Evidence map›Paper›PMID 38634980›Full record

ArticlePhysical and engineering sciences in medicine2024

Accuracy of patient-specific CT organ doses from Monte Carlo simulations: influence of CT-based voxel models.

Gwenny Verfaillie, Jeff Rutten, Yves D'Asseler, Klaus Bacher

Abstract read
In one paragraph

Article in Physical and engineering sciences in medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

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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

4 citing papers in PubMed.

  1. Anatomy-resolved digital twin framework for personalized light dosimetry in lung therapies.Proceedings of the National Academy of Sciences of the United States of America · 2026
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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

4 authors.

Gwenny VerfaillieDepartment of Human Structure and Repair, Ghent University, Ghent, Belgium. gwenny.verfaillie@ugent.be.ORCID http://orcid.org/0000-0002-9185-7717
Jeff RuttenDepartment of Human Structure and Repair, Ghent University, Ghent, Belgium.ORCID http://orcid.org/0000-0002-8121-5972
Yves D'Asseler *Department of Nuclear Medicine, Ghent University Hospital, Ghent, Belgium.ORCID http://orcid.org/0000-0002-8807-2257
Klaus Bacher *Department of Human Structure and Repair, Ghent University, Ghent, Belgium.ORCID http://orcid.org/0000-0001-6420-6006

Funding

H2020 Euratom 755523H2020 Euratom 945196
6 · The paper itself

Abstract

Monte Carlo simulations using patient CT images as input are the gold standard to perform patient-specific dosimetry. However, in standard clinical practice patient's CT images are limited to the reconstructed CT scan range. In this study, organ dose calculations were performed with ImpactMC for chest and cardiac CT using whole-body and anatomy-specific voxel models to estimate the accuracy of CT organ doses based on the latter model. When the 3D patient model is limited to the CT scan range, CT organ doses from Monte Carlo simulations are the most accurate for organs entirely in the field of view. For these organs only the radiation dose related to scatter from the rest of the body is not incorporated. For organs lying partially outside the field of view organ doses are overestimated by not accounting for the non-irradiated tissue mass. This overestimation depends strongly on the amount of the organ volume located outside the field of view. To get a more accurate estimation of the radiation dose to these organs, the ICRP reference organ masses and densities could form a solution. Except for the breast, good agreement in dose was found for most organs. Voxel models generated from clinical CT examinations do not include the overscan in the z-direction. The availability of whole-body voxel models allowed to study this influence as well. As expected, overscan induces slightly higher organ doses.

Indexed as

Monte Carlo MethodRadiation DosageTomography, X-Ray ComputedHumansPhantoms, ImagingRadiometryComputed Tomography (CT)Monte CarloOrgan dosePatient-specific dosimetry

Identifiers

PMID38634980
PMCPMC11408396

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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.