Evidence map›Paper›PMID 37646805›Full record

ArticleEuropean radiology2024

CT acquisition parameter selection in the real world: impacts on radiation dose and variation amongst 155 institutions.

Yifei Wang, Philip Chu, Timothy P Szczykutowicz, Carly Stewart, Rebecca Smith-Bindman

Open access · hybridAbstract read
In one paragraph

Article in European radiology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
1.6field-weighted citation impact, top 16% 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

6 citing papers in PubMed, 7 citations in OpenAlex.

  1. Article
  2. Article
  3. Article
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  5. AI for image quality and patient safety in CT and MRI.European radiology experimental · 2025
    Review
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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

5 authors at 2 institutions in 1 country.

Yifei WangDepartment of Epidemiology and Biostatistics, University of California San Francisco, 550 16Th Street, San Francisco, CA, 94158, USA. yifei.wang@ucsf.edu.ORCID http://orcid.org/0000-0002-1632-2050
Philip ChuDepartment of Epidemiology and Biostatistics, University of California San Francisco, 550 16Th Street, San Francisco, CA, 94158, USA.
Timothy P SzczykutowiczDepartments of Radiology, Medical Physics, and Biomedical Engineering, University of Wisconsin-Madison, Madison, WI, USA.
Carly StewartDepartment of Epidemiology and Biostatistics, University of California San Francisco, 550 16Th Street, San Francisco, CA, 94158, USA.
Rebecca Smith-BindmanDepartment of Epidemiology and Biostatistics, University of California San Francisco, 550 16Th Street, San Francisco, CA, 94158, USA.
University of California, San Francisco · USUniversity of Wisconsin–Madison · US

Funding

CT DOSE CollaboratoryR01CA181191 · NCI · UNIVERSITY OF CALIFORNIA, SAN FRANCISCO · PI SMITH-BINDMAN, REBECCA · 2014 to 2019
$6.2M
NCI NIH HHS R01 CA181191Patient-Centered Outcomes Research Institute DI-2018C1-11375
6 · The paper itself

Abstract

objectiveQuantify the relationship between CT acquisition parameters and radiation dose, how often parameters are adjusted in real-world practice, and their degree of contribution to real-world dose distribution. Identify discrepancies between parameters that are impactful in theory and impactful in practice.

methodsThis study analyses 1.3 million consecutive adult routine abdomen exams performed between November 2015 and Jan 2021 included in the University of California, San Francisco International CT Dose Registry of 155 institutions. We calculated geometric standard deviation (gSD) for five parameters (kV, mAs, spiral pitch, number of phases, scan length) to assess variation in practice. A Gaussian mixed regression model was performed to predict the radiation dose-length product (DLP) using the parameters. Three conceptualizations of "impact" were computed for each parameter. To reflect the theoretical impact, we predict the increase in DLP per 10% (and 15%) increase in the parameter. To reflect the real-world practical impact, we predict the increase in DLP per gSD increase in the parameter.

resultsAmong studied examinations, mAs, number of phases, and scan length were frequently manipulated (gSD 1.52-1.70); kV was rarely manipulated (gSD 1.07). Theoretically, kV is the most impactful parameter (29% increase in DLP per 10% increase in kV, versus 5-9% increase for other parameters). In real-world practice, kV is less impactful; for each gSD increase in kV, the DLP increases by 20%, versus 22-69% for other parameters.

conclusionDespite the potential impact of kV on radiation dose, this parameter is rarely manipulated in common practice and this potential remains untapped. CLINICAL RELEVANCE STATEMENT: CT beam energy (kV) modulation has the potential to strongly reduce radiation over-dosage to the patient, theoretically more so than similar degrees of modulation in other CT acquisition parameters. Despite this, beam energy modulation rarely occurs in practice, leaving its potential untapped. KEY POINTS: • The relationship between CT acquisition parameter selection and radiation dose roughly coincided with established theoretical understanding. • CT acquisition parameters differ from each other in frequency and magnitude of manipulation, with beam energy (kV) being rarely manipulated. • Beam energy (kV) has the potential to substantially impact radiation dose, but because it is rarely manipulated, it is the least impactful CT acquisition parameter affecting radiation dose in practice.

Indexed as

Tomography, X-Ray ComputedAdultHumansRadiation DosageDose–response relationship, RadiationRadiation dosageTomography, X-ray computed

Identifiers

PMID37646805
PMCPMC10873435
OpenAlexW4386272344

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.