Mr. David C. Zaretsky Profile

David C. Zaretsky

at Univ at Buffalo

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Proceedings Article | 29 March 2024 Poster + Paper

Segmentation of spinal computed tomography to produce biomechanically accurate patient-specific surgical models

Kaelyn Button, David Zaretsky, Kasey Pfleging, Megan Malueg, Marissa Kruk, Jeffrey Mullin, Ciprian Ionita

Proceedings Volume 12928, 129281W (2024) https://doi.org/10.1117/12.3006426

KEYWORDS: 3D modeling, Anatomy, 3D printing, Printing, Tissues, Materials properties, Surgery, Image segmentation, Spine

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Purpose: Improved Adult Spinal Deformity (ASD) surgery outcomes can be achieved with precise anatomical and biomechanical models. Traditional cadavers, limited by scarcity and cost, may not fully meet specific anatomical needs. Patient-tailored 3D-printed spine models offer a promising alternative. This study, leveraging medical image segmentation, CAD, and advanced 3D printing techniques, explores the potential of patient-specific 3D-printed spine models. Materials & Methods: 3DSlicer was used for image segmentation, and MeshMixer for model smoothing. 3D-printing, with Stratasys J750™, specialized for anatomical modeling, integrated materials simulating bone structures, soft tissues, and intervertebral discs. Quantitative material testing was completed on samples of each printed tissue type individually, qualitative model testing and quantitative pilot displacement-controlled load flexion/extension testing were completed on L3-L5 vertebral models. Results: The 3D-printing process for these detailed functional unit models was completed within approximately 20 hours. Qualitative assessment by neurosurgical residents affirmed the models' resemblance to human spinal tissue in a simulated procedural context. However, mechanical testing of the material samples revealed discrepancies when compared to established biomechanical properties in the literature. This suggests that while the models provide a degree of procedural realism, their material properties require further refinement to fully replicate the biomechanical characteristics of actual spinal tissues. Additional testing on the L3-L5 model is planned to further investigate these findings. Conclusions: Using medical image segmentation and advanced 3D-printing techniques, we introduce a method for swiftly generating anatomically and biomechanically accurate spine models tailored to individual patients. This approach has transformative potential for ASD pre-surgical planning.

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