Purpose: To compare an unbiased method for estimation of the diffusion coefficient to the quick, but biased, log-linear (LL) method in the presence of noisy magnitude data.
Materials And Methods: The magnitude operation changes the signal distribution in magnetic resonance (MR) images from Gaussian to Rician. If not properly taken into account, this will introduce a bias in the estimated diffusion coefficient. We compare two methods by means of Monte Carlo simulations. The first one applies least-squares fitting of the measured signal to the median (MD) value of the probability density function. The second method is uncorrected LL estimation. We also perform a high-resolution diffusion tensor experiment.
Results: The uncorrected LL estimator is heavily biased at low signal-to-noise ratios. The bias has a significant effect on image quality. The MD estimator is accurate and produces images with excellent contrast.
Conclusion: In the presence of noisy magnitude data, unbiased estimation is essential in diffusion measurements and diffusion tensor imaging.
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