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Abstract #4447

Deep learning motion compensation for Cartesian and spiral trajectories

Quan Dou1, Xue Feng1, Zhixing Wang1, Daniel Weller2, and Craig Meyer1

1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States

Movement of the subject during MRI acquisition causes image quality degradation. In this study we adopted a deep CNN to correct motion-corrupted brain images. To get paired training datasets, synthetic motion artifacts were added by simulating k-space data along different sampling trajectories. Quantitative evaluation showed that the CNN significantly improved the image quality. The spiral trajectory performed better than the Cartesian trajectory both before and after the motion deblurring. A network trained with an L1 loss function achieved better RMSE and SSIM than one trained with an L2 loss function after convergence. Overall, deep learning yields rapid and flexible motion compensation.

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