Keywords: Machine Learning/Artificial Intelligence, Motion CorrectionMyocardial T1 and T2 mapping play an important role in the assessment of cardiovascular disease. 3D whole-heart joint T1/T2 water/fat mapping approaches have been recently proposed, however they require long reconstruction times. Recently a Machine learning based reconstruction was proposed for joint motion correction and motion corrected image reconstruction of undersampled free-breathing single contrast 3D coronary MR angiography. Here, we extend this approach for non-rigid motion-corrected reconstructions for multi-contrast data for joint T1/T2 mapping. The proposed approach achieves good agreement with reference techniques and comparable image quality to state-of-the-art methods albeit in 1200 times shorter reconstruction times.
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