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Abstract #3576

The Parallel Kalman Filter: An Efficient Tool to Deal with Real-Time Non Central χ Noise Correction

Veronique Brion1, Olivier Riff1, Maxime Descoteaux2, Jean-Franois Mangin1, Denis Le Bihan1, Cyril Poupon1, Fabrice Poupon1

1NeuroSpin, CEA/IBM, Gif-sur-Yvette, France; 2Sherbrooke University, Sherbrooke, Canada

This abstract proposes a novel real-time non central χ noise correction method for diffusion-weighted MR data that are known to be particularly sensitive to noise, as the diffusion indicator in the tissues corresponds to a signal loss. The technique 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. The results on simulated and real HARDI data show that it outperforms the standard Kalman Filter approach since non-Gaussian noise distributions are directly embedded in the process through their Gaussian mixture approximation.