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Obtaining T1-T2 distribution functions from 1-dimensional T1 and T2 measurements: The pseudo 2-D relaxation model
RISE, SP – Sveriges Tekniska Forskningsinstitut.
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2016 (English)In: Journal of magnetic resonance, ISSN 1090-7807, E-ISSN 1096-0856, Vol. 269, 186-195 p.Article in journal (Refereed) Published
Abstract [en]

We present the pseudo 2-D relaxation model (P2DRM), a method to estimate multidimensional probability distributions of material parameters from independent 1-D measurements. We illustrate its use on 1-D T1 and T2 relaxation measurements of saturated rock and evaluate it on both simulated and experimental T1-T2 correlation measurement data sets. Results were in excellent agreement with the actual, known 2-D distribution in the case of the simulated data set. In both the simulated and experimental case, the functional relationships between T1 and T2 were in good agreement with the T1-T2 correlation maps from the 2-D inverse Laplace transform of the full 2-D data sets. When a 1-D CPMG experiment is combined with a rapid T1 measurement, the P2DRM provides a double-shot method for obtaining a T1-T2 relationship, with significantly decreased experimental time in comparison to the full T1-T2 correlation measurement. © 2016 Elsevier Inc.

Place, publisher, year, edition, pages
2016. Vol. 269, 186-195 p.
Keyword [en]
Heterogeneity, Inverse Laplace transform, Inverse-gamma distribution, Lognormal distribution, Multidimensional distribution function, Porous media, Relaxation correlation, T1, T2, Distribution functions, Inverse problems, Inverse transforms, Laplace transforms, Porous materials, Inverse gamma distribution, Log-normal distribution, Multidimensional distributions, Relaxation correlations, T1, T2, Probability distributions
National Category
Natural Sciences
Identifiers
URN: urn:nbn:se:ri:diva-27636DOI: 10.1016/j.jmr.2016.06.009Scopus ID: 2-s2.0-84975855895OAI: oai:DiVA.org:ri-27636DiVA: diva2:1059546
Note

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Available from: 2016-12-22 Created: 2016-12-21 Last updated: 2016-12-22Bibliographically approved

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