Premium
Multi‐component T 2 ∗ relaxation modelling in human Achilles tendon: Quantifying chemical shift information in ultra‐short echo time imaging
Author(s) -
Anjum Muhammad A. R.,
Gonzalez Felix M.,
Swain Anshuman,
Leisen Johannes,
Hosseini Zahra,
Singer Adam,
Umpierrez Monica,
Reiter David A.
Publication year - 2021
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.28686
Subject(s) - component (thermodynamics) , relaxation (psychology) , biological system , t2 relaxation , resonance (particle physics) , magnetic resonance imaging , echo (communications protocol) , nuclear magnetic resonance , dispersion (optics) , matrix (chemical analysis) , achilles tendon , biomedical engineering , chemistry , computer science , tendon , physics , optics , thermodynamics , anatomy , chromatography , medicine , computer network , radiology , particle physics , biology
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 R 2 , 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.