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Wavelet-domain TI Wiener-like filtering for complex MR data denoising.

Kai Hu1, Qiaocui Cheng1, Xieping Gao1

  • 1The MOE Key Laboratory of Intelligent Computing and Information Processing, Xiangtan University, Xiangtan, 411105, China; College of Information Engineering, Xiangtan University, Xiangtan, 411105, China.

Magnetic Resonance Imaging
|May 31, 2016
PubMed
Summary

This study introduces a novel wavelet-domain translation-invariant (TI) Wiener-like filtering algorithm for magnetic resonance (MR) image denoising. The advanced method effectively reduces Rician noise in MR images, improving image quality and analysis.

Keywords:
DenoisingMagnetic resonance (MR) imagesRician noiseTranslation-invariant (TI)Wavelet transformWiener-like filtering

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Area of Science:

  • Medical Imaging
  • Signal Processing
  • Computational Science

Background:

  • Magnetic resonance (MR) images suffer from noise, particularly Rician noise in magnitude images, which complicates analysis.
  • Signal-dependent noise in low signal-to-noise ratio (SNR) MR images is challenging to reduce.
  • Existing wavelet-domain filtering methods, like Wirestam's, offer noise reduction but can introduce artifacts.

Purpose of the Study:

  • To develop a translation-invariant (TI) Wiener-like filtering algorithm for enhanced noise reduction in complex MR data.
  • To improve upon existing wavelet-domain denoising techniques by addressing signal translation artifacts and enhancing adaptivity.
  • To provide a more robust and efficient method for denoising MR images.

Main Methods:

  • Proposed a wavelet-domain translation-invariant (TI) Wiener-like filtering algorithm.
  • Integrated Stein's Unbiased Risk Estimator (SURE) thresholding with Wiener-like filters for adaptive hard-thresholding.
  • Applied a preliminary Wiener-like filter to the Gaussian noise in the original complex MR image data.

Main Results:

  • The proposed TI algorithm effectively suppresses artifacts caused by signal translations.
  • Adaptive thresholding using SURE improved the scale adaptability of the denoising process.
  • Experiments on simulated and real MR data (T1-weighted, DW) demonstrated superior performance over other methods.

Conclusions:

  • The developed wavelet-domain TI Wiener-like filtering algorithm offers significant improvements in MR image denoising.
  • The algorithm demonstrates enhanced efficiency and robustness compared to existing denoising techniques.
  • This method provides a valuable tool for improving the quality of MR images for diagnostic and research purposes.