Evidence map›Paper›PMID 40363343›Full record

ArticleSensors (Basel, Switzerland)2025

Standardization of Lung CT Number Using COPD Gene2 Phantom Under Various Scanning Protocols.

Hoondong Song, Hanjoo Jang, Jongduk Baek

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 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. Review
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

3 authors.

Hoondong SongSchool of Integrated Technology, Yonsei University, Incheon 21983, Republic of Korea.ORCID 0009-0002-1196-414X
Hanjoo JangDepartment of Artificial Intelligence, College of Computing, Yonsei University, Seoul 03722, Republic of Korea.ORCID 0000-0001-8899-5062
Jongduk BaekDepartment of Artificial Intelligence, College of Computing, Yonsei University, Seoul 03722, Republic of Korea.ORCID 0000-0002-2532-5413

Funding

National Research Foundation of Korea RS-2023-00219019National Research Foundation of Korea RS-2023-00240135
6 · The paper itself

Abstract

Lung computed tomography (CT) images are widely used to diagnose chronic obstructive pulmonary disease (COPD) by evaluating signs of lung tissue destruction. Accurate diagnosis requires standardizing the CT numbers in lung CT images to distinguish between normal and damaged tissue. The CT number standardization method proposed by Chen-Mayer et al., which uses the linearity of Martinez's formula, showed promising results in phantom studies. However, our findings reveal that the CT number of water varies significantly, depending on scanning conditions and the characteristics of its container, making it an unreliable reference for lung CT number standardization. To enhance the standardization method, we modified the approach to exclude water and used only solid foams from the COPD gene2 phantom as references. To evaluate the proposed method, we collected 234 CT images of the COPD gene2 phantom from 8 different CT scanners and assessed performance by analyzing CT number standard deviations and variations. The modification resulted in improved reliability and consistency in CT number standardization. Additionally, for a detailed analysis, we segmented the dataset based on CT dose index (CTDI), X-ray tube potential, and reconstruction algorithms to examine the impact of different scanning protocols on standardization performance.

Indexed as

LungPhantoms, ImagingPulmonary Disease, Chronic ObstructiveTomography, X-Ray ComputedAlgorithmsHumansImage Processing, Computer-Assistedchronic obstructive pulmonary disease (COPD)computed tomography (CT)COPD gene2 phantomCT number standardization

Identifiers

PMID40363343
PMCPMC12074219

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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.