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Abstract #1556

Accelerated whole-heart mDixon coronary MRA with a deep learning constrained CompressedSENSE: a feasibility study

Xi Wu1,2, Jiayu Sun1, Xiaoyong Zhang3, and Zhigang Wu4
1Department of Radiology, West China Hospital of Sichuan University, Chengdu, China, 2North Sichuan Medical College, Nanchong, China, 3Philips Healthcare, Chengdu, China, 4Philips Healthcare, Shenzhen, China


Currently, modified dixon (mDixon) gradient echo sequence is widely used for respiratory navigated whole-heart coronary magnetic resonance angiography (MRA) for the evaluation of coronary anatomy and abnormalities. However, the main drawback of this approach is that the scan time is longer and prone to interference with motion artifacts. In this study, we investigated the utility of whole-heart coronaryMRA using accelerated mDixonwith compressed sensing (CS) and artificial intelligence(AI) technologies at 3Tesla. The initial results showedtheCS-AI mDixontechnique has potential to be the most viable alternative to enhance the clinical workflow of coronary MRA.

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