ArticleThe British journal of radiology2018
Screening for lung cancer using sub-millisievert chest CT with iterative reconstruction algorithm: image quality and nodule detectability.
Article in The British journal of radiology, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05398887 (Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology), which is not on this map. Cited by 21 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Utilization and Effectiveness of Ultra-low-dose Chest Computed Tomography Using Innovative CT Denoising Solution Based on Deep Learning Technology
Who cites it
21 citing papers in PubMed, 44 citations in OpenAlex.
- Article
- Deep learning reconstruction improves computer-aided pulmonary nodule detection and measurement accuracy for ultra-low-dose chest CT.BMC medical imaging · 2025Article
- Deep learning based ultra-low dose fan-beam computed tomography image enhancement algorithm: Feasibility study in image quality for radiotherapy.Journal of applied clinical medical physics · 2024Article
- Impact of deep learning image reconstruction on volumetric accuracy and image quality of pulmonary nodules with different morphologies in low-dose CT.Cancer imaging : the official publication of the International Cancer Imaging Society · 2024Article
- Standardized uptake valueCancer medicine · 2024Observational
- Value of deep learning reconstruction of chest low-dose CT for image quality improvement and lung parenchyma assessment on lung window.European radiology · 2024Article
- Lung Cancer Screening with Low-Dose CT: What We Have Learned in Two Decades of ITALUNG and What Is Yet to Be Addressed.Diagnostics (Basel, Switzerland) · 2023Review
- Establishing a metastasis-related diagnosis and prognosis model for lung adenocarcinoma through CRISPR library and TCGA database.Journal of cancer research and clinical oncology · 2023Article
- Significance of thyroid transcription factor 1 and Napsin A for prompting the status ofJournal of thoracic disease · 2022Article
- Single CT Appointment for Double Lung and Colorectal Cancer Screening: Is the Time Ripe?Diagnostics (Basel, Switzerland) · 2022Review
- Turkish Thoracic Society Early Career Members Task Force Group's Virtual Congress Notes: European Respiratory Society International Congress 2020.Turkish thoracic journal · 2022Article
- Identifying a 6-Gene Prognostic Signature for Lung Adenocarcinoma Based on Copy Number Variation and Gene Expression Data.Oxidative medicine and cellular longevity · 2022Article
- Latest CT technologies in lung cancer screening: protocols and radiation dose reduction.Translational lung cancer research · 2021Review
- Identification of 5-Gene Signature Improves Lung Adenocarcinoma Prognostic Stratification Based on Differential Expression Invasion Genes of Molecular Subtypes.BioMed research international · 2020Article
- Ceramide Pathway Regulators Predict Clinical Prognostic Risk and Affect the Tumor Immune Microenvironment in Lung Adenocarcinoma.Frontiers in oncology · 2020Article
- Ultralow-dose CT with knowledge-based iterative model reconstruction (IMR) in evaluation of pulmonary tuberculosis: comparison of radiation dose and image quality.European radiology · 2019Observational
- Volumetric assessment of solid pulmonary nodules on ultralow-dose CT: a phantom study.Journal of thoracic disease · 2019Article
- Management of incidental pulmonary nodule in CT: a survey by the Italian College of Chest Radiology.La Radiologia medica · 2019Article
- Lactate dehydrogenase and serum tumor markers for predicting metastatic status in geriatric patients with lung adenocarcinoma.Cancer biomarkers : section A of Disease markers · 2019Article
- Radiation burden and associated cancer risk for a typical population to be screened for lung cancer with low-dose CT: A phantom study.European radiology · 2018Article
Corrections and comments
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Authors and funding
6 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objective: To investigate the image quality and nodules detectability using ultra-low dose (ULD) protocol with iterative model reconstruction (IMR) algorithm when compared to routine low dose (LD) chest CT in lung cancer screening.
methods: Chest CT scans were acquired using a 256-slice scanner for 300 subjects. The scan protocol for the ULD group was 120 kVp/17 mAs while for the LD group was 120 kVp/30 mAs. All images were reconstructed with filtered back projection (FBP), hybrid iterative reconstruction (HIR) and IMR algorithms. Effective dose was recorded. Image quality assessments were performed by two radiologists. SD of CT attenuation was measured as objective image noise. The number of non-calcified nodules detected in both groups with different reconstruction algorithms were calculated and compared.
results: The effective dose of ULD group (0.67 ± 0.08 mSv) was about 44% reduced compared with LD group (1.20 ± 0.08 mSv) (p < 0.01). IMR improved image quality and reduced image noise significantly than HIR and FBP in both groups (all, p < 0.01). IMR enabled a higher number of nodule detected compared to FBP and HIR in both LD and ULD groups, especially for solid nodules less than 4 mm.
conclusion: IMR may improve the diagnostic accuracy of ULD CT lung screening with potential nodule detectability improvement. ADVANCES IN KNOWLEDGE:: IMR enables significant reduction of the image noise and improvement of image quality in sub-mSv (66% reduction) chest scans.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.