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Abstract #4172

Non-contrast-enhanced mDixon water-fat separation whole-heart coronary MRA with a deep learning constrained Compressed SENSE reconstruction

Wenyun Liu1, Lei Zhang1, Cheng Li2, Yuejiao Sun1, Ying Qiu1, Yi Zhu3, Ke Jiang3, Shuo Wang1, and Huimao Zhang1
1Department of Radiology, The First Hospital of Jilin University, Changchun, China, 2Department of Cardiovascular center, The First Hospital of Jilin University, Changchun, China, 3Philips Healthcare, Beijing, China


The conventional 3D whole-heart free-breathing coronary MR angiography suffers from a long scan time. However, using very high acceleration factors leads to degradation of image quality due to insufficient noise removal. In this study, we use Compressed-SENSE Artificial Intelligence (CS-AI) framework to acquire highly accelerated 3D non-contrast-enhanced mDixon water-fat separation whole-heart CMRA. The result shows that CS-AI reconstruction can significantly decrease scan time with sufficient image quality compared to Compressed-SENSE(CS) and might be clinically useful in assessment of coronary artery disease.

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