CT scan range planning in clinical practice: An international survey of scan range extension, quality assurance, and AI integration.
Authors
Affiliations (5)
Affiliations (5)
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland; Faculty of Applied Medical Sciences, Department of Medical Imaging, The Hashemite University, Zarqa, Jordan. Electronic address: [email protected].
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland; Health Sciences Research Centre, UCL University College, Odense, Denmark.
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland.
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland; Department of Medical Imaging and Intervention, King Abdullah Medical City (KAMC), Makkah, Saudi Arabia.
- Discipline of Medical Imaging and Radiation Therapy, University College Cork, Cork, Ireland; Faculty of Health Sciences, Syddansk Universitet - University of Southern Denmark, Denmark; Faculty of Medicine, University of Sydney, Australia.
Abstract
Computed tomography (CT) scan range planning is a modifiable determinant of radiation exposure but remains highly variable in clinical practice. This study aimed to characterize current practices, identify contributors to scan range extension, examine quality assurance mechanisms, and assess the availability and use of Artificial Intelligence (AI)-assisted tools. An international cross-sectional online survey of diagnostic radiographers performing CT examinations was conducted. A purpose-developed questionnaire, informed by expert review and pilot testing, was delivered via the Qualtrics™ platform and disseminated through radiography societies, professional networks, and social media. Data were collected between January and May 2026. Responses were analyzed using descriptive and exploratory statistical methods. A total of 230 respondents from 42 countries were included. Scan range planning was primarily governed by local institutional frameworks (57%), with reliance on radiographer-led or shared decision-making (each 39%). Patient-related factors, particularly motion and cooperation, were widely reported to influence boundary selection (up to 89%). Scan range extension was most commonly reported in chest and abdominal examinations, primarily driven by radiographer caution to avoid repeat imaging (57%, n = 109). Among respondents with access to AI-assisted tools (44%, n = 84), 58% reported use for all CT examinations and 42% reported selective use. Scan range extension reflects clinically driven decision-making under uncertainty in environments with limited standardization and inconsistent quality assurance. Reducing variability requires coordinated strategies that strengthen governance, support informed decision-making, and integrate technological solutions, including AI-assisted tools, into CT workflows. CT scan range planning determines how much of the body is included in a scan and can affect the amount of radiation a patient receives. This international survey found considerable variation in scan range planning practices, with decisions often influenced by patient characteristics, clinical uncertainty, and local departmental procedures. Quality assurance processes and training were not consistently implemented, and the use of AI-assisted planning tools varied across departments. These findings highlight the need for clearer guidance, stronger quality assurance measures, and appropriate integration of AI-assisted tools to support more consistent and effective CT practice.