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Multi-component relaxation modelling in human Achilles tendon: Quantifying chemical shift information in ultra-short
Muhammad A R Anjum1, Felix M Gonzalez1, Anshuman Swain1
1Department of Radiology & Imaging Sciences, School of Medicine, Emory University, Atlanta, Georgia, USA.
Magnetic Resonance in Medicine
|February 16, 2021
Summary
Multi-component modeling accurately quantifies Achilles tendon (AT) components using ultra-short echo time (UTE) MRI. This advanced method reduces bias compared to bi-component models, improving quantitative analysis of tendon matrix constituents.
Area of Science:
- Biomedical Engineering
- Medical Imaging
- Biophysics
Background:
- Quantitative magnetic resonance imaging (MRI) of biological tissues like the Achilles tendon (AT) is crucial for understanding tissue properties.
- Ultra-short echo time (UTE) sequences provide enhanced sensitivity to rapidly relaxing components in tissues.
- Existing bi-component relaxation models may not fully capture the complexity of signal decay in AT.
Purpose of the Study:
- To evaluate multi-component relaxation modeling for quantifying on- and off-resonance signals in multi-echo UTE data of human AT.
- To compare the bias and dispersion errors of multi-component model parameters against a bi-component model.
- To assess the reliability of multi-component modeling for AT matrix water and non-water constituents.
Main Methods:
- Demonstration of multi-component modeling for quantitative multi-echo UTE analysis of AT.
- Utilized a novel method to determine the number of MR-visible off-resonance components.
- Analyzed UTE data from six healthy volunteers and ex vivo bovine AT proton NMR measurements.
- Presented Cramer-Rao lower bound expressions for multi- and bi-component models to compare parameter estimate variances.
- Numerically characterized bias error in bi-component estimates.
Main Results:
- Consistently detected two off-resonance components in both human volunteers and bovine AT data.
- The multi-component model showed a superior quality of fit compared to the bi-component model.
- A marginal increase in estimate variance was observed with the multi-component model.
- Bi-component estimates exhibited significant bias, particularly in the T2* parameter, when off-resonance components were present.
Conclusions:
- Multi-component modeling offers more reliable quantification of tendon matrix water components in AT.
- This approach also enables the quantitation of additional non-water matrix constituents.
- Further research is required to fully interpret the origin of observed off-resonance signals, with preliminary assignments to lipid and proteoglycan chemical groups.
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