ArticleQuantitative imaging in medicine and surgery2022
The influence of a deep learning image reconstruction algorithm on the image quality and auto-analysis of pulmonary nodules at ultra-low dose chest CT: a phantom study.
Article in Quantitative imaging in medicine and surgery, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Effectiveness of AI for Enhancing Computed Tomography Image Quality and Radiation Protection in Radiology: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2025Pooled it
- Low-dose versus standard-dose computed tomography-guided biopsy for pulmonary nodules: a randomized controlled trial.Journal of cardiothoracic surgery · 2023Trial
- 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
- Clinical value of ultra-low-dose chest CT with deep learning image reconstruction in the detection and characterization of pulmonary nodules.Radiologie (Heidelberg, Germany) · 2026Article
- Artificial Intelligence in Lung Cancer: A Narrative Review of Recent Advances in Diagnosis, Biomarker Discovery, and Drug Development.Pharmaceutics · 2026Review
- Clinical value of the 70-kVp ultra-low-dose CT pulmonary angiography with deep learning image reconstruction.European radiology · 2026Article
- Application of a pulmonary nodule detection program using AI technology to ultra-low-dose CT: differences in detection ability among various image reconstruction methods.Japanese journal of radiology · 2025Article
- Deep learning-based image domain reconstruction enhances image quality and pulmonary nodule detection in ultralow-dose CT with adaptive statistical iterative reconstruction-V.European radiology · 2025Article
- Article
- Detection, measurement, and diagnosis of lung nodules by ultra-low-dose CT in lung cancer screening: a systematic review.BJR open · 2024Article
- The value of a deep learning image reconstruction algorithm in whole-brain computed tomography perfusion in patients with acute ischemic stroke.Quantitative imaging in medicine and surgery · 2023Article
- Detectability and Volumetric Accuracy of Pulmonary Nodules in Low-Dose Photon-Counting Detector Computed Tomography: An Anthropomorphic Phantom Study.Diagnostics (Basel, Switzerland) · 2023Article
- Article
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Abstract
Background: To investigate the effect of a new deep learning image reconstruction (DLIR) algorithm on the detection, characterization and image quality of pulmonary nodules (PNs) in ultra-low dose chest computed tomography (CT) in comparison with the adaptive statistical iterative reconstruction (ASIR-V) algorithm. Methods: Nine artificial pulmonary nodules [six ground glass nodules (GGNs) and three solid nodules (SNs); density: -800 HU, -630 HU, 100 HU; diameter: 12 mm, 10 mm, 8 mm] were randomly placed in a thorax anthropomorphic phantom (Lungman, Kyoto Kagaku Inc.) and scanned on a 256-row CT (Revolution CT, GE Healthcare). Eight scans were performed at 70 kVp with different tube currents (20, 30, 50, 70, 90, 100, 120, 150 mA). Raw data were reconstructed using the filtered back projection (FBP), ASIR-V (30%, 50%, 80%) and DLIR (Low, Medium, High; TrueFidelity™) at 0.625 mm thickness. The effective radiation dose was recorded. All images were automatically analyzed using a commercially available artificial intelligence software (Intelligent 4D Imaging System for Chest CT 5.5, YITU Healthcare) and CT value, standard deviation (SD), long and short diameters of each nodule and SD of air (background) were measured. The detection rate, deformation degree (long diameter/short diameter), signal-to-noise ratio (SNR) and contrast-to-noise ratio (CNR) of pulmonary nodules were calculated. Results: Nodule CT values were the same in all mA settings for all three types of reconstruction algorithms (all P>0.05). DLIR groups had significantly lower SD and higher SNR and CNR values, with better overall image quality than ASIR-V and FBP groups at each mA, ranging from 65-85% reduction in SD, 67-83% increase in SNR with DLIR-H over 50%ASIR-V and 75-91% reduction in SD and 77-89% increase in SNR with DLIR-H over FBP (all P<0.05). At ultra-low dose conditions (30 mA), the DLIR-H images had the highest detection rate of PNs (100%). In addition, the DLIR-M had a minimal negative effect on the characterization of PNs. Conclusions: DLIR algorithm can be a potential reconstruction technique to optimize image quality and improve detection rate of PNs in ultra-low dose lung screening.
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