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Abstract #0425

Physiological noise reduction for multi-inversion time ASL

Kevin Murphy 1 , Anja Hayen 2 , Mari Herigstad 2 , and Kyle T.S. Pattinson 2

1 CUBRIC, School of Psychology, Cardiff University, Cardiff, Wales, United Kingdom, 2 Nuffield Dept Clinical Neurosciences & FMRIB Centre, University of Oxford, United Kingdom

It has previously been demonstrated that the optimal approach to removing physiological noise from ASL data is to separate tags and controls first. The purpose of this study is to extend this finding to determine the optimal approach for multiple inversion time ASL data. In this study we find that both the naive approach of not separating the data and the approach of separating tags from controls introduce far more noise than they remove. Separating both TIs and tags/controls alleviates this problem allowing for good repeatability of signal across TIs and improved fits of the kinetic curve model.

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