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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