ArticleRadiological physics and technology2025
Noise-related inaccuracies in the quantitative evaluation of CT artifacts.
Article in Radiological physics and technology, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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
2 citing papers in PubMed.
- Optimal energy level of virtual monoenergetic images for iron detectability in dual-energy CT: a Phantom study.Radiological physics and technology · 2026Article
- Reduction of metal artifact from cryoprobe using tilt CT scanning with metal artifact reduction algorithms for CT-guided cryoablation.Radiological physics and technology · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
4 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Accuracies of measuring the artifact index (AI), a quantitative artifact evaluation index in X-ray CT images, were investigated. The AI is calculated based not only on the standard deviation (SD) of the artifact area in the image, but also on the SD of noise components for considering the noise influence. However, conventional measurement methods may not follow this consideration, for example the non-uniformity of the noise distribution is not taken into account, resulting in reducing the accuracy of AI. To address this problem, this study aims to clarify the impact of noise SD measuring (NSDM) error on AI accuracy and improve the accuracy by reducing the NSDM error. Experimental results demonstrated that the conventional noise measurement methods reduced the accuracy of the AI. Specifically, AI inaccuracy due to the NSDM error is severe in the case of weak artifacts and under high noise conditions. Furthermore, the AI accuracy can be improved by reducing the influence of the NSDM error through image smoothing or by correcting NSDM through noise distribution estimation. These results showed that AI can be affected by NSDM errors practically even though it is robust against noise in principle. The impact of NSDM errors must be avoided for reliable artifact evaluation.
Indexed as
Identifiers
39776374What 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.