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Related Experiment Video

Updated: Jan 10, 2026

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

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Published on: November 7, 2017

d-MAR: Deep Metal Artifact Reduction via Diffusion-Driven Domain Transformations.

Yi Guo, Zhixiong Zeng, Yuyan Song

IEEE Journal of Biomedical and Health Informatics

|November 26, 2025

Summary

This summary is machine-generated.

Metal artifacts in CT scans hinder diagnosis. A new diffusion-driven method, d-MAR, bridges simulated and real image domains, significantly improving metal artifact reduction for clearer medical imaging.

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Last Updated: Jan 10, 2026

Optimized Setup and Protocol for Magnetic Domain Imaging with In Situ Hysteresis Measurement

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Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Protocol for the Evaluation of MRI Artifacts Caused by Metal Implants to Assess the Suitability of Implants and the Vulnerability of Pulse Sequences

Published on: May 17, 2018

Area of Science:

Medical Imaging
Artificial Intelligence
Computer Vision

Background:

Metal implants cause severe artifacts in CT images, reducing diagnostic accuracy.
Supervised metal artifact reduction (MAR) models struggle with domain gaps between simulated and real data.
Unsupervised MAR methods offer limited artifact suppression and training stability.

Purpose of the Study:

To introduce d-MAR, a novel framework for effective metal artifact reduction.
To address the domain gap challenge in MAR by transforming real images into a simulated domain.
To improve the generalization capability of MAR models across different devices and protocols.

Main Methods:

Developed a diffusion-driven domain transformation framework (d-MAR) between real and simulated image domains.
Utilized diffusion models as a transformation bridge, employing conditional input and sampling enhancement.
Leveraged Fourier-extracted low-frequency image components for domain alignment without random generation, preserving anatomical fidelity.

Main Results:

d-MAR successfully reduced metal artifacts from real clinical data using models trained on simulated data.
The method demonstrated consistent outperformance over conventional MAR techniques in quantitative metrics and visual quality.
Evaluations on diverse datasets (Clinical Head, Clinical Body, dental CBCT) confirmed strong generalization capabilities.

Conclusions:

d-MAR effectively bridges the domain gap for metal artifact reduction in CT imaging.
The proposed framework enhances diagnostic reliability by improving image quality in the presence of metal implants.
d-MAR offers a robust and generalizable solution for real-world clinical applications.