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Abstract #0021

Free-breathing High-resolution Spiral Real-time Cardiac Cine Imaging at 1.5 T with DEep learning-based Spiral Image REconstruction (DESIRE)

Junyu Wang1, Ruixi Zhou1,2, Xitong Wang1, Marina Awad1, and Michael Salerno1,3,4,5
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2School of Artificial Intelligence, Beijing University of Posts and Telecommunications, Beijing, China, 3Medicine, University of Virginia, Charlottesville, VA, United States, 4Radiology, University of Virginia, Charlottesville, VA, United States, 5Medicine, Stanford University, Stanford, CA, United States


Cardiac magnetic resonance (CMR) real-time cine, which does not require breath-holding or ECG gating, is clinically useful particularly for patients with impaired breath-hold capacity and/or arrhythmias. Spiral acquisitions, which provide high acquisition efficiency and insensitivity to motion artifacts, can require a long reconstruction time particularly for compressed-sensing or other iterative reconstruction techniques. As such they cannot provide immediate feedback to the imager. Here, we sought to develop high-resolution real-time cine imaging at 1.5 T using fast spiral acquisitions and deep learning-based rapid imaging reconstruction for both bSSFP and GRE imaging, to make high-quality and online reconstruction for cine imaging feasible.

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