Bundle-specific tractography with incorporated anatomical and orientational priors
NeuroImage. 2019-02-01; 186: 382-398
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Rheault F(1), St-Onge E(2), Sidhu J(2), Maier-Hein K(3), Tzourio-Mazoyer N(4), Petit L(4), Descoteaux M(2).
(1)Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Canada. Electronic address: .
(2)Sherbrooke Connectivity Imaging Laboratory (SCIL), Université de Sherbrooke, Canada.
(3)Division of Medical Image Computing, German Cancer Research Center (DKFZ), Heidelberg, Germany.
(4)Groupe d’Imagerie Neurofonctionnelle, IMN, UMR5293, CNRS, CEA, Université de Bordeaux, France.
Anatomical white matter bundles vary in shape, size, length, and complexity, making diffusion MRI tractography reconstruction of some bundles more difficult than others. As a result, bundles reconstruction often suffers from a poor spatial extent recovery. To fill-up the white matter volume as much and as best as possible, millions of streamlines can be generated and filtering techniques applied to address this issue. However, well-known problems and biases are introduced such as the creation of a large number of false positives and over-representation of easy-to-track parts of bundles and under-representation of hard-to-track. To address these challenges, we developed a Bundle-Specific Tractography (BST) algorithm. It incorporates anatomical and orientational prior knowledge during the process of streamline tracing to increase reproducibility,
sensitivity, specificity and efficiency when reconstructing certain bundles of interest. BST outperforms classical deterministic, probabilistic, and global tractography methods. The increase in anatomically plausible streamlines, with larger spatial coverage, helps to accurately represent the full shape of bundles, which could greatly enhance and robustify tract-based and connectivity-based neuroimaging studies.