Abstract #0431

Automatic Assessment of MR Image Quality with Deep Learning

Jifan Li¹, Shuo Chen¹, Qiang Zhang¹, Huiyu Qiao¹, Xihai Zhao¹, Chun Yuan^1,2, 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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