U-JET: Preliminary results of a convolutional neural network approach for distortion-free image reconstruction of PROPELLER data

Arterial 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.