Latent-space Unfolding for MRI Reconstruction

To circumvent the problems caused by prolonged acquisition periods, compressed sensing MRI enjoys a high usage profile to accelerate the recovery of high-quality images from under-sampled k-space data. Most current solutions dedicate to solving 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 achieve a performance promotion yet with the guarantee of running efficiency, in this work, we propose a latent-space unfolding network (LsUNet). Specifically, by an elaborately designed reversible network, the inputs are first mapped to a channel-lifted latent space, which taps the potential of capturing spatial-invariant features sufficiently. Within the latent 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 applied to achieve the expected estimation. LsUNet enjoys high interpretability due to the physically induced modules, which not only facilitates an intuitive understanding of the internal operating mechanism but also endows it with high generalization ability. Comprehensive experiments on different datasets and various sampling rates/patterns demonstrate the advantages of our proposal over the latest methods both visually and numerically.