Rician Noise Removal by Non-Local Means Filtering for Low Signal-to-Noise Ratio MRI: Applications to DT-MRI
By Nicolas Wiest-Daesslé, S. Prima, Pierrick Coupé, S. Morrissey, C. Barillot. International Conference on Medical Image Computing and Computer-Assisted Intervention, 2008
314 citations recorded by the source corpus.
Subfields: Nuclear and High Energy Physics, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/0af2802df80b4e7b14d617263552cdc30d1d0817
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In Radiology, Nuclear Medicine and Imaging
- Thomas R. Knösche
- Robert Turner
- Derek K. Jones
- José Vicente Manjón
- Vladimir S Fonov
- Jens C. Pruessner
- Pierrick Coupé
- D. Louis Collins
Citations in the corpus
- Patch-based segmentation using expert priors: Application to hippocampus and ventricle segmentation (2011, methodology): “To remove the intensity bias introduced by the Rician nature of noise, a Rician adaptation of nonlocal means (Wiest-Daessle et al., 2008) was also used.”
- Image Denoising Methods. A New Nonlocal Principle (2010, unknown): “Improvements or adaptations of NL-means have been proposed in cryon microscopy [29], fluorescence microscopy [9], magnetic resonance imaging (MRI) [61], [26], [91], [68], multispectral MRI [62], [12], and diffusion tensor MRI (DT-MRI) [90].”
- A survey on the magnetic resonance image denoising methods (2014, methodology): “Several variations on the NLM algorithm have been proposed for denoising MR images [26–37].”
- Nonlocal Transform-Domain Filter for Volumetric Data Denoising and Reconstruction (2013, unknown): “BM4D AND THE OB-NLM3D [10], OB-NLM3D-WM [11], [18], ODCT3D [8], AND PRI-NLM3D [8] FILTER.”
- Denoising of 3D magnetic resonance images by using higher-order singular value decomposition (2015, methodology): “The NLM filter has been adapted to denoise MR images and demonstrated better performance than the above mentioned gradient and wavelet-based filters (Coupe et al., 2008; Manjon et al., 2008; Wiest-Daessle et al., 2008).”
- White matter integrity, fiber count, and other fallacies: The do's and don'ts of diffusion MRI (2013, methodology): “, 1996, 1997)] and appropriate models of the noise are invoked (Aja-Fernández et al., 2008; Brion et al., 2011; Clarke et al., 2008; Dietrich et al., 2008; Koay et al., 2009; Kristoffersen, 2009; Tristán-Vega et al., 2012; Wiest-Daessle et al., 2008) (as discussed”