Learning global brain microstructure maps using trainable sparse encoders

Diffusion-Weighted Magnetic Resonance Imaging is the only non-invasive technique available to infer the underlying brain tissue microstructure. Currently, one of the promising methods for microstructure imaging is signal modelling using convex formulation, e.g. using the COMMIT framework. Despite the benefits introduced with such a framework, an important limitation is the long convergence time, making the method unappealing for clinical applications. In order to address this limitation, we propose to use a neural network to learn the sparse encode representation of the data and performed an end-to-end reconstruction of the microstructure estimates directly from the diffusion-weighted data. Our results show that the neural network can accurately estimate the microstructure maps, 4 orders of magnitude faster than the convex formulation.

Presented at:
26th IEEE International Conference on Image Processing (ICIP 2019), Taipei, Taiwan, September 22-25, 2019
Sep 22 2019
Accepted paper. Conference Programme soon available at: http://2019.ieeeicip.org/?action=page4&id=6

 Record created 2019-05-01, last modified 2020-04-20

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