Abstract #1702

Breath-hold Whole Heart Coronary MRA with Parallel Imaging, Compressed Sensing and Deep Learning reconstruction

Mitsuharu Miyoshi¹, Atsushi Nozaki¹, Shigeo Okuda², Masahiro Jinzaki², and Tetsuya Wakayama¹

¹Global MR application and workflow, GE Healthcare Japan, Tokyo, Japan, ²Department of Radiology, Keio University School of Medicine, Tokyo, Japan

Synopsis

Keywords: Heart, Cardiovascular, Coronary Artery

For Breath-hold Whole Heart Coronary MRA, we developed the combination of Parallel Imaging and Compressed Sensing to accelerate scan time and Deep Learning reconstruction to improve the image quality. With the combination of these techniques, we could obtain Coronary MRA with 1.8mm isotropic acquisition voxel size in a possible breath-hold scan time. Deep Learning recon effectively improved the SNR and image quality.

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