Inhomogeneity of the radiofrequency field (B1) is one of the main problems in quantitative MRI. Leveraging from the unique ability of deep learning, we propose a data driven strategy to derive quantitative B1 map from a single qualitative MR image without specific requirements on the weighting of the input image. B1 estimation is accomplished using a self-attention deep convolutional neural network, which makes efficient use of local and non-local information. Without additional data acquisition, an accurate estimation of B1 map is achieved, which is useful for the compensation of field inhomogeneity in T1 mapping as well as for other applications.
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