An automatic registration method for frameless stereotaxy, image guided surgery, and enhanced reality visualization

There is a need for frameless guidance systems to help surgeons plan the exact location for incisions, to define the margins of tumors, and to precisely identify locations of neighboring critical structures. The authors have developed an automatic technique for registering clinical data, such as segmented magnetic resonance imaging (MRI) or computed tomography (CT) reconstructions, with any view of the patient on the operating table. The authors demonstrate on the specific example of neurosurgery. The method enables a visual mix of live video of the patient and the segmented three-dimensional (3-D) MRI or CT model. This supports enhanced reality techniques for planning and guiding neurosurgical procedures and allows us to interactively view extracranial or intracranial structures nonintrusively. Extensions of the method include image guided biopsies, focused therapeutic procedures, and clinical studies involving change detection over time sequences of images.

[1]  W. Grimson,et al.  Model-Based Recognition and Localization from Sparse Range or Tactile Data , 1984 .

[2]  W. Eric L. Grimson,et al.  Localizing Overlapping Parts by Searching the Interpretation Tree , 1987, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[3]  Richard Szeliski,et al.  An Analysis of the Elastic Net Approach to the Traveling Salesman Problem , 1989, Neural Computation.

[4]  Guido Gerig,et al.  Medical Imaging and Computer Vision: An Integrated Approach for Diagnosis and Planning , 1989, DAGM-Symposium.

[5]  C. Pelizzari,et al.  Accurate Three‐Dimensional Registration of CT, PET, and/or MR Images of the Brain , 1989, Journal of computer assisted tomography.

[6]  K. K. Tan,et al.  The brain: integrated three-dimensional display of MR and PET images. , 1989, Radiology.

[7]  R. Kikinis,et al.  Three-dimensional segmentation of MR images of the head using probability and connectivity. , 1990, Journal of computer assisted tomography.

[8]  William Grimson,et al.  Object recognition by computer - the role of geometric constraints , 1991 .

[9]  T. Turkington,et al.  Correlation of SPECT images of a three-dimensional brain phantom using a surface fitting technique , 1991, Conference Record of the 1991 IEEE Nuclear Science Symposium and Medical Imaging Conference.

[10]  David N. Levin,et al.  Interactive 3-D Patient-Image Registration , 1991, IPMI.

[11]  Matching 3-D Smooth Surfaces with their 2-D Projections using 3-D Distance Maps , 1991, Geometric Reasoning for Perception and Action.

[12]  P. Cinquin,et al.  Matching Of Medical Images For Computed And Robot Assisted Surgery , 1991, Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society Volume 13: 1991.

[13]  Chin-Tu Chen,et al.  Automated three-dimensional registration of medical images , 1991, Medical Imaging.

[14]  G. Champleboux,et al.  From accurate range imaging sensor calibration to accurate model-based 3D object localization , 1992, Proceedings 1992 IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[15]  Nicholas Ayache,et al.  Smoothing and Matching of 3-D Space Curves , 1992, ECCV.

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

[17]  Philippe Cinquin,et al.  Accurate calibration of cameras and range imaging sensor: the NPBS method , 1992, Proceedings 1992 IEEE International Conference on Robotics and Automation.

[18]  Shimon Edelman,et al.  Bringing the Grandmother back into the Picture: A Memory-Based View of Object Recognition , 1990, Int. J. Pattern Recognit. Artif. Intell..

[19]  Richard A. Robb,et al.  New approach to 3-D registration of multimodality medical images by surface matching , 1992, Other Conferences.

[20]  Xiaoping Hu,et al.  Retrospective Fusion of Radiographic and MR Data for Localization of Subdural Electrodes , 1992, Journal of computer assisted tomography.

[21]  R. Kikinis,et al.  Video registration using fiducials for surgical enhanced reality , 1993, Proceedings of the 15th Annual International Conference of the IEEE Engineering in Medicine and Biology Societ.

[22]  Richard Szeliski,et al.  Matching 3D anatomical surfaces with nonrigid deformations using octree splines , 1993, Optics & Photonics.

[23]  André Guéziec,et al.  Large deformable splines, crest lines and matching , 1993, 1993 (4th) International Conference on Computer Vision.

[24]  M. Viergever,et al.  Medical image matching-a review with classification , 1993, IEEE Engineering in Medicine and Biology Magazine.

[25]  Nicholas Ayache,et al.  New developments on geometric hashing for curve matching , 1993, Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[26]  William Wells,et al.  Statistical object recognition , 1993 .

[27]  Gary H. Glover,et al.  MR susceptibility misregistration correction , 1993, IEEE Trans. Medical Imaging.

[28]  Jean-Philippe Thirion,et al.  Extremal points: definition and application to 3D image registration , 1994, 1994 Proceedings of IEEE Conference on Computer Vision and Pattern Recognition.

[29]  Jacques Feldmar,et al.  Rigid and Affine Registration of Smooth Surfaces using Differential Properties , 1994, ECCV.

[30]  Tomas Lozano-Perez,et al.  Automatic : Registration for Multiple Sclerosis Change Detection , 2004 .

[31]  Nicholas Ayache,et al.  Medical computer vision, virtual reality and robotics , 1995, Image Vis. Comput..

[32]  J. P. Mellor.  Realtime Camera Calibration for Enhanced Reality Visualization , 1995, CVRMed.