OCUCFormer: An Over-Complete Under-Complete Transformer Network for accelerated MRI reconstruction

Many deep learning-based architectures have been proposed for accelerated Magnetic Resonance Imaging (MRI) reconstruction. However, existing encoder-decoder-based popular networks have a few shortcomings: (1) They focus on the anatomy structure at the expense of fine details, hindering their performance in generating faithful reconstructions; (2) Lack of long-range dependencies yields sub-optimal recovery of fine structural details. In this work, we propose an Over-Complete Under-Complete Transformer network (OCUCFormer) which focuses on better capturing fine edges and details in the image and can extract the long-range relations between these features for improved single-coil (SC) and multi-coil (MC) MRI reconstruction. Our model computes long-range relations in the highest resolutions using Restormer modules for improved acquisition and restoration of fine anatomical details. Towards learning in the absence of fully sampled ground truth for supervision, we show that our model trained with under-sampled data in a self-supervised fashion shows a superior recovery of fine structures compared to other works. We have extensively evaluated our network for SC and MC MRI reconstruction on brain, cardiac, and knee anatomies for $4\times$ and $5\times$ acceleration factors. We report significant improvements over popular deep learning-based methods when trained in supervised and self-supervised modes. We have also performed experiments demonstrating the strengths of extracting fine details and the anatomical structure and computing long-range relations within over-complete representations. Code for our proposed method is available at: https://github.com/alfahimmohammad/OCUCFormer-main.