A comparative simulation study of bayesian fitting approaches to intravoxel incoherent motion modeling in diffusion-weighted MRI.

PURPOSE: To assess the performance of various least squares and Bayesian modeling approaches to parameter estimation in intravoxel incoherent motion (IVIM) modeling of diffusion-weighted MRI data.
METHODS: Simulated tissue models of different type (breast/liver) and morphology (discrete/continuous) were used to generate noisy data according to the IVIM model at several signal-to-noise ratios. IVIM parameter maps were generated using six different approaches, including full nonlinear least squares (LSQ), segmented least squares (SEG), Bayesian modeling with a Gaussian shrinkage prior (BSP) and Bayesian modeling with a spatial homogeneity prior (FBM), plus two modified approaches. Estimators were compared by calculating the median absolute percentage error and deviation, and median percentage bias.
RESULTS: The Bayesian modeling approaches consistently outperformed the least squares approaches, with lower relative error and deviation, and provided cleaner parameter maps with reduced erroneous heterogeneity. However, a weakness of the Bayesian approaches was exposed, whereby certain tissue features disappeared completely in regions of high parameter uncertainty. Lower error and deviation were generally afforded by FBM compared with BSP, at the cost of higher bias.
CONCLUSIONS: Bayesian modeling is capable of producing more visually pleasing IVIM parameter maps than least squares approaches, but their potential to mask certain tissue features demands caution during implementation. Magn Reson Med 78:2373-2387, 2017.