Assessment of lipids in skeletal muscle by LCModel and AMARES

Purpose To process single voxel spectra of the human skeletal muscle by using an advanced method for accurate, robust, and efficient spectral fitting (AMARES) and by linear combination of model spectra (LCModel). To determine absolute concentrations of extra‐ (EMCL) and intramyocellular lipids (IMCL...

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Bibliographic Details
Published inJournal of magnetic resonance imaging Vol. 30; no. 5; pp. 1124 - 1129
Main Authors Weis, Jan, Johansson, Lars, Ortiz-Nieto, Francisco, Ahlström, Håkan
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Subscription Services, Inc., A Wiley Company 01.11.2009
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Summary:Purpose To process single voxel spectra of the human skeletal muscle by using an advanced method for accurate, robust, and efficient spectral fitting (AMARES) and by linear combination of model spectra (LCModel). To determine absolute concentrations of extra‐ (EMCL) and intramyocellular lipids (IMCL). Materials and Methods Single‐voxel proton magnetic resonance spectroscopy (PRESS) was used to obtain the spectra of the calf muscles. Unsuppressed water line was used as a concentration reference. A new prior knowledge for AMARES was proposed to estimate the concentrations of EMCL and IMCL. The prior knowledge was derived from the spectrum of vegetable oil. The results were compared with the values estimated by LCModel. Absolute concentrations of total lipid content in millimoles per kilogram wet weight were used for the comparisons. Results Absolute concentrations of total lipid content in skeletal muscle were estimated by AMARES and LCModel. Very good correlation of the total fat (EMCL + IMCL) and IMCL concentrations was achieved between both data processing approaches. Conclusion Assessment the absolute concentrations of muscular lipids by AMARES and LCModel can be performed with comparable accuracy. J. Magn. Reson. Imaging 2009. © 2009 Wiley‐Liss, Inc.
Bibliography:Swedish Research Council - No. K2006-71X-06676-24-3
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ark:/67375/WNG-4XCH15DH-V
ArticleID:JMRI21900
ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ISSN:1053-1807
1522-2586
1522-2586
DOI:10.1002/jmri.21900