Noise-driven anisotropic diffusion filtering of MRI
IEEE Transactions on Image Processing
Adaptive noise filtering for accurate and precise iffusion estimation in fiber crossings
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part I
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Parallel MRI leads to magnitude data corrupted by noise described in most cases as following a Rician or a non central χ distribution. And yet, very few correction methods perform a non central χ noise removal. However, this correction step, adapted to the correct noise model, is of very much importance, especially when working with Diffusion Weighted MR data yielding a low SNR. We propose an extended Linear Minimum Mean Square Error estimator (LMMSE), which is adapted to deal with non central χ distributions. We demonstrate on simulated and real data that the extended LMMSE outperforms the original LMMSE on images corrupted by a non central χ noise.