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Concentration and effective T 2 relaxation times of macromolecules at 3T
Author(s) -
Landheer Karl,
Gajdošík Martin,
Treacy Michael,
Juchem Christoph
Publication year - 2020
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.28282
Subject(s) - macromolecule , relaxation (psychology) , nuclear magnetic resonance , chemistry , physics , biology , neuroscience , biochemistry
Purpose We aimed to investigate the concentration and effective T 2 relaxation time of macromolecules assessed with an ultra‐short TE sLASER sequence in 2 brain regions, the occipital and frontal cortex, in both genders at 3T. Methods An optimized sLASER sequence was used in conjunction with a double‐inversion preparation module to null the metabolites. Eight equally spaced TEs were chosen from 20.1 to 62.1 ms, and the macromolecules were modeled by 10 line broadened singlets. The amplitude of each of the macromolecule signals was extracted at each TE and fit to a monoexponential function to extract the respective effective T 2 values. Absolute quantification of the macromolecule resonances was performed using water signal as a reference. A total of 10 young healthy adult subjects (5 females) were scanned, with spectra being obtained from both the frontal and occipital cortex. Differences in the effective T 2 relaxation times and concentrations were investigated between both regions and genders. Results A wide disparity was observed between the effective T 2 values of the individual resonances; however, no significant differences between gender or region for any of the measured macromolecule concentration or effective T 2 values were found. Conclusion The effective T 2 relaxation times and concentration of 10 different macromolecule resonances were measured and found to be well represented by the monoexponential model. These results will be useful for absolute quantification of macromolecules in future studies, or in the generation of synthetic basis sets for optimization or machine learning.