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dc.contributor.authorReci, A
dc.contributor.authorSederman, Andy
dc.contributor.authorGladden, Lynn
dc.date.accessioned2018-11-01T14:01:57Z
dc.date.available2018-11-01T14:01:57Z
dc.date.issued2017-11
dc.identifier.issn1090-7807
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/284477
dc.description.abstractA new method of regularization of 1D and 2D NMR relaxation and diffusion experiments is proposed and a robust algorithm for its implementation is introduced. The new form of regularization, termed the Modified Total Generalized Variation (MTGV) regularization, offers a compromise between distinguishing discrete and smooth features in the reconstructed distributions. The method is compared to the conventional method of Tikhonov regularization and the recently proposed method of L1 regularization, when applied to simulated data of 1D spin-lattice relaxation, T1, 1D spin-spin relaxation, T2, and 2D T1-T2 NMR experiments. A range of simulated distributions composed of two lognormally distributed peaks were studied. The distributions differed with regard to the variance of the peaks, which were designed to investigate a range of distributions containing only discrete, only smooth or both features in the same distribution. Three different signal-to-noise ratios were studied: 2000, 200 and 20. A new metric is proposed to compare the distributions reconstructed from the different regularization methods with the true distributions. The metric is designed to penalise reconstructed distributions which show artefact peaks. Based on this metric, MTGV regularization performs better than Tikhonov and L1 regularization in all cases except when the distribution is known to only comprise of discrete peaks, in which case L1 regularization is slightly more accurate than MTGV regularization.
dc.format.mediumPrint-Electronic
dc.languageeng
dc.publisherElsevier BV
dc.titleRetaining both discrete and smooth features in 1D and 2D NMR relaxation and diffusion experiments.
dc.typeArticle
prism.endingPage47
prism.publicationDate2017
prism.publicationNameJ Magn Reson
prism.startingPage39
prism.volume284
dc.identifier.doi10.17863/CAM.31853
dcterms.dateAccepted2017-08-24
rioxxterms.versionofrecord10.1016/j.jmr.2017.08.017
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserved
rioxxterms.licenseref.startdate2017-11
dc.contributor.orcidSederman, Andy [0000-0002-7866-5550]
dc.contributor.orcidGladden, Lynn [0000-0001-9519-0406]
dc.identifier.eissn1096-0856
rioxxterms.typeJournal Article/Review
pubs.funder-project-idEngineering and Physical Sciences Research Council (EP/K039318/1)
rioxxterms.freetoread.startdate2018-11-30


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