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Multi-component T2 relaxation modelling in human Achilles tendon: Quantifying chemical shift information in ultra-short echo time imaging.
Anjum, Muhammad A R; Gonzalez, Felix M; Swain, Anshuman; Leisen, Johannes; Hosseini, Zahra; Singer, Adam; Umpierrez, Monica; Reiter, David A.
Afiliação
  • Anjum MAR; Department of Radiology & Imaging Sciences, School of Medicine, Emory University, Atlanta, Georgia, USA.
  • Gonzalez FM; Department of Radiology & Imaging Sciences, School of Medicine, Emory University, Atlanta, Georgia, USA.
  • Swain A; Department of Radiology & Imaging Sciences, School of Medicine, Emory University, Atlanta, Georgia, USA.
  • Leisen J; School of Chemistry and Biochemistry, Georgia Institute of Technology, Atlanta, Georgia, USA.
  • Hosseini Z; MR R&D Collaborations, Siemens Medical Solutions Inc., Atlanta, Georgia, USA.
  • Singer A; Department of Radiology & Imaging Sciences, School of Medicine, Emory University, Atlanta, Georgia, USA.
  • Umpierrez M; Department of Radiology & Imaging Sciences, School of Medicine, Emory University, Atlanta, Georgia, USA.
  • Reiter DA; Department of Radiology & Imaging Sciences, School of Medicine, Emory University, Atlanta, Georgia, USA.
Magn Reson Med ; 86(1): 415-428, 2021 07.
Article em En | MEDLINE | ID: mdl-33590557
PURPOSE: To examine multi-component relaxation modelling for quantification of on- and off-resonance relaxation signals in multi-echo ultra-short echo time (UTE) data of human Achilles tendon (AT) and compare bias and dispersion errors of model parameters to that of the bi-component model. THEORY AND METHODS: Multi-component modelling is demonstrated for quantitative multi-echo UTE analysis of AT and supported using a novel method for determining number of MR-visible off-resonance components, UTE data from six healthy volunteers, and analysis of proton NMR measurements from ex vivo bovine AT. Cramer-Rao lower bound expressions are presented for multi- and bi-component models and parameter estimate variances are compared. Bias error in bi-component estimates is characterized numerically. RESULTS: Two off-resonance components were consistently detected in all six volunteers and in bovine AT data. Multi-component model exhibited superior quality of fit, with a marginal increase in estimate variance, when compared to the bi-component model. Bi-component estimates exhibited notable bias particularly in R2,1∗ in the presence of off-resonance components. CONCLUSION: Multi-component modelling more reliably quantifies tendon matrix water components while also providing quantitation of additional non-water matrix constituents. Further work is needed to interpret the origin of the observed off-resonance signals with preliminary assignments made to chemical groups in lipids and proteoglycans.
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Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Tendão do Calcâneo Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Revista: Magn Reson Med Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos

Texto completo: 1 Bases de dados: MEDLINE Assunto principal: Tendão do Calcâneo Tipo de estudo: Prognostic_studies Limite: Animals / Humans Idioma: En Revista: Magn Reson Med Assunto da revista: DIAGNOSTICO POR IMAGEM Ano de publicação: 2021 Tipo de documento: Article País de afiliação: Estados Unidos