Synthetic MRI aims to reconstruct multiple MRI contrasts from short measurements of tissue properties. Here, a generalizable physics-informed deep learning-based approach for synthetic MRI was investigated. Acquired data were mapped to effective quantitative parameter maps, here named q*-maps, which are fed to a physical signal model synthesizing four contrasts-weighted images. We demonstrated that from q*-maps, MRI contrasts unseen during training could be synthesized. The proposed method is benchmarked to a standard end-to-end deep learning approach. The two deep learning methods generated similar brain images for healthy subjects and patients with different pathologies.
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