Robust methods for automatic image-to-world registration in cone-beam CT interventional guidance.

PURPOSE Real-time surgical navigation relies on accurate image-to-world registration to align the coordinate systems of the image and patient. Conventional manual registration can present a workflow bottleneck and is prone to manual error and intraoperator variability. This work reports alternative means of automatic image-to-world registration, each method involving an automatic registration marker (ARM) used in conjunction with C-arm cone-beam CT (CBCT). The first involves a Known-Model registration method in which the ARM is a predefined tool, and the second is a Free-Form method in which the ARM is freely configurable. METHODS Studies were performed using a prototype C-arm for CBCT and a surgical tracking system. A simple ARM was designed with markers comprising a tungsten sphere within infrared reflectors to permit detection of markers in both x-ray projections and by an infrared tracker. The Known-Model method exercised a predefined specification of the ARM in combination with 3D-2D registration to estimate the transformation that yields the optimal match between forward projection of the ARM and the measured projection images. The Free-Form method localizes markers individually in projection data by a robust Hough transform approach extended from previous work, backprojected to 3D image coordinates based on C-arm geometric calibration. Image-domain point sets were transformed to world coordinates by rigid-body point-based registration. The robustness and registration accuracy of each method was tested in comparison to manual registration across a range of body sites (head, thorax, and abdomen) of interest in CBCT-guided surgery, including cases with interventional tools in the radiographic scene. RESULTS The automatic methods exhibited similar target registration error (TRE) and were comparable or superior to manual registration for placement of the ARM within ∼200 mm of C-arm isocenter. Marker localization in projection data was robust across all anatomical sites, including challenging scenarios involving the presence of interventional tools. The reprojection error of marker localization was independent of the distance of the ARM from isocenter, and the overall TRE was dominated by the configuration of individual fiducials and distance from the target as predicted by theory. The median TRE increased with greater ARM-to-isocenter distance (e.g., for the Free-Form method, TRE increasing from 0.78 mm to 2.04 mm at distances of ∼75 mm and 370 mm, respectively). The median TRE within ∼200 mm distance was consistently lower than that of the manual method (TRE = 0.82 mm). Registration performance was independent of anatomical site (head, thorax, and abdomen). The Free-Form method demonstrated a statistically significant improvement (p = 0.0044) in reproducibility compared to manual registration (0.22 mm versus 0.30 mm, respectively). CONCLUSIONS Automatic image-to-world registration methods demonstrate the potential for improved accuracy, reproducibility, and workflow in CBCT-guided procedures. A Free-Form method was shown to exhibit robustness against anatomical site, with comparable or improved TRE compared to manual registration. It was also comparable or superior in performance to a Known-Model method in which the ARM configuration is specified as a predefined tool, thereby allowing configuration of fiducials on the fly or attachment to the patient.

[1]  Nassir Navab,et al.  Fiducial-Free Registration Procedure for Navigated Bronchoscopy , 2007, MICCAI.

[2]  Yoshito Otake,et al.  High-performance C-arm cone-beam CT guidance of thoracic surgery , 2012, Medical Imaging.

[3]  M. Flower,et al.  Automated CT marker segmentation for image registration in radionuclide therapy. , 2001, Physics in medicine and biology.

[4]  Yoshito Otake,et al.  An electromagnetic “Tracker-in-Table” configuration for X-ray fluoroscopy and cone-beam CT-guided surgery , 2012, International Journal of Computer Assisted Radiology and Surgery.

[5]  J H Siewerdsen,et al.  Automatic image-to-world registration based on x-ray projections in cone-beam CT-guided interventions. , 2009, Medical physics.

[6]  Nassir Navab,et al.  Interventions under Video-Augmented X-Ray Guidance: Application to Needle Placement , 2000, MICCAI.

[7]  S Hassfeld,et al.  Semiautomated registration using new markers for assessing the accuracy of a navigation system. , 2002, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[8]  C Schmidgunst,et al.  Calibration model of a dual gain flat panel detector for 2D and 3D x-ray imaging. , 2007, Medical physics.

[9]  E. W. Shrigley Medical Physics , 1944, British medical journal.

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

[11]  T E Marchanta,et al.  Automatic tracking of implanted fiducial markers in cone beam CT projection images , 2012 .

[12]  F. Mora-Camino,et al.  Studies in Fuzziness and Soft Computing , 2011 .

[13]  Paul J Keall,et al.  A method for robust segmentation of arbitrarily shaped radiopaque structures in cone-beam CT projections. , 2011, Medical physics.

[14]  John Wong,et al.  Flat-panel cone-beam CT on a mobile isocentric C-arm for image-guided brachytherapy , 2002, SPIE Medical Imaging.

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

[16]  Nassir Navab,et al.  Camera Augmented Mobile C-Arm (CAMC): Calibration, Accuracy Study, and Clinical Applications , 2010, IEEE Transactions on Medical Imaging.

[17]  Berthold K. P. Horn,et al.  Closed-form solution of absolute orientation using unit quaternions , 1987 .

[18]  R. Marmulla,et al.  Image-to-patient registration techniques in head surgery. , 2006, International journal of oral and maxillofacial surgery.

[19]  J H Siewerdsen,et al.  An innovative phantom for quantitative and qualitative investigation of advanced x-ray imaging technologies. , 2005, Physics in medicine and biology.

[20]  A. Deguet,et al.  The cisst libraries for computer assisted intervention systems , 2008, The MIDAS Journal.

[21]  Jiann-Der Lee,et al.  Fast-MICP for frameless image-guided surgery. , 2010, Medical physics.

[22]  K. Cleary,et al.  Image-guided interventions: technology review and clinical applications. , 2010, Annual review of biomedical engineering.

[23]  Leo Joskowicz,et al.  Localization and registration accuracy in image guided neurosurgery: a clinical study , 2008, International Journal of Computer Assisted Radiology and Surgery.

[24]  Oliver Beuing,et al.  Kyphoplasty interventions using a navigation system and C-arm CT data: first clinical results , 2009, Medical Imaging.

[25]  Paul J Keall,et al.  Implementation of a new method for dynamic multileaf collimator tracking of prostate motion in arc radiotherapy using a single kV imager. , 2010, International journal of radiation oncology, biology, physics.

[26]  C. Maurer,et al.  The Impact of Fiducial Distribution on Headset-Based Registration in Image-Guided Sinus Surgery , 2003, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

[27]  William E. Lorensen,et al.  The NA-MIC Kit: ITK, VTK, pipelines, grids and 3D slicer as an open platform for the medical image computing community , 2006, 3rd IEEE International Symposium on Biomedical Imaging: Nano to Macro, 2006..

[28]  Jeffrey H. Siewerdsen,et al.  High-performance intraoperative cone-beam CT on a mobile C-arm: an integrated system for guidance of head and neck surgery , 2009, Medical Imaging.

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

[30]  Nesreen Alsbou,et al.  An algorithm to extract three‐dimensional motion by marker tracking in the kV projections from an on‐board imager: four‐dimensional cone‐beam CT and tumor tracking implications , 2011, Journal of applied clinical medical physics.

[31]  Jeffrey H. Siewerdsen,et al.  Effect of fiducial configuration on target registration error in intraoperative cone-beam CT guidance of head and neck surgery , 2008, 2008 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[32]  R L Galloway,et al.  The process and development of image-guided procedures. , 2001, Annual review of biomedical engineering.

[33]  J. Fitzpatrick,et al.  Image-guided surgery: what is the accuracy? , 2005, Current opinion in otolaryngology & head and neck surgery.

[34]  W Mao,et al.  Fast internal marker tracking algorithm for onboard MV and kV imaging systems. , 2008, Medical physics.

[35]  Richard E. Colbeth,et al.  Multiple-gain-ranging readout method to extend the dynamic range of amorphous silicon flat-panel imagers , 2004, SPIE Medical Imaging.

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

[37]  Z. Yaniv Evaluation of spherical fiducial localization in C-arm cone-beam CT using patient data. , 2010, Medical physics.

[38]  Rainer Graumann,et al.  3D soft tissue imaging with a mobile C-arm , 2007, Comput. Medical Imaging Graph..

[39]  L. Feldkamp,et al.  Practical cone-beam algorithm , 1984 .

[40]  Larry S. Davis,et al.  Model-based object pose in 25 lines of code , 1992, International Journal of Computer Vision.

[41]  Jeffrey H Siewerdsen Cone-Beam CT with a Flat-Panel Detector: From Image Science to Image-Guided Surgery. , 2011, Nuclear instruments & methods in physics research. Section A, Accelerators, spectrometers, detectors and associated equipment.

[42]  W. Eric L. Grimson,et al.  An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[43]  Gabor Fichtinger,et al.  FTRAC-A robust fluoroscope tracking fiducial. , 2005, Medical physics.

[44]  Jay B. West,et al.  Predicting error in rigid-body point-based registration , 1998, IEEE Transactions on Medical Imaging.

[45]  Jonathan C Irish,et al.  Visualization of anterior skull base defects with intraoperative cone‐beam CT , 2009, Head & neck.

[46]  Jay B. West,et al.  Fiducial Point Placement and the Accuracy of Point-based, Rigid Body Registration , 2001, Neurosurgery.

[47]  Ziv Yaniv Localizing spherical fiducials in C-arm based cone-beam CT. , 2009, Medical physics.

[48]  S. Schafer,et al.  Mobile C-arm cone-beam CT for guidance of spine surgery: Image quality, radiation dose, and integration with interventional guidance. , 2011, Medical physics.

[49]  Harley Chan,et al.  Intraoperative use of cone-beam computed tomography in a cadaveric ossified cochlea model , 2009, Otolaryngology--head and neck surgery : official journal of American Academy of Otolaryngology-Head and Neck Surgery.

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

[51]  Branislav Jaramaz,et al.  Medical Image Computing and Computer-Assisted Intervention – MICCAI 2000 , 2000, Lecture Notes in Computer Science.

[52]  Cai Grau,et al.  Robust automatic segmentation of multiple implanted cylindrical gold fiducial markers in cone-beam CT projections. , 2011, Medical physics.

[53]  B. Wilson,et al.  Volume CT with a flat-panel detector on a mobile, isocentric C-arm: pre-clinical investigation in guidance of minimally invasive surgery. , 2005, Medical physics.

[54]  Daniel Mirota,et al.  TREK: an integrated system architecture for intraoperative cone-beam CT-guided surgery , 2011, International Journal of Computer Assisted Radiology and Surgery.