Evaluation of variable line-shape models and prior information in automated 1H spectroscopic imaging analysis.

Published

Journal Article

Analysis of in vivo short TE 1H spectra is complicated by broad baseline signal contributions and resonance line-shape distortions. Although the assumptions of ideal metabolite resonance line-shapes and slowly varying baseline signals can be used to separate these signals, the presence of broad or asymmetric line-shapes can invalidate this model. More complex line-shape models are computationally expensive or difficult to constrain, particularly for the low signal-to-noise commonly found for in vivo MR spectroscopic imaging applications. In this study, two time-domain models for fitting variable spectral line-shapes are examined, one using B-splines and another using summed sinusoids. The methods were verified using both phantom and human data, and Monte Carlo simulations were used to evaluate variations in calculated metabolite amplitudes due to interactions between the baseline and line-shape estimations. Additional studies investigated the use of prior line-shape information, obtained from either a water MRSI measurement or calculations from B(0) maps, to determine parameter starting values or optimization constraints. Both line-shape models showed the ability to fit the variety of line-shapes present in both the phantom and human MRSI data, with similar or improved accuracy over a Gaussian line-shape model; however, this improvement resulted in only minor improvement for the high-SNR phantom data and moderate improvements in regions with asymmetry for the fitted in vivo metabolite images. The use of prior line-shape information was of most benefit when applied toward setting optimization constraints but was of limited benefit when used to define initial starting values.

Full Text

Duke Authors

Cited Authors

  • Soher, BJ; Maudsley, AA

Published Date

  • December 2004

Published In

Volume / Issue

  • 52 / 6

Start / End Page

  • 1246 - 1254

PubMed ID

  • 15562473

Pubmed Central ID

  • 15562473

International Standard Serial Number (ISSN)

  • 0740-3194

Digital Object Identifier (DOI)

  • 10.1002/mrm.20295

Language

  • eng

Conference Location

  • United States