A level-wise spine registration framework to account for large pose changes

• Published in: International journal for computer assisted radiology and surgery Vol. 16; no. 6; pp. 943 - 953
• Main Authors: Cai, Yunliang, Wu, Shaoju, Fan, Xiaoyao, Olson, Jonathan, Evans, Linton, Lollis, Scott, Mirza, Sohail K., Paulsen, Keith D., Ji, Songbai
• Format: Journal Article
• Language: English
• Published: Cham Springer International Publishing 01.06.2021; Springer Nature B.V
• Subjects: Computed tomography; Computer Imaging; Computer Science; Forecasting; Health Informatics; Image registration; Imaging; Magnetic resonance imaging; Medical imaging; Medicine; Medicine & Public Health; Original Article; Pattern Recognition and Graphics; Point pairs; Principal components analysis; Radiology; Registration; Surgery; Swine; Vision; Stereovision; Spine imaging; Image registration; Patient registration; Deep learning networks; NLPCA
• ISSN: 1861-6410; 1861-6429
• DOI: 10.1007/s11548-021-02395-0

Purposes Accurate and efficient spine registration is crucial to success of spine image guidance. However, changes in spine pose cause intervertebral motion that can lead to significant registration errors. In this study, we develop a geometrical rectification technique via nonlinear principal component analysis (NLPCA) to achieve level-wise vertebral registration that is robust to large changes in spine pose. Methods We used explanted porcine spines and live pigs to develop and test our technique. Each sample was scanned with preoperative CT (pCT) in an initial pose and rescanned with intraoperative stereovision (iSV) in a different surgical posture. Patient registration rectified arbitrary spinal postures in pCT and iSV into a common, neutral pose through a parameterized moving-frame approach. Topologically encoded depth projection 2D images were then generated to establish invertible point-to-pixel correspondences. Level-wise point correspondences between pCT and iSV vertebral surfaces were generated via 2D image registration. Finally, closed-form vertebral level-wise rigid registration was obtained by directly mapping 3D surface point pairs. Implanted mini-screws were used as fiducial markers to measure registration accuracy. Results In seven explanted porcine spines and two live animal surgeries (maximum in-spine pose change of 87.5 mm and 32.7 degrees averaged from all spines), average target registration errors (TRE) of 1.70  ±  0.15 mm and 1.85 ± 0.16 mm were achieved, respectively. The automated spine rectification took 3–5 min, followed by an additional 30 secs for depth image projection and level-wise registration. Conclusions Accuracy and efficiency of the proposed level-wise spine registration support its application in human open spine surgeries. The registration framework, itself, may also be applicable to other intraoperative imaging modalities such as ultrasound and MRI, which may expand utility of the approach in spine registration in general.