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Parameter estimation using macroscopic diffusion MRI signal models
NeuroSpin, CEA Saclay Center, 91191 Gif-sur-Yvette Cedex, France.
Laboratoire de Physique de la Matière Condensée, CNRS—Ecole Polytechnique, F-91128 Palaiseau Cedex, France.
INRIA Saclay-Equipe DEFI, CMAP, Ecole Polytechnique, Route de Saclay, Palaiseau Cedex, 91128, France.ORCID iD: 0000-0002-3213-0040
NeuroSpin, CEA Saclay Center, 91191 Gif-sur-Yvette Cedex, France.
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2015 (English)In: Physics in Medicine and Biology, ISSN 0031-9155, E-ISSN 1361-6560, Vol. 60, no 8, p. 3389-3413Article in journal (Refereed) Published
Abstract [en]

Macroscopic models of the diffusion MRI (dMRI) signal can be helpful in understanding the relationship between the tissue microstructure and the dMRI signal. We study the least squares problem associated with estimating tissue parameters such as the cellular volume fraction, the residence times and the effective diffusion coefficients using a recently developed macroscopic model of the dMRI signal called the Finite Pulse Kärger model that generalizes the original Kärger model to non-narrow gradient pulses. In order to analyze the quality of the estimation in a controlled way, we generated synthetic noisy dMRI signals by including the effect of noise on the exact signal produced by the Finite Pulse Kärger model. The noisy signals were then fitted using the macroscopic model. Minimizing the least squares, we estimated the model parameters. The bias and standard deviations of the estimated model parameters as a function of the signal to noise ratio (SNR) were obtained. We discuss the choice of the b-values, the least square weights, the extension to experimentally obtained dMRI data as well as noise correction.

Place, publisher, year, edition, pages
2015. Vol. 60, no 8, p. 3389-3413
Keywords [en]
diffusion MRI, macroscopic model, parameter estimation
National Category
Medical and Health Sciences
Research subject
Applied and Computational Mathematics
Identifiers
URN: urn:nbn:se:kth:diva-240128DOI: 10.1088/0031-9155/60/8/3389ISI: 000352525200027PubMedID: 25831194Scopus ID: 2-s2.0-84927618778OAI: oai:DiVA.org:kth-240128DiVA, id: diva2:1270204
Note

QC 20181213

Available from: 2018-12-12 Created: 2018-12-12 Last updated: 2020-05-11Bibliographically approved

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Publisher's full textPubMedScopushttps://doi.org/10.1088/0031-9155/60/8/3389

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