Diffusion Weighted Image Denoising Using Overcomplete Local PCA
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.
Details can be found in:
Manjon J.V, Coupé P., Concha L., Buades A, Collins D.L., Robles M. Diffusion Weighted Image Denoising using overcomplete Local PCA. PLoS ONE 8(9): e73021. doi:10.1371/journal.pone.0073021.2013.
Demo data and the proposed method can be found here:
Version 1.0 (February 2014)
We have also developed a full package comparing several DWI denosing methods. You can find it here.