Keywords: Safety, Safety, specific absorption rate; deep learning; parallel transmit; convolutional neural network; subject-specific SAR assessment; ultra-high field MRI
Motivation: The methods presented for on-line local SAR evaluation require access to geometric design details of the transmit coil which are not always available.
Goal(s): Evaluate the generalization capabilities of deep learning-based methods when they are used to assess the local SAR distribution for coils not included in the training data.
Approach: We built a diverse synthetic dataset four different coils and trained a neural network: using only samples from each coil, and using samples from all coils except one.
Results: Including a reasonably wide variety of coils in the training process enables local SAR assessment without knowing the design details of the coil.
Impact: The lack of access to design details of the coil makes it challenging to transition the more advanced local SAR assessment methods into clinical practice. Training with a diverse set of coils could enable local SAR assessment without coil information.
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