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Abstract #3117

Estimation of motion-corrupted data using parallel imaging for carotid artery vessel wall imaging

Robert Frost1,2,3, Luca Biasiolli4, Linqing Li5, Aaron T. Hess4, and Peter Jezzard3

1Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, MA, United States, 2Department of Radiology, Harvard Medical School, Boston, MA, United States, 3FMRIB Centre, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 4Oxford Centre for Clinical Magnetic Resonance Research, Division of Cardiovascular Medicine, University of Oxford, Oxford, United Kingdom, 5Section on Magnetic Resonance Spectroscopy, National Institute of Mental Health, Bethesda, MD, United States

Carotid artery imaging is hampered by motion artefacts, including those caused by occasional swallowing motion. In this work, we extend previous work on intelligent reacquisition to estimate and replace motion-corrupted data. ‘Bad’ phase-encode lines were synthesized from surrounding ‘good’ lines using parallel imaging techniques. Estimation and replacement of corrupted data reduced background ghosting levels in comparison to the previous reacquisition approach. A small number of reacquisitions was maintained to ensure good quality data at the centre of k-space because these are essential for image quality and for calibration of the parallel imaging-based estimation.

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