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Abstract #1103

High-resolution Spiral First-pass Myocardial Perfusion Imaging using DEep learning-based rapid Spiral Image REconstruction (DESIRE)

Junyu Wang1, Daniel Weller2, Patricia Rodriguez Lozano3, and Michael Salerno1,3,4
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States, 3Medicine, University of Virginia, Charlottesville, VA, United States, 4Radiology, University of Virginia, Charlottesville, VA, United States

First-pass contrast-enhanced myocardial perfusion imaging is valuable for evaluating coronary artery disease (CAD). Spiral perfusion imaging techniques, using a motion-compensated L1-SPIRiT based reconstruction, are capable of whole-heart high-resolution perfusion imaging. However, this reconstruction is performed off-line and takes ~1 hour per slice. To address this limitation, we developed a DEep learning-based Spiral Image REconstruction technique (DESIRE) for spiral first-pass myocardial perfusion imaging, for both single-slice (SS) and simultaneous multi-slice (SMS) MB=2 acquisitions, to provide fast and high-quality image reconstruction and make rapid online reconstruction feasible. High image quality was demonstrated using the proposed technique for healthy volunteers and patients.

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