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.