Keywords: Safety, Parallel Transmit & Multiband, Specific absorption rate (SAR), ultra-high field MRI, deep learningPatient motion affects the specific absorption rate (SAR), a safety parameter in MRI. SAR is often calculated using so-called Q-matrices. We used conditional generative adversarial networks (cGANs) to estimate the effect of motion on magnitude from Q-matrices, which were extracted from body models simulated in a parallel-transmit (pTx) coil tuned to operate at 7T. Networks trained on Q-matrices from two positions were extrapolated to nine others. Network-predicted Q-matrices corresponded well with simulated ground truth motion-affected Q-matrices.
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