Keywords: AI/ML Image Reconstruction, Machine Learning/Artificial Intelligence, Compressed Sensing, Parallel Imaging
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.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords