A Robust Method for Analysing One and Two-Dimensional Dynamic NMR Data

Abstract

A new method combining Anahess and Inverse Laplace Transform, for analysing one- and two-dimensional dynamic NMR data has been developed. When using the Inverse Laplace Transform (ILT) algorithm only to generate solutions from one- and two-dimensional data sets, the solutions are generally ill-posed resulting in an infinite number of possible solutions. Another approach named Anahess represents a discrete method that minimizes the number of components that is sufficient to fit the data sets properly and limits the number of solutions to a single and unique one. In this work it is shown that using the Anahess only is at the least as accurate as the ILT approach in extracting the correct T1–T2-values from a set of synthetic data. However, the Anahess is a discrete approach and does not reproduce continuous T1–T2-distributions, as is the case for many systems being investigated. Thus, a method for producing distributions of T1–T2-values from the Anahess discrete fit is provided in this work. In contrast to the ILT approach, the T1–T2-distribution produced from the Anahess discrete fit is unique as it is produced from a single set of T1–T2-values. The significant difference in performance of the two approaches, ILT and Anahess, for analysing the one- and two-dimensional dynamic NMR data is documented on synthetic data sets as well as on real data sets. It is also important to note that the analysis of the data sets using the Anahess approach does not require any user input as smoothing factor, field of view and number of grid points.