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Related Experiment Video

Updated: Aug 9, 2025

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

An Innovative Metal Artifact Reduction Algorithm based on Res-U-Net GANs.

Ziheng Zhang^1,2, Minghan Yang¹, Lei Xu³

¹Hefei Institutes of Physical Science, Chinese Academy of Sciences, Box: 230031, Hefei, China.

Current Medical Imaging

|February 17, 2023

Summary

This summary is machine-generated.

Metal artifacts in CT scans obscure medical images. A novel Res-U-Net Generative Adversarial Network (GAN) effectively removes these artifacts, enhancing image quality for better diagnosis.

Keywords:

X-ray computed tomography (CT)artifact reduction deep learning generative adversarial networks (GANs)image restoration metal implants

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Area of Science:

Medical Imaging
Artificial Intelligence
Image Processing

Background:

Metal implants in patients undergoing X-ray computed tomography (CT) scans cause severe artifacts.
These artifacts degrade image quality, hindering accurate medical diagnosis.

Purpose of the Study:

To develop an effective algorithm for metal artifact reduction (MAR) in CT images.
To improve the quality of CT images affected by metallic implants.

Main Methods:

Proposed a Generative Adversarial Network (GAN)-based algorithm named Res-U-Net GANs.
The approach utilizes a generator with residual blocks and a U-Net structure, incorporating skip connections.
Employed a weighted joint loss function for training the model.

Main Results:

The Res-U-Net GANs method effectively suppresses noise and removes metal artifacts.
Quantitative evaluation using SSIM, PSNR, and RMSE showed high performance (mean SSIM: 0.977, PSNR: 39.044, RMSE: 0.011).
The algorithm demonstrated excellent performance on clinical datasets, successfully removing artifacts from clinical CT images.

Conclusions:

The proposed algorithm successfully removes metal artifacts from CT images.
It restores crucial image details, significantly aiding radiologists in their assessments.
This technique offers a valuable tool for improving diagnostic accuracy in the presence of metallic implants.