A SIGNAL-ENHANCEMENT ALGORITHM FOR THE QUANTIFICATION OF NMR DATA IN THE TIME-DOMAIN

A new enhancement algorithm is presented for cleaning up NMR data befo re estimating signal parameters using a subspace-based method. The pro posed algorithm is based on the minimum variance estimation method, wh ich starts from a very rectangular (instead of a square) Hankel struct ured data matrix in order to make the corresponding signal-only data m atrix orthogonal to the noise, then computes an estimate of the signal -only data matrix, and finally restores the Hankel structure of the co mputed estimate. This algorithm has remarkable practical advantages ov er Cadzow's and nonenhanced algorithms in both resolution performance and computational efficiency that make it well suited to the quantitat ive time-domain analysis of NMR measurement data. The convergence of t he enhancement procedure is found to be redundant when followed by an SVD-based estimator such as HTLS, offering drastic reduction in the co mputational cost. Extensive computer simulations on NMR signals with o verlapping peaks have been carried out to evaluate the new algorithm a fter one iteration, its convergence, and its combination with Cadzow's method. The enhancement algorithms are applied to the parameter estim ation of real-world NMR measurement data as well. In particular, the n ewly proposed algorithm is recommended for estimating the parameters o f overlapping peaks when the signal-to-noise ratio is low and prior kn owledge is hardly available. (C) 1994 Academic Press,Inc.