A parallel implementation of 2-D/3-D image registration for computer-assisted surgery

Image registration is a technique usually used for aligning two different images taken at different times and/or from different viewing points. A key challenge for medical image registration is to minimise computation time with a small alignment error in order to realise computer-assisted surgery. In this paper, we present the design and implementation of a parallel two-dimensional/three-dimensional (2-D/3-D) image registration method for computer-assisted surgery. Our method exploits data parallelism and speculative parallelism, aiming at making computation time short enough to carry out registration tasks during surgery. Our experiments show that exploiting both parallelisms reduces computation time on a cluster of 64 PCs from a few tens of minutes to less than a few tens of seconds, a clinically compatible time.

[1]  W. Eric L. Grimson,et al.  2D-3D rigid registration of X-ray fluoroscopy and CT images using mutual information and sparsely sampled histogram estimators , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[2]  Fumihiko Ino,et al.  High-performance computing service over the Internet for intraoperative image processing , 2004, IEEE Transactions on Information Technology in Biomedicine.

[3]  Fumihiko Ino,et al.  A data distributed parallel algorithm for nonrigid image registration , 2005, Parallel Comput..

[4]  Ron Kikinis,et al.  Real-Time Biomechanical Simulation of Volumetric Brain Deformation for Image Guided Neurosurgery , 2000, ACM/IEEE SC 2000 Conference (SC'00).

[5]  Benoit M. Dawant,et al.  Surface-based registration of CT images to physical space for image-guided surgery of the spine: a sensitivity study , 1998, IEEE Transactions on Medical Imaging.

[6]  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.

[7]  D. R. Fish,et al.  A patient-to-computed-tomography image registration method based on digitally reconstructed radiographs. , 1994, Medical physics.

[8]  Tony Pan,et al.  Image processing for the grid: a toolkit for building grid-enabled image processing applications , 2003, CCGrid 2003. 3rd IEEE/ACM International Symposium on Cluster Computing and the Grid, 2003. Proceedings..

[9]  Charles L. Seitz,et al.  Myrinet: A Gigabit-per-Second Local Area Network , 1995, IEEE Micro.

[10]  Marc Levoy,et al.  Display of surfaces from volume data , 1988, IEEE Computer Graphics and Applications.

[11]  Nicholas Ayache,et al.  3D-2D Projective Registration of Free-Form Curves and Surfaces , 1997, Comput. Vis. Image Underst..

[12]  David J. Hawkes,et al.  A Comparison of 2D-3D Intensity-Based Registration and Feature-Based Registration for Neurointerventions , 2002, MICCAI.

[13]  L. Joskowicz,et al.  FRACAS: a system for computer-aided image-guided long bone fracture surgery. , 1998, Computer aided surgery : official journal of the International Society for Computer Aided Surgery.

[14]  Torsten Rohlfing,et al.  Nonrigid image registration in shared-memory multiprocessor environments with application to brains, breasts, and bees , 2003, IEEE Transactions on Information Technology in Biomedicine.

[15]  Robert J. Maciunas,et al.  Registration of head CT images to physical space using a weighted combination of points and surfaces [image-guided surgery] , 1998, IEEE Transactions on Medical Imaging.

[16]  D. Hill,et al.  Medical image registration , 2001, Physics in medicine and biology.

[17]  Jürgen Weese,et al.  Voxel-based 2-D/3-D registration of fluoroscopy images and CT scans for image-guided surgery , 1997, IEEE Transactions on Information Technology in Biomedicine.

[18]  Nobuhiko Hata,et al.  An Autostereoscopic Display System for Image-Guided Surgery Using High-Quality Integral Videography with High Performance Computing , 2003, MICCAI.

[19]  Nicholas Ayache,et al.  Grid powered nonlinear image registration with locally adaptive regularization , 2004, Medical Image Anal..

[20]  Henry Fuchs,et al.  A sorting classification of parallel rendering , 1994, IEEE Computer Graphics and Applications.

[21]  William H. Press,et al.  Numerical Recipes in FORTRAN - The Art of Scientific Computing, 2nd Edition , 1987 .

[22]  Benoit M. Dawant,et al.  Retrospective intermodality registration techniques for images of the head: surface-based versus volume-based , 1999, IEEE Transactions on Medical Imaging.

[23]  Hiroshi Tezuka,et al.  The design and implementation of zero copy MPI using commodity hardware with a high performance network , 1998, ICS '98.

[24]  Peter Kazanzides,et al.  Anatomy-based registration of CT-scan and intraoperative X-ray images for guiding a surgical robot , 1998, IEEE Transactions on Medical Imaging.

[25]  Richard Szeliski,et al.  Recovering the Position and Orientation of Free-Form Objects from Image Contours Using 3D Distance Maps , 1995, IEEE Trans. Pattern Anal. Mach. Intell..

[26]  Ron Kikinis,et al.  A High Performance Computing Approach to the Registration of Medical Imaging Data , 1998, Parallel Comput..