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Abstract #3476

Accelerated free-breathing radial cine imaging via GROG-interpolated DL-ESPIRiT

Kanghyun Ryu1, Zhitao Li1, Christopher M. Sandino2, and Shreyas S. Vasanawala1
1Radiology, Stanford University, Stanford, CA, United States, 2Electrical Engineering, Stanford University, Stanford, CA, United States


Deep Learning based reconstruction methods have been vastly explored for accelerating Cartesian-based cardiac cine imaging via using unrolled neural networks. However, for non-Cartesian trajectories such as radial, these networks require substantial modifications (i.e., NUFFT-based data consistency) and requires collecting separate radial-based dataset, which may not be common in the clinics.

Here, we investigate a method to transfer the radial k-space data to the Cartesian domain using GROG-based interpolation. We show that DL-ESPIRiT trained with Cartesian cine dataset (with pseudo radial-like under sampling pattern) can be generalizable to reconstruct actual accelerated radial cine acquired on a scanner.

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