ArticleJapanese journal of radiology2025
Application of a pulmonary nodule detection program using AI technology to ultra-low-dose CT: differences in detection ability among various image reconstruction methods.
Article in Japanese journal of radiology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Diagnostic performance of commercial AI systems versus participating radiologists for pulmonary nodule detection in routine clinical practice.Japanese journal of radiology · 2026Article
- Chest CT-detected pulmonary nodules across the COVID-19 pandemic in Guangzhou, China: a regression discontinuity-in-time study, 2013-2023.Translational lung cancer research · 2026Article
- Deep Learning Reconstruction Specialized for Inner Ear: Improving Image Quality and Anatomical Structure Visualization as Compared with Conventional Hybrid-Type Iterative Reconstruction on High-Definition CT.Diagnostics (Basel, Switzerland) · 2026Article
- Artificial Intelligence-Assisted Pathogen Detection: Algorithms, Biosensing Platforms, and Applications.Biosensors · 2026Review
- Artificial intelligence-based lung nodule detection for pulmonary arteriovenous fistulas on chest computed tomography.World journal of radiology · 2026Article
- Artificial intelligence construction: a review of the bridge between CT imaging features of lung ground-glass nodules adenocarcinoma and carcinogenic driver genes.Journal of cancer research and clinical oncology · 2026Review
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8 authors.
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Abstract
purposeThis study aimed to investigate the performance of an artificial intelligence (AI)-based lung nodule detection program in ultra-low-dose CT (ULDCT) imaging, with a focus on the influence of various image reconstruction methods on detection accuracy.
methodsA chest phantom embedded with artificial lung nodules (solid and ground-glass nodules [GGNs]; diameters: 12 mm, 8 mm, 5 mm, and 3 mm) was scanned using six combinations of tube currents (160 mA, 80 mA, and 10 mA) and voltages (120 kV and 80 kV) on a Canon Aquilion One CT scanner. Images were reconstructed using filtered back projection (FBP), hybrid iterative reconstruction (HIR), model-based iterative reconstruction (MBIR), and deep learning reconstruction (DLR). Nodule detection was performed using an AI-based lung nodule detection program, and performance metrics were analyzed across different reconstruction methods and radiation dose protocols.
resultsAt the lowest dose protocol (80 kV, 10 mA), FBP showed a 0% detection rate for all nodule sizes. HIR and DLR consistently achieved 100% detection rates for solid nodules ≥ 5 mm and GGNs ≥ 8 mm. No method detected 3 mm GGNs under any protocol. DLR demonstrated the highest detection rates, even under ultra-low-dose settings, while maintaining high image quality.
conclusionAI-based lung nodule detection in ULDCT is strongly dependent on the choice of image reconstruction method.
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