ArticleThe British journal of radiology2023
Impact of a new deep-learning-based reconstruction algorithm on image quality in ultra-high-resolution CT: clinical observational and phantom studies.
Article in The British journal of radiology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Deep learning‑based ultra-high-resolution CT imaging of viral pneumonia at admission and after discharge.BMC medical imaging · 2026Article
- Diagnostic utility of chest wall vessel involvement sign on ultra-high-resolution CT for primary lung cancer infiltrating the chest wall.European radiology · 2025Article
- Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.Abdominal radiology (New York) · 2025Article
- Ultra-high resolution computed tomography with deep-learning-reconstruction: diagnostic ability in the assessment of gastric cancer and the depth of invasion.Abdominal radiology (New York) · 2024Article
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Authors and funding
8 authors.
Funding
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Abstract
objectivesTo demonstrate the effect of an improved deep learning-based reconstruction (DLR) algorithm on Ultra-High-Resolution Computed Tomography (U-HRCT) scanners.
methodsClinical and phantom studies were conducted. Thirty patients who underwent contrast-enhanced CT examination during the follow-up period were enrolled. Images were reconstructed using improved DLR [termed, New DLR,
resultsThe overall image quality of New DLR was better than that of the Conv DLR (4.2 ± 0.4 and 3.3 ± 0.4, respectively;
conclusionsThe new DLR algorithm improved the image quality within a practical reconstruction time. ADVANCES IN KNOWLEDGE: The new DLR enables us to choose whether to improve image quality or reduce the dose.
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