Surgical Planning Laboratory - Brigham & Women's Hospital - Boston, Massachusetts USA - a teaching affiliate of Harvard Medical School

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Biomechanical Model as a Registration Tool for Image-guided Neurosurgery: Evaluation Against BSpline Registration

Institution:
1Intelligent Systems for Medicine Laboratory, The University of Western Australia, Perth, Australia.
2Surgical Planning Laboratory, Brigham & Women’s Hospital, Harvard Medical School, Boston, MA, USA.
3Computational Radiology Laboratory, Children’s Hospital, Harvard Medical School, Boston, MA, USA.
4Institute of Mechanics and Advanced Materials, Cardiff School of Engineering, Cardiff University, Wales, UK.
Publication Date:
Nov-2013
Journal:
Ann Biomed Eng
Volume Number:
41
Issue Number:
11
Pages:
2409-25
Citation:
Ann Biomed Eng. 2013 Nov;41(11):2409-25.
PubMed ID:
23771299
PMCID:
PMC3939696
Keywords:
Brain, Non-rigid Registration, Intra-operative MRI, Biomechanics, Edge detection, Hausdorff distance, Cerebral gliomas
Appears in Collections:
NAC, NA-MIC, NCIGT, SLICER, SPL
Sponsors:
P41 EB015902/EB/NIBIB NIH HHS/United States
P41 RR019703/RR/NCRR NIH HHS/United States
P41 EB015898/EB/NIBIB NIH HHS/United States
R01 EB008015/EB/NIBIB NIH HHS/United States
R01 LM010033/LM/NLM NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Mostayed A., Garlapati R.R., Joldes G.R., Wittek A., Roy A., Kikinis R., Warfield S.K., Miller K. Biomechanical Model as a Registration Tool for Image-guided Neurosurgery: Evaluation Against BSpline Registration. Ann Biomed Eng. 2013 Nov;41(11):2409-25. PMID: 23771299. PMCID: PMC3939696.
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In this paper we evaluate the accuracy of warping of neuro-images using brain deformation predicted by means of a patient-specific biomechanical model against registration using a BSpline-based free form deformation algorithm. Unlike the BSpline algorithm, biomechanics-based registration does not require an intra-operative MR image which is very expensive and cumbersome to acquire. Only sparse intra-operative data on the brain surface is sufficient to compute deformation for the whole brain. In this contribution the deformation fields obtained from both methods are qualitatively compared and overlaps of Canny edges extracted from the images are examined. We define an edge based Hausdorff distance metric to quantitatively evaluate the accuracy of registration for these two algorithms. The qualitative and quantitative evaluations indicate that our biomechanics-based registration algorithm, despite using much less input data, has at least as high registration accuracy as that of the BSpline algorithm.

Additional Material
1 File (184.058kB)
Mostayed-AnnBiomedEng2013-fig2.jpg (184.058kB)