Abstract
Spinal stenosis is a condition resulting in the compression of the neural elements due to narrowing of the spinal canal. Anatomical factors including enlargement of the facet joints, thickening of the ligaments, and bulging or collapse of the intervertebral discs contribute to the compression. Decompression surgery alleviates spinal stenosis through a laminectomy involving the resection of bone and ligament. Spinal decompression surgery requires appropriate planning and variable strategies depending on the specific situation. Given the potential for neural complications, there exist significant barriers to residents and fellows obtaining adequate experience performing spinal decompression in the operating room. Virtual teaching tools exist for learning instrumentation which can enhance the quality of orthopaedic training, building competency and procedural understanding. However, virtual simulation tools are lacking for decompression surgery. The aim of this work was to develop an open-source 3D virtual simulator as a teaching tool to improve orthopaedic training in spinal decompression.
A custom step-wise spinal decompression simulator workflow was built using 3D Slicer, an open-source software development platform for medical image visualization and processing. The procedural steps include multimodal patient-specific loading and fusion of Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) data, bone threshold-based segmentation, soft tissue segmentation, surgical planning, and a laminectomy and spinal decompression simulation. Fusion of CT and MRI elements was achieved using Fiducial-Based Registration which aligned the scans based on manually placed points allowing for the identification of the relative position of soft and hard tissues. Soft tissue segmentation of the spinal cord, the cerebrospinal fluid, the cauda equina, and the ligamentum flavum was performed using Simple Region Growing Segmentation (with manual adjustment allowed) involving the selection of structures on T1 and/or T2-weighted scans. A high-fidelity 3D model of the bony and soft tissue anatomy was generated with the resulting surgical exposure defined by labeled vertebrae simulating the central surgical incision. Bone and soft tissue resecting tools were developed by customizing manual 3D segmentation tools. Simulating a laminectomy was enabled through bone and ligamentum flavum resection at the site of compression. Elimination of the stenosis enabled decompression of the neural elements simulated by interpolation of the undeformed anatomy above and below the site of compression using Fill Between Slices to reestablish pre-compression neural tissue anatomy.
The completed workflow allows patient specific simulation of decompression procedures by staff surgeons, fellows and residents. Qualitatively, good visualization was achieved of merged soft tissue and bony anatomy. Procedural accuracy, the design of resecting tools, and modeling of the impact of bone and ligament removal was found to adequately encompass important challenges in decompression surgery.
This software development project has resulted in a well-characterized freely accessible tool for simulating spinal decompression surgery. Future work will integrate and evaluate the simulator within existing orthopaedic resident competency-based curriculum and fellowship training instruction. Best practices for effectively teaching decompression in tight areas of spinal stenosis using virtual simulation will also be investigated in future work.
