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Article: Higher order positive semidefinite diffusion tensor imaging

TitleHigher order positive semidefinite diffusion tensor imaging
Authors
KeywordsApparent Diffusion Coefficient
Convex Optimization Problem
Invariants
Positive Semidefinite Diffusion Tensor
Z-Eigenvalue
Issue Date2010
Citation
SIAM Journal On Imaging Sciences, 2010, v. 3 n. 3, p. 416-433 How to Cite?
AbstractDue to the well-known limitations of diffusion tensor imaging, high angular resolution diffusion imaging (HARDI) is used to characterize non-Gaussian diffusion processes. One approach to analyzing HARDI data is to model the apparent diffusion coefficient (ADC) with higher order diffusion tensors. The diffusivity function is positive semidefinite. In the literature, some methods have been proposed to preserve positive semidefiniteness of second order and fourth order diffusion tensors. None of them can work for arbitrarily high order diffusion tensors. In this paper, we propose a comprehensive model to approximate the ADC profile by a positive semidefinite diffusion tensor of either second or higher order. We call this the positive semidefinite diffusion tensor (PSDT) model. PSDT is a convex optimization problem with a convex quadratic objective function constrained by the nonnegativity requirement on the smallest Z-eigenvalue of the diffusivity function. The smallest Z-eigenvalue is a computable measure of the extent of positive definiteness of the diffusivity function. We also propose some other invariants for the ADC profile analysis. Experiment results show that higher order tensors could improve the estimation of anisotropic diffusion and that the PSDT model can depict the characterization of diffusion anisotropy which is consistent with known neuroanatomy. © 2010 Society for Industrial and Applied Mathematics.
Persistent Identifierhttp://hdl.handle.net/10722/155599
ISSN
2015 Impact Factor: 2.687
2015 SCImago Journal Rankings: 2.052
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorQi, Len_US
dc.contributor.authorYu, Gen_US
dc.contributor.authorWu, EXen_US
dc.date.accessioned2012-08-08T08:34:18Z-
dc.date.available2012-08-08T08:34:18Z-
dc.date.issued2010en_US
dc.identifier.citationSIAM Journal On Imaging Sciences, 2010, v. 3 n. 3, p. 416-433en_US
dc.identifier.issn1936-4954en_US
dc.identifier.urihttp://hdl.handle.net/10722/155599-
dc.description.abstractDue to the well-known limitations of diffusion tensor imaging, high angular resolution diffusion imaging (HARDI) is used to characterize non-Gaussian diffusion processes. One approach to analyzing HARDI data is to model the apparent diffusion coefficient (ADC) with higher order diffusion tensors. The diffusivity function is positive semidefinite. In the literature, some methods have been proposed to preserve positive semidefiniteness of second order and fourth order diffusion tensors. None of them can work for arbitrarily high order diffusion tensors. In this paper, we propose a comprehensive model to approximate the ADC profile by a positive semidefinite diffusion tensor of either second or higher order. We call this the positive semidefinite diffusion tensor (PSDT) model. PSDT is a convex optimization problem with a convex quadratic objective function constrained by the nonnegativity requirement on the smallest Z-eigenvalue of the diffusivity function. The smallest Z-eigenvalue is a computable measure of the extent of positive definiteness of the diffusivity function. We also propose some other invariants for the ADC profile analysis. Experiment results show that higher order tensors could improve the estimation of anisotropic diffusion and that the PSDT model can depict the characterization of diffusion anisotropy which is consistent with known neuroanatomy. © 2010 Society for Industrial and Applied Mathematics.en_US
dc.languageengen_US
dc.relation.ispartofSIAM Journal on Imaging Sciencesen_US
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.subjectApparent Diffusion Coefficienten_US
dc.subjectConvex Optimization Problemen_US
dc.subjectInvariantsen_US
dc.subjectPositive Semidefinite Diffusion Tensoren_US
dc.subjectZ-Eigenvalueen_US
dc.titleHigher order positive semidefinite diffusion tensor imagingen_US
dc.typeArticleen_US
dc.identifier.emailWu, EX:ewu1@hkucc.hku.hken_US
dc.identifier.authorityWu, EX=rp00193en_US
dc.description.naturepublished_or_final_versionen_US
dc.identifier.doi10.1137/090755138en_US
dc.identifier.scopuseid_2-s2.0-78651578983en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-78651578983&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume3en_US
dc.identifier.issue3en_US
dc.identifier.spage416en_US
dc.identifier.epage433en_US
dc.identifier.isiWOS:000285500900007-
dc.identifier.scopusauthoridQi, L=7202149952en_US
dc.identifier.scopusauthoridYu, G=7403528626en_US
dc.identifier.scopusauthoridWu, EX=7202128034en_US

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