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Abstract #1322

Evaluating Rigid Motion-Affected, U-NET-Estimated Q-Matrices with Parallel RF Transmission

Katherine Anna Blanter1, Alix Plumley1, Alper Gungor2, Shaihan Malik3,4, and Emre Kopanoglu1
1Cardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom, 2Bilkent University, Ankara, Turkey, 3Imaging Physics and Engineering, King's College London, London, United Kingdom, 4School of Biomedical Engineering and Imaging Sciences, King's College London, London, United Kingdom

Synopsis

Keywords: Safety, RF Pulse Design & Fields

Motivation: Patient motion may exacerbate SAR exposure, and may necessitate large corrective safety factors. A position adaptive safety model would facilitate high-performance scanning without compromising safety.

Goal(s): We test the efficiency of using neural networks for estimating the effect of patient motion on local SAR for ultrahigh-field MRI.

Approach: We trained U-Nets to estimate the effect of patient motion on Q-matrices, and compared network-estimated SAR with ground-truth after-motion SAR for realistic parallel-transmit pulses.

Results: Patient motion has a statistically-significant effect on local SAR, but network-estimated safety models can recover a faithful representation of the ground-truth after-motion local SAR.

Impact: The proposed approach needs a smaller corrective safety factor, which may enable higher-performance scanning without compromising safety, when using ultrahigh-field MRI for subjects who may not remain still.

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Keywords