Automated whole‐brain N‐acetylaspartate proton MRS quantification. (5th September 2014)

• Record Type: Journal Article
• Authors: Soher, Brian J.; Wu, William E.; Tal, Assaf; Storey, Pippa; Zhang, Ke; Babb, James S.; Kirov, Ivan I.; Lui, Yvonne W.; Gonen, Oded
• Is Part Of: NMR in biomedicine. Volume 27:Number 11(2014:Nov.)
• Page Start: 1275
• Page End: 1284
• Publication Date: 2014-09-05
• Subjects: Nuclear magnetic resonance -- Periodicals; Magnetic Resonance Spectroscopy -- Periodicals; 574
• DOI: 10.1002/nbm.3185
• Languages: English
• ISSNs: 0952-3480

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>Concentration of the neuronal marker, <italic>N</italic>‐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 (<sup>1</sup>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 <sup>1</sup>H‐MRS from 18 healthy young adults were examined. Non‐localized, whole‐head <sup>1</sup>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 <italic>T</italic><sub>1</sub>‐weighted MRI) to yield WBNAA. A paired‐sample <italic>t</italic> 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 m<sc>m</sc> for integration, 12.8 ± 1.4 m<sc>m</sc> for spectral modeling), the latter's standard deviation was significantly smaller (by ~50%, <italic>p</italic> = 0.026). The within‐subject variability was 11.7% (±1.3 m<sc>m</sc>) for integration <italic>versus</italic> 7.0% (±0.8 m<sc>m</sc>) 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 <sup>1</sup>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. Copyright © 2014 John Wiley & Sons, Ltd.</p> </abstract>