Estimating transcytolemmal water exchange from the Kärger model using a Bayesian method in a rat model of ischemic stroke

Transcytolemal water exchange can be estimated using diffusion-time-dependent diffusion kurtosis imaging acquired at long diffusion times. However, dMRI signals acquired at long diffusion times using STEAM sequences are typically noisy, and fitting of the nonlinear kurtosis model and the Kärger model accumulates fitting errors. Here, we proposed a Bayesian method for estimating transcytolemal exchange time from the Kärger model and compared accuracy and robustness with conventional least square fitting method in both simulated data and rat brain data in a model of transient middle cerebral artery occlusion. Results indicated improved fitting accuracy and robustness against noise using the Bayesian approach.