Graduate Seminar: Improving Scoliosis Correcition Procedures Using Patient-Specific, Soft-Tissue Inclusive Models
By Mr. Austin Tapp, PHD Candidate in the Biomedical Engineering Institute at Old Dominion University
Friday, December 4, 2020
Scoliosis is an abnormal spinal curvature of greater than 10 degrees. Severe scoliotic deformities are usually addressed with a highly invasive procedure: an anterior or posterior spinal fusion approach. The invasiveness is due, in part, to the constraints of current scoliosis surgical planning, which utilizes computed tomography (CT) scans that are unable to discern soft tissue elements. If localization of ligaments and other soft tissues is achieved pre-operatively, corrective procedures can be made safer and more efficient by reducing the number of required ligament releases performed during spinal fusion. Most soft tissue assumptions for surgical planning are accomplished through simplistic, hand-drawn, finite-element models. Compared to these traditional models our use of patient-specific meshes that encompass vertebrae, intervertebral discs, ligaments, and other soft tissue will more accurately guide computer-assisted surgery systems supporting deformity correction. Our anatomically inclusive meshes are produced using a computer-aided design (CAD) mesh that is warped onto a patient CT image through a deformable surface algorithm. Conspicuous structures, such as vertebral bodies, are segmented with deep learning neural networks and the CAD meshes are readily fit onto the CT segmentations. Meanwhile, the soft tissues that surround these structures are locally warped to surmise a contextually appropriate position. Dice coefficients and Hausdorff distance metrics quantitatively demonstrate the accuracy and feasibility of our approach. Preliminary, qualitative outcomes of full spinal columns fit to pre-operative patient images offer a view into the developments nearing finalization. When complete, these anatomically inclusive models will be implemented as the "roadmap" of the "Surgical GPS".