis\u003c/h2\u003e \u003cp\u003eThe 3D-printed canine brain avatar accurately replicated the morphology of the patient\u0026rsquo;s external features. The model maintained a smooth surface finish and well-defined boundaries, closely resembling the input-derived geometry. There was minimal perceivable shrinkage or surface irregularities upon initial inspection. These findings were confirmed when comparing the external anatomy of the patient and the avatar on 3D volume reconstructions of the CT scans (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Initial evaluation of the CT and MRI sequences for two iterations of phantoms showed high levels of signal output for both modalities. Some tissues had notable inequivalences, such as a discontinuity within the right side of the calvarium, which was purposely implemented in the avatar for biopsy training purposes, and therefore expected (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Unlike the patient, a majority of the anatomic structures are replaced with the material used to mimic fat instead of the more abundant muscle tissue in the body. Compared to the patient\u0026rsquo;s original images, the phantom\u0026rsquo;s CT scans produced multiple tissue types that were easily distinguishable from one another using a standard soft tissue window (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eSubjectively, the relative X-ray attenuation of each tissue type appeared to be in a similar range to the relative intensities on the patient\u0026rsquo;s images. MRI of the phantom showed high signal output for multiple material types on T1-weighted imaging. However, there was minimal signal on the T2-weighted series, and therefore, it was not included in further analysis. Specifically, the material used for muscle created an undesired signal void on T1-weighted images; however, the remaining materials used for soft tissue and fluids produced adequate signal intensities to register using our sequence settings (see Table\u0026nbsp;1). On the T1-weighted series, each tissue type was easily distinguishable from each other, and many tissues showed the same intensity relationships to each other as the original patient. Additional MRI pulse sequences were obtained (proton density, 3D-LAVA, 3D-FIESTA) and showed great tissue differentiation within the avatar; however, since these sequences were not performed on the original patient, they were not included in further analysis (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eQuantitative analysis\u003c/h2\u003e \u003cp\u003eSpecific tissue types, such as bone, fluid, and fat, have well-described X-ray attenuation (HU) ranges for CT imaging. The HU values of the corresponding tissue types for our CT phantoms, V2 and V3, are displayed in Table\u0026nbsp;3 with example region of interest (ROI) measurements in Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTable 3.\u003c/strong\u003e Hounsfield units and intensity for selected regions of interest (ROIs) across imaging studies.\u0026nbsp;\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabc\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTissue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient CT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV2 CT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV3 CT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ePatient MRI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV2 MRI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMuscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e125\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVentricle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1321\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e730\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTumor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e791\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1092\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBone\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBackground\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-955\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-939\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eV2 materials produced HU values across the spectrum, predominantly including fat, fluid, and soft tissues. The selected materials for each anatomic structure did not align with the patient\u0026rsquo;s results. Specifically, the material used for the brain was hypoattenuating (darker) compared to the patient\u0026rsquo;s brain, in the range of fat instead of soft tissue. Also, similar HU values were produced by the materials used for the ventricles (water) and tumor (heavy cream and water), unlike the patient\u0026rsquo;s CT scan, where these two tissues can be differentiated. None of the materials used produced signals quantitatively analogous to bone. The third version of the phantom had materials that mimicked similar tissues as the second version, with a new, accurate analog for bone. Materials used for the ventricles and bones matched the HU values of the original patient, with remaining materials producing appropriate relative intensities to each other. The V2 avatar was optimized for MRI testing (Table\u0026nbsp;2). However, it is important to note that, unlike standard CT HU values, MRI signal intensities cannot be directly compared, as they are influenced by the MRI acquisition parameters. In addition, the lack of a control tissue to normalize MRI signal intensity measurements precluded complete comparative quantitative analysis. However, the V2 phantom T1-weighted MRI produced similar intensities as the patient\u0026rsquo;s T2-weighted MRI, with similar proportional intensity spectra between tissue types within each independent scan.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eDimensional analysis\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows the selected measurements conducted for the optimized phantom model. Since the avatars were designed using the patient\u0026rsquo;s CT scan, only measurements from the avatars' CTs were compared. Table\u0026nbsp;4 shows the recorded linear measurements of the original scans and the V2 and V3 phantoms, showing that all selected measurements were within 5% variation of the original patient\u0026rsquo;s dimensions.\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eTable 4.\u003c/strong\u003e Comparative dimensions. Length (in centimeters) of selected measurements for all CT studies. DV \u0026ndash; dorsoventral (whole head); ML \u0026ndash; mediolateral (whole head); BDV \u0026ndash; Brain (only) dorsoventral; BO \u0026ndash; Brain (only) oblique.\u0026nbsp;\u003c/p\u003e\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Tabd\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirection\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePatient CT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eV2 CT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eV2% variation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eV3 CT\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eV3% variation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-2.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e12.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eML\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e13.47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBDV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e4.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-2.49\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBO\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.02\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eHistogram representations of linear intensity profiles along a path from the surface of the head to the deep tumor margin were compared between the actual patient\u0026rsquo;s CT and V3 avatar (Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). Peaks at distances *80*, *150*, and *290* correlate with the muscle-bone, bone-brain, and brain-ventricle interfaces. The V2 phantom had artifactual peaks at each material transition due to the boundary materials used to separate the phantom tissue types (not pictured in Fig.\u0026nbsp;\u003cspan refid=\"Fig7\" class=\"InternalRef\"\u003e7\u003c/span\u003e). The peaks associated with the interface entering the tumor at the end of this trajectory are indistinct, as is true in the patient\u0026rsquo;s CT scan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study presents a complete end-to-end workflow for the creation of a high-resolution, patient-specific canine brain avatar using CT and MRI data, segmentation, multi-material 3D printing, and imaging validation. The successful production of these avatars validates this step-by-step process, with anticipated feasible improvements for each step in the future. This is particularly relevant for preoperative planning in procedures such as brain biopsies or surgeries, where multiple imaging sessions are often required to place anatomic markers for the imaging-to-patient registration used during these procedures. A physical patient-specific avatar may allow clinicians to simulate and select optimal interventional paths, minimizing the risks and costs associated with general anesthesia required to execute this step with the real patient [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. The combination of advanced segmentation and multi-material PolyJet printing enabled high anatomical realism, with clear differentiation of each tissue. Through multiple iterations, refinements to these two steps improved minor surface irregularities, resulting in an externally perceivably similar avatar.\u003c/p\u003e \u003cp\u003eMaterial assignments based on anatomical presets from the Digital Anatomy printer helped replicate expected imaging contrast, produced adequate signal, and proportional intensities. However, many improvements can be made in the future. The material used for brain tissue in the CT V2 phantom was more hyperattenuating than desired, resulting in an incorrect relative intensity among the other structures. Additionally, this version produced minimal signal in T2-weighted MRI sequences, and the material used for muscle in the T1-weighted sequences was not visible. Further testing is required to optimize material selection to emulate T2-weighted MRI imaging features, to improve these drawbacks.\u003c/p\u003e \u003cp\u003eThe phantom preserved the spatial alignment of key anatomical landmarks, closely matching the geometry observed in the original MRI and CT scans. This level of fidelity is essential for validating neuroanatomical relationships and enables potential use in image-guided navigation and biopsy training [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our qualitative and quantitative analysis supports these models to produce high spatial accuracy, including simulated paths that could represent procedures such as brain biopsies. However, although the printer is accurate to 27 microns, variable shrinking and deformation during post-processing will inherently introduce pseudorandom differences between the designed and manufactured phantoms. Also, a longitudinal analysis should be performed to assess the durability of these materials and to determine if structural changes occur over time. Overall, this form of validation lays the groundwork for future studies that may incorporate full 3D registration, trajectory planning metrics, and measurement error analysis.\u003c/p\u003e \u003cp\u003eHere, we established a workflow that is adaptable to other anatomical regions and species. Similar 3D printing approaches have been applied to the spine, liver, and appendicular skeleton for human medical training and CT quality assurance [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. Our results support the feasibility of extending this multi-material and multimodality approach to veterinary models of other organ systems. However, expanding this work will require additional validation across different anatomical regions, as well as greater automation in segmentation and print preparation.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates the feasibility of converting patient-specific clinical imaging to a 3D printed phantom that is visible and anatomically accurate on CT and MRI. Despite a few imperfections with sub-optimal material selection, these models successfully replicated key anatomical structures and imaging characteristics of a real canine patient, offering a platform for surgical planning and veterinary training. Overall, this workflow presents a meaningful advance in versatile phantom development and clinical procedure planning. Future work should aim to automate segmentation, improve overall time and cost efficiency, and perfect material selection to mimic all desired HU/intensities. Ultimately, these phantoms can serve for procedure planning and practice models for patient-specific procedures.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work involved non-experimental animals with consent granted by owners for future use of data for research purposes. Due to the retrospective nature of data usage, ethical approval from a formal committee was not required.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll included patient images and company references were approved prior to publication.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eData used in this study are available from the corresponding author upon reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAuthors declare no potential competing interests with respect to the research and publication of this article.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eW.K.P. conceived and designed the study. W.K.P., R.K., and A.N.B. performed experiments, analyzed data, and wrote the manuscript. J.N.R., R.G, N.A., W. M., M. D., R. M assisted with sample processing and testing. W.K.P., M.D., W.M., N.A., and R.M. provided critical feedback and oversaw the research program. All authors listed reviewed the manuscript and provided feedback with writing and revisions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to acknowledge Stratasys and Axial 3D company representatives for technical support. We want to thank Susan Kmetz, who permitted the usage of CT and MRI imaging of her beloved dog Charlotte for the purpose of this study.\u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eMartelli, N., Serrano, C., van den Brink, H., Pineau, J., Prognon, P., Borget, I., \u0026amp; El Batti, S. (2016). Advantages and disadvantages of 3-dimensional printing in surgery: A systematic review. Surgery, 159(6), 1485\u0026ndash;1500. https://doi.org/10.1016/J.SURG.2015.12.017 \u003c/li\u003e\n\u003cli\u003eAltwal, J., Altwal, J., Wilson, C. H., \u0026amp; Griffon, D. J. (n.d.). Applications of 3-dimensional printing in small-animal surgery: A review of current practices. \u003cem\u003eJohn Wiley \u0026amp; Sons, Ltd\u003c/em\u003e. https://onlinelibrary.wiley.com/doi/epdf/10.1111/vsu.13739\u003c/li\u003e\n\u003cli\u003eMarquez-Grados, F., Vettorato, E., \u0026amp; Corletto, F. (2020). Sevoflurane with opioid or dexmedetomidine infusions in dogs undergoing intracranial surgery: a retrospective observational study. Journal of veterinary science, 21(1), e8. https://doi.org/10.4142/jvs.2020.21.e8 \u003c/li\u003e\n\u003cli\u003eZhu, Y., Guo, S., Ravichandran, D., Ramanathan, A., Sobczak, M. T., Sacco, A., Patil, D., Thummalapalli, S. V., Pulido, T. V., Lancaster, J. N., Yi, J., Cornella, J. L., Lott, D. G., Chen, X., Mei, X., Zhang, Y. S., Wang, L., Wang, X., Zhao, Y., \u0026hellip; Song, K. (2024). 3D‐Printed Polymeric Biomaterials for Health Applications. Advanced Healthcare Materials. https://doi.org/10.1002/adhm.202402571\u003c/li\u003e\n\u003cli\u003ePatpatiya, P., Chaudhary, K. C., Shastri, A., \u0026amp; Sharma, S. (2022). A review on polyjet 3D printing of polymers and multi-material structures. Proceedings of the Institution of Mechanical Engineers, Part C: Journal of Mechanical Engineering Science, 236(14), 7899\u0026ndash;7926. https://doi.org/10.1177/09544062221079506 \u003c/li\u003e\n\u003cli\u003eRuiz, O. G., \u0026amp; Dhaher, Y. Y. (2021). Multi-color and Multi-Material 3D Printing of Knee Joint models. 7(1), 12. https://doi.org/10.1186/S41205-021-00100-0 \u003c/li\u003e\n\u003cli\u003eFilippou, V., \u0026amp; Tsoumpas, C. (2018). Recent advances on the development of phantoms using 3D printing for imaging with CT, MRI, PET, SPECT, and ultrasound. Medical Physics, 45(9). https://doi.org/10.1002/mp.13058 \u003c/li\u003e\n\u003cli\u003eQiu, J., Hou, K., Dyer, B. A., Chen, J. C., Shi, L., Sun, Y., Xu, L., Zhao, H., Li, Z., Chen, T., Li, M., Zhang, F., Zhang, H., \u0026amp; Rong, Y. (2021). Constructing customized multimodal phantoms through 3D printing: a preliminary evaluation. Frontiers in Physics, 9. https://doi.org/10.3389/fphy.2021.605630 \u003c/li\u003e\n\u003cli\u003eHatamikia, S., Gulyas, I., Birkfellner, W., Kronreif, G., Unger, A., Oberoi, G., Lorenz, A., Unger, E., Kettenbach, J., Figl, M., Patsch, J., Strassl, A., Georg, D., \u0026amp; Renner, A. (2022). Realistic 3D printed CT imaging tumor phantoms for validation of image processing algorithms. Physica Medica, 105, 102512. https://doi.org/10.1016/j.ejmp.2022.102512 \u003c/li\u003e\n\u003cli\u003eMitsouras, D., Lee, T. C., Liacouras, P., Ionita, C. N., Pietilla, T., Maier, S. E., \u0026amp; Mulkern, R. V. (2017). Three-dimensional printing of MRI-visible phantoms and MR image-guided therapy simulation: 3D Printing of MRI-Visible Phantoms. Magnetic Resonance in Medicine, 77(2). https://doi.org/10.1002/MRM.26617 \u003c/li\u003e\n\u003cli\u003eHiggins, M., Leung, S., \u0026amp; Radacsi, N. (2022). 3D Printing Surgical Phantoms and their Role in the Visualization of Medical Procedures. Annals of 3D Printed Medicine, 6, 100057. https://doi.org/10.1016/j.stlm.2022.100057 \u003c/li\u003e\n\u003cli\u003eB\u0026uuml;cking, T. M., Hill, E. R., Robertson, J., Maneas, E., Plumb, A., \u0026amp; Nikitichev, D. I. (2017). From medical imaging data to 3D printed anatomical models. PLOS ONE, 12(5). https://doi.org/10.1371/JOURNAL.PONE.0178540 \u003c/li\u003e\n\u003cli\u003eRamirez, M. J. E., Pena, I. J. R., Castillo, R. E. B., Sufianov, A., Goncharov, E. N., S\u0026aacute;nchez, J. A. S., Colome-Hidalgo, M., Nurmukhametov, R. M., C\u0026eacute;spedes, J. R. C., \u0026amp; Montemurro, N. (2023). Development of a 3D Printed Brain Model with Vasculature for Neurosurgical Procedure Visualisation and Training. Advances in Cardiovascular Diseases, 11(2), 330. https://doi.org/10.3390/biomedicines11020330 \u003c/li\u003e\n\u003cli\u003eColes-Black, J., Bolton, D. M., \u0026amp; Chuen, J. (2021). Accessing 3D Printed Vascular Phantoms for Procedural Simulation. Frontiers in Surgery, 7, 626212. https://doi.org/10.3389/FSURG.2020.626212 \u003c/li\u003e\n\u003cli\u003eRossi, T., Williams, A., \u0026amp; Sun, Z. (2023). Three-Dimensional Printed Liver Models for Surgical Planning and Intraoperative Guidance of Liver Cancer Resection: A Systematic Review. Applied Sciences. https://doi.org/10.3390/app131910757 \u003c/li\u003e\n\u003cli\u003eRottoo, S., Frangella, L., Bazalova‐Carter, M., \u0026amp; Masella, O. (2024). Development of a 3D-printed canine head phantom for veterinary radiotherapy. https://doi.org/10.48550/arxiv.2409.19694 \u003c/li\u003e\n\u003cli\u003eLee, H.-R., Adam, G. O., Adam, G. O., Yang, D. K., Tungalag, T., Lee, S.-J., Kim, J.-S., Kang, H.-S., Kim, S.-J., \u0026amp; Kim, N.-S. (2020). An Easy and Economical Way to Produce a Three-Dimensional Bone Phantom in a Dog with Antebrachial Deformities. Open Access Journal, 10(9), 1445. https://doi.org/10.3390/ANI10091445 \u003c/li\u003e\n\u003cli\u003eSchindelin, J., Arganda-Carreras, I., Frise, E., Kaynig, V., Longair, M., Pietzsch, T., Preibisch, S., Rueden, C., Saalfeld, S., Schmid, B., Tinevez, J., White, D. J., Hartenstein, V., Eliceiri, K., Tomancak, P., \u0026amp; Cardona, A. (2012). Fiji: an open-source platform for biological-image analysis. Nature Methods, 9(7), 676\u0026ndash;682. https://doi.org/10.1038/nmeth.2019\u003c/li\u003e\n\u003cli\u003eMeyer-Szary, J., Souza Luis, M., Mikulski, S., Patel, A., Schulz, F., Tretiakow, D., Fercho, J., Jaguszewska, K., Frankiewicz, M., Pawłowska, E., Targoński, R., Szarpak, Ł., Dądela, K., Sabiniewicz, R., \u0026amp; Kwiatkowska, J. (2022). The Role of 3D Printing in Planning Complex Medical Procedures and Training of Medical Professionals\u0026mdash;Cross-Sectional Multispecialty Review. International Journal of Environmental Research and Public Health, 19(6), 3331. https://doi.org/10.3390/ijerph19063331\u003c/li\u003e\n\u003cli\u003eLasso, A., Heffter, T., Rankin, A., Pinter, C., Ungi, T., \u0026amp; Fichtinger, G. (2014). PLUS: open-source toolkit for ultrasound-guided intervention systems. IEEE Transactions on Biomedical Engineering, 61(10), 2527\u0026ndash;2537. https://doi.org/10.1109/TBME.2014.2322864.\u003c/li\u003e\n\u003cli\u003eLeng S, McGee K, Morris J, Alexander A, Kuhlmann J, Vrieze T, et al. Anatomic modeling using 3D printing: quality assurance and optimization. \u003cem\u003e3D Print Med\u003c/em\u003e. 2017;3:6. https://doi.org/10.1186/s41205-017-0014-3\u003c/li\u003e\n\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":"Dog, Phantom, 3D, Biopsy, Neurosurgery, Teaching model, Glioma","lastPublishedDoi":"10.21203/rs.3.rs-6779582/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6779582/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground. \u003c/strong\u003eAdvancements in 3D printing technology have propelled its utility in human and veterinary medicine for teaching, surgical planning, and procedural practice. Complex clinical workflows and a lack of realistic training models have prevented widespread clinical implementation of intracranial interventions in veterinary medicine. Currently, many phantoms produced for medical settings are limited in their anatomical representations or compatible imaging modalities. Computer-aided design bridges the gap between a patient’s imaging data, segmenting individual regions of interest, and selecting materials to produce the same imaging features. Digital Anatomy printer with polyjet systems allows for the use of multiple materials to accomplish these designs and produce a usable patient-specific physical avatar that behaves like the original patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose.\u003c/strong\u003e This study provides a ready-to-use guideline and workflow system of utilizing the Digital Anatomy printer to develop a physical canine brain avatar based on a specific patient’s imaging data that accurately depicts patient anatomy in CT and MRI scans, which can ultimately be used for next-generation procedural planning and practice.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods.\u003c/strong\u003e MRI and CT data from a specific patient with a brain tumor were initially utilized to generate a computer-aided design for an avatar to mimic the original anatomy. Tissues of interest were segmented, and printing materials were selected to mimic the desired imaging properties. Avatars were produced using a 3D polyjet system and underwent CT and MRI scans for evaluation. Avatar material intensity/Hounsfield Units, dimensions, and selected biopsy track were directly compared to the patient data for similarity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults. \u003c/strong\u003eOur avatars showed the desired signal in both CT and MRI, with similar proportional material and tissue intensities across the imaging spectrum and minimal dimensional variation between the avatars and the patient.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion.\u003c/strong\u003e This study established guidelines for canine brain avatar manufacture with DAP and validated the workflow of converting patient-specific clinical imaging to a 3D printed avatar that is anatomically accurate and visible in CT and MRI, producing a unique model for clinicians.\u003c/p\u003e","manuscriptTitle":"CT and MRI-visible canine brain avatar via three-dimensional digital anatomy printing in aiding neurosurgical training and biopsy planning – proof of concept study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-03 07:50:20","doi":"10.21203/rs.3.rs-6779582/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":"92c903c1-0188-4d73-9a63-5a66f9e6dc8d","owner":[],"postedDate":"June 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-12T22:53:24+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-03 07:50:20","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6779582","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6779582","identity":"rs-6779582","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}