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Non-Rigid Registration for Brain MRI: Faster and Cheaper
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Institution: |
1Department of Computer Science, College of William and Mary, Williamsburg, VA 23185, USA 2Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. |
Publication Date: |
May-2010 |
Journal: |
Int. J. Functional Informatics and Personalised Medicine |
Volume Number: |
3 |
Issue Number: |
1 |
Pages: |
48-57 |
Citation: |
International Journal of Functional Informatics and Personalised Medicine. 2010; 3(1):48 - 57. |
Keywords: |
non-rigid registration, GPU, multicore, real-time, brain shift |
Appears in Collections: |
NA-MIC, NAC, SPL |
Sponsors: |
NSF grants: CCF-0916526, CCF-0833081, CSI-719929 NIH R01 AA016748 NIH P41 RR13218 NIH U54 EB005149 |
Generated Citation: |
Liu Y., Fedorov A., Kikinis R., Chrisochoides N. Non-Rigid Registration for Brain MRI: Faster and Cheaper. International Journal of Functional Informatics and Personalised Medicine. 2010; 3(1):48 - 57. |
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| Paper: | Download, View online |
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We study the problem of Non-Rigid Registration (NRR) for intra-operative recovery of brain shift during image-guided neurosurgery. Time-critical nature of the tumour resection procedure presents a major obstacle to the routine clinical use of many available NRR approaches. In this paper, we utilise the resources of a single multicore workstation with an advanced graphics card to parallelise and evaluate an end-to-end implementation of a clinically validated NRR method. The results on clinical brain MRI data show the parallel NRR can reach real-time clinical requirement.
Additional Material
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YLiu-IJFIPM2010-fig4.jpg (35.797kB)