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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: May 2, 2026

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

Published on: July 29, 2013

Metal artifact reduction combined with deep learning image reconstruction algorithm for CT image quality

Huachun Zou^1,2, Zonghuo Wang², Mengya Guo³

¹School of Medical and Information Engineering, Gannan Medical University, Ganzhou, China.

|June 9, 2025

Summary

This summary is machine-generated.

The smart metal artifact reduction (MAR) algorithm combined with deep learning image reconstruction (DLIR-H) effectively reduces metal artifacts and improves CT image quality, especially at higher tube voltages.

Keywords:

CT Deep learning image reconstruction Diagnostic performance Image quality Metal artifact reduction

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Last Updated: May 2, 2026

Construction of a Preclinical Multimodality Phantom Using Tissue-mimicking Materials for Quality Assurance in Tumor Size Measurement

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Published on: July 29, 2013

Hybrid µCT-FMT imaging and image analysis

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Published on: June 4, 2015

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
Radiology
Computed Tomography (CT)

Background:

Metal artifacts significantly degrade CT image quality, hindering accurate diagnosis.
Optimizing scanning parameters is crucial for effective metal artifact reduction (MAR).

Purpose of the Study:

To evaluate the impact of the MAR algorithm and various scanning parameters on metal artifact reduction and image quality.
To determine the optimal CT protocol for clinical application in the presence of metal implants.

Main Methods:

A phantom with a pacemaker was scanned at standard (3 mSv) and low (0.5 mSv) doses with varying tube voltages (70, 100, 120 kVp).
Reconstruction algorithms included adaptive statistical iterative reconstruction-V (ASIR-V) and deep learning image reconstruction (DLIR-H), with and without MAR.
Quantitative analysis of artifact index, noise, and signal-to-noise ratio (SNR), alongside qualitative assessment, was performed.

Main Results:

DLIR-H and higher tube voltages resulted in lower noise (p < 0.001).
MAR and high tube voltages significantly reduced artifact index (p < 0.001).
Low-dose (0.5 mSv) 120 kVp DLIR-H MAR showed comparable artifact reduction to standard-dose (3 mSv) 70 kVp ASIR-V MAR.

Conclusions:

The MAR algorithm combined with DLIR-H significantly reduces metal artifacts and enhances image quality.
High kVp tube voltages further improve artifact reduction and image quality.
This combination offers a promising approach for optimizing CT protocols in patients with metal implants.