Multi-channel framelet denoising of diffusion-weighted images

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

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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

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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