Segmenting crossing fiber geometries using fluid mechanics tensor distribution function tractography

Authors:
Nathan Hageman;Alex Leow;David Shattuck;Liang Zhan;Paul Thompson;Siwei Zhu;Arthur Toga
Affiliations:
Laboratory of Neuroimaging, UCLA School of Medicine Department of Neurology, Los Angeles, CA;Laboratory of Neuroimaging, UCLA School of Medicine Department of Neurology, Los Angeles, CA;Laboratory of Neuroimaging, UCLA School of Medicine Department of Neurology, Los Angeles, CA;Laboratory of Neuroimaging, UCLA School of Medicine Department of Neurology, Los Angeles, CA;Laboratory of Neuroimaging, UCLA School of Medicine Department of Neurology, Los Angeles, CA;Laboratory of Neuroimaging, UCLA School of Medicine Department of Neurology, Los Angeles, CA;Laboratory of Neuroimaging, UCLA School of Medicine Department of Neurology, Los Angeles, CA
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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Abstract

We introduce a fluid mechanics based tractography method that estimates the most likely connection path between points in a tensor distribution function (TDF) dataset. We simulated the flow of an artificial fluid whose properties are related to the underlying TDF dataset. The resulting fluid velocity was used as a metric of connection strength. We validated our algorithm using a digital phantom dataset based on a pattern with two intersecting tracts. When compared to a TDF streamline method and our single tensor fluid mechanics tractography algorithm, our method was able to segment intersecting tracts at a finer spatial resolution. Our method was successfully applied to human control data to segment a major fiber pathway, the corpus callosum, even in problematic regions with crossing fiber geometries.