The purpose of this work was to develop and evaluate a novel deep learning-based framework termed Reference-free Latent map-eXtracting MANTIS (Relax-MANTIS) for efficient MR parameter mapping. Our approach incorporated end-to-end CNN mapping, the concept of cyclic loss to enforce data fidelity and without the need of explicit training references. Our results demonstrated that the proposed framework produced accurate and robust T1 mapping in knee and low-SNR lung UTE MRI. The good quantitative agreement to the reference method suggests that Relax-MANTIS allows potentially accelerated quantitative mapping without modifications of scan protocol and sequence for high-resolution knee and whole lung T1 quantification.
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