Keywords: Machine Learning/Artificial Intelligence, Machine Learning/Artificial Intelligence, Quality controlThis study investigated the feasibility and performance of quality assessment of hepatic magnetic resonance (MR) images using a deep-learning-based segmentation and radiomics approach. We used a pre-trained deep learning model to segment the liver on different contrast-enhanced MR phases and then extracted quantitative features to assess the image quality by a machine learning method. The results showed that the radiomics model had a high performance for image quality identification in both training and test sets. This suggests that it was feasible to automate the identification of image quality by using radiomics approaches.
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