Fast multi-compartment Microstructure Fingerprinting in brain white matter
(2024) Frontiers in Neuroscience — Vol. 18, p. 1400499 [1-17] (2024)
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- Abstract
- We proposed two deep neural network based methods to accelerate the estimation of microstructural features of crossing fascicles in the white matter. Both methods focus on the acceleration of a multi-dictionary matching problem, which is at the heart of Microstructure Fingerprinting, an extension of Magnetic Resonance Fingerprinting to diffusion MRI. The first acceleration method uses efficient sparse optimization and a dedicated feed-forward neural network to circumvent the inherent combinatorial complexity of the fingerprinting estimation. The second acceleration method relies on a feed-forward neural network that uses a spherical harmonics representation of the DW-MRI signal as input. The first method exhibits a high interpretability while the second method achieves a greater speedup factor. The accuracy of the results and the speedup factors of several orders of magnitude obtained on in vivo brain data suggest the potential of our methods for a fast quantitative estimation of microstructural features in complex white matter configurations.
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Citations
Dessain, Q., Fuchs, C., Macq, B., & Rensonnet, G. (2024). Fast multi-compartment Microstructure Fingerprinting in brain white matter. Frontiers in Neuroscience, 18, 1400499 [1-17]. https://doi.org/10.3389/fnins.2024.1400499 (Original work published 2024)