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Abstract #2874

Reduction of motion artefacts in multi-shot 3D GRASE Arterial Spin Labelling using Autofocus

David L Thomas1,2, Fabio Nery3, Isky Gordon3, Chris A Clark3, Sebastien Ourselin2, Xavier Golay1, David Atkinson4, and Enrico De Vita1,5

1Department of Brain Repair and Rehabilitation, UCL Institute of Neurology, London, United Kingdom, 2Translational Imaging Group, UCL, London, United Kingdom, 3Developmental Imaging and Biophysics Section, UCL Institute of Child Health, London, United Kingdom, 4Centre for Medical Imaging, UCL, London, United Kingdom, 5Lysholm Department of Neuroradiology, National Hospital for Neurology and Neurosurgery, London, United Kingdom

Multi-shot 3D acquisition schemes offer an efficient method to obtain ASL data with good SNR and spatial resolution. However, multi-shot techniques are susceptible to motion-induced artefacts that can severely degrade image quality. In this work, we investigate the use of the autofocus algorithm to correct k-space phase inconsistencies caused by inter-shot motion, and demonstrate its effectiveness at improving image sharpness and removing artefacts in motion-corrupted 3-shot 3D GRASE data. As such, autofocus offers a retrospective approach to improve the quality of multi-shot ASL data, with the associated improvements in CBF quantification accuracy and reproducibility.

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