A Unified Reconstruction Framework for Arbitrarily Accelerated MR Imaging

Motivation: MRI reconstruction models are typically trained for specific acceleration factors. Multiple model variants are required to accommodate varying acceleration factors in practice. Goal(s): We aim to develop a unified reconstruction model capable of handling arbitrarily accelerated MRI scans. Approach: We first use self-supervised learning to obtain feature representations of fully-sampled and arbitrarily sub-sampled data. Then, we employ latent diffusion model to map feature representations of sub-sampled data to those of fully-sampled data. Results: Experiments show that the use of feature transferring in our unified model brings an average performance gain of 3.86dB in PSNR for acceleration factors of 2x, 3x, and 4x. Impact: We adopt feature representation transfer in the field of MRI reconstruction to address a practical issue largely overlooked by existing studies. Our adaptive reconstruction model can significantly simplify the deployment of MR reconstruction model and reduce the development costs.