title: Rician noise removal by non-Local Means filtering for low signal-to-noise ratio MRI: applications to DT-MRI.
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.
authors.affiliation: INRIA, VisAGeS Project-Team, F-35042 Rennes, France.
authors.forename: Nicolas
authors.initials: N
authors.lastname: Wiest-Daesslé
authors.forename: Sylvain
authors.initials: S
authors.lastname: Prima
authors.forename: Pierrick
authors.initials: P
authors.lastname: Coupé
authors.forename: Sean Patrick
authors.initials: SP
authors.lastname: Morrissey
authors.forename: Christian
authors.initials: C
authors.lastname: Barillot
mesh_terms: D000465:Algorithms; D016477:Artifacts; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity