A deep learning method for image‐based subject‐specific local SAR assessment - Meliadò, E.F.; Raaijmakers, A.J.E; Sbrizzi, A.; Steensma, B.R.; Maspero, M.; Savenije, M.H.F.; Luijten, P.R.; Berg, C.A.T. (Magnetic resonance in medicine, 2020)
Keywords: Algorithms; Artificial neural networks; Computer Simulation; convolutional neural network; Databases, Factual; Deep Learning; Full Papers—Computer Processing and Modeling; Healthy Volunteers; Humans; Image Processing, Computer-Assisted - methods; In vivo methods and tests; Machine learning; Magnetic Resonance Imaging; Male; Mathematical models; Models, Statistical; Neural networks; Neural Networks, Computer; parallel transmit; Phantoms, Imaging; Prostate; Prostate - diagnostic imaging; Prostatic Neoplasms - diagnostic imaging; Qualitative analysis; Reproducibility of Results; Safety factors; Safety margins; Signal-To-Noise Ratio; specific absorption rate; subject‐specific SAR assessment; Teaching methods; ultrahigh‐field MRI; deep learning; ultrahigh-field MRI; specific absorption rate; parallel transmit; subject-specific SAR assessment; convolutional neural network