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Abstract #2670

Respiratory motion in DENSE MRI: Introduction of a new motion model and use of deep learning for motion correction

Mohamad Abdi1, Daniel S Weller1,2, and Frederick H Epstein1,3
1Biomedical Engineering, University of Virginia, Charlottesville, VA, United States, 2Electrical and Computer Engineering, University of Virginia, Charlottesville, VA, United States, 3Radiology, University of Virginia, Charlottesville, VA, United States

Conventionally in MRI, respiratory motion leads to shifts of tissue position in the image domain that correspond to linear phase errors in the k-space domain. For DENSE, in addition to position shifts, respiratory motion is displacement-encoded in the stimulated echo, leading to a constant phase error in the k-space domain. We show that in segmented DENSE acquisitions, motion compensation can be applied using per-segment linear and constant phase corrections. As constant phase corrections using image-based navigators are challenging, we show that deep leaning is potentially an effective solution using simulated training data.

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