Open AccessSupport vector machine learning‐based cerebral blood flow quantification for arterial spin labeling MRI
Author(s) -
Wang Ze
Publication year - 2014
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.22445
Subject(s) - cerebral blood flow , support vector machine , artificial intelligence , multivariate statistics , voxel , pattern recognition (psychology) , arterial spin labeling , computer science , univariate , perfusion scanning , perfusion , machine learning , medicine , radiology
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.