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White Matter Bundle Registration and Population Analysis Based on Gaussian Processes
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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;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: |
R01 MH82918 (MH) funded by NIMH NIH HHS P41 RR13218 (RR) funded by NCRR NIH HHS R01 MH074794 (MH) funded by NIMH NIH HHS R01 MH092862 (MH) funded by NIMH NIH HHS R01 MH5074 (MH) funded by NIMH NIH HHS P41 RR019703 (RR) funded by NCRR NIH HHS U54 EB005149 (EB) funded by NIBIB NIH HHS |
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;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
1 File (100.298kB)
Wassermann-IPMI2011-fig1.jpg (100.298kB)