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 Weller^1,2, and Frederick H Epstein^1,3

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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