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

Synopsis

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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