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Dual-Domain Denoising Diffusion Probabilistic Model for Metal Artifact Reduction.

Wenjun Xia1, Chuang Niu1, Grigorios M Karageorgos2

  • 1Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180 USA.

IEEE Transactions on Radiation and Plasma Medical Sciences
|March 9, 2026
PubMed
Summary
This summary is machine-generated.

This study introduces a novel metal artifact reduction (MAR) algorithm for computed tomography (CT) using dual-domain denoising diffusion probabilistic models (DDPM). The method effectively removes metal artifacts, improving diagnostic image quality.

Keywords:
Computed tomography (CT)denoising diffusion probabilistic model (DDPM)dual-domainmetal artifact reduction

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Image Processing

Background:

  • Metal artifacts in computed tomography (CT) images hinder diagnosis and treatment planning.
  • Effective metal artifact reduction (MAR) is crucial for clinical CT applications.
  • Existing methods often struggle with artifact removal and image quality preservation.

Purpose of the Study:

  • To develop and evaluate a novel MAR algorithm using dual-domain denoising diffusion probabilistic models (DDPM).
  • To improve the quality of CT images degraded by metal artifacts.
  • To address hallucination issues common in generic DDPM applications in medical imaging.

Main Methods:

  • Pre-processing using linear interpolation (LI) and a convolutional neural network (CNN) for initial reprojection.
  • Employing two specialized DDPM networks: one for sinogram synthesis and another for image domain optimization.
  • Dual-domain approach combining sinogram and image domain processing for comprehensive artifact removal.

Main Results:

  • The sinogram-domain DDPM successfully reconstructs high-quality sinograms.
  • The image-domain DDPM effectively eliminates residual artifacts, significantly enhancing overall image quality.
  • The proposed algorithm demonstrates superior performance compared to generic DDPMs, mitigating hallucination issues.

Conclusions:

  • The dual-domain DDPM-based MAR algorithm offers a significant improvement in CT image quality.
  • This method enhances the clinical applicability of DDPMs in medical imaging by overcoming limitations.
  • The synergistic use of specialized DDPMs in both sinogram and image domains is key to its success.