Abstract
Diffusion MRI (DW-MRI) allows for the detailed exploration of the brain white matter microstructure, with applications in both research and the clinic. However, state-of-the-art methods for microstructure estimation suffer from known limitations, such as the overestimation of the mean axon diameter, and the infeasibility of fitting diameter distributions. In this study, we propose to eschew current modeling-based approaches in favor of a novel, simulation-assisted machine learning approach. In particular, we train machine learning (ML) algorithms on a large dataset of simulated diffusion MRI signals from white matter regions with different axon diameter distributions and packing densities. We show, on synthetic data, that the trained models provide an accurate and efficient estimation of microstructural parameters in-silico and from DW-MRI data with moderately high b-values (4000 s/mm\(^2\)). Further, we show, on in-vivo data, that the estimators trained from simulations can provide parameter estimates which are close to the values expected from histology.
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Acknowledgments
This work was supported by EPFL through the use of the facilities of its Scientific IT and Application Support Center. We gratefully acknowledge the support of NVIDIA Corporation with the donation of the Titan Xp GPU used for this research. This work is supported by the Swiss National Science Foundation under grant number CRSII5_170873 (Sinergia project).
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Rafael-Patino, J. et al. (2020). DWI Simulation-Assisted Machine Learning Models for Microstructure Estimation. In: Bonet-Carne, E., Hutter, J., Palombo, M., Pizzolato, M., Sepehrband, F., Zhang, F. (eds) Computational Diffusion MRI. Mathematics and Visualization. Springer, Cham. https://doi.org/10.1007/978-3-030-52893-5_11
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