Abstract #2709
An automated post processing analysis to increase detectability of cerebral blood flow arterial spin labeling images in the presence of head motion
Zahra Shirzadi 1 , David E Crane 1 , Benjamin I Goldstein 2,3 , Sandra E Black 1,4 , and Bradley J MacIntosh 1,5
1
HSF Canadian Partnership for Stroke
Recovery, Sunnybrook Research Institute, Toronto,
Ontario, Canada,
2
Evaluative
Clinical Sciences, Brain Sciences Research Program,
Sunnybrook Research Institute, Toronto, Ontario, Canada,
3
Departments
of Psychiatry and Pharmacology, Faculty of Medicine,
University of Toronto, Toronto, Ontario, Canada,
4
Division
of Neurology, Faculty of Medicine, University of
Toronto, Toronto, Ontario, Canada,
5
Department
of Medical Biophysics, University of Toronto, Toronto,
Ontario, Canada
Arterial spin labeling (ASL) can be used to provide
quantitative cerebral blood flow (CBF) images; however,
the intrinsic low signal-to-noise ratio (SNR) limits its
clinical applications. We propose a novel analysis
approach that works in automated fashion by rejecting
portions of the ASL data based on whether head motion
during the individual difference image reduced the CBF
sensitivity. Compared to conventional ASL analysis, our
approach improved SNR by 9% (p-value<0.001) among 67
participants ranging in age from 14 to 88 years old. We
also conducted between groups comparison to assess the
impact of age and brain disorders on image quality.
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