Motion-resolved parallel-transmit (pTx) B1-maps can be predicted using neural networks, facilitating online pulse re-design – a prospective solution to motion. However, networks require large training-datasets. Since different pTx coils inherently produce different B1-distributions, it is unclear whether coil-specific training-datasets are needed. Here, we train networks on simulated data from one coil-model and test on 6 differently-sized coil-models. While performance was optimal for the coil on which networks were trained, B1-prediction yielded lower error than that caused by motion in ≥91% of magnitude, and ≥55% of phase evaluations for 5 out of the 6 models, demonstrating some generalisability across coils.
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