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Abstract #3371

Reducing false-positive connections using hierarchical microstructure-informed tractography

Mario Ocampo-Pineda1, Simona Schiavi1, Matteo Frigo1,2, Muhamed Barakovic3, Gabriel Girard3,4, Maxime Descoteaux5,6, Jean-Philippe Thiran3,4, and Alessandro Daducci1,3,4

1Computer Science Department, University of Verona, Verona, Italy, 2Athena Project-Team, Inria Sophia-Antipolis Méditerranée, Université Côte d'Azur, Nice, France, 3Signal Processing Laboratory 5 (LTS5), École Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 4Radiology Department, University Hospital Center (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland, 5Sherbrooke Connectivity Imaging Laboratory (SCIL), University of Sherbrooke, Sherbrooke, QC, Canada, 6Department of Nuclear Medicine and Radiobiology, Faculty of Medicine and Health Science, Sherbrooke Molecular Imaging Center, Sherbrooke, QC, Canada

Tractography has proven particularly effective for studying non-invasively the neuronal architecture of the brain, but recent studies have showed that the high incidence of false-positives can significantly bias any connectivity analysis. Last year we presented a method that extended COMMIT framework to consider the prior knowledge that white matter fibers are organized in bundles. Inspired by this, here we propose another extension to further improve the quality of the tractography reconstructions. We introduce a novel regularization term based on the multilevel hierarchy organization of the human brain and we test the results on both synthetic phantom and in vivo data.

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