ArticleEuropean radiology2019
Deep learning reconstruction improves image quality of abdominal ultra-high-resolution CT.
Article in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. It is linked to trial NCT05651360 (Diagnostic Performance of Deep Learning Image Reconstruction in Low Dose CT for the Detection of Acute Abdominal Conditions), which is not on this map. Cited by 161 papers, 1 of them a synthesis that pooled it.
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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.
Diagnostic Performance of Deep Learning Image Reconstruction in Low Dose CT for the Detection of Acute Abdominal Conditions
Who cites it
161 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
- Low dose of contrast agent and low radiation liver computed tomography with deep-learning-based contrast boosting model in participants at high-risk for hepatocellular carcinoma: prospective, randomized, double-blind study.European radiology · 2023Trial
- High-strength deep learning image reconstruction in coronary CT angiography at 70-kVp tube voltage significantly improves image quality and reduces both radiation and contrast doses.European radiology · 2022Trial
- Advantages and disadvantages of single-source dual-energy whole-body CT angiography with 50% reduced iodine dose at 40 keV reconstruction.The British journal of radiology · 2021Trial
- Low-dose CT angiography using ASiR-V for potential living renal donors: a prospective analysis of image quality and diagnostic accuracy.European radiology · 2020Trial
- Potential radiation dose reduction in computed tomography with deep learning reconstruction: a retrospective monocentric study.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Improved delineation of the cystic artery using super-resolution deep learning reconstruction in contrast-enhanced abdominal computed tomography.Radiological physics and technology · 2026Article
- Deep Learning Reconstruction Versus Hybrid Iterative Reconstruction for Acute Cerebral Infarction Detection on 135 kVp Non-Contrast Brain CT.Clinical neuroradiology · 2026Article
- Evaluation of image quality between hybrid iterative reconstruction and deep learning reconstruction in low dose abdominopelvic CT in low body mass index individuals.Scientific reports · 2026Article
- Image Quality Advancements in Low-Dose Pediatric CT Using Super-Resolution Deep-Learning Reconstruction.Journal of imaging informatics in medicine · 2026Article
- Implementing generative artificial intelligence in precision oncology: safety, governance, and significance.Journal of hematology & oncology · 2026Review
- Improved Image Quality of Virtual Monochromatic Images with Deep Learning Image Reconstruction Algorithm on Dual-Energy CT in Patients with Pancreatic Ductal Adenocarcinoma.Journal of imaging informatics in medicine · 2026Article
- Phantom-based performance comparison of two commercial deep learning CT reconstruction algorithms with super- and normal-resolution settings.European radiology experimental · 2026Article
- AI-based denoising improves image quality in HCC volume perfusion CT without affecting Milan classification.BMC medical imaging · 2026Article
- Article
- Artificial intelligence (AI) and CT in abdominal imaging: image reconstruction and beyond.Abdominal radiology (New York) · 2026Review
- Artificial intelligence in imaging diagnosis of liver tumors: current status and future prospects.Abdominal radiology (New York) · 2026Review
- Radiation Dose Reduction and Image Quality Improvement of UHR CT of the Neck by Novel Deep-learning Image Reconstruction.Clinical neuroradiology · 2025Article
- Development and reliability of a systematic method to evaluate lumbar paraspinal muscle size and quality in computed tomography images.North American Spine Society journal · 2025Article
- Deep learning reconstruction for improved image quality of ultra-high-resolution brain CT angiography: application in moyamoya disease.Japanese journal of radiology · 2025Article
101 more citing papers are in PubMed but not listed here.
Corrections and comments
- Erratum issued
Authors and funding
9 authors.
Funding
Abstract
objectivesDeep learning reconstruction (DLR) is a new reconstruction method; it introduces deep convolutional neural networks into the reconstruction flow. This study was conducted in order to examine the clinical applicability of abdominal ultra-high-resolution CT (U-HRCT) exams reconstructed with a new DLR in comparison to hybrid and model-based iterative reconstruction (hybrid-IR, MBIR).
methodsOur retrospective study included 46 patients seen between December 2017 and April 2018. A radiologist recorded the standard deviation of attenuation in the paraspinal muscle as the image noise and calculated the contrast-to-noise ratio (CNR) for the aorta, portal vein, and liver. The overall image quality was assessed by two other radiologists and graded on a 5-point confidence scale ranging from 1 (unacceptable) to 5 (excellent). The difference between CT images subjected to hybrid-IR, MBIR, and DLR was compared.
resultsThe image noise was significantly lower and the CNR was significantly higher on DLR than hybrid-IR and MBIR images (p < 0.01). DLR images received the highest and MBIR images the lowest scores for overall image quality.
conclusionsDLR improved the quality of abdominal U-HRCT images. KEY POINTS: • The potential degradation due to increased noise may prevent implementation of ultra-high-resolution CT in the abdomen. • Image noise and overall image quality for hepatic ultra-high-resolution CT images improved with deep learning reconstruction as compared to hybrid- and model-based iterative reconstruction.
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Registered trials
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.