  Knowledge of the noise distribution in diffusion MRI is the centerpiece to
quantify uncertainties arising from the acquisition process. Accurate
estimation beyond textbook distributions often requires information about the
acquisition process, which is usually not available. We introduce two new
automated methods using the moments and maximum likelihood equations of the
Gamma distribution to estimate all unknown parameters using only the magnitude
data. A rejection step is used to make the framework automatic and robust to
artifacts. Simulations were created for two diffusion weightings with parallel
imaging. Furthermore, MRI data of a water phantom with different combinations
of parallel imaging were acquired. Finally, experiments on freely available
datasets are used to assess reproducibility when limited information about the
acquisition protocol is available. Additionally, we demonstrated the
applicability of the proposed methods for a bias correction and denoising task
on an in vivo dataset. A generalized version of the bias correction framework
for non integer degrees of freedom is also introduced. The proposed framework
is compared with three other algorithms with datasets from three vendors,
employing different reconstruction methods. Simulations showed that assuming a
Rician distribution can lead to misestimation of the noise distribution in
parallel imaging. Results showed that signal leakage in multiband can also lead
to a misestimation of the noise distribution. Repeated acquisitions of in vivo
datasets show that the estimated parameters are stable and have lower
variability than compared methods. Results show that the proposed methods
reduce the appearance of noise at high b-value. The proposed algorithms herein
can estimate both parameters of the noise distribution automatically, are
robust to signal leakage artifacts and perform best when used on acquired noise
maps.
