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A kernel-based image denoising method for improving parametric image generation.

Hsuan-Ming Huang1, Chieh Lin2

  • 1Institute of Medical Device and Imaging, College of Medicine, National Taiwan University, No.1, Sec. 1, Jen Ai Rd., Zhongzheng Dist., Taipei City 100, Taiwan.

Medical Image Analysis
|April 26, 2019
PubMed
Summary
This summary is machine-generated.

This study introduces a novel kernel-based image denoising method to reduce noise in diffusion-weighted magnetic resonance imaging (DW-MRI) data. The method enhances the quality of intravoxel incoherent motion (IVIM) parametric images, offering a better bias-variance trade-off than existing techniques.

Keywords:
Diffusion-weighted imagingGeneral threshold filteringIntra-voxel incoherent motionKernel methodTotal difference

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Area of Science:

  • Medical Imaging
  • Image Processing
  • Biophysics

Background:

  • High noise levels pose a significant challenge in pixel-wise analysis of medical imaging data.
  • Existing kernel-based methods for dynamic positron emission tomography reconstruction inspired this work.
  • Diffusion-weighted magnetic resonance imaging (DW-MRI) is susceptible to noise, impacting quantitative analysis.

Purpose of the Study:

  • To develop and evaluate a novel kernel-based image denoising method for DW-MRI data.
  • To assess the impact of denoised DW-MRI data on the quality of intravoxel incoherent motion (IVIM) parametric images.
  • To compare the proposed denoising method against the local principal component analysis (LPCA) method.

Main Methods:

  • A kernel-based image denoising method was developed, minimizing a kernel-based lp-norm regularized problem.
  • The general-threshold filtering algorithm was combined with total difference for solving the denoising problem.
  • The proposed method was applied to simulated DW-MRI data with Rician noise and tested on in vivo data, with comparisons to LPCA.

Main Results:

  • Computer simulations demonstrated that the proposed method achieves a superior bias-variance trade-off compared to LPCA.
  • The method effectively reduces variance while preserving important image edges in parametric maps.
  • Denoised DW-MRI data using the proposed method significantly improved the quality of IVIM parametric images in vivo.

Conclusions:

  • The proposed kernel-based image denoising method is effective for improving DW-MRI data quality.
  • This technique offers a valuable tool for enhancing the accuracy and reliability of IVIM parametric imaging.
  • The method provides a better bias-variance trade-off and edge preservation than LPCA for DW-MRI denoising.