ReviewEuropean radiology2019
The evolution of image reconstruction for CT-from filtered back projection to artificial intelligence.
Review in European radiology, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 220 papers, 8 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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
220 citing papers in PubMed, 8 syntheses or guidelines pooled it.
- How is 3D modeling in metabolic surgery utilized and what is its clinical benefit: a systematic review and meta-analysis.International journal of surgery (London, England) · 2025Pooled it
- Mastering CT-based radiomic research in lung cancer: a practical guide from study design to critical appraisal.The British journal of radiology · 2025Guideline
- 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
- Pooled it
- Applications of deep learning to reduce the need for iodinated contrast media for CT imaging: a systematic review.International journal of computer assisted radiology and surgery · 2023Pooled it
- Computed Tomography of the Head : A Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose.Clinical neuroradiology · 2023Pooled it
- Machine learning applications for early detection of esophageal cancer: a systematic review.BMC medical informatics and decision making · 2023Pooled it
- Computed Tomography of the Spine : Systematic Review on Acquisition and Reconstruction Techniques to Reduce Radiation Dose.Clinical neuroradiology · 2023Pooled it
- Trial
- Evaluation of image quality on low contrast media with deep learning image reconstruction algorithm in prospective ECG-triggering coronary CT angiography.The international journal of cardiovascular imaging · 2024Trial
- Low-contrast-dose liver CT using low monoenergetic images with deep learning-based denoising for assessing hepatocellular carcinoma: a randomized controlled noninferiority trial.European radiology · 2023Trial
- 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
- 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
- Effect Evaluation of Platelet-Rich Plasma Combined with Vacuum Sealing Drainage on Serum Inflammatory Factors in Patients with Pressure Ulcer by Intelligent Algorithm-Based CT Image.Computational and mathematical methods in medicine · 2022Trial
- Spectral CT predicts initial bleeding risk in cirrhotic patients with esophagogastric varices.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
- CBCT Dose Is Reduced with Angular Tube Current Modulation While Maintaining Image Quality and Reducing Photon Starvation Artifacts.Annals of biomedical engineering · 2026Article
- Patient Shielding in Computed Tomography: Analysis of Influencing Factors and a Conceptual Framework for Decision-Making.Healthcare (Basel, Switzerland) · 2026Review
- Understanding Photon-Counting CT: Physics, Detector Technology, and Image Reconstructions.Sensors (Basel, Switzerland) · 2026Review
- Comparing the detectability of pulmonary nodules on two ultra-high resolution CT scanners: a preliminary phantom study.European radiology · 2026Article
160 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
2 authors.
Funding
Abstract
The first CT scanners in the early 1970s already used iterative reconstruction algorithms; however, lack of computational power prevented their clinical use. In fact, it took until 2009 for the first iterative reconstruction algorithms to come commercially available and replace conventional filtered back projection. Since then, this technique has caused a true hype in the field of radiology. Within a few years, all major CT vendors introduced iterative reconstruction algorithms for clinical routine, which evolved rapidly into increasingly advanced reconstruction algorithms. The complexity of algorithms ranges from hybrid-, model-based to fully iterative algorithms. As a result, the number of scientific publications on this topic has skyrocketed over the last decade. But what exactly has this technology brought us so far? And what can we expect from future hardware as well as software developments, such as photon-counting CT and artificial intelligence? This paper will try answer those questions by taking a concise look at the overall evolution of CT image reconstruction and its clinical implementations. Subsequently, we will give a prospect towards future developments in this domain. KEY POINTS: • Advanced CT reconstruction methods are indispensable in the current clinical setting. • IR is essential for photon-counting CT, phase-contrast CT, and dark-field CT. • Artificial intelligence will potentially further increase the performance of reconstruction methods.
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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.