A DeepSAR method is provided in which local SAR is predicted using a three-dimensional convolutional neural network (CNN). More specifically, a patient-specific local specific absorption rate (SAR) prediction method is provided. A three-dimensional convolutional neural network (CNN) is trained using pairs of SAR maps and B1+ maps for different channel weights. The CNN has an input and an output, and is then provided as a computational device to compute SAR maps. As input to the trained CNN, measured B1+ maps, simulated B1+ maps or a combination thereof are used. The trained CNN then computes and output SAR maps in a form of a generative adversarial network (GAN) to predict a three-dimensional real-valued SAR map with both real and imaginary components to be used for various applications in high field Magnetic Resonance Imaging (MRI).
DeepSAR: Specific Absorption Rate (SAR) prediction and management with a neural network approach