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

For many years, the development of CT has been driven by technological advances in CT scanners to improve their performance, reduce radiation dose, and expand their clinical capabilities. Meanwhile, another focus of CT development has emerged-on improving and automating the scanning workflow and on approaches to better display the clinical information of CT images, supported by artificial intelligence (AI). In this review, we provide an overview of AI-driven functionalities integrated into the CT system. We discuss 3D camera-based patient positioning, patient characterization, and scan guidance, and automated scan range definition based on the localizer scan. We present approaches to automatically predict patient-specific individual scan delays for CT angiographic scans, and we discuss algorithms for AI-supported initial image quality checks. Finally, we present modern approaches that enable automatic calculation of anatomically aligned reconstructions and advanced visualizations directly on the scanner. We focus on clinically established methods, but we also present some prototype algorithms as a glimpse into the future.

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Publication Details

Journal
British Journal of Radiology
Published
2025-06-16
DOI
https://doi.org/10.1093/bjr/tqaf129
Citations
5
Primary Topic
Advanced X-ray and CT Imaging
Type
article
Field-Weighted Citation Impact
3.37
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article

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

Michael Sühling, Thomas G. Flohr
5 citations
British Journal of Radiology
Advanced X-ray and CT Imaging
3.37
article

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

Michael Sühling, Thomas G. Flohr
article en
5 citations

Abstract

For many years, the development of CT has been driven by technological advances in CT scanners to improve their performance, reduce radiation dose, and expand their clinical capabilities. Meanwhile, another focus of CT development has emerged-on improving and automating the scanning workflow and on approaches to better display the clinical information of CT images, supported by artificial intelligence (AI). In this review, we provide an overview of AI-driven functionalities integrated into the CT system. We discuss 3D camera-based patient positioning, patient characterization, and scan guidance, and automated scan range definition based on the localizer scan. We present approaches to automatically predict patient-specific individual scan delays for CT angiographic scans, and we discuss algorithms for AI-supported initial image quality checks. Finally, we present modern approaches that enable automatic calculation of anatomically aligned reconstructions and advanced visualizations directly on the scanner. We focus on clinically established methods, but we also present some prototype algorithms as a glimpse into the future.

British Journal of RadiologyVol. 98(1175)
Maastricht University Medical Centre (NL), University Medical Center (US), Maastricht University (NL), University Hospital of Zurich (CH), Siemens Healthcare (United States) (US)
Openalex Percentile: Top 8%
Advanced X-ray and CT Imaging
3.37
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