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fMRI-DTI Modeling via Landmark Distance Atlases for Prediction and Detection of Fiber Tracts
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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 Lab, 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, Atlas, White matter, Neuroimaging, Structure–function |
Appears in Collections: |
NCIGT, LMI, NAC, SLICER, SPL |
Sponsors: |
NIH 1R21CA156943-01A1 P41 RR019703 P01 CA067165 R01 MH074794 R25 CA089017 R01 MH092862 P41 RR13218 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)