Automated whole-brain N-acetylaspartate proton MRS quantification.

Journal Article

Concentration of the neuronal marker, N-acetylaspartate (NAA), a quantitative metric for the health and density of neurons, is currently obtained by integration of the manually defined peak in whole-head proton ((1) H)-MRS. Our goal was to develop a full spectral modeling approach for the automatic estimation of the whole-brain NAA concentration (WBNAA) and to compare the performance of this approach with a manual frequency-range peak integration approach previously employed. MRI and whole-head (1) H-MRS from 18 healthy young adults were examined. Non-localized, whole-head (1) H-MRS obtained at 3 T yielded the NAA peak area through both manually defined frequency-range integration and the new, full spectral simulation. The NAA peak area was converted into an absolute amount with phantom replacement and normalized for brain volume (segmented from T1 -weighted MRI) to yield WBNAA. A paired-sample t test was used to compare the means of the WBNAA paradigms and a likelihood ratio test used to compare their coefficients of variation. While the between-subject WBNAA means were nearly identical (12.8 ± 2.5 mm for integration, 12.8 ± 1.4 mm for spectral modeling), the latter's standard deviation was significantly smaller (by ~50%, p = 0.026). The within-subject variability was 11.7% (±1.3 mm) for integration versus 7.0% (±0.8 mm) for spectral modeling, i.e., a 40% improvement. The (quantifiable) quality of the modeling approach was high, as reflected by Cramer-Rao lower bounds below 0.1% and vanishingly small (experimental - fitted) residuals. Modeling of the whole-head (1) H-MRS increases WBNAA quantification reliability by reducing its variability, its susceptibility to operator bias and baseline roll, and by providing quality-control feedback. Together, these enhance the usefulness of the technique for monitoring the diffuse progression and treatment response of neurological disorders.

Full Text

Duke Authors

Cited Authors

  • Soher, BJ; Wu, WE; Tal, A; Storey, P; Zhang, K; Babb, JS; Kirov, II; Lui, YW; Gonen, O

Published Date

  • November 2014

Published In

Volume / Issue

  • 27 / 11

Start / End Page

  • 1275 - 1284

PubMed ID

  • 25196714

Electronic International Standard Serial Number (EISSN)

  • 1099-1492

International Standard Serial Number (ISSN)

  • 0952-3480

Digital Object Identifier (DOI)

  • 10.1002/nbm.3185

Language

  • eng