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Optimising chest computed tomography (CT) scan ranges in paediatric patients to reduce radiation exposure
1Department of Radiology, The Second Hospital of Tianjin Medical University, Tianjin 300211, China.
Insights
Optimizing scan ranges for pediatric unenhanced chest CT scans accurately covers lung boundaries. This precise protocol reduces radiation exposure by 17-19% while maintaining diagnostic quality for children.
Area of Science:
- Radiology
- Medical Imaging
- Pediatric Imaging
Background:
- Unenhanced chest computed tomography (CT) scans are crucial for diagnosing pediatric conditions.
- Accurate positioning of scan ranges is essential to balance diagnostic needs with minimizing radiation exposure in children.
- Traditional CT protocols may lead to suboptimal coverage or unnecessary radiation dose due to imprecise boundary settings.
Purpose of the Study:
- To establish precise upper and lower scan boundaries for unenhanced chest CT in pediatric patients.
- To minimize radiation risks by optimizing scan range while ensuring complete lung parenchyma coverage.
- To validate a new protocol for improved accuracy and dose reduction in pediatric chest CT.
Main Methods:
- A two-cohort study involving 3174 children (aged 3-14 years) undergoing unenhanced chest CT.
- Retrospective analysis of lung boundary variability in Cohort 1 (n=1894) to determine optimal scan ranges.
- Validation of a precise protocol (upper border at first rib, lower border 35-40 mm below costophrenic angle) in Cohort 2 (n=1,280) and calculation of radiation dose (effective dose, ED; dose-length product, DLP).
Main Results:
- Optimized scan ranges reduced z-axis coverage by 4.7% (3-6 years) and 6.1% (7-14 years).
- The optimized protocol demonstrated a <5% probability of missing lung parenchyma at the upper and lower boundaries.
- Radiation dose reductions observed: 19.2% ED and 17.7% DLP for 3-6 years; 17.0% ED and 17.0% DLP for 7-14 years.
Conclusions:
- The proposed precise scanning protocol effectively covers lung parenchyma in pediatric unenhanced chest CT.
- This protocol achieves significant radiation dose reduction (17-19%) without compromising diagnostic quality.
- The strategy offers a scalable solution for pediatric imaging, with potential for further enhancement through multicenter validation and AI automation.
Aim:
The aim of this study was to accurately position the scan range of unenhanced chest computed tomography (CT) scans for paediatric patients by clarifying the upper and lower scan boundary ranges to meet clinical needs while minimising radiation risks.
Materials And Methods:
This two-cohort study included 3174 children (aged 3-14 years) undergoing unenhanced chest CT scans. Cohort 1 (n=1894) was used to retrospectively analyse lung boundary variability to determine optimal scan ranges, while cohort 2 (n=1,280) was used to validate the accuracy in delineating lung boundaries and reducing radiation dose compared with traditional protocols. Traditional protocol: upper lung border at the first rib's upper edge; lower border below the diaphragm (technician dependent), covering apex to base. Precise protocol: upper border aligns with the first rib (0 mm offset); lower border 35 mm (3-6 years) or 40 mm (7-14 years) below costophrenic angle. Radiation dose calculation: effective dose (ED) derived from dose-length product (DLP) using age-specific conversion coefficients (k = 0.018 mSv·mGy-1·cm-1).
Results:
Scan range optimisation reduced z-axis coverage by 4.7% (3-6 years) and 6.1% (7-14 years). The probability of missed lung parenchyma was <5% for both upper and lower boundaries using the optimised protocol. Radiation dose reductions were as follows: 3-6 year: ED decreased by 19.2% (0.09 mSv) and DLP by 17.7% (4.5 mGy-1cm1); 7-14 years: ED decreased by 17.0% (0.21 mSv) and DLP by 17.0% (11.7 mGy-1cm1).
Conclusion:
The proposed protocol achieves precise lung coverage while reducing radiation exposure by 17-19%. This strategy balances diagnostic quality and patient safety, offering a scalable solution for paediatric unenhanced chest CT imaging. Future multicentre validation and artificial intelligence (AI)-driven automation could further enhance clinical adoption.
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