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Learned spatiotemporal correlation priors for CEST image denoising using incorporated global-spectral convolution

Huan Chen1, Xinran Chen1, Liangjie Lin2

  • 1Department of Electronic Science, Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance, School of Electronic Science and Engineering, National Model Microelectronics College, Xiamen University, Xiamen, China.

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|June 19, 2023
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Summary

A new deep learning method, DECENT, effectively denoises Chemical Exchange Saturation Transfer (CEST) images by leveraging spatiotemporal correlations. This advanced technique outperforms existing methods in low signal-to-noise ratio scenarios.

Keywords:
MRIchemical exchange saturation transferconvolution neural networkdeep learningdenoising

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Area of Science:

  • Biomedical Imaging
  • Artificial Intelligence in Medical Imaging
  • Image Processing

Background:

  • Chemical Exchange Saturation Transfer (CEST) imaging is crucial for various medical applications.
  • Low signal-to-noise ratio (SNR) in CEST images presents a significant challenge for accurate analysis.
  • Existing denoising methods often struggle with the complex noise patterns in CEST data.

Purpose of the Study:

  • To develop a novel deep learning-based method, Denoising CEST Network (DECENT), for effective CEST image denoising.
  • To exploit the inherent spatiotemporal correlation within CEST images for improved noise reduction.
  • To provide a robust and efficient solution for enhancing CEST image quality in low SNR conditions.

Main Methods:

  • DECENT utilizes a dual-pathway U-Net architecture with varying convolution kernel sizes to capture global and spectral features.
  • A fusion pathway integrates features from both parallel pathways for comprehensive denoising.
  • The method was validated using numerical simulations, phantom experiments, and in vivo studies on mouse brains and human skeletal muscle.

Main Results:

  • DECENT demonstrated superior denoising performance compared to state-of-the-art methods like NLmCED, MLSVD, and BM4D, as evidenced by peak SNR (PSNR) and structural similarity index (SSIM) metrics.
  • The network effectively reduced Rician noise in simulated and experimental low SNR CEST images.
  • DECENT offers a computationally efficient alternative, avoiding complex parameter tuning and lengthy iterative processes.

Conclusions:

  • DECENT successfully leverages the spatiotemporal correlation prior of CEST images to restore noise-free observations from noisy data.
  • The proposed deep learning approach significantly outperforms existing denoising techniques.
  • DECENT offers a promising tool for improving the diagnostic utility of CEST imaging in clinical and research settings.