Abstract #3868

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

Yuriko Suzuki^1,2, Thomas Okell², Joseph G. Woods^2,3, and Michael Chappell^1,2

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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