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Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI: applications to DT-MRI.

Nicolas Wiest-Daesslé 1 Sylvain Prima 1 Pierrick Coupé 1 Sean Patrick Morrissey 1, 2 Christian Barillot 1 
1 VisAGeS - Vision, Action et Gestion d'informations en Santé
INSERM - Institut National de la Santé et de la Recherche Médicale : U746, Inria Rennes – Bretagne Atlantique , IRISA-D5 - SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE
Abstract : Diffusion-Weighted MRI (DW-MRI) is subject to random noise yielding measures that are different from their real values, and thus biasing the subsequently estimated tensors. The Non-Local Means (NLMeans) filter has recently been proposed to denoise MRI with high signal-to-noise ratio (SNR). This filter has been shown to allow the best restoration of image intensities for the estimation of diffusion tensors (DT) compared to state-of-the-art methods. However, for DW-MR images with high b-values (and thus low SNR), the noise, which is strictly Rician-distributed, can no longer be approximated as additive white Gaussian, as implicitly assumed in the classical formulation of the NLMeans. High b-values are typically used in high angular resolution diffusion imaging (HARDI) or q-space imaging (QSI), for which an optimal restoration is critical. In this paper, we propose to adapt the NLMeans filter to Rician noise corrupted data. Validation is performed on synthetic data and on real data for both conventional MR images and DT images. Our adaptation outperforms the original NLMeans filter in terms of peak-signal-to-noise ratio (PSNR) for DW-MRI.
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Submitted on : Monday, October 20, 2008 - 6:00:55 PM
Last modification on : Monday, June 27, 2022 - 3:05:54 AM
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Nicolas Wiest-Daesslé, Sylvain Prima, Pierrick Coupé, Sean Patrick Morrissey, Christian Barillot. Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI: applications to DT-MRI.. 11th International Conference on Medical Image Computing and Computer-Assisted Intervention, Sep 2008, New York, United States. pp.171-9, ⟨10.1007/978-3-540-85990-1_21⟩. ⟨inserm-00332388⟩



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