Evidence map›Paper›PMID 40558773›Full record

ArticleJournal of imaging2025

Performance of A Statistical-Based Automatic Contrast-to-Noise Ratio Measurement on Images of the ACR CT Phantom.

Choirul Anam, Riska Amilia, Ariij Naufal, Heri Sutanto, Wahyu S Budi, Geoff Dougherty

Abstract read
In one paragraph

Article in Journal of imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Choirul AnamDepartment of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Indonesia.ORCID 0000-0003-0156-6797
Riska AmiliaDepartment of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Indonesia.ORCID 0000-0002-7077-439X
Ariij NaufalDepartment of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Indonesia.ORCID 0000-0002-4218-6259
Heri SutantoDepartment of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Indonesia.ORCID 0000-0001-7119-9633
Wahyu S BudiDepartment of Physics, Faculty of Sciences and Mathematics, Diponegoro University, Jl. Prof. Soedarto SH, Tembalang, Semarang 50275, Indonesia.
Geoff DoughertyDepartment of Applied Physics and Medical Imaging, California State University Channel Islands, Camarillo, CA 93012, USA.ORCID 0000-0003-3187-3595

Funding

Diponegoro University 2025
6 · The paper itself

Abstract

This study evaluates the performance of a statistical-based automatic contrast-to-noise ratio (CNR) measurement method on images of the ACR CT phantom under varying imaging parameters. A statistical automatic method for segmenting low-contrast objects and for measuring CNR was recently introduced. The method employs a 25 mm region of interest (ROI), rotated in 2° clockwise steps, to identify the low-contrast object by locating the maximum CT value. The CNR was measured on images acquired with different parameters: tube voltage (80-140 kVp), tube current (80-200 mA), slice thickness (1.25-10 mm), field of view (190-230 mm), and convolution kernel (edge, ultra, lung, bone, chest, standard). The automatic results were compared to manual measurements. The automatic method accurately identified the largest low-contrast object. The CNR values from the automatic and manual methods showed no significant difference (

Indexed as

ACR CT phantomcontrast-to-noise ratioimage qualitynoise

Identifiers

PMID40558773
PMCPMC12194584

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Registered trials

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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.