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Article: Tensor estimation for double-pulsed diffusional kurtosis imaging

TitleTensor estimation for double-pulsed diffusional kurtosis imaging
Authors
Issue Date2017
PublisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291099-1492
Citation
NMR in Biomedicine, 2017, v. 30 n. 7, p. e3722 How to Cite?
AbstractDouble-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.
Persistent Identifierhttp://hdl.handle.net/10722/240232
ISSN
2017 Impact Factor: 3.031
2015 SCImago Journal Rankings: 1.533
ISI Accession Number ID

 

DC FieldValueLanguage
dc.contributor.authorShaw, CB-
dc.contributor.authorHui, SK-
dc.contributor.authorHelpern, JA-
dc.contributor.authorJensen, JH-
dc.date.accessioned2017-04-19T08:21:38Z-
dc.date.available2017-04-19T08:21:38Z-
dc.date.issued2017-
dc.identifier.citationNMR in Biomedicine, 2017, v. 30 n. 7, p. e3722-
dc.identifier.issn0952-3480-
dc.identifier.urihttp://hdl.handle.net/10722/240232-
dc.description.abstractDouble-pulsed diffusional kurtosis imaging (DP-DKI) represents the double diffusion encoding (DDE) MRI signal in terms of six-dimensional (6D) diffusion and kurtosis tensors. Here a method for estimating these tensors from experimental data is described. A standard numerical algorithm for tensor estimation from conventional (i.e. single diffusion encoding) diffusional kurtosis imaging (DKI) data is generalized to DP-DKI. This algorithm is based on a weighted least squares (WLS) fit of the signal model to the data combined with constraints designed to minimize unphysical parameter estimates. The numerical algorithm then takes the form of a quadratic programming problem. The principal change required to adapt the conventional DKI fitting algorithm to DP-DKI is replacing the three-dimensional diffusion and kurtosis tensors with the 6D tensors needed for DP-DKI. In this way, the 6D diffusion and kurtosis tensors for DP-DKI can be conveniently estimated from DDE data by using constrained WLS, providing a practical means for condensing DDE measurements into well-defined mathematical constructs that may be useful for interpreting and applying DDE MRI. Data from healthy volunteers for brain are used to demonstrate the DP-DKI tensor estimation algorithm. In particular, representative parametric maps of selected tensor-derived rotational invariants are presented.-
dc.languageeng-
dc.publisherJohn Wiley & Sons Ltd. The Journal's web site is located at http://onlinelibrary.wiley.com/journal/10.1002/%28ISSN%291099-1492-
dc.relation.ispartofNMR in Biomedicine-
dc.rightsNMR in Biomedicine. Copyright © John Wiley & Sons Ltd.-
dc.rightsThis is the peer reviewed version of the following article: NMR in Biomedicine, 2017, v. 30 n. 7, p. e3722, which has been published in final form at https://doi.org/10.1002/nbm.3722. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.-
dc.titleTensor estimation for double-pulsed diffusional kurtosis imaging-
dc.typeArticle-
dc.identifier.emailHui, SK: edshui@hku.hk-
dc.identifier.authorityHui, SK=rp01832-
dc.description.naturepostprint-
dc.identifier.doi10.1002/nbm.3722-
dc.identifier.hkuros271741-
dc.identifier.volume30-
dc.identifier.issue7-
dc.identifier.spagee3722-
dc.identifier.epagee3722-
dc.identifier.isiWOS:000405325100011-
dc.publisher.placeUnited Kingdom-

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