Frontiers of Medical Image Reconstruction with Deep Unfolding

Abstract

Shortening acquisition time in medical imaging, particularly in Magnetic Resonance Imaging (MRI), has been a long-standing challenge involving a trade-off with image quality. This article provides an overview of “deep unfolding,” a technology emerging as a powerful solution to this problem. Deep unfolding is a hybrid approach that fuses classical iterative reconstruction algorithms based on physical models with deep learning that learns features from data, thereby achieving both high performance and interpretability. This paper first explains its fundamental principles and then traces its evolution in MRI reconstruction, from pioneering models like ADMM-Net and ISTA-Net, to advanced architectures such as MoDL that integrate powerful priors like U-Net, and finally to the latest architectures applying Transformers. Next, we discuss the trend of self-supervised learning, which overcomes the need for fully-sampled ground-truth data―a major barrier to clinical application. Furthermore, a theoretical interpretation of the behavior of learned parameters is introduced to explain why deep unfolding achieves rapid convergence. Finally, as a fundamental challenge for the technology’s widespread clinical adoption, we address the issue of generalization to out-of-distribution data and provide a perspective on future research directions.