File Download

There are no files associated with this item.

  Links for fulltext
     (May Require Subscription)
Supplementary

Article: Multiple-window spectrum estimation applied to in vivo NMR spectroscopy

TitleMultiple-window spectrum estimation applied to in vivo NMR spectroscopy
Authors
Issue Date1996
Citation
Journal Of Magnetic Resonance - Series B, 1996, v. 110 n. 2, p. 138-149 How to Cite?
AbstractMultiple-window spectrum estimation (MWSE) is a method of deriving frequency spectra from time series. A set of apodizing windows is applied to the time data and each windowed data set is Fourier transformed. The windows are prolate spheroidal sequences. These form the orthonormal set of functions that is maximally concentrated in both time and frequency domains. An iterative algorithm is then applied to the data set to find a leastsquares estimate of the power spectrum. In addition, statistical tests may be applied to determine the existence of periodic components at particular frequencies, their amplitudes, phases, and positions. The method is quantitative and makes no lineshape assumptions. Computer simulations were used to compare MWSE performance with that of conventional Fourier-transform processing with quantification by curve fitting. Signal-to-noise ratio, spectral resolution, linearity, and susceptibility to artifacts were compared. MWSE gives similar signal-to-noise ratio and spectral resolution to Fourier-transform data and is linear over three orders of magnitude but is much more robust with respect to artifacts. In particular, data truncation introduces no baseline distortion, broad baseline humps are removed automatically, and large solvent peaks may be easily removed without affecting adjacent lines. No separate phase correction is required. MWSE gives more accurate quantitative spectra, particularly when the time data are imperfect. The method is, therefore, particularly appropriate for processing in vivo data. The utility of the MWSE method is demonstrated on in vivo 'H, 31P, and 13C NMR spectroscopy data. e 1996 Academic Press, Inc.
Persistent Identifierhttp://hdl.handle.net/10722/155423
ISSN
ISI Accession Number ID
References

 

DC FieldValueLanguage
dc.contributor.authorJohnson, Gen_US
dc.contributor.authorThomson, DJen_US
dc.contributor.authorWu, EXen_US
dc.contributor.authorWilliams, SCRen_US
dc.date.accessioned2012-08-08T08:33:25Z-
dc.date.available2012-08-08T08:33:25Z-
dc.date.issued1996en_US
dc.identifier.citationJournal Of Magnetic Resonance - Series B, 1996, v. 110 n. 2, p. 138-149en_US
dc.identifier.issn1064-1866en_US
dc.identifier.urihttp://hdl.handle.net/10722/155423-
dc.description.abstractMultiple-window spectrum estimation (MWSE) is a method of deriving frequency spectra from time series. A set of apodizing windows is applied to the time data and each windowed data set is Fourier transformed. The windows are prolate spheroidal sequences. These form the orthonormal set of functions that is maximally concentrated in both time and frequency domains. An iterative algorithm is then applied to the data set to find a leastsquares estimate of the power spectrum. In addition, statistical tests may be applied to determine the existence of periodic components at particular frequencies, their amplitudes, phases, and positions. The method is quantitative and makes no lineshape assumptions. Computer simulations were used to compare MWSE performance with that of conventional Fourier-transform processing with quantification by curve fitting. Signal-to-noise ratio, spectral resolution, linearity, and susceptibility to artifacts were compared. MWSE gives similar signal-to-noise ratio and spectral resolution to Fourier-transform data and is linear over three orders of magnitude but is much more robust with respect to artifacts. In particular, data truncation introduces no baseline distortion, broad baseline humps are removed automatically, and large solvent peaks may be easily removed without affecting adjacent lines. No separate phase correction is required. MWSE gives more accurate quantitative spectra, particularly when the time data are imperfect. The method is, therefore, particularly appropriate for processing in vivo data. The utility of the MWSE method is demonstrated on in vivo 'H, 31P, and 13C NMR spectroscopy data. e 1996 Academic Press, Inc.en_US
dc.languageengen_US
dc.relation.ispartofJournal of Magnetic Resonance - Series Ben_US
dc.titleMultiple-window spectrum estimation applied to in vivo NMR spectroscopyen_US
dc.typeArticleen_US
dc.identifier.emailWu, EX:ewu1@hkucc.hku.hken_US
dc.identifier.authorityWu, EX=rp00193en_US
dc.description.naturelink_to_subscribed_fulltexten_US
dc.identifier.scopuseid_2-s2.0-3743114628en_US
dc.relation.referenceshttp://www.scopus.com/mlt/select.url?eid=2-s2.0-3743114628&selection=ref&src=s&origin=recordpageen_US
dc.identifier.volume110en_US
dc.identifier.issue2en_US
dc.identifier.spage138en_US
dc.identifier.epage149en_US
dc.identifier.isiWOS:A1996TY73700005-
dc.identifier.scopusauthoridJohnson, G=10240157400en_US
dc.identifier.scopusauthoridThomson, DJ=35566405500en_US
dc.identifier.scopusauthoridWu, EX=7202128034en_US
dc.identifier.scopusauthoridWilliams, SCR=35419560700en_US
dc.identifier.issnl1064-1866-

Export via OAI-PMH Interface in XML Formats


OR


Export to Other Non-XML Formats