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Denoise diffusion-weighted images using higher-order singular value decomposition.

Xinyuan Zhang1, Jie Peng1, Man Xu1

  • 1School of Biomedical Engineering, Guangdong Provincial Key Laboratory of Medical Image Processing, Southern Medical University, Guangzhou, China.

Neuroimage
|April 19, 2017
PubMed
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This summary is machine-generated.

This study introduces a novel denoising method for diffusion-weighted (DW) magnetic resonance imaging (MRI) that reduces stripe artifacts. The enhanced Higher-order Singular Value Decomposition (HOSVD) technique improves image quality and diffusion parameter estimation.

Area of Science:

  • Medical Imaging
  • Signal Processing

Background:

  • Noise significantly impacts quantitative analysis in diffusion-weighted (DW) magnetic resonance imaging (MRI), particularly at high b-values or spatial resolutions.
  • Higher-order singular value decomposition (HOSVD) is an effective transform for exploiting data sparseness, with patch-based HOSVD showing promise in various MRI contrasts.

Purpose of the Study:

  • To investigate the feasibility of denoising DW MRI data using HOSVD.
  • To address stripe artifacts in patch-based HOSVD denoising of DW MRI due to low signal-to-noise ratios.

Main Methods:

  • Proposed a novel two-stage denoising method combining global HOSVD prefiltering with patch-based HOSVD.
  • Utilized HOSVD bases from prefiltered images to guide the denoising of noisy patches in original DW data.
  • Validated the method using simulated and in vivo DW MRI datasets.
Keywords:
DenoisingDiffusion tensor imaging (DTI)Diffusion-weighted imaging (DWI)Higher-order singular value decomposition (HOSVD)Magnetic resonance imaging (MRI)

Related Experiment Videos

Main Results:

  • The proposed method significantly reduced stripe artifacts compared to conventional patch-based HOSVD.
  • Achieved superior denoising quality and diffusion parameter estimation compared to two state-of-the-art methods.
  • Demonstrated effective artifact reduction in homogeneous regions of DW MRI.

Conclusions:

  • The novel HOSVD-based prefiltering approach effectively guides patch-based denoising for DW MRI.
  • This method enhances the reliability of quantitative analysis in DW MRI by improving image quality and parameter estimation.
  • The proposed technique offers a significant advancement in DW MRI denoising.