Estimating non-Gaussian diffusion model parameters in the presence of physiological noise and Rician signal bias

To assess the effects of Rician bias and physiological noise on parameter estimation for non-Gaussian diffusion models.

At high b-values, there are deviations from monoexponential signal decay known as non-Gaussian diffusion. Magnitude images have a Rician distribution, which introduces a bias that appears as non-Gaussian diffusion. A second factor that complicates parameter estimation is physiological noise. It has an intensity that depends on the b-value in a complicated manner. Hence, the signal distribution is unknown a priori. By measuring a large number of averages, however, the variance at each b-value can be estimated. Using Monte Carlo simulations, we compared uncorrected estimation to a corrected scheme that involves fitting to the mean value of the Rician distribution. We also evaluated effects of weighting with the inverse of the estimated variance in least-squares fitting. A human brain experiment illustrates parameter estimation effects and identifies brain regions affected by physiological noise.

The simulations show that the corrected estimator is very accurate. The uncorrected estimator is heavily biased. In the human brain experiment, the magnitude of the relative bias ranges from 6%-31%, depending on the diffusion model. Weighting has negligible effects on accuracy, but improves precision in the presence of physiological noise. At low b-values, physiological noise is prominent in cerebrospinal fluid. At high b-values there is physiological noise in white matter structures near the ventricles.

Bias correction is essential and weighting may be beneficial. Physiological noise has significant effects.