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Abstract #1848

Deep learning-based Automatic Analysis for Free-breathing High-resolution Spiral Real-time Cardiac Cine Imaging at 3T

Marina Awad1, Junyu Wang1, Xue Feng1, Ruixi Zhou1, and Michael Salerno1,2,3,4
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, 4Medicine, Stanford University, Stanford, CA, United States

Synopsis

Cardiac real-time cine imaging is useful for patients who cannot hold their breath or have irregular heart rhythms. Free breathing high-resolution real-time cardiac cine images are acquired efficiently using spiral acquisitions, and rapidly reconstructed using our DESIRE framework. However, quantifying the EF from free-breathing real-time imaging is limited by the lack of an EKG signal to define the cardiac cycle, and through-plane cardiac motion resulting from free-breathing. We developed a DL-based segmentation technique to determine the end-expiratory phase and determine end-systole and end-diastole on a slice by slice basis to accurately quantify EF from spiral real-time cardiac cine imaging.

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