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Conference Paper: Model selection and estimation of multi-compartment models in diffusion MRI with a rician noise model
Title | Model selection and estimation of multi-compartment models in diffusion MRI with a rician noise model |
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Authors | |
Issue Date | 2013 |
Publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ |
Citation | The 23rd Biennial International Conference on Information Processing in Medical Imaging (IPMI 2013), Asilomar, CA., 28 June03 July 2013. In Lecture Notes in Computer Science, 2013, v. 7917, p. 644-655 How to Cite? |
Abstract | Multi-compartment models in diffusion MRI (dMRI) are used to describe complex white matter fiber architecture of the brain. In this paper, we propose a novel multi-compartment estimation method based on the ball-and-stick model, which is composed of an isotropic diffusion compartment (“ball”) as well as one or more perfectly linear diffusion compartments (“sticks”). To model the noise distribution intrinsic to dMRI measurements, we introduce a Rician likelihood term and estimate the model parameters by means of an Expectation Maximization (EM) algorithm. This paper also addresses the problem of selecting the number of fiber compartments that best fit the data, by introducing a sparsity prior on the volume mixing fractions. This term provides automatic model selection and enables us to discriminate different fiber populations. When applied to simulated data, our method provides accurate estimates of the fiber orientations, diffusivities, and number of compartments, even at low SNR, and outperforms similar methods that rely on a Gaussian noise distribution assumption. We also apply our method to in vivo brain data and show that it can successfully capture complex fiber structures that match the known anatomy. |
Description | LNCS v. 7917 entitled: Information processing in medical imaging : 23rd international conference, IPMI 2013 ... proceedings |
Persistent Identifier | http://hdl.handle.net/10722/191547 |
ISBN | |
ISSN | 2020 SCImago Journal Rankings: 0.249 |
DC Field | Value | Language |
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dc.contributor.author | Zhu, X | en_US |
dc.contributor.author | Gur, Y | en_US |
dc.contributor.author | Wang, WP | en_US |
dc.contributor.author | Fetcher, PT | en_US |
dc.date.accessioned | 2013-10-15T07:10:16Z | - |
dc.date.available | 2013-10-15T07:10:16Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The 23rd Biennial International Conference on Information Processing in Medical Imaging (IPMI 2013), Asilomar, CA., 28 June03 July 2013. In Lecture Notes in Computer Science, 2013, v. 7917, p. 644-655 | en_US |
dc.identifier.isbn | 978-3-642-38867-5 | - |
dc.identifier.issn | 0302-9743 | - |
dc.identifier.uri | http://hdl.handle.net/10722/191547 | - |
dc.description | LNCS v. 7917 entitled: Information processing in medical imaging : 23rd international conference, IPMI 2013 ... proceedings | - |
dc.description.abstract | Multi-compartment models in diffusion MRI (dMRI) are used to describe complex white matter fiber architecture of the brain. In this paper, we propose a novel multi-compartment estimation method based on the ball-and-stick model, which is composed of an isotropic diffusion compartment (“ball”) as well as one or more perfectly linear diffusion compartments (“sticks”). To model the noise distribution intrinsic to dMRI measurements, we introduce a Rician likelihood term and estimate the model parameters by means of an Expectation Maximization (EM) algorithm. This paper also addresses the problem of selecting the number of fiber compartments that best fit the data, by introducing a sparsity prior on the volume mixing fractions. This term provides automatic model selection and enables us to discriminate different fiber populations. When applied to simulated data, our method provides accurate estimates of the fiber orientations, diffusivities, and number of compartments, even at low SNR, and outperforms similar methods that rely on a Gaussian noise distribution assumption. We also apply our method to in vivo brain data and show that it can successfully capture complex fiber structures that match the known anatomy. | - |
dc.language | eng | en_US |
dc.publisher | Springer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/ | - |
dc.relation.ispartof | Lecture Notes in Computer Science | en_US |
dc.rights | The original publication is available at www.springerlink.com | - |
dc.title | Model selection and estimation of multi-compartment models in diffusion MRI with a rician noise model | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Wang, WP: wenping@cs.hku.hk | en_US |
dc.identifier.authority | Wang, WP=rp00186 | en_US |
dc.identifier.hkuros | 225506 | en_US |
dc.identifier.volume | 7917 | - |
dc.identifier.spage | 644 | - |
dc.identifier.epage | 655 | - |
dc.publisher.place | Germany | - |
dc.customcontrol.immutable | sml 131106 | - |
dc.identifier.issnl | 0302-9743 | - |