Anatomical priors for global probabilistic diffusion tractography
Name
Yendiki-2009-Anatomical priors for global probabilistic diffusion tractography.pdf
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Author(s)
Yendiki, Anastasia
Stevens, Allison
Augustinack, Jean
Salat, David
Zollei, Lilla
Fischl, Bruce
Date Issued
August 2009
Journal
Proceedings of the 2009 IEEE International Symposium on Biomedical Imaging: From Nano to Macro (ISBI '09)
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Citation
Yendiki, Anastasia et al. “Anatomical Priors for Global Probabilistic Diffusion Tractography.” IEEE International Symposium on Biomedical Imaging: From Nano to Macro, 2009. ISBI '09., 2009. 630–633. © 2009 IEEE
Version
Final published version
Abstract
We investigate the use of anatomical priors in a Bayesian framework for diffusion tractography. We compare priors that utilize different types of information on the white-matter pathways to be reconstructed. This information includes manually labeled paths from a set of training subjects and anatomical segmentation labels obtained from T1-weighted MR images of the same subjects. Our results indicate that the use of prior information increases robustness to end-point ROI size and yields solutions that agree with expert-drawn manual labels, obviating the need for manual intervention on any new test subjects.
MIT Department
Harvard University--MIT Division of Health Sciences and Technology
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1109/ISBI.2009.5193126
