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Abstract #0431

Automatic Assessment of MR Image Quality with Deep Learning

Jifan Li1, Shuo Chen1, Qiang Zhang1, Huiyu Qiao1, Xihai Zhao1, Chun Yuan1,2, and Rui Li1

1Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Beijing, China, 2Vascular Imaging Laboratory, Department of Radiology, University of Washington, Seattle, WA, United States

In this study, we aimed to develop a convolutional neural network (CNN) to assess the quality of multi-contrast carotid plaque MR images automatically. The network was trained on large amount of plaque images combined with image quality scores labeled by experienced radiologists. Transfer learning was utilized to take the advantage of state-of-the-art CNN pre-trained on ImageNet dataset. The accuracy of image quality estimation achieved 86.0% with preprocessing and fine-tuning of the network.

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