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dc.contributor.authorEdmunds, K J
dc.contributor.authorÁrnadóttir, Í
dc.contributor.authorGíslason, M K
dc.contributor.authorCarraro, U
dc.contributor.authorGargiulo, P
dc.date.accessioned2017-02-13T15:30:47Z
dc.date.available2017-02-13T15:30:47Z
dc.date.issued2016
dc.date.submitted2017
dc.identifier.citationNonlinear Trimodal Regression Analysis of Radiodensitometric Distributions to Quantify Sarcopenic and Sequelae Muscle Degeneration. 2016, 2016:8932950 Comput Math Methods Meden
dc.identifier.issn1748-6718
dc.identifier.pmid28115982
dc.identifier.doi10.1155/2016/8932950
dc.identifier.urihttp://hdl.handle.net/2336/620107
dc.descriptionEfst á síðunni er hægt að nálgast greinina í heild sinni með því að smella á hlekkinn To access publisher's full text version of this article, please click on the hyperlink in Additional Links field or click on the hyperlink at the top of the page marked Files.en
dc.description.abstractMuscle degeneration has been consistently identified as an independent risk factor for high mortality in both aging populations and individuals suffering from neuromuscular pathology or injury. While there is much extant literature on its quantification and correlation to comorbidities, a quantitative gold standard for analyses in this regard remains undefined. Herein, we hypothesize that rigorously quantifying entire radiodensitometric distributions elicits more muscle quality information than average values reported in extant methods. This study reports the development and utility of a nonlinear trimodal regression analysis method utilized on radiodensitometric distributions of upper leg muscles from CT scans of a healthy young adult, a healthy elderly subject, and a spinal cord injury patient. The method was then employed with a THA cohort to assess pre- and postsurgical differences in their healthy and operative legs. Results from the initial representative models elicited high degrees of correlation to HU distributions, and regression parameters highlighted physiologically evident differences between subjects. Furthermore, results from the THA cohort echoed physiological justification and indicated significant improvements in muscle quality in both legs following surgery. Altogether, these results highlight the utility of novel parameters from entire HU distributions that could provide insight into the optimal quantification of muscle degeneration.
dc.description.sponsorshipEuropean Commissionen
dc.language.isoenen
dc.publisherHindawi Pub. Corpen
dc.relation.urlhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC5223076/pdf/CMMM2016-8932950.pdfen
dc.rightsArchived with thanks to Computational and mathematical methods in medicineen
dc.subjectStoðkerfi (líffærafræði)en
dc.subjectÖldrunen
dc.subjectSneiðmyndatökuren
dc.subjectVöðvasjúkdómaren
dc.subjectRAM12en
dc.subjectRES12en
dc.subject.meshMuscle, Skeletalen
dc.subject.meshTomography, X-Ray Computeden
dc.subject.meshBody Compositionen
dc.subject.meshAgingen
dc.titleNonlinear Trimodal Regression Analysis of Radiodensitometric Distributions to Quantify Sarcopenic and Sequelae Muscle Degeneration.en
dc.typeArticleen
dc.contributor.department[ 1 ] Reykjavik Univ, Inst Biomed & Neural Engn, IS-101 Reykjavik, Iceland Show the Organization-Enhanced name(s) [ 2 ] IRCCS Fdn Osped San Camillo, Via Alberoni 70, I-30126 Venice, Italy [ 3 ] Landspitali, Dept Rehabil, IS-101 Reykjavik, Icelanden
dc.identifier.journalComputational and mathematical methods in medicineen
dc.rights.accessOpen Access - Opinn aðganguren
refterms.dateFOA2018-09-12T16:26:09Z
html.description.abstractMuscle degeneration has been consistently identified as an independent risk factor for high mortality in both aging populations and individuals suffering from neuromuscular pathology or injury. While there is much extant literature on its quantification and correlation to comorbidities, a quantitative gold standard for analyses in this regard remains undefined. Herein, we hypothesize that rigorously quantifying entire radiodensitometric distributions elicits more muscle quality information than average values reported in extant methods. This study reports the development and utility of a nonlinear trimodal regression analysis method utilized on radiodensitometric distributions of upper leg muscles from CT scans of a healthy young adult, a healthy elderly subject, and a spinal cord injury patient. The method was then employed with a THA cohort to assess pre- and postsurgical differences in their healthy and operative legs. Results from the initial representative models elicited high degrees of correlation to HU distributions, and regression parameters highlighted physiologically evident differences between subjects. Furthermore, results from the THA cohort echoed physiological justification and indicated significant improvements in muscle quality in both legs following surgery. Altogether, these results highlight the utility of novel parameters from entire HU distributions that could provide insight into the optimal quantification of muscle degeneration.


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