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Toward Real-Time Image Guided Neurosurgery Using Distributed and Grid Computing
Institution:
 1Department of Computer Science, College of William and Mary, Williamsburg, VA, USA.
2Computational Radiology Laboratory, Departments of Radiology, Children’s Hospital and Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 02115, USA.
3Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
4Department of Neurosurgery, Brigham and Women's Hospital, Harvard Medical School, 75 Francis Street, Boston, MA 02115, USA.
Publisher:
 IEEE Conference on Supercomputing
Publication Date:
 Oct-2006
Citation:
 Proceedings of the ACM/IEEE conference on Supercomputing 2006; 37-50.
Keywords:
 Distributed computing, Grid computing, Medical imaging, Image registration
Appears in Collections:
SPL, CRL, SLICER
Generated Citation:
 Chrisochoides N., Fedorov A., Kot A., Archip N., Black P.M., Clatz O., Golby A.J., Kikinis R., Warfield S.K. Toward Real-Time Image Guided Neurosurgery Using Distributed and Grid Computing. Proceedings of the ACM/IEEE conference on Supercomputing 2006; 37-50.
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Neurosurgical resection is a therapeutic intervention in the treatment of brain tumors. Precision of the resection can be improved by utilizing Magnetic Resonance Imaging (MRI) as an aid in decision making during Image Guided Neurosurgery (IGNS). Image registration adjusts pre-operative data according to intra-operative tissue deformation. Some of the approaches increase the registration accuracy by tracking image landmarks through the whole brain volume. High computational cost used to render these techniques inappropriate for clinical applications. In this paper we present a parallel implementation of a state of the art registration method, and a number of needed incremental improvements. Overall, we reduced the response time for registration of an average dataset from about an hour and for some cases more than an hour to less than seven minutes, which is within the time constraints imposed by neurosurgeons. For the first time in clinical practice we demonstrated, that with the help of distributed computing non-rigid MRI registration based on volume tracking can be computed intra-operatively.

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