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Article: Kurtosis analysis of neural diffusion organization

TitleKurtosis analysis of neural diffusion organization
Authors
Issue Date2015
PublisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/ynimg
Citation
NeuroImage, 2015, v. 106, p. 391-403 How to Cite?
AbstractA computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.
Persistent Identifierhttp://hdl.handle.net/10722/207233
ISSN
2015 Impact Factor: 5.463
2015 SCImago Journal Rankings: 4.464

 

DC FieldValueLanguage
dc.contributor.authorHui, ESK-
dc.contributor.authorGlenn, GR-
dc.contributor.authorHelpern, JA-
dc.contributor.authorJensen, JH-
dc.date.accessioned2014-12-19T09:18:31Z-
dc.date.available2014-12-19T09:18:31Z-
dc.date.issued2015-
dc.identifier.citationNeuroImage, 2015, v. 106, p. 391-403-
dc.identifier.issn1053-8119-
dc.identifier.urihttp://hdl.handle.net/10722/207233-
dc.description.abstractA computational framework is presented for relating the kurtosis tensor for water diffusion in brain to tissue models of brain microstructure. The tissue models are assumed to be comprised of non-exchanging compartments that may be associated with various microstructural spaces separated by cell membranes. Within each compartment the water diffusion is regarded as Gaussian, although the diffusion for the full system would typically be non-Gaussian. The model parameters are determined so as to minimize the Frobenius norm of the difference between the measured kurtosis tensor and the model kurtosis tensor. This framework, referred to as kurtosis analysis of neural diffusion organization (KANDO), may be used to help provide a biophysical interpretation to the information provided by the kurtosis tensor. In addition, KANDO combined with diffusional kurtosis imaging can furnish a practical approach for developing candidate biomarkers for neuropathologies that involve alterations in tissue microstructure. KANDO is illustrated for simple tissue models of white and gray matter using data obtained from healthy human subjects.-
dc.languageeng-
dc.publisherAcademic Press. The Journal's web site is located at http://www.elsevier.com/locate/ynimg-
dc.relation.ispartofNeuroImage-
dc.rightsNOTICE: this is the author’s version of a work that was accepted for publication in NeuroImage. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently published in NeuroImage, 2015, v. 106, p. 391-403. DOI: 10.1016/j.neuroimage.2014.11.015-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.titleKurtosis analysis of neural diffusion organization-
dc.typeArticle-
dc.identifier.emailHui, ESK: edshui@hku.hk-
dc.identifier.authorityHui, SK=rp01832en_US
dc.description.naturepostprint-
dc.identifier.doi10.1016/j.neuroimage.2014.11.015-
dc.identifier.pmid25463453-
dc.identifier.hkuros241730-
dc.identifier.volume106-
dc.identifier.spage391-
dc.identifier.epage403-
dc.publisher.placeUnited States-

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