  The key to dynamic or multi-contrast magnetic resonance imaging (MRI)
reconstruction lies in exploring inter-frame or inter-contrast information.
Currently, the unrolled model, an approach combining iterative MRI
reconstruction steps with learnable neural network layers, stands as the
best-performing method for MRI reconstruction. However, there are two main
limitations to overcome: firstly, the unrolled model structure and GPU memory
constraints restrict the capacity of each denoising block in the network,
impeding the effective extraction of detailed features for reconstruction;
secondly, the existing model lacks the flexibility to adapt to variations in
the input, such as different contrasts, resolutions or views, necessitating the
training of separate models for each input type, which is inefficient and may
lead to insufficient reconstruction. In this paper, we propose a two-stage MRI
reconstruction pipeline to address these limitations. The first stage involves
filling the missing k-space data, which we approach as a physics-based
reconstruction problem. We first propose a simple yet efficient baseline model,
which utilizes adjacent frames/contrasts and channel attention to capture the
inherent inter-frame/-contrast correlation. Then, we extend the baseline model
to a prompt-based learning approach, PromptMR, for all-in-one MRI
reconstruction from different views, contrasts, adjacent types, and
acceleration factors. The second stage is to refine the reconstruction from the
first stage, which we treat as a general video restoration problem to further
fuse features from neighboring frames/contrasts in the image domain. Extensive
experiments show that our proposed method significantly outperforms previous
state-of-the-art accelerated MRI reconstruction methods.
