Abstract #2874

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

David L Thomas^1,2, Fabio Nery³, Isky Gordon³, Chris A Clark³, Sebastien Ourselin², Xavier Golay¹, David Atkinson⁴, and Enrico De Vita^1,5

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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