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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Hageman, N. Leow, A. Shattuck, D. Liang Zhan Thompson, P. Siwei Zhu Toga, A. |
| Copyright Year | 2009 |
| Description | Author affiliation: Laboratory of Neuroimaging, UCLA School of Medicine Department of Neurology, Los Angeles, CA, USA (Hageman, N.; Leow, A.; Shattuck, D.; Liang Zhan; Thompson, P.; Siwei Zhu; Toga, A.) |
| 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. |
| Starting Page | 1390 |
| Ending Page | 1393 |
| File Size | 380588 |
| Page Count | 4 |
| File Format | |
| ISBN | 9781424439317 |
| ISSN | 19457928 |
| DOI | 10.1109/ISBI.2009.5193325 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2009-06-28 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Geometry Tensile stress Distribution functions Biomedical imaging Image segmentation Diffusion tensor imaging Biomedical measurements Image reconstruction Partial differential equations Magnetic resonance imaging biomedical image processing partial differential equations fluid flow image segmentation magnetic resonance imaging |
| Content Type | Text |
| Resource Type | Article |
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