Evidence map›Paper›PMID 41553466›Full record

ArticlePediatric radiology2026

Attenuation-based ultra-low-dose lung computed tomography at 0.1 mSv to 0.3 mSv effective dose in children.

Max-Johann Sturm, Christian J Kellenberger, Franco Rupcich, Sebastian Tschauner, Michael Zellner

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Article in Pediatric radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

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1citing papers in PubMed
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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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1 citing paper in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

5 authors.

Max-Johann SturmDepartment of Diagnostic Imaging, University Children's Hospital Zurich, Lenggstrasse 30, Zurich, 8008, Switzerland.
Christian J KellenbergerDepartment of Diagnostic Imaging, University Children's Hospital Zurich, Lenggstrasse 30, Zurich, 8008, Switzerland.
Franco RupcichGE Healthcare, Waukesha, USA.
Sebastian TschaunerDepartment of Radiology, Medical University of Graz, Graz, Austria.
Michael ZellnerDepartment of Diagnostic Imaging, University Children's Hospital Zurich, Lenggstrasse 30, Zurich, 8008, Switzerland. michael.zellner@kispi.uzh.ch.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundRadiation dose reduction is essential in paediatric lung computed tomography (CT). Advances in energy-integrating detector CT and deep-learning reconstruction may enable ultra-low-dose imaging comparable to photon-counting CT.

objectiveTo evaluate the radiation dose and performance of an ultra-low-dose lung CT protocol using a wide-detector energy-integrating CT system in paediatric patients, focusing on effective radiation dose and diagnostic image quality. MATERIALS AND

methodsA total of 277 low-dose lung CT scans from 106 paediatric patients (age range, 113 days to 17.85 years) were retrospectively analysed. All scans were acquired in axial mode using a 256-slice-multidetector CT scanner with deep learning image reconstruction and attenuation-based Auto Prescription. Radiation dose parameters, including volume CT dose index, dose-length product, size-specific dose estimate, and effective dose, were calculated. Signal-to-noise ratio and contrast-to-noise ratio were assessed in standardised anatomical regions. Patients were stratified by age, and statistical analysis was conducted to evaluate dose trends and image quality metrics.

resultsThere were significant differences between all age groups for all dose parameters (Kruskal-Wallis test, P<0.05). The median effective dose increased with age, ranging from 0.12 mSv (interquartile range (IQR) 0.09-0.14 mSv) in the 0-5-year group to 0.23 mSv (IQR 0.21-0.25 mSv) in adolescents aged 15 years to <18 years. Contrast-to-noise ratio and signal-to-noise ratio exhibited age-dependent variation with a small increase in older age groups. One-sided non-inferiority testing demonstrated that the signal-to-noise ratio and contrast-to-noise ratio in the youngest age group (0-5 years) were not significantly inferior to those in the ≥15-year group (P<0.05). All examinations were deemed diagnostically sufficient by board-certified paediatric radiologists. Non-disruptive artefacts such as cardiac motion and step artefacts occurred frequently but did not impair interpretation.

conclusionsUltra-low-dose lung CT using wide-detector energy-integrating CT with deep-learning image reconstruction allows for routine diagnostic imaging in children at radiation doses ranging from 0.12 mSv to 0.23 mSv, comparable to those reported for newer photon-counting CT systems. This approach provides a robust, clinically viable strategy for minimizing radiation exposure while maintaining diagnostic image quality.

Indexed as

Radiation DosageTomography, X-Ray ComputedAdolescentChildChild, PreschoolFemaleHumansInfantInfant, NewbornMaleRadiographic Image Interpretation, Computer-AssistedRetrospective StudiesSignal-To-Noise RatioChildrenMultidetector computed tomographyPaediatric imagingPaediatric lungRadiation dose

Identifiers

PMID41553466
PMCPMC12957640

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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.