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dc.contributor.authorReci, A
dc.contributor.authorSederman, AJ
dc.contributor.authorGladden, LF
dc.date.accessioned2018-11-01T14:02:15Z
dc.date.available2018-11-01T14:02:15Z
dc.date.issued2018-09
dc.identifier.issn1090-7807
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/284485
dc.description.abstractSampling strategies are often central to experimental design. Choosing efficiently which data to acquire can improve the estimation of parameters and reduce the acquisition time. This work is focused on designing optimal sampling patterns for Nuclear Magnetic Resonance (NMR) applications, illustrated with respect to the best estimate of the parameters characterising a lognormal distribution. Lognormal distributions are commonly used as fitting models for distributions of spin-lattice relaxation time constants, spin-spin relaxation time constants and diffusion coefficients. A method for optimising the choice of points to be sampled is presented which is based on the Cramér-Rao Lower Bound (CRLB) theory. The method's capabilities are demonstrated experimentally by applying it to the problem of estimating the emulsion droplet size distribution from a pulsed field gradient (PFG) NMR diffusion experiment. A difference of <5% is observed between the predictions of CRLB theory and the PFG NMR experimental results. It is shown that CLRB theory is stable down to signal-to-noise ratios of ∼10. A sensitivity analysis for the CRLB theory is also performed. The method of optimizing sampling patterns is easily adapted to distributions other than lognormal and to other aspects of experimental design; case studies of optimising the sampling scheme for a fixed acquisition time and determining the potential for reduction in acquisition time for a fixed parameter estimation accuracy are presented. The experimental acquisition time is typically reduced by a factor of 3 using the proposed method compared to a constant gradient increment approach that would usually be used.
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherElsevier BV
dc.titleOptimising magnetic resonance sampling patterns for parametric characterisation.
dc.typeArticle
prism.endingPage43
prism.publicationDate2018
prism.publicationNameJ Magn Reson
prism.startingPage35
prism.volume294
dc.identifier.doi10.17863/CAM.31861
dcterms.dateAccepted2018-06-18
rioxxterms.versionofrecord10.1016/j.jmr.2018.06.010
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2018-09
dc.contributor.orcidSederman, Andy [0000-0002-7866-5550]
dc.contributor.orcidGladden, Lynn [0000-0001-9519-0406]
dc.identifier.eissn1096-0856
rioxxterms.typeJournal Article/Review
cam.issuedOnline2018-06-19
rioxxterms.freetoread.startdate2019-09-30


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