Journal of medical radiation sciences · 2026
Radiation Dose Optimisation Through Artificial Intelligence (AI)-based Auto-Thorax Collimation.
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Lee Derek Yoon-Sang, Chan Nicole, Knight Richard, Hayre Christopher
Аннотация
INTRODUCTION: Artificial intelligence (AI) is increasingly adopted in digital radiography to enhance workflow efficiency, standardisation and support for radiation dose optimisation. Auto-thorax collimation (ATC) has been introduced to automate field size selection in chest X-ray (CXR) imaging; however, its clinical performance and impact on collimation consistency in routine chest X-ray practice require further evaluation.
METHODS: A single-centre retrospective study analysed 400 posteroanterior (PA) erect CXRs sourced from the Picture Archiving and Communication System (PACS) and local image archives. Collimation size was measured in both the superior-inferior and lateral dimensions. An additional, 200 erect CXRs were collected to assess the repeat rate due to collimation errors. Statistical analysis was performed using two-sample t-tests for collimation measurements and a two-proportion Z-test for repeat rates. The coefficient of variation (CV) was calculated to quantify the variability in collimation field size for each operator across both ATC and manual collimation methods.
RESULTS: ATC demonstrated tighter inferior and left lateral collimation than manual collimation (p < 0.05), while manual collimation showed tighter superior collimation. Collimation consistency was greater with ATC, as evidenced by lower CV values across operators. Repeat rates were comparable between ATC (7%) and manual collimation (8%).
CONCLUSION: AI-based collimation provides collimation results similar to manual performance, with improved standardisation and reproducibility. Its consistent output suggests potential benefits in high-turnover environments, enhancing workflow efficiency whilst optimising radiation safety.
METHODS: A single-centre retrospective study analysed 400 posteroanterior (PA) erect CXRs sourced from the Picture Archiving and Communication System (PACS) and local image archives. Collimation size was measured in both the superior-inferior and lateral dimensions. An additional, 200 erect CXRs were collected to assess the repeat rate due to collimation errors. Statistical analysis was performed using two-sample t-tests for collimation measurements and a two-proportion Z-test for repeat rates. The coefficient of variation (CV) was calculated to quantify the variability in collimation field size for each operator across both ATC and manual collimation methods.
RESULTS: ATC demonstrated tighter inferior and left lateral collimation than manual collimation (p < 0.05), while manual collimation showed tighter superior collimation. Collimation consistency was greater with ATC, as evidenced by lower CV values across operators. Repeat rates were comparable between ATC (7%) and manual collimation (8%).
CONCLUSION: AI-based collimation provides collimation results similar to manual performance, with improved standardisation and reproducibility. Its consistent output suggests potential benefits in high-turnover environments, enhancing workflow efficiency whilst optimising radiation safety.