Denoising functional MR images: a comparison of wavelet denoising and Gaussian smoothing

By Alle Meije Wink, Jos B. T. M. Roerdink. IEEE Transactions on Medical Imaging, 2004

308 citations recorded by the source corpus.

Subfields: Computational Mechanics, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/1447abde68017e50953b3a93c9cc36e17343a621

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In Computational Mechanics

In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Comparative evaluation of despeckle filtering in ultrasound imaging of the carotid artery (2005, background): “These filters are making use of a realistic distribution of the wavelet coefficients [2], [15], [25]–[30] in which only the useful wavelet coefficients are used.”
Integrated wavelet processing and spatial statistical testing of fMRI data (2004, unknown): “Wavelets have also been applied to fMRI analysis as a pure spatial denoising preprocessing step (Hilton et al., 1996; Wink and Roerdink, 2004).”
WSPM: Wavelet-based statistical parametric mapping (2007, background): “Several approaches have been proposed, such as variance reconstruction or estimation of the residual in the spatial domain (Desco et al., 2005; Aston et al., 2005), Bayesian modeling (Turkheimer et al., 2006; Flandin and Penny, 2007), optimizing statistical power while controlling false discovery…”
Bayesian fMRI data analysis with sparse spatial basis function priors (2007, methodology): “As compared to monoresolution Gaussian smoothing, spatial wavelet-based denoising techniques for fMRI have been shown to better preserve image sharpness and retain the original shapes of active regions (Wink and Roerdink, 2004).”
A survey on the magnetic resonance image denoising methods (2014, background): “Since the work of Donoho and Johnstone [45,46], over the last two decades, there are many wavelet based noise reduction schemes for MRI [47–66].”
A Bayesian spatiotemporal model for very large data sets (2010, unknown): “There has been much interest in modelling the spatial response of fMRI over the past decade (Flandin and Penny, 2007; Friman et al., 2003; Gossl et al., 2001; Harrison et al., 2008a; Hartvig and Jensen, 2000; Kiebel et al., 2000; Kim et al., 2005; Penny et al., 2005b; Sole et al., 2001; Tabelow et a”