ReviewInsights into imaging2025
Objective assessment of diagnostic image quality in CT scans: what radiologists and researchers need to know.
Review in Insights into imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
4 citing papers in PubMed.
- Vascular attenuation and volumetric lung iodine density in dual-layer spectral CT pulmonary angiography: a randomized controlled trial comparing three contrast doses.European radiology · 2026Trial
- Assessment of image quality and detectability in computed tomography: Inter-laboratory comparison of Fourier-based metrics.Journal of applied clinical medical physics · 2026Article
- Automated contrast-to-noise ratio analysis in chest CT: validation of an open-source segmentation approach.Insights into imaging · 2026Article
- Effects of recumbency and tube current on image quality of canine elbow computed tomography.Frontiers in veterinary science · 2026Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
objectivesQuantifying diagnostic image quality (IQ) is not straightforward but essential for optimizing the balance between IQ and radiation dose, and for ensuring consistent high-quality images in CT imaging. This review provides a comprehensive overview of advanced objective reference-free IQ assessment methods for CT scans, beyond standard approaches.
methodsA literature search was performed in PubMed and Web of Science up to June 2024 to identify studies using advanced objective image quality methods on clinical CT scans. Only reference-free methods, which do not require a predefined reference image, were included. Traditional methods relying on the standard deviation of the Hounsfield units, the signal-to-noise ratio or contrast-to-noise ratio, all within a manually selected region-of-interest, were excluded. Eligible results were categorized by IQ metric (i.e., noise, contrast, spatial resolution and other) and assessment method (manual, automated, and artificial intelligence (AI)-based).
resultsThirty-five studies were included that proposed or employed reference-free IQ methods, identifying 12 noise assessment methods, 4 contrast assessment methods, 14 spatial resolution assessment methods and 7 others, based on manual, automated or AI-based approaches.
conclusionThis review emphasizes the transition from manual to fully automated approaches for IQ assessment, including the potential of AI-based methods, and it provides a reference tool for researchers and radiologists who need to make a well-considered choice in how to evaluate IQ in CT imaging. CRITICAL RELEVANCE STATEMENT: This review examines the challenge of quantifying diagnostic CT image quality, essential for optimization studies and ensuring consistent high-quality images, by providing an overview of objective reference-free diagnostic image quality assessment methods beyond standard methods. KEY POINTS: Quantifying diagnostic CT image quality remains a key challenge. This review summarizes objective diagnostic image quality assessment techniques beyond standard metrics. A decision tree is provided to help select optimal image quality assessment techniques.
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What OpenQuestion holds
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.