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Abstract #2873

Free-breathing High-resolution Spiral Real-time Cardiac Cine Imaging using DEep learning-based rapid Spiral Image REconstruction (DESIRE)

Junyu Wang1, Ruixi Zhou1, and Michael Salerno1,2,3
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2Medicine, University of Virginia, Charlottesville, VA, United States, 3Radiology, University of Virginia, Charlottesville, VA, United States

Cardiac real-time cine imaging is valuable for patients who cannot hold their breath or have irregular heart rhythms. Spiral acquisitions, which provide high acquisition efficiency, make high-resolution cardiac cine real-time imaging feasible. However, the reconstruction for under-sampled non-Cartesian real-time imaging is time-consuming, and hence cannot provide rapid feedback. We sought to develop a DEep learning-based rapid Spiral Image REconstruction technique (DESIRE) for spiral real-time cardiac cine imaging with free-breathing, 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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