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Related Experiment Video

Updated: Jul 27, 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

Neural network guided sinogram-domain iterative algorithm for artifact reduction.

Gengsheng L Zeng^1,2

¹Department of Computer Science, Utah Valley University, Salt Lake City, Utah, USA.

Medical Physics

|June 6, 2023

Summary

This summary is machine-generated.

This study introduces a novel neural network approach for reducing metal artifacts in X-ray CT scans when the underlying physics are unknown. The method effectively minimizes artifacts, improving image quality in computed tomography tasks.

Keywords:

artifacts computed tomography image reconstruction iterative algorithms neural network

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Area of Science:

Medical Imaging
Artificial Intelligence
Computational Science

Background:

Metal artifacts in X-ray CT pose challenges due to complex, unmodeled physics, especially with unknown materials and wide X-ray spectra.
Accurate mathematical modeling of artifact creation is difficult in many real-world scenarios.

Purpose of the Study:

To develop and evaluate a neural network-based objective function for iterative artifact reduction in computed tomography (CT).
To address artifact reduction when the artifact creation model is unknown or difficult to define mathematically.

Main Methods:

A convolutional neural network was trained to recognize artifacts using a hypothetical, unpredictable projection data distortion model.
The trained neural network served as the objective function for an iterative artifact reduction algorithm in CT.
Gradient descent optimization was employed, with the gradient calculated via the chain rule, evaluating the objective function in the image domain.

Main Results:

Learning curves demonstrated a decrease in the objective function with increasing iterations, indicating convergence.
Post-treatment images showed significant reduction in metal artifacts.
Quantitative analysis using the Sum Square Difference (SSD) metric confirmed the method's effectiveness.

Conclusions:

Utilizing a neural network as an objective function offers a promising solution for artifact reduction in complex scenarios where physical modeling is challenging.
This methodology holds potential for real-world applications in medical imaging and other fields requiring artifact mitigation.