File Download
Supplementary
-
Citations:
- Appears in Collections:
postgraduate thesis: Multi-compartment model estimation and analysis in high angular resolution diffusion imaging
Title | Multi-compartment model estimation and analysis in high angular resolution diffusion imaging |
---|---|
Authors | |
Advisors | |
Issue Date | 2014 |
Publisher | The 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 |
Abstract | Diffusion 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. |
Degree | Doctor of Philosophy |
Subject | Diffusion magnetic resonance imaging |
Dept/Program | Computer Science |
Persistent Identifier | http://hdl.handle.net/10722/206696 |
HKU Library Item ID | b5223987 |
DC Field | Value | Language |
---|---|---|
dc.contributor.advisor | Wang, WP | - |
dc.contributor.advisor | Wong, KKY | - |
dc.contributor.author | Zhu, Xinghua | - |
dc.contributor.author | 朱星华 | - |
dc.date.accessioned | 2014-11-25T03:53:19Z | - |
dc.date.available | 2014-11-25T03:53:19Z | - |
dc.date.issued | 2014 | - |
dc.identifier.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 | - |
dc.identifier.uri | http://hdl.handle.net/10722/206696 | - |
dc.description.abstract | Diffusion 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.language | eng | - |
dc.publisher | The University of Hong Kong (Pokfulam, Hong Kong) | - |
dc.relation.ispartof | HKU Theses Online (HKUTO) | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.rights | The author retains all proprietary rights, (such as patent rights) and the right to use in future works. | - |
dc.subject.lcsh | Diffusion magnetic resonance imaging | - |
dc.title | Multi-compartment model estimation and analysis in high angular resolution diffusion imaging | - |
dc.type | PG_Thesis | - |
dc.identifier.hkul | b5223987 | - |
dc.description.thesisname | Doctor of Philosophy | - |
dc.description.thesislevel | Doctoral | - |
dc.description.thesisdiscipline | Computer Science | - |
dc.description.nature | published_or_final_version | - |
dc.identifier.doi | 10.5353/th_b5223987 | - |
dc.identifier.mmsid | 991037035639703414 | - |