Automated characterization of noise distributions in diffusion MRI data

Purpose: To understand and characterize noise distributions in parallel imaging for diffusion MRI. Theory and Methods: Two new automated methods using the moments and the maximum likelihood equations of the Gamma distribution were developed. Simulations using stationary and spatially varying noncentral chi noise distributions were created for two diffusion weightings with SENSE or GRAPPA reconstruction and 8, 12 or 32 receiver coils. Furthermore, MRI data of a water phantom with different combinations of multiband and SENSE acceleration were acquired on a 3T scanner along with noise-only measurements. Finally, an in vivo dataset was acquired at 3T using multiband acceleration and GRAPPA reconstruction. Estimation of the noise distribution was performed with the proposed methods and compared with 3 other existing algorithms. Results: Simulations showed that assuming a Rician distribution can lead to misestimation in parallel imaging. Results on the acquired datasets showed that signal leakage in multiband can lead to a misestimation of the parameters. Noise maps are robust to these artifacts, but may misestimate parameters in some cases. The proposed algorithms herein can estimate both parameters of the noise distribution, are robust to signal leakage artifacts and perform best when used on acquired noise maps. Conclusion: Misestimation of the correct noise distribution can hamper further processing such as bias correction and denoising, especially when the measured distribution differs too much from the actual signal distribution e.g., due to artifacts. The use of noise maps can yield more robust estimates than the use of diffusion weighted images as input for algorithms.