Self-Supervised Learning for MRI Reconstruction with a Parallel Network Training Framework

TL;DR

This paper introduces a self-supervised learning framework for MRI reconstruction that does not require fully-sampled reference data, enabling effective high-acceleration MRI imaging using only undersampled data.

Contribution

The proposed method allows MRI reconstruction training solely on undersampled data by using parallel networks and novel loss functions, eliminating the need for time-consuming fully-sampled references.

Findings

Achieves competitive reconstruction performance at high acceleration rates (4 and 8).

Demonstrates effectiveness on an open brain MRI dataset.

Flexible framework applicable to existing deep learning methods.

Abstract

Image reconstruction from undersampled k-space data plays an important role in accelerating the acquisition of MR data, and a lot of deep learning-based methods have been exploited recently. Despite the achieved inspiring results, the optimization of these methods commonly relies on the fully-sampled reference data, which are time-consuming and difficult to collect. To address this issue, we propose a novel self-supervised learning method. Specifically, during model optimization, two subsets are constructed by randomly selecting part of k-space data from the undersampled data and then fed into two parallel reconstruction networks to perform information recovery. Two reconstruction losses are defined on all the scanned data points to enhance the network's capability of recovering the frequency information. Meanwhile, to constrain the learned unscanned data points of the network, a difference loss is designed to enforce consistency between the two parallel networks. In this way, the reconstruction model can be properly trained with only the undersampled data. During the model evaluation, the undersampled data are treated as the inputs and either of the two trained networks is expected to reconstruct the high-quality results. The proposed method is flexible and can be employed in any existing deep learning-based method. The effectiveness of the method is evaluated on an open brain MRI dataset. Experimental results demonstrate that the proposed self-supervised method can achieve competitive reconstruction performance compared to the corresponding supervised learning method at high acceleration rates (4 and 8). The code is publicly available at \url{https://github.com/chenhu96/Self-Supervised-MRI-Reconstruction}.