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Abstract #1038

Experimental Validation of Subject-Specific Local SAR Assessment by Deep Learning

E.F. Meliadò1,2, A.J.E Raaijmakers1,3, B.R. Steensma1, A. Sbrizzi1, P.R. Luijten1, and C.A.T. van den Berg1

1Center for Image Sciences, University Medical Center Utrecht, Utrecht, Netherlands, 2MR Code BV, Zaltbommel, Netherlands, 3Biomedical Image Analysis, Eindhoven University of Technology, Eindhoven, Netherlands

Subject-specific local SAR assessment is one of the outstanding challenges of ultra-high field MRI. In this work, we present experimental, in-vivo results on four healthy subjects of a novel deep learning approach which provides online subject-specific local SAR distributions based on measured B1+ maps. Results are validated by creating a subject-specific model of each subject and calculate a reference local SAR distribution off-line by FDTD simulations. The results show that a Convolutional Neural Network (CNN) trained with synthetic MR data enables accurate subject-specific local SAR prediction during an MRI examination.

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