Abstract #4662

Accelerated 3D Non-Cartesian Reconstruction with Deep Learning

Mario O. Malavé¹, Srivathsan P. Koundinyan¹, Christopher M. Sandino¹, Frank Ong², Joseph Y. Cheng^1,3, 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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