Ze Wang1. 1. Department of Psychiatry, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania; Department of Radiology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania.
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.
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.
Authors: Elisabeth A Wilde; Sylvain Bouix; David F Tate; Alexander P Lin; Mary R Newsome; Brian A Taylor; James R Stone; James Montier; Samuel E Gandy; Brian Biekman; Martha E Shenton; Gerald York Journal: Brain Imaging Behav Date: 2015-09 Impact factor: 3.978
Authors: Lei Zhang; Danfeng Xie; Yiran Li; Aldo Camargo; Donghui Song; Tong Lu; Jean Jeudy; David Dreizin; Elias R Melhem; Ze Wang Journal: J Magn Reson Imaging Date: 2021-11-06 Impact factor: 5.119