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Abstract #3566

Computational frameworks for multi-fascicle models: impact on microstructural descriptors

Benjamin Schloesing1,2, Maxime Taquet3, Jean-Philippe Thiran2, Simon Keith Warfield1, and Benoit Scherrer1

1Computational Radiology Laboratory,Boston Children’s Hospital, Harvard Medical School, Boston, MA, United States, 2Signal Processing Lab (LTS5), École polytechnique fédérale de Lausanne, Lausanne, Switzerland, 3University of Oxford, Oxford, United Kingdom

When performing operations on multi-fascicle DCI models, the need to preserve microstructural descriptors such as fFA and fMD is important. In this work we compared different multi-fascicle computational frameworks by assessing their impact on microstructure properties. Specifically, we investigated the impact after geometrical transformation and averaging of multi-fascicle models, two key operations when carrying out population studies. We found that Euclidean and log-Euclidean frameworks resulted in a decrease of fFA and fMD. More surprisingly, the values of microstructural descriptors depended on the number of subjects. The quaternion framework, in contrast, was the best at preserving microstructural features.

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