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Abstract #3493

Machine Learning-Based Cerebral Blood Flow Quantification for ASL MRI

Ze Wang1, Anna Rose Childress1, John A. Detre2

1Psychiatry, University of Pennsylvania, Philadelphia, PA, United States; 2Neurology, University of Pennsylvania, Philadelphia, PA, United States

Arterial spin labeling (ASL) is not stable across time but no one has taken this into account during perfusion quantification. Due to the systematic labeling and control labeling, ASL CBF quantification is a natural two-class data classification process. Based on this phenomenon, we used a powerful machine learning algorithm, the support vector machine (SVM), to extract the spin labeling function from the ASL data and used it for CBF quantification. The method demonstrated significantly improved temporal SNR and spatial image quality for CBF quantification using normal healthy subjects data and data from patients with Alzheimers Disease.