Literature DB >> 31814197

What is the optimal schedule for multiparametric MRS? A magnetic resonance fingerprinting perspective.

Alexey Kulpanovich1, Assaf Tal1.   

Abstract

Clinical magnetic resonance spectroscopy (MRS) mainly concerns itself with the quantification of metabolite concentrations. Metabolite relaxation values, which reflect the microscopic state of specific cellular and sub-cellular environments, could potentially hold additional valuable information, but are rarely acquired within clinical scan times. By varying the flip angle, repetition time and echo time in a preset way (termed a schedule), and matching the resulting signals to a pre-generated dictionary - an approach dubbed magnetic resonance fingerprinting - it is possible to encode the spins' relaxation times into the acquired signal, simultaneously quantifying multiple tissue parameters for each metabolite. Herein, we optimized the schedule to minimize the averaged root mean square error (RMSE) across all estimated parameters: concentrations, longitudinal and transverse relaxation time, and transmitter inhomogeneity. The optimal schedules were validated in phantoms and, subsequently, in a cohort of healthy volunteers, in a 4.5 mL parietal white matter single voxel and an acquisition time under 5 minutes. The average intra-subject, inter-scan coefficients of variation (CVs) for metabolite concentrations, T1 and T2 relaxation times were found to be 3.4%, 4.6% and 4.7% in-vivo, respectively, averaged over all major singlets. Coupled metabolites were quantified using the short echo time schedule entries and spectral fitting, and reliable estimates of glutamate+glutamine, glutathione and myo-inositol were obtained.
© 2019 John Wiley & Sons, Ltd.

Entities:  

Keywords:  MRF; MRS; MRSF; T1 relaxation; T2 relaxation; magnetic resonance fingerprinting; magnetic resonance spectroscopy; multiparametric MRS

Mesh:

Year:  2019        PMID: 31814197      PMCID: PMC9244865          DOI: 10.1002/nbm.4196

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.478


  58 in total

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Authors:  Berkin Bilgic; Elfar Adalsteinsson; Mark A Griswold; Lawrence L Wald; Kawin Setsompop
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  1 in total

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  1 in total

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