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Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations

2020-06-27 03:52:22
Dongdong Chen, Mike E. Davies, Mohammad Golbabaee
arXiv_CV
                 
Abstract

Consistency of the predictions with respect to the physical forward model is pivotal for reliably solving inverse problems. This consistency is mostly un-controlled in the current end-to-end deep learning methodologies proposed for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we propose ProxNet, a learned proximal gradient descent framework that directly incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism. The ProxNet adopts a compact neural proximal model for de-aliasing and quantitative inference, that can be flexibly trained on scarce MRF training datasets. Our numerical experiments show that the ProxNet can achieve a superior quantitative inference accuracy, much smaller storage requirement, and a comparable runtime to the recent deep learning MRF baselines, while being much faster than the dictionary matching schemes. Code has been released at this https URL.

Abstract (translated)

URL

https://arxiv.org/abs/2006.15271

PDF

https://arxiv.org/pdf/2006.15271.pdf

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