Automatic geometric rectification for patient registration in image-guided spinal surgery

Yunliang Cai; Jonathan D. Olson; Xiaoyao Fan; Linton T. Evans; Keith D. Paulsen; David W. Roberts; Sohail K. Mirza; S. Scott Lollis; Songbai Ji

doi:10.1117/12.2216193

18 March 2016 Automatic geometric rectification for patient registration in image-guided spinal surgery

Yunliang Cai, Jonathan D. Olson, Xiaoyao Fan, Linton T. Evans, Keith D. Paulsen, David W. Roberts, Sohail K. Mirza, S. Scott Lollis, Songbai Ji

Author Affiliations +

Proceedings Volume 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling; 97860E (2016) https://doi.org/10.1117/12.2216193
Event: SPIE Medical Imaging, 2016, San Diego, California, United States

Abstract

Accurate and efficient patient registration is crucial for the success of image-guidance in open spinal surgery. Recently, we have established the feasibility of using intraoperative stereovision (iSV) to perform patient registration with respect to preoperative CT (pCT) in human subjects undergoing spinal surgery. Although a desired accuracy was achieved, the method required manual segmentation and placement of feature points on reconstructed iSV and pCT surfaces. In this study, we present an improved registration pipeline to eliminate these manual operations. Specifically, automatic geometric rectification was performed on spines extracted from pCT and iSV into pose-invariant shapes using a nonlinear principal component analysis (NLPCA). Rectified spines were obtained by projecting the reconstructed 3D surfaces into an anatomically determined orientation. Two-dimensional projection images were then created with image intensity values encoding feature "height" in the dorsal-ventral direction. Registration between the 2D depth maps yielded an initial point-wise correspondence between the 3D surfaces. A refined registration was achieved using an iterative closest point (ICP) algorithm. The technique was successfully applied to two explanted and one live porcine spines. The computational cost of the registration pipeline was less than 1 min, with an average target registration error (TRE) less than 2.2 mm in the laminae area. These results suggest the potential for the pose-invariant, rectification-based registration technique for clinical application in human subjects in the future.

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Yunliang Cai, Jonathan D. Olson, Xiaoyao Fan, Linton T. Evans, Keith D. Paulsen, David W. Roberts, Sohail K. Mirza, S. Scott Lollis, and Songbai Ji "Automatic geometric rectification for patient registration in image-guided spinal surgery", Proc. SPIE 9786, Medical Imaging 2016: Image-Guided Procedures, Robotic Interventions, and Modeling, 97860E (18 March 2016); https://doi.org/10.1117/12.2216193

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