Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data

TL;DR

This paper introduces an unsupervised deep learning approach for MRI reconstruction that does not require fully-sampled training data, improving robustness and reducing computational costs compared to supervised models and classical methods.

Contribution

The authors propose a novel unsupervised training strategy for unrolled neural networks in CS-MRI, eliminating the need for ground-truth images and enhancing robustness.

Findings

Achieves lower loss than classical optimization methods.

Demonstrates superior robustness to unseen data.

Computationally more efficient than traditional solvers.

Abstract

Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing under-sampled MR images, offering the potential to reduce scan times. Classical techniques minimize a regularized least-squares cost function using an expensive iterative optimization procedure. Recently, deep learning models have been developed that model the iterative nature of classical techniques by unrolling iterations in a neural network. While exhibiting superior performance, these methods require large quantities of ground-truth images and have shown to be non-robust to unseen data. In this paper, we explore a novel strategy to train an unrolled reconstruction network in an unsupervised fashion by adopting a loss function widely-used in classical optimization schemes. We demonstrate that this strategy achieves lower loss and is computationally cheap compared to classical optimization solvers while also exhibiting superior robustness compared to supervised models. Code is available at https://github.com/alanqrwang/HQSNet.