A deep learning method for image‐based subject‐specific local SAR assessment

Purpose Local specific absorption rate (SAR) cannot be measured and is usually evaluated by offline numerical simulations using generic body models that of course will differ from the patient's anatomy. An additional safety margin is needed to include this intersubject variability. In this work...

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Veröffentlicht in:	Magnetic resonance in medicine Jg. 83; H. 2; S. 695 - 711
Hauptverfasser:	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.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	United States Wiley Subscription Services, Inc 01.02.2020 John Wiley and Sons Inc
Schlagworte:	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
ISSN:	0740-3194, 1522-2594, 1522-2594
Online-Zugang:	Volltext
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