Work

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

Year: 2014

Type: article

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.

Source: Human Brain Mapping

DOI: 10.1002/hbm.22445

Author: Ze Wang

Institution: University of Pennsylvania

Language: English

Topic: Advanced MRI Techniques and Applications

Subfield: Radiology, Nuclear Medicine and Imaging

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

Funders: National Institute on Drug Abuse, National Institute on Deafness and Other Communication Disorders, National Institute of Biomedical Imaging and Bioengineering, National Center for Research Resources