Evidence map›Paper›PMID 42669967›Full record

ArticleJournal of applied clinical medical physics2026

Quantifying variation in DLP- to- effective-dose conversion factors in CT using a Monte Carlo-based dose-estimation software.

Jelena M Mihailovic, Renxin Chu, Da Zhang, Andrew M Hernandez, Kai Yang, Lifeng Yu

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Article in Journal of applied clinical medical physics, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Jelena M MihailovicDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.ORCID https://orcid.org/0000-0002-1608-0637
Renxin ChuDepartment of Radiology, UVA Health University Hospital, Charlottesville, Virginia, USA.
Da ZhangDepartment of Radiology, Boston Children's Hospital, Boston, Massachusetts, USA.
Andrew M HernandezDepartment of Radiology, UC Davis Health, Sacramento, California, USA.ORCID https://orcid.org/0000-0002-1806-7073
Kai YangDepartment of Radiology, Massachusetts General Hospital, Boston, Massachusetts, USA.ORCID https://orcid.org/0000-0002-7428-8401
Lifeng YuDepartment of Radiology, Mayo Clinic, Rochester, Minnesota, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundA common method to estimate the effective dose in CT is to multiply the dose length product in a CT scan by a conversion factor (k). The k-factors are determined by dividing the effective dose (calculated using a weighted summation of organ doses) by the dose length product (DLP) for specific digital phantoms. The k-factors determined in this way may vary substantially depending on the effective dose calculation methods and phantom models used. PURPOSE: The purpose of this study was to estimate variation associated with different acquisition and patient characteristics in the quantification of k-factors.

methodsK-factors were calculated using VirtualDoseCT for a diverse set of computational phantoms representing different patient models, scanner models, and seven CT exams. The tube potential varied from 80 to 120 kV. The scan length was varied by ± 5 cm relative to the default setting. The CTDIvol was set to 10 mGy when tube current modulation was not used; otherwise, the software default was used. A multivariable regression method was employed to derive an empirical formula for k-factors, along with model-uncertainty analysis and Sobol's sensitivity analysis to identify the influence of each variable.

resultsK-factors for all exams increased with kV. Negative dependence of k-factors on BMI was observed, with lower values in males than in females. Moderate to high variation was observed in the quantification of k-factors without TCM, which remained unchanged with TCM. Based on Sobol's indices (0.8-1), k-factors were influenced most by scan length and patient size for adults, and scan length and age for pediatric phantoms. DISCUSSION: The study showed that a large spread in k-factors was predominantly influenced by scan length, patient size and age. The heterogeneity in k-factors may lead to large heterogeneity in the effective dose estimate if the DLP×k method is used. These results indicate that age and size specific k-factors to estimate effective dose from DLP may be warranted.

conclusionsA set of k-factors should be established to account for these factors and improve effective dose estimations.

Indexed as

Image Processing, Computer-AssistedMonte Carlo MethodPhantoms, ImagingRadiotherapy Planning, Computer-AssistedSoftwareTomography, X-Ray ComputedAdultAlgorithmsFemaleHumansMaleOrgans at RiskRadiation Dosagecomputed tomographyeffective doseeffective dose conversion factorsuncertainty analysis

Identifiers

PMID42669967
PMCPMC13527227

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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.