S³CNet: self-supervised Siamese cooperative network for accelerating magnetic resonance imaging reconstruction

Deep learning (DL)-based magnetic resonance imaging (MRI) reconstruction methods have been widely applied to accelerate data acquisition. However, most of these methods rely heavily on large-scale fully sampled labeled data for supervision, which is often difficult to obtain in clinical practice, thereby limiting their practical application.

To address the aforementioned limitation, this study proposes a self-supervised Siamese cooperative network (S³CNet) for high-quality MRI reconstruction using highly undersampled k-space data. The goal is to reduce reliance on fully sampled data while maintaining excellent reconstruction performance.

A unified two-stage self-supervised reconstruction framework was designed, which is compatible with different sampling patterns and network architectures. In the first stage, undersampled k-space data were randomly split into two disjoint subsets. By introducing measurement consistency loss to align reconstructed k-space with measured data and cross-consistency loss to suppress noise via Noise2Noise-inspired supervision, the framework effectively suppresses noise propagation from measured data to reconstructed results. In the second stage, the denoised outputs of the first stage were used as pseudo-labels, and reconstruction consistency loss was adopted to constrain the sampling-reconstruction pipeline, which enhances the network's generalization ability to unseen data. These three losses constitute a hybrid self-supervised loss function, which enables effective training of the Siamese network without the need for fully sampled ground truth.

Experimental results on the T1- and T2-weighted brain FastMRI dataset showed that S³CNet outperformed state-of-the-art self-supervised methods under various undersampling rates and sampling patterns. Notably, S³CNet's performance was comparable to that of fully supervised methods.

S³CNet effectively overcomes the critical dependency of DL-based MRI reconstruction on fully sampled training data. It achieves both high reconstruction quality and strong generalization, providing a practical solution for accelerated clinical MRI.