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Diffusion-based model as a generalizable denoiser for optoacoustic imaging.

Yuan Xu1, Xiang Liu2, Xia Li3

  • 1Institute of Biomedical Engineering, Department of Information Technology and Electrical Engineering, ETH, Zurich, 8093, Switzerland; Institute of Biomedical Engineering and Institute of Pharmacology and Toxicology, Faculty of Medicine, University of Zurich, 8057, Switzerland; Department of Mechanical and Process Engineering, ETH, Zurich, 8092, Switzerland.

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Summary
This summary is machine-generated.

This study introduces an advanced denoising method for optoacoustic imaging, enhancing image quality from low-cost systems. The novel framework effectively reduces noise and artifacts, improving diagnostic potential.

Keywords:
Deep learningDenoisingLow-costOptoacoustic imagingPhotoacoustic imaging

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Area of Science:

  • Biomedical Imaging
  • Medical Physics
  • Image Processing

Background:

  • Optoacoustic imaging is increasingly used in research and clinical settings, but low-cost systems often suffer from image artifacts and noise.
  • Advanced denoising techniques are crucial for improving the performance and diagnostic utility of these systems.

Purpose of the Study:

  • To develop a novel denoising framework for optoacoustic imaging systems.
  • To address heterogeneous noise sources and preserve critical image details in optoacoustic images.

Main Methods:

  • A new denoising framework based on improved denoising diffusion probabilistic models (DDPMs) was developed.
  • The model uses Gaussian noise as a carrier to suppress various distortions and an iterative mechanism for artifact removal.
  • The method was tested using laser-diode and light-emitting-diode sources.

Main Results:

  • The proposed method demonstrated consistent performance improvements compared to state-of-the-art techniques.
  • It effectively suppressed both Gaussian and non-Gaussian noise while preserving essential image features.
  • Experimental results confirmed the framework's superiority over benchmark methods like BM3D and Deep Image Prior.

Conclusions:

  • The novel DDPM-based denoising framework significantly enhances optoacoustic image quality.
  • This advancement is vital for maximizing the diagnostic potential of cost-effective optoacoustic imaging systems, especially in resource-limited environments.