Evidence map›Paper›PMID 37559745›Full record

ArticleDiagnostic and interventional radiology (Ankara, Turkey)2023

Ultra-low-dose spectral-detector computed tomography for the accurate quantification of pulmonary nodules: an anthropomorphic chest phantom study.

Xiuxiu Zhou, Hanxiao Zhang, Xiaoxing Jin, Xiaohui Zhang, Xiaomei Lu, Qun Han, Xiaoge Xiong, Tian Liu, Yan Feng, Wenting Tu and 5 more

Open access · diamondAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Diagnostic and interventional radiology (Ankara, Turkey), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
0.2field-weighted citation impact, top 52% of its field
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

2 citing papers in PubMed, 2 citations in OpenAlex.

  1. Article
  2. 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

15 authors at 1 institution in 2 countries.

Xiuxiu ZhouDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.ORCID 0000-0003-2405-0566
Hanxiao ZhangDepartment of Radiology, Xuzhou Medical University, School of Medical Imaging, Xuzhou, China.ORCID 0009-0002-2811-669X
Xiaoxing JinDepartment of Radiology Medicine, The Second People's Hospital of Linhai, Linhai, China.ORCID 0009-0001-5133-6626
Xiaohui ZhangDepartment of Clinical Science, Philips Healthcare, Shanghai, China.ORCID 0000-0002-4575-0775
Xiaomei LuCT Clinical Science, Philips Healthcare, Shanghai, China.ORCID 0000-0003-1201-6029
Qun HanDepartment of Clinical Science, Philips Healthcare, Shanghai, China.ORCID 0000-0002-2875-8644
Xiaoge XiongSchool of Statistics and Management, Shanghai University of Finance and Economics, Shanghai, China.ORCID 0009-0007-4546-6460
Tian LiuDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.ORCID 0009-0005-9131-5992
Yan FengDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.ORCID 0009-0006-6865-8978
Wenting TuDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.ORCID 0000-0003-1010-7189
Taohu ZhouDepartment of Radiology, Weifang Medical University, School of Medical Imaging, Shanghai, China.ORCID 0000-0001-6208-2249
Yanming GeDepartment of Radiology, Weifang Medical University, School of Medical Imaging, Shanghai, China.ORCID 0009-0008-2398-5152
Peng DongDepartment of Radiology, Weifang Medical University, School of Medical Imaging, Shanghai, China.ORCID 0009-0004-3363-3688
Shiyuan LiuDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.ORCID 0000-0003-3420-0310
Li FanDepartment of Radiology, Second Affiliated Hospital of Naval Medical University, Shanghai, China.ORCID 0000-0003-4722-3933
Weifang Medical University · CN

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

purposeTo assess the quantification accuracy of pulmonary nodules using virtual monoenergetic images (VMIs) derived from spectral-detector computed tomography (CT) under an ultra-low-dose scan protocol.

methodsA chest phantom consisting of 12 pulmonary nodules was scanned using spectral-detector CT at 100 kVp/10 mAs, 100 kVp/20 mAs, 120 kVp/10 mAs, and 120 kVp/30 mAs. Each scanning protocol was repeated three times. Each CT scan was reconstructed utilizing filtered back projection, hybrid iterative reconstruction, iterative model reconstruction (IMR), and VMIs of 40-100 keV. The signal-to-noise ratio and air noise of images, absolute differences, and absolute percentage measurement errors (APEs) of the diameter, density, and volume of the four scan protocols and ten reconstruction images were compared.

resultsWith each fixed reconstruction image, the four scanning protocols exhibited no significant differences in APEs for diameter and density (all

conclusionCompared with IMR, reconstruction at 100 kVp/10 mAs and 50 keV provides a more accurate quantification of pulmonary nodules, and the radiation dose is reduced by 77.8% compared with that at 120 kVp/30 mAs, demonstrating great potential for ultra-low-dose spectral-detector CT.

Indexed as

HominidaeMultiple Pulmonary NodulesAlgorithmsAnimalsHumansPhantoms, ImagingRadiation DosageRadiographic Image Interpretation, Computer-AssistedTomography, X-Ray Computedchest phantomlow-dose computed tomographypulmonary nodulereconstruction algorithmSpectral computed tomography

Identifiers

PMID37559745
PMCPMC10679552
OpenAlexW4385715956

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

None linked

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.