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 Zhou^1,2, Xitong Wang¹, Marina Awad¹, and Michael Salerno^1,3,4,5

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

Synopsis

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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