Abstract #2727

Retrospective Motion Correction of multi-shot 3D GRASE Arterial Spin Labelling using ESPIRiT reconstruction

Jack Highton¹, Enrico De Vita², and David Thomas^3,4,5

¹UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ²School of Biomedical Engineering and Imaging Sciences, Kings College London, London, United Kingdom, ³Dementia Research Centre, UCL Queen Square Institute of Neurology, University College London, London, United Kingdom, ⁴Neuroradiological Academic Unit, Department of Brain Repair and Rehabilitation, University College London, London, United Kingdom, ⁵Wellcome Centre for Human Neuroimaging, University College London, London, United Kingdom

A retrospective motion correction method is presented, for multi-shot 3D GRASE ASL. ESPIRiT is used to calculate coil sensitivity fields, using raw ASL k-space data. These are used to reconstruct complete images from the interleaved fractions of k-space sampled during each shot using SENSE. Therefore, typical image registration can be used to correct inter-shot motion. The method was tested using a simulation of 3D GRASE ASL and an equivalent experiment, where the subject was trained to nod or keep still. The motion correction reduced artefacts, and increased the correlation between cerebral blood flow measurements acquired with and without motion.

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