ArticleEuropean radiology2020
Image quality and dose reduction opportunity of deep learning image reconstruction algorithm for CT: a phantom study.
Article in European radiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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 175 papers, 2 of them syntheses that pooled it.
What it found
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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
175 citing papers in PubMed, 2 syntheses or guidelines pooled it, 317 citations in OpenAlex.
- 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
- Deep-learning CT reconstruction in clinical scans of the abdomen: a systematic review and meta-analysis.Abdominal radiology (New York) · 2023Pooled it
- Feasibility study of "double-low" scanning protocol combined with artificial intelligence iterative reconstruction algorithm for abdominal computed tomography enhancement in patients with obesity.BMC medical imaging · 2025Trial
- Deep learning reconstruction algorithm and high-concentration contrast medium: feasibility of a double-low protocol in coronary computed tomography angiography.European radiology · 2025Trial
- Low tube voltage and deep-learning reconstruction for reducing radiation and contrast medium doses in thin-slice abdominal CT: a prospective clinical trial.European radiology · 2024Trial
- 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
- Deep learning reconstruction for 40-keV virtual monoenergetic CT of colon cancer: evaluation of image quality and edge sharpness.Abdominal radiology (New York) · 2026Article
- Evaluation of deep-learning iterative reconstruction combined with high-frequency kernels in CT: a task-based image quality study.European radiology experimental · 2026Article
- Deep learning image reconstruction for 50-keV virtual monoenergetic dual-energy CT of the thyroid: a prospective "dual-low" dose study.European radiology · 2026Article
- Potential radiation dose reduction in computed tomography with deep learning reconstruction: a retrospective monocentric study.Diagnostic and interventional radiology (Ankara, Turkey) · 2026Article
- Performance of Commercial Deep Learning-Based Radiation Dose Optimization Software in Pediatric Radiology: A Systematic Review.Children (Basel, Switzerland) · 2026Review
- Deep learning image reconstruction improves visualization of arterial phase hyperenhancement and washout appearance on dual-energy CT for hepatocellular carcinoma: a non-inferiority study.Abdominal radiology (New York) · 2026Article
- Impact of image reconstruction algorithms on histogram analysis of iodine concentration for assessing chemotherapy response in pancreatic ductal adenocarcinoma.Abdominal radiology (New York) · 2026Article
- Optimization of hypovascular liver lesion detectability in dual-energy CT using deep learning image reconstruction: a phantom study for potential iodine dose reduction.European radiology experimental · 2026Article
- Reduced environmental impact in body CT imaging with deep learning reconstruction: experience of a high-volume tertiary referral center.Insights into imaging · 2026Article
- CT Radiation Dose Reduction With Preserved Diagnostic Performance: How Far Have We Come Over 25 Years?AJR. American journal of roentgenology · 2026Review
- Coronary CT Angiography in PCI Planning: Advances, Clinical Applications, and Challenges.Journal of cardiovascular development and disease · 2026Review
- Improving image quality and diagnostic confidence for PRETEXT staging in pediatric hepatoblastoma using thin-slice and low-energy virtual monochromatic images in dual-energy CT with deep learning image reconstruction algorithm.BMC medical imaging · 2026Article
- Reconstruction algorithms and arm positioning effects on abdominal CT image quality and radiation dose: a phantom study.European radiology experimental · 2026Article
- Additional Dose Reduction Potential of Vendor-Agnostic Deep Learning Model: A Phantom Study.Journal of the Korean Society of Radiology · 2026Article
115 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesTo assess the impact on image quality and dose reduction of a new deep learning image reconstruction (DLIR) algorithm compared with a hybrid iterative reconstruction (IR) algorithm.
methodsData acquisitions were performed at seven dose levels (CTDI
resultsNPS peaks were higher with AV50 than with all DLIR levels and only higher with DLIR-H than with AV100. The average NPS spatial frequencies were higher with DLIR than with IR. For all DLIR levels, TTF
conclusionsNew DLIR algorithm reduced noise and improved spatial resolution and detectability without changing the noise texture. Images obtained with DLIR seem to indicate a greater potential for dose optimization than those with hybrid IR. KEY POINTS: • This study assessed the impact on image quality and radiation dose of a new deep learning image reconstruction (DLIR) algorithm as compared with hybrid iterative reconstruction (IR) algorithm. • The new DLIR algorithm reduced noise and improved spatial resolution and detectability without perceived alteration of the texture, commonly reported with IR. • As compared with IR, DLIR seems to open further possibility of dose optimization.
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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.