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Abstract #3868

A novel motion correction for ASL-fMRI with multi-PLD: non-parametric Gaussian Processes prediction of background suppression

Yuriko Suzuki1,2, Thomas Okell2, Joseph G. Woods2,3, and Michael Chappell1,2
1Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, United Kingdom, 2Wellcome Centre for Integrative Neuroimaging, FMRIB, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United Kingdom, 3Department of Radiology, University of California, La Jolla, CA, United States

For fMRI using ASL, multi-PLD acquisitions may have advantages, improving reliability and specificity. However, the varying static-tissue signal in multi-PLD ASL can confound motion estimation when conventional motion correction is applied. In this study, we propose a novel framework using Gaussian processes to address this problem, in which motionless ASL images are predicted, so that they can be used as a reference for motion correction for each ASL volume. Simulation and in-vivo studies show the new motion correction framework using Gaussian Processes eliminates the influence of multi-PLD and provides a suitable reference for each volume.

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