Optimal Surgical Planning Guidance for Spinal Fusion Surgery Using CT Based Spinal Pedicle Segmentation

Abstract

Exact information about the shape of a lumbar pedicle and high precision of planning in the preoperative phase can increase operation accuracy and safety during computer-aided spinal fusion surgery, which requires extreme caution on the part of the surgeon, due to the complexity and delicacy of the procedure. In this thesis, a robust framework for segmenting the lumbar pedicle in computed tomography (CT) images and an advanced preoperative planning framework for lumbar spinal fusion are presented.Firstly, chapter 2 is concern with the spinal pedicle segmentation framework that takes a CT image, which includes the lumbar pedicle as input, and provides the segmented lumbar pedicle in the form of three-dimensional voxel sets. This multistep approach begins with two-dimensional dynamic thresholding using local optimal thresholds, followed by procedures to recover the spine geometry in a high curvature environment. A subsequent canal reference determinationusing proposed thinning-based integrated cost (tbIC) is then performed. Based on the obtained segmented vertebra and canal reference, the edge of the spinal pedicle is segmented. This framework has been tested on 84 lumbar vertebrae of 19 patients requiring spinal fusion. It was successfully applied, resulting in an average success rate of 93.22% and a final mean error of 0.14±0.05mm. Precision errors were smaller than 1%for spine pedicle volumes. Intra- and inter-operator precision errors were not significantly different.Next, in chapter 3, the advanced preoperative planning framework is described, which is based on lumbar spinal pedicle data obtained from computed tomography (CT) images, and provides optimal insertion trajectories and pedicle screw sizes. The proposed approach begins with a safety margin estimation for each potential insertion trajectory that passes through the pedicle volume, followed by procedures to collect a set of insertion trajectories that satisfy operation safety objectives. Among the trajectory candidates, the insertion trajectory, which maximizes the insertable depth of a pedicle screw into the vertebral body, is then chosen as optimal, because the insertable depth enhances the strength of the transpedicular screw-vertebra interface after spinal fusion surgery. The radius of a pedicle screw was chosen as 70% of the pedicle radius. This framework has been tested on 108 spinal pedicles of 12 patients requiring spinal fusion. It was successfully applied,resulting in an average success rate of 100% and a final safety margin of 2.1±0.1mm. Planning accuracy and usefulness of the proposed surgical planner show significant differences compared witha conventional manual planner.The combination of above two approaches can guarantee a conservative safety margin about 2mm during the preoperative phase, which is higher than the amount of the error chain using conventionalrobot-assisted spinal fusion (~1.5mm). Pedicle screws can remain at the sponge bone region of the spinal pedicle despite potential errors induced during registration or actual screw insertion. Resulting optimal preoperative planning solution can contribute to operational safety during the intraoperative phase via registration between preoperative CT and intraoperative fluoroscope,a navigation system, and/or precise pose guidance of an assistive robot.