An expectation-maximisation approach for simultaneous pixel classification and tracer kinetic modelling in dynamic contrast enhanced-magnetic resonance imaging.
Traditionally, tracer kinetic modelling and pixel classification of DCE-MRI studies are accomplished separately, although they could greatly benefit from each other. In this article, we propose an expectation-maximisation scheme for simultaneous pixel classification and compartmental modelling of DC...
| Publicado en: | Medical & Biological Engineering & Computing Vol. 49; no. 4; pp. 485 - 496 |
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| Autores principales: | , , , , , , , , , |
| Formato: | research Journal Article |
| Publicado: |
Springer Nature
Apr2011
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=104571024&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 104571024 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 01400118 PO0 jtl: Medical & Biological Engineering & Computing issn: 01400118 maglogo: N pubinfo: dt: Apr2011 vid: 49 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 104571024 NLM21046274 2010997745 10.1007/s11517-010-0695-x NLM21046274 104571024 ppf: 485 ppct: 11 formats: fmt: @attributes: type: P tig: atl: An expectation-maximisation approach for simultaneous pixel classification and tracer kinetic modelling in dynamic contrast enhanced-magnetic resonance imaging. aug: au: Sansone M Fusco R Petrillo A Petrillo M Bracale M Sansone, Mario Fusco, Roberta Petrillo, Antonella Petrillo, Mario Bracale, Marcello affil: Department of Biomedical, Electronic and Telecommunication Engineering, University Federico II of Naples, via Claudio 21, 80131 Naples, Italy sug: subj: Magnetic Resonance Imaging Methods Neoplasms Diagnosis Algorithms Contrast Media Pharmacokinetics Human Image Interpretation, Computer Assisted Methods Models, Biological ab: Traditionally, tracer kinetic modelling and pixel classification of DCE-MRI studies are accomplished separately, although they could greatly benefit from each other. In this article, we propose an expectation-maximisation scheme for simultaneous pixel classification and compartmental modelling of DCE-MRI studies. The key point in the proposed scheme is the estimation of the kinetic parameters (K(trans) and K(ep)) of the two-compartmental model. Typically, they are estimated via nonlinear least-squares fitting. In our scheme, by exploiting the iterative nature of the EM algorithm, we use instead a Taylor expansion of the modelling equation. We developed the theoretical framework for the particular case of two classes and evaluated the performances of the algorithm by means of simulations. Results indicate that the accuracy of the proposed method supersedes the traditional pixel-by-pixel scheme and approaches the theoretical lower bound imposed by the Cramer-Rao theorem. Preliminary results on real data were also reported. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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