Improved intravoxel incoherent motion analysis of diffusion weighted imaging by data driven Bayesian modeling

In addition to the diffusion coefficient, fitting the intravoxel incoherent motion model to multiple b-value diffusion-weighted MR data gives pseudo-diffusion measures associated with rapid signal attenuation at low b-values that are of use in the assessment of a number of pathologies. When summary measures are required, such as the average parameter for a region of interest, least-squares based methods give adequate estimation accuracy. However, using least-squares methods for pixel-wise fitting typically gives noisy estimates, especially for the pseudo-diffusion parameters, which limits the applicability of the approach for assessing spatial features and heterogeneity. In this article, a Bayesian approach using a shrinkage prior model is proposed and is shown to substantially reduce estimation uncertainty so that spatial features in the parameters maps are more clearly apparent. The Bayesian approach has no user-defined parameters, so measures of parameter variation (heterogeneity) over regions of interest are determined by the data alone, whereas it is shown that for the least-squares estimates, measures of variation are essentially determined by user-defined constraints on the parameters. Use of a Bayesian shrinkage prior approach is, therefore, recommended for intravoxel incoherent motion modeling.