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Abstract #0428

Deep Learning Method for Non-Cartesian Off-resonance Artifact Correction

David Y Zeng1, Jamil Shaikh2, Dwight G Nishimura1, Shreyas S Vasanawala2, and Joseph Y Cheng2

1Electrical Engineering, Stanford University, Stanford, CA, United States, 2Radiology, Stanford University, Stanford, CA, United States

3D cones trajectories have the flexibility to be more scan-time efficient than 3D Cartesian trajectories, especially with long readouts. However, long readouts are subject to blurring from off-resonance, limiting the efficiency. We propose a convolutional residual network to correct for off-resonance artifacts to allow for reduced scan time. Fifteen exams were acquired with both conservative readout durations and readouts 2.4x as long. Long-readout images were corrected with the proposed method. The corrected long-readout images had non-inferior (p<0.01) reader scores in all features examined compared to conservative readout images.

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