Quantification of neurochemical concentrations from 1H MR spectra is challenged by incomplete knowledge of contributing signals. Some experimental conditions hinder the acquisition of artifact-free spectra and impede the acquisition of condition-specific macromolecule (MM) spectra. This work studies differences caused by fitting solutions routinely employed to manage resonances from MM and lipids. High quality spectra (free of residual water and lipid artifacts and for which condition-specific MM spectra are available) are used to understand the influences of spline baseline flexibility and noncondition-specific MM on neurochemical quantification. Fitting with moderate spline flexibility or using noncondition-specific MM led to quantification that differed from when an appropriate, fully specified model was used. This occurred for all neurochemicals to an extent that varied in magnitude among and within approaches. The spline baseline was more tortuous when less constrained and when used in combination with noncondition-specific MM. Increasing baseline flexibility did not reproduce concentrations quantified under appropriate conditions when spectra were fitted using a MM spectrum measured from a mismatched cohort. Using the noncondition-specific MM spectrum led to quantification differences that were comparable in size with using a fitting model that had moderate freedom, and these influences were additive. Although goodness of fit was better with greater fitting flexibility, quantification differed from when fitting with a fully specified model that is appropriate for low noise data. Notable GABA and PE concentration differences occurred with lower estimates of measurement error when fitting with greater spline flexibility or noncondition-specific MM. These data support the need for improved metrics of goodness of fit. Attempting to correct for artifacts or absence of a condition-specific MM spectrum via increased spline flexibility and usage of noncondition-specific MM spectra cannot replace artifact-free data quantified with a condition-specific MM spectrum.
Bibliographical noteFunding Information:
The authors thank: Stephen Provencher for providing the definition of and clarification on the usage of the LCModel control parameter, DKNTMN; Laura Hemmy, PhD and J. Riley McCarten, MD for screening older adults; James S. Hodges, PhD for discussion of statistical analysis; Dinesh K. Deelchand for help with figures; Edward J. Auerbach, PhD for implementing the FAST (EST)MAP and STEAM sequences on the Siemens platform; Pierre-Francois Van de Moortele, MD, PhD, and Julien Sein, PhD for the T1-flattening script; and Emily Kittelson for image segmentation. We also acknowledge the University of Minnesota Retiree's Volunteer Center for assistance with recruiting older adult candidates.
This work was supported by the National Institutes of Health [grant numbers R01AG055591, R01AG039396, P41 EB015894, P30 NS076408] and the W.M. Keck Foundation.
© 2019 John Wiley & Sons, Ltd.
- 7 T
- magnetic resonance spectroscopy
- ultrahigh field
PubMed: MeSH publication types
- Journal Article