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Support vector machine learning-based cerebral blood flow quantification for arterial spin labeling MRI

Authors

  • Ze Wang

    Corresponding author
    1. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
    2. Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
    • Correspondence to: Ze Wang, Treatment Research Center, University of Pennsylvania, 3900 Chestnut St., Philadelphia, Pennsylvania 19104. E-mail: zewang@mail.med.upenn.edu

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Abstract

Purpose: To develop a multivariate machine learning classification-based cerebral blood flow (CBF) quantification method for arterial spin labeling (ASL) perfusion MRI. Methods: The label and control images of ASL MRI were separated using a machine-learning algorithm, the support vector machine (SVM). The perfusion-weighted image was subsequently extracted from the multivariate (all voxels) SVM classifier. Using the same pre-processing steps, the proposed method was compared with standard ASL CBF quantification method using synthetic data and in-vivo ASL images. Results: As compared with the conventional univariate approach, the proposed ASL CBF quantification method significantly improved spatial signal-to-noise-ratio (SNR) and image appearance of ASL CBF images. Conclusion: the multivariate machine learning-based classification is useful for ASL CBF quantification. Hum Brain Mapp 35:2869–2875, 2014. © 2013 Wiley Periodicals, Inc.

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