Multi-channel framelet denoising of diffusion-weighted images

Geng Chen, Jian Zhang, Yong Zhang, Bin Dong, Dinggang Shen, Pew Thian Yap

Research output: Contribution to journalArticle

1 Citation (Scopus)

Abstract

Diffusion MRI derives its contrast from MR signal attenuation induced by the movement of water molecules in microstructural environments. Associated with the signal attenuation is the reduction of signal-to-noise ratio (SNR). Methods based on total variation (TV) have shown superior performance in image noise reduction. However, TV denoising can result in stair-casing effects due to the inherent piecewise-constant assumption. In this paper, we propose a tight wavelet frame based approach for edge-preserving denoising of diffusion-weighted (DW) images. Specifically, we employ the unitary extension principle (UEP) to generate frames that are discrete analogues to differential operators of various orders, which will help avoid stair-casing effects. Instead of denoising each DW image separately, we collaboratively denoise groups of DW images acquired with adjacent gradient directions. In addition, we introduce a very efficient method for solving an ℓ0 denoising problem that involves only thresholding and solving a trivial inverse problem. We demonstrate the effectiveness of our method qualitatively and quantitatively using synthetic and real data.

Original languageEnglish
Article numbere0211621
JournalPLoS One
Volume14
Issue number2
DOIs
Publication statusPublished - 2019 Feb 1

Fingerprint

Stairs
Water Movements
Diffusion Magnetic Resonance Imaging
Signal-To-Noise Ratio
Noise abatement
Inverse problems
Magnetic resonance imaging
Signal to noise ratio
methodology
Molecules
Water
water
Direction compound
wavelet

ASJC Scopus subject areas

  • Biochemistry, Genetics and Molecular Biology(all)
  • Agricultural and Biological Sciences(all)

Cite this

Chen, G., Zhang, J., Zhang, Y., Dong, B., Shen, D., & Yap, P. T. (2019). Multi-channel framelet denoising of diffusion-weighted images. PLoS One, 14(2), [e0211621]. https://doi.org/10.1371/journal.pone.0211621

Multi-channel framelet denoising of diffusion-weighted images. / Chen, Geng; Zhang, Jian; Zhang, Yong; Dong, Bin; Shen, Dinggang; Yap, Pew Thian.

In: PLoS One, Vol. 14, No. 2, e0211621, 01.02.2019.

Research output: Contribution to journalArticle

Chen, G, Zhang, J, Zhang, Y, Dong, B, Shen, D & Yap, PT 2019, 'Multi-channel framelet denoising of diffusion-weighted images', PLoS One, vol. 14, no. 2, e0211621. https://doi.org/10.1371/journal.pone.0211621
Chen, Geng ; Zhang, Jian ; Zhang, Yong ; Dong, Bin ; Shen, Dinggang ; Yap, Pew Thian. / Multi-channel framelet denoising of diffusion-weighted images. In: PLoS One. 2019 ; Vol. 14, No. 2.
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