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Abstract #1050

Super Resolution MRI Using 3D Generative Adversarial Network: Towards Single Breath-Hold Coronary MR Angiography

Yibin Xie1, Ruiyuan Lin2, Yuhua Chen1,3, Yubo Zhang2, Feng Shi1, Yanan Fei2, Zixin Deng1,3, Derenik Haghverdian2, Madhvi Kannan2, Hyuk-Jae Chang4, C.-C. Jay Kuo2, and Debiao Li1,3

1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, CA, United States, 2Ming Hsieh Department of Electrical Engineering, University of Southern California, Los Angeles, CA, United States, 3Department of Bioengineering, University of California, Los Angeles, Los Angeles, CA, United States, 4Division of Cardiology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea

Coronary MRA is an attractive imaging tool to offer noninvasive, radiation-free evaluation of coronary artery disease. However, long scan time and sensitivity to motion limit its current clinical applications. In this paper, we propose a super resolution reconstruction framework based on 3D generative adversarial network (GAN) to allow substantial acceleration (10x plus) and potentially whole-heart coronary MRA within a breath-hold. Preliminary results demonstrated significantly improved vessel sharpness and image quality metrics in highly under-sampled coronary MRA dataset.

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