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Diffusion Weighted Image Denoising Using Overcomplete Local PCA

Author

Listed:
  • José V Manjón
  • Pierrick Coupé
  • Luis Concha
  • Antonio Buades
  • D Louis Collins
  • Montserrat Robles

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.

Suggested Citation

  • José V Manjón & Pierrick Coupé & Luis Concha & Antonio Buades & D Louis Collins & Montserrat Robles, 2013. "Diffusion Weighted Image Denoising Using Overcomplete Local PCA," PLOS ONE, Public Library of Science, vol. 8(9), pages 1-12, September.
    • Handle: RePEc:plo:pone00:0073021
    DOI: 10.1371/journal.pone.0073021
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    Cited by:

    1. Thomas Schult & Till-Karsten Hauser & Uwe Klose & Helene Hurth & Hans-Heino Ehricke, 2019. "Fiber visualization for preoperative glioma assessment: Tractography versus local connectivity mapping," PLOS ONE, Public Library of Science, vol. 14(12), pages 1-22, December.
    2. Jia Yang & Barbara Carl & Christopher Nimsky & Miriam H A Bopp, 2020. "The impact of position-orientation adaptive smoothing in diffusion weighted imaging—From diffusion metrics to fiber tractography," PLOS ONE, Public Library of Science, vol. 15(5), pages 1-18, May.
    3. Nan-kuei Chen & Hing-Chiu Chang & Ali Bilgin & Adam Bernstein & Theodore P Trouard, 2018. "A diffusion-matched principal component analysis (DM-PCA) based two-channel denoising procedure for high-resolution diffusion-weighted MRI," PLOS ONE, Public Library of Science, vol. 13(4), pages 1-19, April.

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