Keywords: Machine Learning/Artificial Intelligence, ArtifactsArterial Spin Labeling has great potential in clinics as a non-invasive alternative to Dynamic Contrast Enhanced imaging. However, motion sensitivity needs to be tackled by readout techniques such as 3D GRASE PROPELLER, which unfortunately shows a high sensitivity to geometric distortion. Analytical separation of motion and distortion effects is computationally demanding and might fail in some situations. To this aim, a U-NET based convolutional neural network approach is demonstrated which might overcome this limitation.
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