The Parallel Kalman Filter: An efficient tool to deal with real-time non central χ noise correction of HARDI data

We propose a novel real-time non central χ (nc-χ) noise correction method for diffusion-weighted MR data that are known to be particularly sensitive to noise, especially at high b-values. This technique aims to be real-time during the acquisition to get any map stemming from the Diffusion Tensor Imaging (DTI) and the High Angular Resolution Diffusion Imaging (HARDI) models corrected from nc-χ noise. The method is based on a Parallel Kalman Filter which is well adapted for non-Gaussian noise distributions, and which is as suitable for real time purposes as the standard Kalman filter (KF). The results on simulated and real HARDI data show that it outperforms the standard KF approach since non-Gaussian noise distributions are directly embedded in the process through their Gaussian mixture approximation.