Robust 3D–2D image registration: application to spine interventions and vertebral labeling in the presence of anatomical deformation

We present a framework for robustly estimating registration between a 3D volume image and a 2D projection image and evaluate its precision and robustness in spine interventions for vertebral localization in the presence of anatomical deformation. The framework employs a normalized gradient information similarity metric and multi-start covariance matrix adaptation evolution strategy optimization with local-restarts, which provided improved robustness against deformation and content mismatch. The parallelized implementation allowed orders-of-magnitude acceleration in computation time and improved the robustness of registration via multi-start global optimization. Experiments involved a cadaver specimen and two CT datasets (supine and prone) and 36 C-arm fluoroscopy images acquired with the specimen in four positions (supine, prone, supine with lordosis, prone with kyphosis), three regions (thoracic, abdominal, and lumbar), and three levels of geometric magnification (1.7, 2.0, 2.4). Registration accuracy was evaluated in terms of projection distance error (PDE) between the estimated and true target points in the projection image, including 14 400 random trials (200 trials on the 72 registration scenarios) with initialization error up to ±200 mm and ±10°. The resulting median PDE was better than 0.1 mm in all cases, depending somewhat on the resolution of input CT and fluoroscopy images. The cadaver experiments illustrated the tradeoff between robustness and computation time, yielding a success rate of 99.993% in vertebral labeling (with 'success' defined as PDE <5 mm) using 1,718 664 ± 96 582 function evaluations computed in 54.0 ± 3.5 s on a mid-range GPU (nVidia, GeForce GTX690). Parameters yielding a faster search (e.g., fewer multi-starts) reduced robustness under conditions of large deformation and poor initialization (99.535% success for the same data registered in 13.1 s), but given good initialization (e.g., ±5 mm, assuming a robust initial run) the same registration could be solved with 99.993% success in 6.3 s. The ability to register CT to fluoroscopy in a manner robust to patient deformation could be valuable in applications such as radiation therapy, interventional radiology, and an assistant to target localization (e.g., vertebral labeling) in image-guided spine surgery.

[1]  Stephen D. Laycock,et al.  GPU Accelerated Generation of Digitally Reconstructed Radiographs for 2-D/3-D Image Registration , 2012, IEEE Transactions on Biomedical Engineering.

[2]  John A. Nelder,et al.  A Simplex Method for Function Minimization , 1965, Comput. J..

[3]  G C Sharp,et al.  Auto-masked 2D/3D image registration and its validation with clinical cone-beam computed tomography. , 2012, Physics in medicine and biology.

[4]  Boštjan Likar,et al.  “Gold standard” data for evaluation and comparison of 3D/2D registration methods , 2004, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[5]  Graeme P. Penney,et al.  Increasing the Automation of a 2D-3D Registration System , 2013, IEEE Transactions on Medical Imaging.

[6]  J. Kettenbach,et al.  Stochastic rank correlation: a robust merit function for 2D/3D registration of image data obtained at different energies. , 2009, Medical physics.

[7]  Alan H. Daniels,et al.  Wrong‐site Spine Surgery , 2013, The Journal of the American Academy of Orthopaedic Surgeons.

[8]  György Cserey,et al.  Fast DRR generation for 2D to 3D registration on GPUs. , 2012, Medical physics.

[9]  T. Peters,et al.  2D-3D registration of coronary angiograms for cardiac procedure planning and guidance. , 2005, Medical physics.

[10]  Kawal S. Rhode,et al.  Intensity-based 2-D - 3-D registration of cerebral angiograms , 2003, IEEE Transactions on Medical Imaging.

[11]  Ramin Shahidi,et al.  Evaluation of Intensity-Based 2D-3D Spine Image Registration Using Clinical Gold-Standard Data , 2003, WBIR.

[12]  Joyoni Dey,et al.  Targeted 2D/3D registration using ray normalization and a hybrid optimizer. , 2006, Medical physics.

[13]  Richard D. Komistek,et al.  A robust method for registration of three-dimensional knee implant models to two-dimensional fluoroscopy images , 2003, IEEE Transactions on Medical Imaging.

[14]  Russell H. Taylor,et al.  Information Processing in Computer-Assisted Interventions - Second International Conference, IPCAI 2011, Berlin, Germany, June 22, 2011. Proceedings , 2011, IPCAI.

[15]  L. Darrell Whitley,et al.  The dispersion metric and the CMA evolution strategy , 2006, GECCO.

[16]  Guoan Li,et al.  The accuracy and repeatability of an automatic 2D-3D fluoroscopic image-model registration technique for determining shoulder joint kinematics. , 2012, Medical engineering & physics.

[17]  Michael Brady,et al.  Improved Optimization for the Robust and Accurate Linear Registration and Motion Correction of Brain Images , 2002, NeuroImage.

[18]  Jürgen Weese,et al.  A comparison of similarity measures for use in 2-D-3-D medical image registration , 1998, IEEE Transactions on Medical Imaging.

[19]  Pedro Larrañaga,et al.  Towards a New Evolutionary Computation - Advances in the Estimation of Distribution Algorithms , 2006, Towards a New Evolutionary Computation.

[20]  Peter Kazanzides,et al.  Intraoperative Image-based Multiview 2D/3D Registration for Image-Guided Orthopaedic Surgery: Incorporation of Fiducial-Based C-Arm Tracking and GPU-Acceleration , 2012, IEEE Transactions on Medical Imaging.

[21]  Shiju Yan,et al.  Improving accuracy of XRII image distortion correction using a new hybrid image processing method: performance assessment. , 2011, Medical physics.

[22]  Wolfgang Birkfellner,et al.  Fast DRR Generation for 2D/3D Registration , 2005, MICCAI.

[23]  William M. Wells,et al.  Medical Image Computing and Computer-Assisted Intervention — MICCAI’98 , 1998, Lecture Notes in Computer Science.

[24]  A. Fenster,et al.  2D-3D rigid registration to compensate for prostate motion during 3D TRUS-guided biopsy. , 2013, Medical physics.

[25]  Yoshito Otake,et al.  Automatic localization of target vertebrae in spine surgery using fast CT-to-fluoroscopy (3D-2D) image registration , 2012, Medical Imaging.

[26]  Hongkai Wang,et al.  A method of 2D/3D registration of a statistical mouse atlas with a planar X-ray projection and an optical photo , 2013, Medical Image Anal..

[27]  Sartaj Sahni,et al.  Leaf sequencing algorithms for segmented multileaf collimation. , 2003, Physics in medicine and biology.

[28]  S. Schafer,et al.  Automatic localization of vertebral levels in x-ray fluoroscopy using 3D-2D registration: a tool to reduce wrong-site surgery , 2012, Physics in medicine and biology.

[29]  G. T. Timmer,et al.  Stochastic global optimization methods part I: Clustering methods , 1987, Math. Program..

[30]  Nikolaus Hansen,et al.  Evaluating the CMA Evolution Strategy on Multimodal Test Functions , 2004, PPSN.

[31]  Wolfgang Birkfellner,et al.  High-performance GPU-based rendering for real-time, rigid 2D/3D-image registration and motion prediction in radiation oncology. , 2012, Zeitschrift fur medizinische Physik.

[32]  Reshma Munbodh,et al.  2D-3D registration for prostate radiation therapy based on a statistical model of transmission images. , 2009, Medical physics.

[33]  James C. Gee,et al.  Point similarity measures for non-rigid registration of multi-modal data , 2003, Comput. Vis. Image Underst..

[34]  Jing J. Liang,et al.  Problem Definitions and Evaluation Criteria for the CEC 2005 Special Session on Real-Parameter Optimization , 2005 .

[35]  Wolfgang Birkfellner,et al.  A faster method for 3D/2D medical image registration—a simulation study , 2003, Physics in medicine and biology.

[36]  William Hoff,et al.  In vivo determination of normal and anterior cruciate ligament-deficient knee kinematics. , 2005, Journal of biomechanics.

[37]  Graeme P. Penney,et al.  An Image-Guided Surgery System to Aid Endovascular Treatment of Complex Aortic Aneurysms: Description and Initial Clinical Experience , 2011, IPCAI.

[38]  M. J. D. Powell,et al.  An efficient method for finding the minimum of a function of several variables without calculating derivatives , 1964, Comput. J..

[39]  Jim Graham,et al.  Automatic Inference and Measurement of 3D Carpal Bone Kinematics From Single View Fluoroscopic Sequences , 2013, IEEE Transactions on Medical Imaging.

[40]  Uros Mitrovic,et al.  3D-2D Registration of Cerebral Angiograms: A Method and Evaluation on Clinical Images , 2013, IEEE Transactions on Medical Imaging.

[41]  James C. Gee,et al.  Biomedical Image Registration , 2003, Lecture Notes in Computer Science.

[42]  Nikolaus Hansen,et al.  A restart CMA evolution strategy with increasing population size , 2005, 2005 IEEE Congress on Evolutionary Computation.

[43]  Bostjan Likar,et al.  A review of 3D/2D registration methods for image-guided interventions , 2012, Medical Image Anal..

[44]  Mohamed Cheriet,et al.  Nonrigid 2D/3D Registration of Coronary Artery Models With Live Fluoroscopy for Guidance of Cardiac Interventions , 2012, IEEE Transactions on Medical Imaging.

[45]  Christian Roux,et al.  2-D–3-D Frequency Registration Using a Low-Dose Radiographic System for Knee Motion Estimation , 2013, IEEE Transactions on Biomedical Engineering.

[46]  Anne Auger,et al.  Performance evaluation of an advanced local search evolutionary algorithm , 2005, 2005 IEEE Congress on Evolutionary Computation.

[47]  Brian Cabral,et al.  Accelerated volume rendering and tomographic reconstruction using texture mapping hardware , 1994, VVS '94.

[48]  Gabor Fichtinger,et al.  Monitoring tumor motion by real time 2D/3D registration during radiotherapy , 2012, Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology.

[49]  Daniel Rueckert,et al.  Fast generation of digitally reconstructed radiographs using attenuation fields with application to 2D-3D image registration , 2005, IEEE Transactions on Medical Imaging.

[50]  David J. Hawkes,et al.  A comparison of a similarity-based and a feature-based 2-D-3-D registration method for neurointerventional use , 2005, IEEE Transactions on Medical Imaging.

[51]  Guy Marchal,et al.  Multimodality image registration by maximization of mutual information , 1997, IEEE Transactions on Medical Imaging.

[52]  Torsten Hopp,et al.  Automatic multimodal 2D/3D breast image registration using biomechanical FEM models and intensity-based optimization , 2013, Medical Image Anal..

[53]  Graeme P. Penney,et al.  Standardized evaluation methodology for 2-D-3-D registration , 2005, IEEE Transactions on Medical Imaging.

[54]  Paul J. Besl,et al.  A Method for Registration of 3-D Shapes , 1992, IEEE Trans. Pattern Anal. Mach. Intell..

[55]  Lixu Gu,et al.  Non-rigid 2D-3D Registration Based on Support Vector Regression Estimated Similarity Metric , 2008, MIAR.

[56]  Nassir Navab,et al.  Deformable 2D-3D Registration of Vascular Structures in a One View Scenario , 2009, IEEE Transactions on Medical Imaging.

[57]  Max A. Viergever,et al.  General intensity transformations and differential invariants , 1994, Journal of Mathematical Imaging and Vision.

[58]  Nassir Navab,et al.  3D Reconstruction from Projection Matrices in a C-Arm Based 3D-Angiography System , 1998, MICCAI.

[59]  Mohammad Alfawareh,et al.  The Prevalence of Wrong Level Surgery Among Spine Surgeons , 2008, Spine.

[60]  Stephen D. Laycock,et al.  Fast reconstructed radiographs from octree-compressed volumetric data , 2013, International Journal of Computer Assisted Radiology and Surgery.

[61]  Nikolaus Hansen,et al.  The CMA Evolution Strategy: A Comparing Review , 2006, Towards a New Evolutionary Computation.

[62]  Max A. Viergever,et al.  Image registration by maximization of combined mutual information and gradient information , 2000, IEEE Transactions on Medical Imaging.

[63]  Jon Louis Bentley,et al.  Multidimensional binary search trees used for associative searching , 1975, CACM.