Keywords: Image Reconstruction, Image Reconstruction
Motivation: Self-supervised neural network reconstruction improves multi-shot diffusion MRI (dMRI), yet suffers from prohibitively long computation times.
Goal(s): To develop a zero-shot self-supervised learning method for fast multi-shot dMRI reconstruction.
Approach: We propose a physics-guided neural network that operates in both k- and image-spaces to combine information from different EPI shots. We show that reconstruction quality can be improved with a novel sampling mask strategy, and that faster training is possible with a new training strategy. Finally, we extend our results to SMS acquisitions.
Results: Our results show that the proposed method provides improved and fast reconstructions compared to 2-shot LORAKS and 2-shot ZS-SSL.
Impact: The proposed physics-guided self-supervised learning method provides fast and high-quality reconstruction of multi-shot diffusion MRI volumes, while also eliminating the need for external training datasets.
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