Artificial intelligence integration into the CT system with a focus on CT planning and scanning-review

Area of Science:

• Radiology
• Medical Imaging
• Artificial Intelligence in Medicine

Background:

• Computed tomography (CT) development historically focused on hardware improvements and clinical capabilities.
• A parallel advancement in CT development involves optimizing scanning workflows and image display using artificial intelligence (AI).

Purpose of the Study:

• To provide a comprehensive overview of AI-driven functionalities integrated into CT systems.
• To discuss current and future AI applications in improving CT scanning and image analysis.

Main Methods:

• Review of AI-driven functionalities in CT systems, including patient positioning, characterization, and scan guidance.
• Discussion of automated scan range definition, prediction of scan delays for CT angiography, and AI-supported image quality checks.
• Presentation of AI algorithms for automatic image reconstruction and advanced visualizations on the scanner.

Main Results:

• AI integration offers automated patient positioning, scan guidance, and scan range definition.
• AI enables prediction of patient-specific scan delays for CT angiography and supports image quality checks.
• Advanced AI methods facilitate automatic, anatomically aligned reconstructions and visualizations directly on CT scanners.

Conclusions:

• AI is transforming CT systems by automating complex tasks and enhancing image interpretation.
• The integration of AI streamlines the CT workflow, improves diagnostic accuracy, and expands clinical applications.
• Future developments in AI promise further innovations in CT technology, enhancing both efficiency and diagnostic power.