Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations

• URL: http://arxiv.org/abs/2006.15271v3
• Date: Mon, 6 Jul 2020 11:51:40 GMT
• Title: Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations
• Authors: Dongdong Chen, Mike E. Davies and Mohammad Golbabaee
• Abstract summary: ProxNet is a learned proximal gradient descent framework that incorporates the forward acquisition and Bloch dynamic models within a recurrent learning mechanism. 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.
• Score: 27.259916894535404
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 https://github.com/edongdongchen/PGD-Net.

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