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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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