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

✍ Scribed by H. Ratiney; M. Sdika; Y. Coenradie; S. Cavassila; D. van Ormondt; D. Graveron-Demilly

• Book ID: 102541341
• Publisher: John Wiley and Sons
• Year: 2005
• Tongue: English
• Weight: 482 KB
• Volume: 18
• Category: Article
• ISSN: 0952-3480
• DOI: 10.1002/nbm.895

✦ Synopsis

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 1 H 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 Crame ´r-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of 1 H in vitro data, 1 H in vivo short echo-time and 31 P human brain signals at 1.5 T, as well as 1 H spectroscopic imaging data of human brain at 1.5 T, is demonstrated.