Abstract #0381

Rapid 3D SPirAl Respiratory and Cardiac Self-gated (SPARCS) Cine Imaging with a DEep learning-based rapid Spiral Image REconstruction (DESIRE)

Xitong Wang¹, Junyu Wang¹, Shen Zhao¹, Ruixi Zhou², Yang Yang³, and Michael Salerno¹

¹Stanford Univeristy, Stanford, CA, United States, ²Beijing University of Posts and Telecommunications, Beijing, China, ³Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, CA, United States

Synopsis

Keywords: Machine Learning/Artificial Intelligence, CardiovascularWe developed a rapid 3D self-gated cardiac cine technique, using a variable-density undersampled randomized stack of spiral gradient echo sequence to perform cine evaluation of the whole left ventricle. Our proposed slice-by-slice deep learning-based imaging reconstruction technique for self-gated free-breathing 3D stack of spiral cardiac cine imaging can produce cine images with high temporal (40 ms) and spatial resolution (1.25x1.25x8 mm) within a 20s acquisition time with <1s deep learning inference time.

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