Intra voxel analysis in magnetic resonance imaging.

A technique for analyzing the composition of each voxel, in the magnetic resonance imaging (MRI) framework, is presented. By combining different acquisitions, a novel methodology, called intra voxel analysis (IVA), for the detection of multiple tissues and the estimation of their spin-spin relaxation times is proposed. The methodology exploits the sparse Bayesian learning (SBL) approach in order to solve a highly underdetermined problem imposing the solution sparsity. IVA, developed for spin echo imaging sequence, can be easily extended to any acquisition scheme. For validating the approach, simulated and real data sets are considered. Monte Carlo simulations have been implemented for evaluating the performances of IVA compared to methods existing in literature. Two clinical datasets acquired with a 3T scanner have been considered for validating the approach. With respect to other approaches presented in literature, IVA has proved to be more effective in the voxel composition analysis, in particular in the case of few acquired images. Results are interesting and very promising: IVA is expected to have a remarkable impact on the research community and on the diagnostic field.