On the numerical inversion of the Laplace transform for Nuclear Magnetic Resonance relaxometry

In this paper we study several different methods both
deterministic and stochastic to solve the Nuclear Magnetic
Resonance (NMR) relaxometry problem. This
problem is strongly related to finding a non-negative
function given a finite number of values of its
Laplace transform embedded in noise. Some of the methods considered here are
new. We also propose a procedure which exploits and combines the main features
of these methods. To show the performances of this
procedure, some results of applying it to
synthetic data are finally reported.