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Proceedings Paper

Integration of patient specific modeling and advanced image processing techniques for image-guided neurosurgery
Author(s): Neculai Archip; Andriy Fedorov; Bryn Lloyd; Nikos Chrisochoides; Alexandra Golby; Peter M. Black; Simon K. Warfield
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Paper Abstract

A major challenge in neurosurgery oncology is to achieve maximal tumor removal while avoiding postoperative neurological deficits. Therefore, estimation of the brain deformation during the image guided tumor resection process is necessary. While anatomic MRI is highly sensitive for intracranial pathology, its specificity is limited. Different pathologies may have a very similar appearance on anatomic MRI. Moreover, since fMRI and diffusion tensor imaging are not currently available during the surgery, non-rigid registration of preoperative MR with intra-operative MR is necessary. This article presents a translational research effort that aims to integrate a number of state-of-the-art technologies for MRI-guided neurosurgery at the Brigham and Women's Hospital (BWH). Our ultimate goal is to routinely provide the neurosurgeons with accurate information about brain deformation during the surgery. The current system is tested during the weekly neurosurgeries in the open magnet at the BWH. The preoperative data is processed, prior to the surgery, while both rigid and non-rigid registration algorithms are run in the vicinity of the operating room. The system is tested on 9 image datasets from 3 neurosurgery cases. A method based on edge detection is used to quantitatively validate the results. 95% Hausdorff distance between points of the edges is used to estimate the accuracy of the registration. Overall, the minimum error is 1.4 mm, the mean error 2.23 mm, and the maximum error 3.1 mm. The mean ratio between brain deformation estimation and rigid alignment is 2.07. It demonstrates that our results can be 2.07 times more precise then the current technology. The major contribution of the presented work is the rigid and non-rigid alignment of the pre-operative fMRI with intra-operative 0.5T MRI achieved during the neurosurgery.

Paper Details

Date Published: 13 March 2006
PDF: 8 pages
Proc. SPIE 6141, Medical Imaging 2006: Visualization, Image-Guided Procedures, and Display, 61411E (13 March 2006); doi: 10.1117/12.653930
Show Author Affiliations
Neculai Archip, Children's Hospital, Harvard Medical School (United States)
Andriy Fedorov, College of William and Mary (United States)
Bryn Lloyd, Children's Hospital, Harvard Medical School (United States)
Nikos Chrisochoides, College of William and Mary (United States)
Alexandra Golby, Brigham and Women's Hospital (United States)
Peter M. Black, Brigham and Women's Hospital (United States)
Simon K. Warfield, Children's Hospital, Harvard Medical School (United States)

Published in SPIE Proceedings Vol. 6141:
Medical Imaging 2006: Visualization, Image-Guided Procedures, and Display
Kevin R. Cleary; Robert L. Galloway, Jr., Editor(s)

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