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Abstract #0780

An Automated Method for Subject Specific Global SAR Prediction in Parallel Transmission

Leeor Alon1,2, Cem Murat Deniz1,2, Riccardo Lattanzi1, Graham Wiggins1, Ryan Brown1, Daniel K. Sodickson1,2, Yudong Zhu1

1Center for Biomedical Imaging, Department of Radiology, NYU School of Medicine, NYU School of Medicine, New York, NY, United States; 2Sackler Institute of Graduate Biomedical Sciences, NYU School of Medicine, New York, NY, United States

Current SAR measurement schemes are missing the capability to track and manage SAR under in-vivo conditions. Existing hardware schemes monitor forward and reflective power in real time only, but offer no prediction capability and tend to considerably overestimate SAR by assuming complete constructive interference of electric fields. In this study, we present, and demonstrate in vivo, a rapid and simple calibration method for the accurate prediction of subject specific global power deposition on an 8-channel transmit 7T MR system. This global SAR prediction capability is scalable to parallel transmit systems with any number of transmit channels.