Abstract #1050

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

Yibin Xie¹, Ruiyuan Lin², Yuhua Chen^1,3, Yubo Zhang², Feng Shi¹, Yanan Fei², Zixin Deng^1,3, Derenik Haghverdian², Madhvi Kannan², Hyuk-Jae Chang⁴, C.-C. Jay Kuo², and Debiao Li^1,3

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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