PUBLICATION DATE

2014-07

TITLE

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

ISSUE

7

VOLUME

35

ISSN (print)

N/A

ISSN (electronic)

N/A

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.

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17 TRIPLES      14 PREDICATES      18 URIs      10 LITERALS

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1 articles:d2f465d2936bdff1da75f9a93f2c9cd7 sg: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.
2 sg:doi 10.1002/hbm.22445
3 sg:doiLink http://dx.doi.org/10.1002/hbm.22445
4 sg:isFundedPublicationOf grants:10c9d004fbc8a9b4a1aceb2ca44be2eb
5 grants:79955db5ad1e01b613838644d32fc70a
6 grants:a068f29f500e4ceef2d6300d48981b26
7 grants:c289325f64e62e101cb70bac478c1f28
8 sg:issue 7
9 sg:language English
10 sg:license http://scigraph.springernature.com/explorer/license/
11 sg:publicationYear 2014
12 sg:publicationYearMonth 2014-07
13 sg:scigraphId d2f465d2936bdff1da75f9a93f2c9cd7
14 sg:title Support vector machine learning-based cerebral blood flow quantification for arterial spin labeling MRI.
15 sg:volume 35
16 rdf:type sg:Article
17 rdfs:label Article: Support vector machine learning-based cerebral blood flow quantification for arterial spin labeling MRI.
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