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Abstract #0560

Fast multi-compartment microstructure fingerprinting using deep neural networks

Quentin Dessain1, Nicolas Delinte1, Benoit Macq1, and Gaëtan Rensonnet1
1ICTEAM, Université Catholique de Louvain, Louvain La Neuve, Belgium


We estimate microstructural features of crossing fascicles in the white matter by using a fast multi-compartment fingerprinting, an extension of MR fingerprinting to diffusion MRI. The acceleration uses efficient sparse optimization and a dedicated feed-forward neural network to circumvent the inherent combinatorial complexity of the fingerprinting estimation. The accuracy of the results and the speedup factors obtained on in vivo brain data suggest the potential of our method for a fast quantitative estimation of microstructural features in complex white matter configurations.

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