Human brain mapping · 2014
Support vector machine learning-based cerebral blood flow quantification for arterial spin labeling MRI.
СтатьяПоддержка NIH: внешняя программаНазвание и аннотация: Перевод пока не подготовлен
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Wang Ze
Аннотация
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.
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.