ArticleThe British journal of radiology2021
Superior objective and subjective image quality of deep learning reconstruction for low-dose abdominal CT imaging in comparison with model-based iterative reconstruction and filtered back projection.
Article in The British journal of radiology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers, 1 of them a synthesis that pooled it.
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19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Efficacy Evaluation of 64-Slice Spiral Computed Tomography Images in Laparoscopic-Assisted Distal Gastrectomy for Gastric Cancer under the Reconstruction Algorithm.Contrast media & molecular imaging · 2022Trial
- Evaluation of a super-resolution deep learning reconstruction algorithm in abdominal CT imaging-A qualitative and quantitative performance analysis.Journal of applied clinical medical physics · 2026Article
- Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.Abdominal radiology (New York) · 2026Review
- Article
- Effects of Computed Tomography Technical Parameters on Body-Composition Analysis.Korean journal of radiology · 2025Review
- Deep learning-based reconstruction improves the image quality of low-dose CT enterography in patients with inflammatory bowel disease.Abdominal radiology (New York) · 2025Article
- Ultra-low-dose hepatic computed tomography with a novel real-time deep learning-based noise reduction algorithm: a prospective cross-sectional analysis of image quality and lesion detection.Quantitative imaging in medicine and surgery · 2025Article
- Evaluation of SR-DLR in low-dose abdominal CT: superior image quality and noise reduction.Abdominal radiology (New York) · 2025Article
- Application of one heartbeat acquisition with motion correction algorithm in CCTA of patients with atrial fibrillation: evaluation of coronary artery stenoses using artificial intelligence assisted diagnostic system.Frontiers in radiology · 2025Article
- A comparison of the image quality between deep learning reconstruction algorithm and iDose4 using low dose abdominopelvic computed tomography for individuals with normal BMI.SAGE open medicine · 2025Article
- Article
- The Dose Optimization and Evaluation of Image Quality in the Adult Brain Protocols of Multi-Slice Computed Tomography: A Phantom Study.Journal of imaging · 2023Article
- CT image quality evaluation in the age of deep learning: trade-off between functionality and fidelity.European radiology · 2023Article
- Deep Learning Image Reconstruction for CT: Technical Principles and Clinical Prospects.Radiology · 2023Review
- Image quality comparison of lower extremity CTA between CT routine reconstruction algorithms and deep learning reconstruction.BMC medical imaging · 2023Article
- Deep learning reconstruction allows low-dose imaging while maintaining image quality: comparison of deep learning reconstruction and hybrid iterative reconstruction in contrast-enhanced abdominal CT.Quantitative imaging in medicine and surgery · 2022Article
- Fully automated image quality evaluation on patient CT: Multi-vendor and multi-reconstruction study.PloS one · 2022Article
- Dependence of Resolution Properties on Display Field-of-view Size in Deep Learning-based Reconstruction Computed Tomography Images for Peripheral Vascular Imaging.Journal of medical physicsArticle
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6 authors.
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Abstract
objectiveThis study aimed to conduct objective and subjective comparisons of image quality among abdominal computed tomography (CT) reconstructions with deep learning reconstruction (DLR) algorithms, model-based iterative reconstruction (MBIR), and filtered back projection (FBP).
methodsDatasets from consecutive patients who underwent low-dose liver CT were retrospectively identified. Images were reconstructed using DLR, MBIR, and FBP. Mean image noise and contrast-to-noise ratio (CNR) were calculated, and noise, artifacts, sharpness, and overall image quality were subjectively assessed. Dunnett's test was used for statistical comparisons.
resultsNinety patients (67 ± 12.7 years; 63 males; mean body mass index [BMI], 25.5 kg/m
conclusionsBased on objective and subjective comparisons, the image quality of DLR was found to be superior to that of MBIR and FBP on low-dose abdominal CT. DLR was the only method for which image noise was not higher for obese patients than for those with a normal BMI. ADVANCES IN KNOWLEDGE: This study provides previously unavailable information on the properties of DLR systems and their clinical utility.
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