PowerSlicing

Abstract

Recently, a new technique for unique non-iterative multi-exponential fitting of time domain NMR data was proposed. The method was termed S licing, because an intrinsic part of the method consisted of taking different parts (slices) of the original matrix data and rearranging the slices into a three-way box of data. Subsequently, a directly calculated model of this box provided T 2-estimates and corresponding amplitudes. The most critical part of this method is the choice of how to slice the original data. In this paper, a new general scheme for this slicing is proposed which (1) is shown to provide more accurate T 2-estimates and (2) leads to a significant speed improvement compared to earlier approaches. The method is called P owerS licing, because it takes slices of lag 2 x ( x=0,1,…, N) where 2 N ⩽ J/2 and J is the number of bins on the time axis. This approach ensures a reasonably high amount of direct constraints and an appropriate representation of both short and long time decays in the decomposition.