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

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

Materials And Methods: 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.

Results: 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.

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