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Computed Tomography

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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Related Experiment Video

Updated: Jan 16, 2026

Advanced Diffusion Imaging in The Hippocampus of Rats with Mild Traumatic Brain Injury

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TDMAR-Net: a frequency-aware tri-domain diffusion network for CT metal artifact reduction.

Wenzhuo Chen¹, Bowen Ning¹, Zekun Zhou²

¹School of Information Engineering, Nanchang University, Nanchang 330031, People's Republic of China.

Physics in Medicine and Biology

|October 2, 2025

Summary

This study introduces TDMAR-Net, a novel diffusion model for reducing metal artifacts in CT images. It effectively enhances image quality by using multi-domain information, outperforming existing unsupervised methods.

Keywords:

computed tomography deep learning diffusion models medical image processing metal artifact reduction

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Published on: May 17, 2018

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Last Updated: Jan 16, 2026

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Published on: August 14, 2019

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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
Computational Science

Background:

Metal implants cause significant artifacts in computed tomography (CT) images, impeding clinical diagnosis.
Existing metal artifact reduction methods have limitations, including residual artifacts and reliance on paired data or multi-domain information.

Purpose of the Study:

To propose TDMAR-Net, a diffusion model-based three-domain neural network for metal artifact reduction and CT image quality enhancement.
To leverage priors from projection, image, and Fourier domains for improved artifact removal.

Main Methods:

Developed TDMAR-Net, a diffusion model utilizing projection, image, and Fourier domains.
Employed a two-stage training strategy: large-scale pretraining and masked data fine-tuning.
Integrated a high-pass filter module in the Fourier domain and processed images in blocks to extract diffusion prior information, iteratively filling artifacts in sinogram and image domains.

Main Results:

TDMAR-Net demonstrated superior performance compared to existing unsupervised methods.
The method effectively removes metal-induced artifacts and enhances CT image quality.
Validation was successful on both synthetic and clinical datasets.

Conclusions:

TDMAR-Net effectively overcomes challenges in cross-domain information sharing for precise and robust metal artifact elimination.
The proposed approach offers significant improvements in CT image quality in the presence of metal implants.