Simulation study of minimally invasive surgical scenes and their effect on hand-held stereovision driven level-wise registration techniques

Abstract

Image guided spinal procedure accuracy is dynamic during surgery. Intervertebral motion during surgery drastically effects the temporal accuracy of a procedure. A hand-held stereovision (HHS) system has been employed in previous studies for intraoperative data collection. These data can be used to deform a CT scan to reflect the current spinal posture. These methods are criticized due to the large exposure required for data collection. Currently, to collect HHS data the spine is exposed out to the lateral boundary of the posterior surface of the transverse process. In modern pedicle screw placements, and laminectomies the exposure is smaller. For this method to remain contemporary, a more robust data collection scheme using a smaller exposure should be employed. In this study, simulated narrow exposures were created by manually segmenting HHS data from a cadaver pig. These 3 datasets are created to drive an existing level-wise registration model to generate 3 updated CTs (uCT). The 3 HHS datasets were manually segmented in the following ways: out to the transverse process, out to the facet joints, and out to the lamina. A fiducial registration error was computed from manually identified mini-screw fiducials in each uCT. The mean values for L2 norms for the transverse process segmentation data, facet segmentation data, and lamina segmentation are 2.04 ± 1.10mm,3.18 ± 2.18mm, and 4.59 ± 2.28mm respectively. Median values are 1.82mm, 2.25mm, 4.35mm respectively. This data shows the need for a more robust deformation model, and HHS system if we wish to have sub 2mm registration accuracy with narrow exposures.