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Abstract #4662

Accelerated 3D Non-Cartesian Reconstruction with Deep Learning

Mario O. Malavé1, Srivathsan P. Koundinyan1, Christopher M. Sandino1, Frank Ong2, Joseph Y. Cheng1,3, and Dwight G. Nishimura1

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Electrical Engineering and Computer Sciences, University of California, Berkeley, CA, United States, 3Radiology, Stanford University, Stanford, CA, United States

In this work, we demonstrate the application of a non-Cartesian unrolled architecture in reconstructing images from undersampled 3D cones datasets. One shown application of this method is for reconstructing undersampled 3D image-based navigators (iNAVs), which enable monitoring of beat-to-beat nonrigid heart motion during a cardiac scan. The proposed non-Cartesian unrolled network architecture provides similar outcomes as l1-ESPiRIT in one-twentieth of the time, and the reconstructions exhibit robustness when using an undersampled 3D cones trajectory.

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