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Conference Paper: Model selection and estimation of multi-compartment models in diffusion MRI with a rician noise model

TitleModel selection and estimation of multi-compartment models in diffusion MRI with a rician noise model
Authors
Issue Date2013
PublisherSpringer 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?
AbstractMulti-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.
DescriptionLNCS v. 7917 entitled: Information processing in medical imaging : 23rd international conference, IPMI 2013 ... proceedings
Persistent Identifierhttp://hdl.handle.net/10722/191547
ISBN
ISSN
2005 Impact Factor: 0.402
2015 SCImago Journal Rankings: 0.252

 

DC FieldValueLanguage
dc.contributor.authorZhu, Xen_US
dc.contributor.authorGur, Yen_US
dc.contributor.authorWang, WPen_US
dc.contributor.authorFetcher, PTen_US
dc.date.accessioned2013-10-15T07:10:16Z-
dc.date.available2013-10-15T07:10:16Z-
dc.date.issued2013en_US
dc.identifier.citationThe 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. 644-655en_US
dc.identifier.isbn978-3-642-38867-5-
dc.identifier.issn0302-9743-
dc.identifier.urihttp://hdl.handle.net/10722/191547-
dc.descriptionLNCS v. 7917 entitled: Information processing in medical imaging : 23rd international conference, IPMI 2013 ... proceedings-
dc.description.abstractMulti-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.languageengen_US
dc.publisherSpringer Verlag. The Journal's web site is located at http://springerlink.com/content/105633/-
dc.relation.ispartofLecture Notes in Computer Scienceen_US
dc.rightsThe original publication is available at www.springerlink.com-
dc.titleModel selection and estimation of multi-compartment models in diffusion MRI with a rician noise modelen_US
dc.typeConference_Paperen_US
dc.identifier.emailWang, WP: wenping@cs.hku.hken_US
dc.identifier.authorityWang, WP=rp00186en_US
dc.identifier.hkuros225506en_US
dc.identifier.volume7917-
dc.identifier.spage644-
dc.identifier.epage655-
dc.publisher.placeGermany-
dc.customcontrol.immutablesml 131106-

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