PGIUN: Physics-Guided Implicit Unrolling Network for Accelerated MRI

Abstract

To cope with the challenges stemming from prolonged acquisition periods, compressed sensing MRI has emerged as a popular technique to accelerate the reconstruction of high-quality images from under-sampled k-space data. Most current solutions endeavor to solve this issue with the pursuit of certain prior properties, yet the treatments are all enforced in the original space, resulting in limited feature information. To boost the performance yet with the guarantee of high running efficiency, in this study, we propose a Physics-Guided Implicit Unrolling Network (PGIUN). Specifically, by an elaborately designed reversible network, the inputs are first mapped to a channel-lifted implicit space, which taps the potential of capturing spatial-invariant features sufficiently. Within this implicit space, we then unfold an accelerated optimization algorithm to iterate an efficient and feasible solution, in which a parallelly dual-domain update is equipped for better feature fusion. Finally, an inverse embedding transformation of the recovered high-dimensional representation is employed to achieve the desired estimation. PGIUN enjoys high interpretability benefiting from the physically induced modules, which not only facilitates an intuitive understanding of the internal working mechanism but also endows it with high generalization ability. Extensive experiments conducted across diverse datasets and varying sampling rates/patterns consistently establish the superiority of our approach over state-of-the-art methods in both visual and quantitative evaluations.