A Semi-Automated Method for Subject-Specific Modeling of the Spinal Canal from Computed Tomography Images and Dynamic Radiographs

Abstract

Shrinkage of the spinal canal may be due to congenital or degenerative conditions, and it causes many spinerelated diseases. We present a semi-automated method to computationally reconstruct spinal canal models from static 3D images and dynamic 2D radiographs of the spine. First, we reconstruct the 3D motion of vertebrae from dynamic radiographs and compute hybrid representations of 3D bone models to facilitate computational modeling. We then use the bone position and orientation and the hybrid representations to computationally reconstruct the mesh structure of the spinal canal across the range of motion. The process requires selecting manually only a few landmark points (approximately 1%-2% of all computed vertices), and thus significantly reduces the amount of manual labor required for reconstructing a detailed geometrical model of the spinal canal. Validation on both a healthy and a fusion patient shows that the generated models can capture subject-specific characteristics of the canals and provide insight into the change of the motion pattern due to the surgery. The automation of the method will allow bioengineers to perform large scale experiments on healthy and injured spine joints and thus gain insight into underlying canal conditions.