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fMRI-DTI Modeling via Landmark Distance Atlases for Prediction and Detection of Fiber Tracts

Institution:
1Golby Lab, Department of Neurosurgery, Brigham and Women's Hospital, Boston, MA, USA.
2Laboratory for Mathematics in Imaging, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
3Surgical Planning Laboratory, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA.
Publisher:
Elsevier Science
Publication Date:
Mar-2012
Journal:
Neuroimage
Volume Number:
60
Issue Number:
1
Pages:
456-70
Citation:
Neuroimage. 2012 Mar;60(1):456-70.
PubMed ID:
22155376
PMCID:
PMC3423975
Keywords:
Diffusion MRI, Functional MRI (fMRI), Atlas, White matter, Neuroimaging, Structure–function
Appears in Collections:
NCIGT, LMI, NAC, SLICER, SPL
Sponsors:
R21 CA156943/CA/NCI NIH HHS/United States
P41 RR019703/RR/NCRR NIH HHS/United States
P01 CA067165/CA/NCI NIH HHS/United States
R01 MH074794/MH/NIMH NIH HHS/United States
R25 CA089017/CA/NCI NIH HHS/United States
R01 MH092862/MH/NIMH NIH HHS/United States
P41 RR013218/RR/NCRR NIH HHS/United States
P41 EB015902/EB/NIBIB NIH HHS/United States
Klarman Family Foundation, and Brain Science Foundation
Generated Citation:
O'Donnell L., Rigolo L., Norton I., Wells III W.M., Westin C-F., Golby A.J. fMRI-DTI Modeling via Landmark Distance Atlases for Prediction and Detection of Fiber Tracts. Neuroimage. 2012 Mar;60(1):456-70. PMID: 22155376. PMCID: PMC3423975.
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The overall goal of this research is the design of statistical atlas models that can be created from normal subjects, but may generalize to be applicable to abnormal brains. We present a new style of joint modeling of fMRI, DTI, and structural MRI. Motivated by the fact that a white matter tract and related cortical areas are likely to displace together in the presence of a mass lesion (brain tumor), in this work we propose a rotation and translation invariant model that represents the spatial relationship between fiber tracts and anatomic and functional landmarks. This landmark distance model provides a new basis for representation of fiber tracts and can be used for detection and prediction of fiber tracts based on landmarks. Our results indicate that the measured model is consistent across normal subjects, and thus suitable for atlas building. Our experiments demonstrate that the model is robust to displacement and missing data, and can be successfully applied to a small group of patients with mass lesions.

Additional Material
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ODonnell-NeuroImage2012-fig10.jpg (88.485kB)