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Abstract #4116

MR/PET motion correction using deep learning

Yasheng Chen1, Cihat Eldeniz1, Richard Laforest1, and Hongyu An1

1Washington Univ. School of Medicine, St. Louis, MO, United States

In abdominal imaging, the simultaneous acquisition of MR and PET provides a unique opportunity to take advantage of MR (which has high spatial and temporal resolution) to resolve the respiratory motion artefacts in PET. But this motion correction scheme requires MR motion scans during the whole PET session. To improve the imaging efficiency, we use simultaneously acquired MR/PET signal to train a PET re-binning motion correction classifier, which can be deployed to correct the motion in the PET scans without concurrently acquired MR motion detection. We have demonstrated the feasibility of this online motion correction scheme with a phantom study.

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