Abstract #2958
Support vector machine classification analysis of Arterial Volume-weighted Arterial Spin Tagging (AVAST) images
Yash S Shah 1 , Luis Hernandez-Garcia 1 , Hesamoddin Jahanian 1 , and Scott J Peltier 1
1
University of Michigan, Ann Arbor, Michigan,
United States
Machine learning has gained tremendous popularity in
fMRI data analysis. This study presents an application
of support vector machines for temporal brain state
classification using multiple acquisition techniques
(Blood Oxygenation Level Dependent, Perfusion-weighted
Arterial Spin Labeling and Arterial Volume-weighted
Arterial Spin Tagging) and highlights the advantages
offered by AVAST. Arterial volume-weighted arterial spin
tagging (AVAST) is a variant of pseudo continuous ASL
technique. In this study, we demonstrate that AVAST
exhibits superior detection sensitivity and temporal
resolution comparable to BOLD while still retaining
desirable properties of standard perfusion-weighted ASL
techniques.
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