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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.
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Related Experiment Video

Updated: Jul 7, 2025

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Author Spotlight: A 3D Digital Model for the Diagnosis and Treatment of Pulmonary Nodules

Published on: May 19, 2023

A novel open-source CADs platform for 3D CT pulmonary analysis.

Keming Mao¹, Xin Jing¹, Gao Wang¹

¹Software College, Northeastern University, Shenyang, China.

Computers in Biology and Medicine

|December 23, 2023

Summary

This study introduces an open-source computer-aided diagnosis (CAD) platform for 3D CT pulmonary nodule analysis. The efficient system enhances early lung cancer detection by offering flexible tools and user-friendly interfaces for medical professionals.

Keywords:

3D pulmonary CT Computer-aided diagnosis system Nodule recognition

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Last Updated: Jul 7, 2025

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3D Printing of Preclinical X-ray Computed Tomographic Data Sets

3D Printing of Preclinical X-ray Computed Tomographic Data Sets

Published on: March 22, 2013

Area of Science:

Medical Imaging
Computer-Aided Diagnosis
Oncology

Background:

Pulmonary nodules are critical indicators for lung cancer.
Early detection of lung nodules can significantly reduce mortality rates.
Existing computer-aided diagnosis (CAD) systems require enhanced functionality and flexibility.

Purpose of the Study:

To propose an efficient, open-source CAD platform for 3D CT pulmonary nodule analysis.
To provide professional doctors with advanced tools for improved diagnostic services.
To create a versatile platform supporting flexible equipment integration and user-friendly operation.

Main Methods:

Developed a CAD platform with core functions: Basic Image Processing, Intelligent Image Analysis, Multi-View Image Visualization, Report Editing and Generation, User Information Management, and Inference Service Monitoring.
Implemented a plugin architecture for seamless integration of state-of-the-art or user-defined algorithms.
Conducted system evaluation through use-case testing.

Main Results:

The proposed platform offers a comprehensive suite of tools for 3D CT pulmonary nodule analysis.
The open-source nature and plugin architecture allow for high flexibility and extensibility.
Use-case testing validated the platform's effectiveness and universality in clinical scenarios.

Conclusions:

The developed CAD platform is effective and versatile for 3D CT pulmonary nodule analysis.
The system supports enhanced early lung cancer detection through advanced diagnostic capabilities.
The open-source platform provides a valuable tool for medical professionals, improving diagnostic services.