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postgraduate thesis: Multi-compartment model estimation and analysis in high angular resolution diffusion imaging

TitleMulti-compartment model estimation and analysis in high angular resolution diffusion imaging
Authors
Advisors
Issue Date2014
PublisherThe University of Hong Kong (Pokfulam, Hong Kong)
Citation
Zhu, X. [朱星华]. (2014). Multi-compartment model estimation and analysis in high angular resolution diffusion imaging. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5223987
AbstractDiffusion weighted magnetic resonance images offer unique insights into the neural networks of in vivo human brain. In this study, we investigate estimation and statistical analysis of multi-compartment models in high angular resolution diffusion imaging (HARDI) involving the Rician noise model. In particular, we address four important issues in multi-compartment diffusion model estimation, namely, the modelling of Rician noise in diffusion weighted (DW) images, the automatic determination of the number of compartments in the diffusion signal, the application of spatial prior on multi-compartment models, and the evaluation of parameter indeterminacy in diffusion models. We propose an expectation maximization (EM) algorithm to estimate the parameters of a multi-compartment model by maximizing the Rician likelihood of the diffusion signal. We introduce a novel scheme for automatically selecting the number of compartments, via a sparsity-inducing prior on the compartment weights. A non-local weighted maximum likelihood estimator is proposed to improve estimation accuracy utilizing repetitive patterns in the image. Experimental results show that the proposed algorithm improves estimation accuracy in low signal-to-noise-ratio scenarios, and it provides better model selection than several alternative strategies. In addition, we derive the Cram´er-Rao Lower Bound (CRLB) of the maximum Rician likelihood estimator for the balland-stick model and general differentiable diffusion models. The CRLB provides a general theoretical tool for comparing diffusion models and examining parameter indeterminacy in the maximum likelihood estimation problem.
DegreeDoctor of Philosophy
SubjectDiffusion magnetic resonance imaging
Dept/ProgramComputer Science
Persistent Identifierhttp://hdl.handle.net/10722/206696

 

DC FieldValueLanguage
dc.contributor.advisorWang, WP-
dc.contributor.advisorWong, KKY-
dc.contributor.authorZhu, Xinghua-
dc.contributor.author朱星华-
dc.date.accessioned2014-11-25T03:53:19Z-
dc.date.available2014-11-25T03:53:19Z-
dc.date.issued2014-
dc.identifier.citationZhu, X. [朱星华]. (2014). Multi-compartment model estimation and analysis in high angular resolution diffusion imaging. (Thesis). University of Hong Kong, Pokfulam, Hong Kong SAR. Retrieved from http://dx.doi.org/10.5353/th_b5223987-
dc.identifier.urihttp://hdl.handle.net/10722/206696-
dc.description.abstractDiffusion weighted magnetic resonance images offer unique insights into the neural networks of in vivo human brain. In this study, we investigate estimation and statistical analysis of multi-compartment models in high angular resolution diffusion imaging (HARDI) involving the Rician noise model. In particular, we address four important issues in multi-compartment diffusion model estimation, namely, the modelling of Rician noise in diffusion weighted (DW) images, the automatic determination of the number of compartments in the diffusion signal, the application of spatial prior on multi-compartment models, and the evaluation of parameter indeterminacy in diffusion models. We propose an expectation maximization (EM) algorithm to estimate the parameters of a multi-compartment model by maximizing the Rician likelihood of the diffusion signal. We introduce a novel scheme for automatically selecting the number of compartments, via a sparsity-inducing prior on the compartment weights. A non-local weighted maximum likelihood estimator is proposed to improve estimation accuracy utilizing repetitive patterns in the image. Experimental results show that the proposed algorithm improves estimation accuracy in low signal-to-noise-ratio scenarios, and it provides better model selection than several alternative strategies. In addition, we derive the Cram´er-Rao Lower Bound (CRLB) of the maximum Rician likelihood estimator for the balland-stick model and general differentiable diffusion models. The CRLB provides a general theoretical tool for comparing diffusion models and examining parameter indeterminacy in the maximum likelihood estimation problem.-
dc.languageeng-
dc.publisherThe University of Hong Kong (Pokfulam, Hong Kong)-
dc.relation.ispartofHKU Theses Online (HKUTO)-
dc.rightsCreative Commons: Attribution 3.0 Hong Kong License-
dc.rightsThe author retains all proprietary rights, (such as patent rights) and the right to use in future works.-
dc.subject.lcshDiffusion magnetic resonance imaging-
dc.titleMulti-compartment model estimation and analysis in high angular resolution diffusion imaging-
dc.typePG_Thesis-
dc.identifier.hkulb5223987-
dc.description.thesisnameDoctor of Philosophy-
dc.description.thesislevelDoctoral-
dc.description.thesisdisciplineComputer Science-
dc.description.naturepublished_or_final_version-
dc.identifier.doi10.5353/th_b5223987-

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