Estimating transcytolemmal water exchange from the Kärger model using a Bayesian method in the human brain

Motivation: Transcytolemal water exchange time (tex) can be estimated using diffusion-time-dependent diffusion kurtosis imaging (tDKI) acquired at long diffusion times(td). However, dMRI signals acquired at long td's using STEAM sequence are noisy, fitting of tDKI model accumulates errors. Goal(s): proposed a Bayesian strategy to improve the accuracy and robustness of tex mapping based on the Kärger model (KM). Approach: we fitted the tex map based on the simulation and in vivo human brain data using Bayesian and the nonlinear least square methods to compare the fitting results. Results: Bayesian fitting significantly reduced the estimation error and variance in the simulation and in vivo scan. Impact: The proposed a Bayesian strategy significantly reduced the estimation error and variance and improved microstructural maps in vivo. And the proposed 10-minute td-dMRI protocol showed potential value for water exchange mapping in the human brain in clinical practice.