Noise Reduction for Magnetic Resonance Images via Adaptive Multiscale Products Thresholding

By Paul Bao, Lei Zhang. IEEE Trans. Medical Imaging, 2003

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Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging (2013, methodology): “Several noise reduction methods in the literature (Bao and Zhang, 2003; Coupé et al., 2008; Gerig et al., 1992) have been applied to MR data from patients with MS.”
DAGAN: Deep De-Aliasing Generative Adversarial Networks for Fast Compressed Sensing MRI Reconstruction (2018, background): “It is of note that although the noise model of magnitude MRI images should follow Rician distribution, the additive Gaussian white noise assumption still holds for the k -space components [70].”
Denoising of Dynamic Contrast-Enhanced MR Images Using Dynamic Nonlocal Means (2010, methodology): “To evaluate the performance of the DNLM algorithm we elected to compare its denoising ability to that of seven other algorithms: Gaussian low pass filter (GLPF), bilateral filter, anisotropic diffusion (AD) [15], wavelet adaptive multiscale products threshold (WAMPT) [ 10 ], traditional wavelet thre”
A survey on the magnetic resonance image denoising methods (2014, methodology): “Bao and Zang [53] proposed a MR image denoising method based on an adaptive multiscale products threshold which incorporates the merits of interscale dependencies into the thresholding technique for denoising.”