Yunliang Cai1, Shaoju Wu1, Xiaoyao Fan2, Jonathan Olson2, Linton Evans2, Scott Lollis3, Sohail K Mirza2, Keith D Paulsen2, Songbai Ji4. 1. Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA. 2. Dartmouth College Dartmouth-Hitchcock Medical Center, 1 Medical Center Dr, Lebanon, NH, 03766, USA. 3. University of Vermont Medical Center, Burlington, VT, 05401, USA. 4. Worcester Polytechnic Institute, 100 Institute Rd, Worcester, MA, 01609, USA. sji@wpi.edu.
Abstract
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.
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.
Authors: Songbai Ji; Xiaoyao Fan; Keith D Paulsen; David W Roberts; Sohail K Mirza; S Scott Lollis Journal: IEEE Trans Biomed Eng Date: 2015-03-26 Impact factor: 4.538
Authors: Sean Gill; Purang Abolmaesumi; Gabor Fichtinger; Jonathan Boisvert; David Pichora; Dan Borshneck; Parvin Mousavi Journal: Med Image Anal Date: 2010-08-04 Impact factor: 8.545
Authors: Songbai Ji; Xiaoyao Fan; Keith D Paulsen; David W Roberts; Sohail K Mirza; S Scott Lollis Journal: Int J Comput Assist Radiol Surg Date: 2015-07-21 Impact factor: 2.924
Authors: A Uneri; T De Silva; J Goerres; M W Jacobson; M D Ketcha; S Reaungamornrat; G Kleinszig; S Vogt; A J Khanna; G M Osgood; J-P Wolinsky; J H Siewerdsen Journal: Phys Med Biol Date: 2017-02-24 Impact factor: 3.609
Authors: J Goerres; A Uneri; T De Silva; M Ketcha; S Reaungamornrat; M Jacobson; S Vogt; G Kleinszig; G Osgood; J-P Wolinsky; J H Siewerdsen Journal: Phys Med Biol Date: 2017-02-08 Impact factor: 4.174