Abstract #3533

Retrospective Eddy Current Artifact Reduction for Balanced SSFP Cine Imaging via Deep Learning

Cynthia Chen¹, Christopher Sandino², Adam Bush³, Frank Ong², and Shreyas Vasanawala³

¹California Institute of Technology, Pasadena, CA, United States, ²Electrical Engineering, Stanford University, Stanford, CA, United States, ³Radiology, Stanford University, Stanford, CA, United States

Eddy currents due to changing magnetic fields reduce diagnostic image quality, especially in SSFP acquisitions. In this work, we propose a deep learning method that successfully reduces eddy current artifacts in 2D cardiac cine imaging using a 3D U-Net architecture. Our method is completely retrospective and does not require any sequence or hardware modifications.

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