Time-domain semi-parametric estimation based on a metabolite basis set

NMR Biomed. 2005 Feb;18(1):1-13. doi: 10.1002/nbm.895.


A novel and fast time-domain quantitation algorithm--quantitation based on semi-parametric quantum estimation (QUEST)--invoking optimal prior knowledge is proposed and tested. This nonlinear least-squares algorithm fits a time-domain model function, made up from a basis set of quantum-mechanically simulated whole-metabolite signals, to low-SNR in vivo data. A basis set of in vitro measured signals can be used too. The simulated basis set was created with the software package NMR-SCOPE which can invoke various experimental protocols. Quantitation of 1H short echo-time signals is often hampered by a background signal originating mainly from macromolecules and lipids. Here, we propose and compare three novel semi-parametric approaches to handle such signals in terms of bias-variance trade-off. The performances of our methods are evaluated through extensive Monte-Carlo studies. Uncertainty caused by the background is accounted for in the Cramér-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of 1H in vitro data, 1H in vivo short echo-time and 31P human brain signals at 1.5 T, as well as 1H spectroscopic imaging data of human brain at 1.5 T, is demonstrated.

Publication types

  • Evaluation Study
  • Research Support, Non-U.S. Gov't
  • Validation Study

MeSH terms

  • Algorithms*
  • Brain / metabolism*
  • Diagnosis, Computer-Assisted / methods*
  • Gene Expression Profiling / methods*
  • Humans
  • Least-Squares Analysis
  • Magnetic Resonance Spectroscopy / methods*
  • Nerve Tissue Proteins / metabolism*
  • Phantoms, Imaging
  • Protons
  • Reproducibility of Results
  • Sensitivity and Specificity


  • Nerve Tissue Proteins
  • Protons