Normalizing data from GABA-edited MEGA-PRESS implementations at 3 Tesla

Ashley D. Harris, Nicolaas A.J. Puts, S. Andrea Wijtenburg, Laura M. Rowland, Mark Mikkelsen, Peter B. Barker, C. John Evans, Richard A.E. Edden

Research output: Contribution to journalArticle

Abstract

Standardization of results is an important milestone in the maturation of any truly quantitative methodology. For instance, a lack of measurement agreement across imaging platforms limits multisite studies, between-study comparisons based on the literature, and inferences based on and the generalizability of results. In GABA-edited MEGA-PRESS, two key sources of differences between implementations are: differences in editing efficiency of GABA and the degree of co-editing of macromolecules (MM). In this work, GABA editing efficiency κ and MM-co-editing μ constants are determined for three widely used MEGA-PRESS implementations (on the most common MRI platforms; GE, Philips, and Siemens) by phantom experiments. Implementation-specific κ,μ-corrections were then applied to two in vivo datasets, one consisted of 8 subject scanned on the three platforms and the other one subject scanned eight times on each platform. Manufacturer-specific κ and μ values were determined as: κGE = 0.436, κSiemens = 0.366 and κPhilips = 0.394 and μGE = 0.83, μSiemens = 0.625 and μPhilips = 0.75. Applying the κ,μ-correction on the Cr-referenced data decreased the coefficient of variation (CV) of the data for both in vivo data sets (multisubjects: uncorrected CV = 13%, κ,μ-corrected CV = 5%, single subject: uncorrected CV = 23%, κ,μ-corrected CV = 13%) but had no significant effect on mean GABA levels. For the water-referenced results, CV increased in the multisubject data (uncorrected CV = 6.7%, κ,μ-corrected CV = 14%) while it decreased in the single subject data (uncorrected CV = 24%, κ,μ-corrected CV = 21%) and manufacturer was a significant source of variance in the κ,μ-corrected data. Applying a correction for editing efficiency and macromolecule contamination decreases the variance between different manufacturers for creatine-referenced data, but other sources of variance remain.

Original languageEnglish (US)
Pages (from-to)8-15
Number of pages8
JournalMagnetic Resonance Imaging
Volume42
DOIs
StatePublished - Oct 1 2017

Fingerprint

Macromolecules
gamma-Aminobutyric Acid
Datasets
Magnetic resonance imaging
Standardization
Contamination
Imaging techniques
Water
Experiments
Creatine

Keywords

  • Cross-platform
  • Editing efficiency
  • GABA
  • Macromolecular co-editing
  • MEGA-PRESS
  • Multi-site

ASJC Scopus subject areas

  • Biophysics
  • Biomedical Engineering
  • Radiology Nuclear Medicine and imaging

Cite this

Harris, A. D., Puts, N. A. J., Wijtenburg, S. A., Rowland, L. M., Mikkelsen, M., Barker, P. B., ... Edden, R. A. E. (2017). Normalizing data from GABA-edited MEGA-PRESS implementations at 3 Tesla. Magnetic Resonance Imaging, 42, 8-15. DOI: 10.1016/j.mri.2017.04.013

Normalizing data from GABA-edited MEGA-PRESS implementations at 3 Tesla. / Harris, Ashley D.; Puts, Nicolaas A.J.; Wijtenburg, S. Andrea; Rowland, Laura M.; Mikkelsen, Mark; Barker, Peter B.; Evans, C. John; Edden, Richard A.E.

In: Magnetic Resonance Imaging, Vol. 42, 01.10.2017, p. 8-15.

Research output: Contribution to journalArticle

Harris, AD, Puts, NAJ, Wijtenburg, SA, Rowland, LM, Mikkelsen, M, Barker, PB, Evans, CJ & Edden, RAE 2017, 'Normalizing data from GABA-edited MEGA-PRESS implementations at 3 Tesla' Magnetic Resonance Imaging, vol 42, pp. 8-15. DOI: 10.1016/j.mri.2017.04.013
Harris AD, Puts NAJ, Wijtenburg SA, Rowland LM, Mikkelsen M, Barker PB et al. Normalizing data from GABA-edited MEGA-PRESS implementations at 3 Tesla. Magnetic Resonance Imaging. 2017 Oct 1;42:8-15. Available from, DOI: 10.1016/j.mri.2017.04.013

Harris, Ashley D.; Puts, Nicolaas A.J.; Wijtenburg, S. Andrea; Rowland, Laura M.; Mikkelsen, Mark; Barker, Peter B.; Evans, C. John; Edden, Richard A.E. / Normalizing data from GABA-edited MEGA-PRESS implementations at 3 Tesla.

In: Magnetic Resonance Imaging, Vol. 42, 01.10.2017, p. 8-15.

Research output: Contribution to journalArticle

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