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White Matter Bundle Registration and Population Analysis Based on Gaussian Processes

Institution:
1Laboratory of Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
2Psychiatry Neuroimaging Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA.
3Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston MA, USA.
Publisher:
Inf Process Med Imaging IPMI 2011
Publication Date:
Jul-2011
Journal:
Inf Process Med Imaging
Volume Number:
22
Pages:
320-32
Citation:
Inf Process Med Imaging. 2011 Jul;22:320-32.
PubMed ID:
21761667
PMCID:
PMC3140022
Keywords:
Diffusion MRI, White Matter Fiber Tracts, Gaussian Processes, Registration
Appears in Collections:
NAC, LMI, NA-MIC, NCIGT, PNL, SLICER, SPL
Sponsors:
P41 RR013218/RR/NCRR NIH HHS/United States
R01 MH082918/MH/NIMH NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
R01 MH092862/MH/NIMH NIH HHS/United States
R01 MH005074/MH/NIMH NIH HHS/United States
P41 RR019703/RR/NCRR NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Wassermann D., Rathi Y., Bouix S., Kubicki M., Kikinis R., Shenton M.E., Westin C-F. White Matter Bundle Registration and Population Analysis Based on Gaussian Processes. Inf Process Med Imaging. 2011 Jul;22:320-32. PMID: 21761667. PMCID: PMC3140022.
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This paper proposes a method for the registration of white matter tract bundles traced from diffusion images and its extension to atlas generation, Our framework is based on a Gaussian process representation of tract density maps. Such a representation avoids the need for point-to-point correspondences, is robust to tract interruptions and reconnections and seamlessly handles the comparison and combination of white matter tract bundles. Moreover, being a parametric model, this approach has the potential to be defined in the Gaussian processes' parameter space, without the need for resampling the fiber bundles during the registration process. We use the similarity measure of our Gaussian process framework, which is in fact an inner product, to drive a diffeomorphic registration algorithm between two sets of homologous bundles which is not biased by point-to-point correspondences or the parametrization of the tracts. We estimate a dense deformation of the underlying white matter using the bundles as anatomical landmarks and obtain a population atlas of those fiber bundles. Finally we test our results in several different bundles obtained from in-vivo data.

Additional Material
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Wassermann-IPMI2011-fig1.jpg (100.298kB)