ArticleInsights into imaging2026
Automated contrast-to-noise ratio analysis in chest CT: validation of an open-source segmentation approach.
Article in Insights into imaging, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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1 citing paper in PubMed.
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13 authors.
Funding
Abstract
objectivesThis study aimed to evaluate the feasibility and accuracy of automated contrast-to-noise ratio (CNR) analysis in chest CT using the open-source body and organ analysis (BOA) framework and to validate segmentation modifications for reproducible image-quality assessment. MATERIALS AND
methodsThis retrospective study analyzed 100 contrast-enhanced chest CTs (mean age 60.2 ± 15 years; 40% female; 50 CTA, 50 CTPA) and validated the approach in an external cancer imaging archive (TCIA) cohort (n = 100). Automated BOA segmentations of the aorta, pulmonary trunk, and paraspinal muscles were modified by fat subtraction and binary erosion and compared with manual measurements from three radiologists. Agreement was assessed using statistical testing, Bland-Altman analysis, and intraclass correlation coefficients (ICC).
resultsUnmodified BOA segmentations yielded significantly lower CNRs than manual measurements (all p < 0.01, mean difference up to 6.3). Fat subtraction and binary erosion progressively reduced deviations, with the optimized variant (m_erode6 combined with a_erode6 or p_erode6) showing no significant differences from radiologists (p ≥ 0.35). In the external TCIA validation cohort (n = 100), agreement was excellent (ICC 0.89-0.93), and Bland-Altman analysis demonstrated minimal bias (Aorta: 0.16 [limits of agreement (LoA) -3.47 to 3.80]; PT: 0.42 [LoA -4.03 to 4.87]).
conclusionsA minimally modified open-source segmentation framework enables fully automated, reproducible CNR assessment in chest CT, achieving expert-level agreement, including robust performance in external validation. This scalable alternative to manual region-of-interest (ROI) measurement streamlines image-quality assessment, facilitates protocol optimization, and provides standardized metrics ready for integration into AI workflows. CRITICAL RELEVANCE STATEMENT: This study provides a validated, fully automated method for quantitative CT image quality assessment, reducing observer dependence and enabling consistent evaluation across scanners, protocols, and institutions, thereby supporting reproducible image quality metrics in clinical routine. KEY POINTS: Automated CNR assessment enables objective and reproducible evaluation of image quality in CTA and CTPA. Adjustments of the segmentation strategy can substantially improve the accuracy of automated measurements. The fully automated approach provides a foundation for standardized and scalable CT image quality analysis in research and clinical practice.
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