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Automatic patient centering in computed tomography: a systematic review and meta-analysis
Yasser H Hadi1,2, Lauren Keaney3, Andrew England3
1Discipline of Medical Imaging and Radiation Therapy, School of Medicine, University College Cork, Cork, Ireland. hadi.y@umail.ucc.ie.
European Radiology
|November 21, 2024
Summary
Automatic patient centering in computed tomography (CT) significantly improves positioning accuracy and reduces preparation time. While radiation dose reduction and image quality improvements are noted, evidence is mixed, supporting integration for optimized patient care.
Area of Science:
- Medical Imaging Technology
- Radiological Physics
Background:
- Computed tomography (CT) relies on precise patient positioning for diagnostic accuracy and safety.
- Manual patient centering is time-consuming and prone to inaccuracies, potentially increasing radiation dose and affecting image quality.
Purpose of the Study:
- To systematically evaluate the impact of automatic patient centering technologies on CT scans.
- To assess effects on positioning accuracy, radiation dose, image quality, and time efficiency.
Main Methods:
- A systematic literature search was conducted across major databases (PubMed, Embase, CINAHL, Scopus, Web of Science) from January 2000 to November 2023.
- Study quality was assessed using the QUADAS-2 tool, with meta-analysis and proportion methodologies used for data synthesis.
Main Results:
- Automatic centering significantly improved positioning accuracy, reducing vertical offsets in thoracic and abdominal CTs.
- Most studies reported radiation dose reductions (5.71-31%) with automatic methods.
- Time efficiency was enhanced, with reduced preparation times compared to manual positioning; image quality results were mixed with minimal noise differences.
Conclusions:
- Automatic patient centering technologies demonstrably enhance positioning accuracy and reduce preparation times in CT examinations.
- Observed reductions in radiation dose and some image quality improvements warrant further investigation.
- Integration of these technologies is recommended to optimize patient care and imaging workflows.
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