Abstract #0909
Correction of artefacts in simultaneous multi-slice multi-PLD arterial spin labelling data using Gaussian Process regression
Jack Toner1,2, Flora Kennedy McConnell1,2,3, Yuriko Suzuki4, Timothy S. Coalson5, Michael P. Harms6, Matthew F. Glasser5,7, and Michael A. Chappell1,2,3,4
1Radiological Sciences, Mental Health & Clinical Neurosciences, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 2Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, Nottingham, United Kingdom, 3Nottingham Biomedical Research Centre, Queens Medical Centre, University of Nottingham, Nottingham, United Kingdom, 4Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 5Department of Neuroscience, Washington University School of Medicine, St Louis, MO, United States, 6Department of Psychiatry, Washington University School of Medicine, St Louis, MO, United States, 7Department of Radiology, Washington University School of Medicine, St Louis, MO, United States
Synopsis
Simultaneous multi-slice (SMS) acquisitions enable higher resolutions to be achieved for arterial spin labelling (ASL) images. However, SMS acquisitions can introduce a banded pattern of intensity within the images. This reduces the quality of motion estimation as the algorithm aligns the bands in preference to the brain structures. We introduce a Gaussian Process model that can be used to correct the banding in SMS multiple post-labelling delay ASL data, which should improve motion correction. We demonstrate its effectiveness on 10 subjects from the Human Connectome Project Aging dataset. We anticipate that the model will generalise to other ASL datasets.
This abstract and the presentation materials are available to members only;
a login is required.
Join Here
