Diffusion Weighted Image Denoising Using Overcomplete Local PCA

By José Vicente Manjón, Pierrick Coupé, Luis Concha, A. Buades, D. Louis Collins, Robles Montserrat. PLoS ONE, 2013

429 citations recorded by the source corpus.

Diffusion Weighted Images (DWI) normally shows a low Signal to Noise Ratio (SNR) due to the presence of noise from the measurement process that complicates and biases the estimation of quantitative diffusion parameters. In this paper, a new denoising methodology is proposed that takes into consideration the multicomponent nature of multi-directional DWI datasets such as those employed in diffusion imaging. This new filter reduces random noise in multicomponent DWI by locally shrinking less significant Principal Components using an overcomplete approach. The proposed method is compared with state-of-the-art methods using synthetic and real clinical MR images, showing improved performance in terms of denoising quality and estimation of diffusion parameters.

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

View Paper: https://www.semanticscholar.org/paper/cd1292c3bcc0e512c7a1b6fa2ac256215aab1255

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In Computer Vision and Pattern Recognition

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

Complex diffusion-weighted image estimation via matrix recovery under general noise models (2018, methodology): “U The DWI phase varies in a largely unpredictable manner during the acquisition mainly due to patient motion during the scan [17].”
Denoising of diffusion MRI using random matrix theory (2016, methodology): “Commonly used criteria include thresholding of the eigenvalues associated with the principal components by an empirically set value (Manjón et al., 2013).”