Journal
JOURNAL OF MAGNETIC RESONANCE
Volume 351, Issue -, Pages -Publisher
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.jmr.2023.107435
Keywords
Data processing; Matrix pencil method; NMR relaxometry
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The matrix pencil method (MPM) is tested for quantitatively processing multiexponential low-field nuclear magnetic resonance T1 relaxometry data. The data is obtained from T1 saturation recovery curves measured in a highly inhomogeneous magnetic field. Different concentrations of a Gd3+ contrast agent doped in 0.9% brine solutions are used as test liquids. MPM shows superior performance in separating and quantifying relaxation components, as well as exploring the sensitivity to relative contribution of each component and resolving systems with multiple components.
The matrix pencil method (MPM) is tested as an approach to quantitatively process multiexponential low-field nuclear magnetic resonance T1 relaxometry data. The data is obtained by measuring T1 satura-tion recovery curves in the highly inhomogeneous magnetic field of a stray-field sensor. 0.9% brine solu-tions, doped with different concentrations of a Gd3+ containing contrast agent, serve as test liquids. Relaxation-times as a function of contrast-agent concentration along with the T1 relaxation curves for combinations of multiple different test liquids are measured, and the results from processing using MPM as well as inverse Laplace transformation as a benchmark are compared. The relaxation-time res-olution limits of both procedures are probed by gradually reducing the difference between the relaxation-times of two liquids measured simultaneously. The sensitivity to quantify the relative contribution of each component to the magnetization build-up curve is explored by changing their volume ratio. Furthermore, the potential to resolve systems with more than two components is tested. For the systems under test, MPM shows superior performance in separating two or three relaxation components, respec-tively and effectively quantifying the time constants. (c) 2023 Elsevier Inc. All rights reserved.
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