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

ABSTRACT:

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, we present a deep learning-based method for image-based subject-specific local SAR assessment. We propose to train a convolutional neural network to learn a "surrogate SAR model" to map the relation between subject-specific B1+ maps and the corresponding local SAR.

Method

Our database of 23 subject-specific models with an 8-transmit channel body array for prostate imaging at 7 T was used to build 5750 training samples. These synthetic complex B1+ maps and local SAR distributions were

SUBMITTER: Meliado EF 

PROVIDER: S-EPMC6899474 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature