Anatomical priors for global probabilistic diffusion tractography

Authors:
Anastasia Yendiki;Allison Stevens;Jean Augustinack;David Salat;Lilla Zollei;Bruce Fischl
Affiliations:
HMS/MGH/MIT Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA;HMS/MGH/MIT Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA;HMS/MGH/MIT Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA;HMS/MGH/MIT Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA;HMS/MGH/MIT Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA;HMS/MGH/MIT Athinoula A. Martinos Center for Biomedical Imaging, Charlestown, MA
Venue:
ISBI'09 Proceedings of the Sixth IEEE international conference on Symposium on Biomedical Imaging: From Nano to Macro
Year:
2009

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Finsler tractography for white matter connectivity analysis of the cingulum bundle

MICCAI'07 Proceedings of the 10th international conference on Medical image computing and computer-assisted intervention - Volume Part I

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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.