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A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis
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Institution: |
1Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA. 2Laboratory of Mathematics in Imaging, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, USA. 3 Surgical Planning Lab, Department of Radiology, Brigham and Women's Hospital, Boston, MA, USA. |
Publisher: |
IEEE Symposium on Biomedical Imaging ISBI |
Publication Date: |
May-2008 |
Volume Number: |
4543943 |
Pages: |
105-108 |
Citation: |
Proc IEEE Int Symp Biomed Imaging. 2008;4543943:105-108. |
PubMed ID: |
19212449 |
PMCID: |
PMC2638065 |
Keywords: |
Diffusion Tensor MRI, Clustering, Anatomical Information, Tract-Oriented Quantitative Analysis, Projects:DTIModeling |
Appears in Collections: |
SPL, LMI, NA-MIC, NAC, NCIGT, SLICER |
Sponsors: |
P41 RR13218-09/NCRR NIH HHS R01 MH074794-02/NIMH NIH HHS R01 NS051826-04/NINDS NIH HHS U41 RR019703-03/NCRR NIH HHS U54 EB005149-04/NIBIB NIH HHS |
Generated Citation: |
Maddah M., Zöllei L., Grimson W.E.L., Westin C-F., Wells III W.M. A Mathematical Framework for Incorporating Anatomical Knowledge in DT-MRI Analysis. Proc IEEE Int Symp Biomed Imaging. 2008;4543943:105-108. PMID: 19212449. PMCID: PMC2638065. |
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| Paper: | Download, View online |
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We propose a Bayesian approach to incorporate anatomical information in the clustering of fiber trajectories. An expectationmaximization (EM) algorithm is used to cluster the trajectories, in which an atlas serves as the prior on the labels. The atlas guides the clustering algorithm and makes the resulting bundles anatomically meaningful. In addition, it provides the seed points for the tractography and initial settings of the EM algorithm. The proposed approach provides a robust and automated tool for tract-oriented analysis both in a single subject and over a population.
Additional Material
1 File (278.162kB)
Mahnaz-ISBI2008-fig2.jpg (278.162kB)