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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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