Abstract

Background ContextComputer-assisted navigation (CAN) may guide spinal instrumentation, and requires alignment of patient anatomy to imaging. Iterative closest-point (ICP) algorithms register anatomical and imaging surface datasets, which may fail in the presence of geometric symmetry (congruence), leading to failed registration or inaccurate navigation. Here we computationally quantify geometric congruence in posterior spinal exposures, and identify predictors of potential navigation inaccuracy.MethodsMidline posterior exposures were performed from C1-S1 in four human cadavers. An optically-based CAN generated surface maps of the posterior elements at each level. Maps were reconstructed to include bilateral hemilamina, or unilateral hemilamina with/without the base of the spinous process. Maps were fitted to symmetrical geometries (cylindrical/spherical/planar) using computational modelling, and the degree of model fit quantified based on the ratio of model inliers to total points.Geometric congruence was subsequently assessed clinically in 11 patients undergoing midline exposures in the cervical/thoracic/lumbar spine for posterior instrumented fusion.ResultsIn cadaveric testing, increased cylindrical/spherical/planar symmetry was seen in the high-cervical and subaxial cervical spine relative to the thoracolumbar spine (p<0.001). Extension of unilateral exposures to include the ipsilateral base of the spinous process decreased symmetry independent of spinal level (p<0.001).In clinical testing, increased cylindrical/spherical/planar symmetry was seen in the subaxial cervical relative to the thoracolumbar spine (p<0.001), and in the thoracic relative to the lumbar spine (p<0.001). Symmetry in unilateral exposures was decreased by 20% with inclusion of the ipsilateral base of the spinous process.ConclusionsGeometric congruence is most evident at C1 and the subaxial cervical spine, warranting greater vigilance in navigation accuracy verification. At all levels, inclusion of the base of the spinous process in unilateral registration decreases the likelihood of geometric symmetry and navigation error. This work is important to allow the extension of line-of-sight based registration techniques to minimally-invasive unilateral approaches.

Highlights

  • Spinal instrumentation traditionally has been placed freehand based on anatomic landmarks, which may be highly variable, or with fluoroscopic guidance resulting in significant radiation exposure to operating room personnel.[1,2,3] Computer-assisted navigation (CAN) may guide spinal instrumentation placement, significantly improving accuracy and minimizing acute and long-term malposition related complications.[4,5,6] Image guidance in CAN may be based on pre-operative imaging, typically CT, or intra-operatively-acquired 3D fluoroscopy or CT; in all cases, navigation requires registration of the image and patient spaces

  • Inclusion of the base of the spinous process in unilateral registration decreases the likelihood of geometric symmetry and navigation error

  • While multiple variations of Iterative closest-point (ICP) have attempted to address the stability of the final alignment between meshes, non-convergence from geometric congruence remains a potential source of registration error in image-guided surgery, leading to failed registration or, worse, successful registration with inaccurate navigation

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Summary

Background

Computer-assisted navigation (CAN) may guide spinal instrumentation, and requires alignment of patient anatomy to imaging. Iterative closest-point (ICP) algorithms register anatomical and imaging surface datasets, which may fail in the presence of geometric symmetry (congruence), leading to failed registration or inaccurate navigation. We computationally quantify geometric congruence in posterior spinal exposures, and identify predictors of potential navigation inaccuracy

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