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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...

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WDBDM: Wavelet-based dual-branch diffusion model for low-dose CT and PET denoising.

Qi Sun1, Tongtong Li1, Guowei Wang1

  • 1School of Information Science and Engineering, Lanzhou University, Lanzhou 730000, China; Gansu Provincial Key Laboratory of Wearable Computing, Lanzhou University, Lanzhou 730000, China.

Computerized Medical Imaging and Graphics : the Official Journal of the Computerized Medical Imaging Society
|June 24, 2026
PubMed
Summary

This study introduces WDBDM, a novel framework using wavelet transforms and diffusion models to denoise low-dose CT and PET scans. WDBDM generates high-quality medical images from low-dose data, reducing radiation exposure risks.

Keywords:
Diffusion modelImage denoisingLow-dose CTLow-dose PETWavelet transforms

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Signal Processing

Background:

  • Low-dose computed tomography (CT) and positron emission tomography (PET) imaging are crucial for disease assessment but raise radiation exposure concerns.
  • Reconstructing high-quality images from low-dose scans is essential to balance diagnostic performance and patient safety.
  • Existing methods struggle to effectively denoise low-dose medical images while preserving diagnostic quality.

Purpose of the Study:

  • To present WDBDM, a wavelet-based dual-branch diffusion framework designed for denoising low-dose CT and PET images.
  • To enhance image quality to levels comparable to normal-dose imaging, thereby reducing radiation risks.
  • To improve the feature representation and denoising performance for medical imaging applications.

Main Methods:

  • The WDBDM framework integrates Discrete Wavelet Transform (DWT), a Low-Frequency Diffusion Branch (LFDB), a High-Frequency Diffusion Branch (HFDB), and a Fusion Module.
  • A Fusion Spatial-Frequency Convolution Module (FSFCM) was developed to extract joint spatial and frequency domain information, enhancing feature representation.
  • A novel recovery network, HLF-MEMNet, was integrated to prevent error propagation and enable bidirectional guidance between frequency components during sampling.

Main Results:

  • WDBDM demonstrated superior denoising performance compared to existing methods across four public datasets and two imaging modalities.
  • The framework effectively generated normal-dose quality images from low-dose CT and PET data.
  • The integration of FSFCM and HLF-MEMNet significantly improved feature extraction and model stability.

Conclusions:

  • WDBDM offers a promising solution for generating high-quality medical images from low-dose scans, significantly reducing radiation exposure.
  • The proposed framework shows excellent denoising capabilities and generalization ability, outperforming current state-of-the-art methods.
  • WDBDM has the potential to improve diagnostic accuracy and patient safety in medical imaging.