Diffusion weighted image denoising using overcomplete local PCA

PLoS One. 2013 Sep 3;8(9):e73021. doi: 10.1371/journal.pone.0073021. eCollection 2013.

Abstract

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.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Brain / physiology
  • Humans
  • Magnetic Resonance Imaging*
  • Principal Component Analysis*
  • Signal-To-Noise Ratio

Grant support

This work has been supported by the Spanish grant TIN2011-26727 from Ministerio de Ciencia e Innovación. This work has been also partially supported by the French grant “HR-DTI” ANR-10-LABX-57 funded by the TRAIL from the French Agence Nationale de la Recherche within the context of the Investments for the Future program. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.