Keywords: Image Reconstruction, Heart, Self-supervised LearningPhysics-guided self-supervised learning (PG-SSL) of MRI reconstruction may provide high spatiotemporal fidelity and fast reconstruction of highly accelerated first-pass myocardial perfusion MRI without ground truth. We sought to develop a SSL model with self-supervised regularization (SSR) using Siamese architecture with stop gradient and re-undersampling block to generate physics-based data augmentation and regularization. PG unrolled network was used as the sub-network in Siamese structure. Self-supervised learning with self-supervised regularization (SSLR) outperformed low rank plus sparse on retrospective rate-8 undersampling single-band data and showed improved SNR and temporal fidelity on prospective multiband whole-heart coverage high resolution perfusion imaging.
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