Feature-Image Variational Network for Accelerated MRI Reconstructions

Motivation: To improve learning-based MRI reconstructions to achieve higher clinical accuracy and detail retention. Goal(s): To introduce modifications in the end-to-end (E2E) variational network (VarNet) to enhance its performance for undersampled MRI reconstructions. Approach: We performed training in feature-space instead of image-space and included an attention layer that leverages the spatial locations of the Cartesian undersampling artifacts. We combined the new network with the E2E VarNet into Feature-Image VarNet to facilitate cross-domain learning. Results: Reconstructions were evaluated using standard metrics and clinical scoring. Feature-Image VarNet outperformed all open-source models in the fastMRI leaderboard and preserved small anatomical details that were blurred with E2E VarNet. Impact: We propose the Feature-Image (FI) variational network (VarNet), which performs cross-domain learning between feature and image spaces. FI VarNet significantly enhances the reconstruction quality of undersampled MRI and could enable clinically acceptable reconstructions at higher acceleration factors than currently possible.