# Compressive MR Fingerprinting reconstruction with Neural Proximal Gradient iterations

arXiv: https://arxiv.org/abs/2006.15271 · HTML: https://mlsanity.com/paper/2006.15271/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Jun 2020.

## Built on by (most cited first, 7 of 7)

- [Equivariant Imaging: Learning Beyond the Range Space](https://mlsanity.com/paper/2103.14756/) (2021)
- [A Plug-and-Play Approach to Multiparametric Quantitative MRI: Image Reconstruction using Pre-Trained Deep Denoisers](https://mlsanity.com/paper/2202.05269/) (2022)
- [Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI](https://mlsanity.com/paper/2211.12786/) (2022)
- [Deep Image Priors for Magnetic Resonance Fingerprinting with pretrained Bloch-consistent denoising autoencoders](https://mlsanity.com/paper/2407.19866/) (2024)
- [StoDIP: Efficient 3D MRF image reconstruction with deep image priors and stochastic iterations](https://mlsanity.com/paper/2408.02367/) (2024)
- [Generalized Deep Learning-based Proximal Gradient Descent for MR Reconstruction](https://mlsanity.com/paper/2211.16881/) (2022)
- [MRI2Qmap: multi-parametric quantitative mapping with MRI-driven denoising priors](https://mlsanity.com/paper/2603.11316/) (2026)

## Abstract (arXiv)

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.