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

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 H-1 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 Cramer-Rao lower bounds calculation. Valuable insight about quantitation precision is obtained from the correlation matrices. Quantitation with QUEST of H-1 in vitro data, H-1 in vivo short echo-time and P-31 human brain signals at 1.5 T, as well as H-1 spectroscopic imaging data of human brain at 1.5 T, is demonstrated. Copyright (C) 2005 John Wiley Sons, Ltd.