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Estimating radiation effective doses from whole body computed tomography scans based on U.S. soldier patient height and weight

BACKGROUND: The purpose of this study is to explore how a patient's height and weight can be used to predict the effective dose to a reference phantom with similar height and weight from a chest abdomen pelvis computed tomography scan when machine-based parameters are unknown. Since machine-bas...

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Autores principales: Prins, Robert D, Thornton, Raymond H, Schmidtlein, C Ross, Quinn, Brian, Ching, Hung, Dauer, Lawrence T
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2011
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224357/
https://www.ncbi.nlm.nih.gov/pubmed/22004072
http://dx.doi.org/10.1186/1471-2342-11-20
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author Prins, Robert D
Thornton, Raymond H
Schmidtlein, C Ross
Quinn, Brian
Ching, Hung
Dauer, Lawrence T
author_facet Prins, Robert D
Thornton, Raymond H
Schmidtlein, C Ross
Quinn, Brian
Ching, Hung
Dauer, Lawrence T
author_sort Prins, Robert D
collection PubMed
description BACKGROUND: The purpose of this study is to explore how a patient's height and weight can be used to predict the effective dose to a reference phantom with similar height and weight from a chest abdomen pelvis computed tomography scan when machine-based parameters are unknown. Since machine-based scanning parameters can be misplaced or lost, a predictive model will enable the medical professional to quantify a patient's cumulative radiation dose. METHODS: One hundred mathematical phantoms of varying heights and weights were defined within an x-ray Monte Carlo based software code in order to calculate organ absorbed doses and effective doses from a chest abdomen pelvis scan. Regression analysis was used to develop an effective dose predictive model. The regression model was experimentally verified using anthropomorphic phantoms and validated against a real patient population. RESULTS: Estimates of the effective doses as calculated by the predictive model were within 10% of the estimates of the effective doses using experimentally measured absorbed doses within the anthropomorphic phantoms. Comparisons of the patient population effective doses show that the predictive model is within 33% of current methods of estimating effective dose using machine-based parameters. CONCLUSIONS: A patient's height and weight can be used to estimate the effective dose from a chest abdomen pelvis computed tomography scan. The presented predictive model can be used interchangeably with current effective dose estimating techniques that rely on computed tomography machine-based techniques.
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spelling pubmed-32243572011-11-30 Estimating radiation effective doses from whole body computed tomography scans based on U.S. soldier patient height and weight Prins, Robert D Thornton, Raymond H Schmidtlein, C Ross Quinn, Brian Ching, Hung Dauer, Lawrence T BMC Med Imaging Research Article BACKGROUND: The purpose of this study is to explore how a patient's height and weight can be used to predict the effective dose to a reference phantom with similar height and weight from a chest abdomen pelvis computed tomography scan when machine-based parameters are unknown. Since machine-based scanning parameters can be misplaced or lost, a predictive model will enable the medical professional to quantify a patient's cumulative radiation dose. METHODS: One hundred mathematical phantoms of varying heights and weights were defined within an x-ray Monte Carlo based software code in order to calculate organ absorbed doses and effective doses from a chest abdomen pelvis scan. Regression analysis was used to develop an effective dose predictive model. The regression model was experimentally verified using anthropomorphic phantoms and validated against a real patient population. RESULTS: Estimates of the effective doses as calculated by the predictive model were within 10% of the estimates of the effective doses using experimentally measured absorbed doses within the anthropomorphic phantoms. Comparisons of the patient population effective doses show that the predictive model is within 33% of current methods of estimating effective dose using machine-based parameters. CONCLUSIONS: A patient's height and weight can be used to estimate the effective dose from a chest abdomen pelvis computed tomography scan. The presented predictive model can be used interchangeably with current effective dose estimating techniques that rely on computed tomography machine-based techniques. BioMed Central 2011-10-17 /pmc/articles/PMC3224357/ /pubmed/22004072 http://dx.doi.org/10.1186/1471-2342-11-20 Text en Copyright ©2011 Prins et al; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/2.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
spellingShingle Research Article
Prins, Robert D
Thornton, Raymond H
Schmidtlein, C Ross
Quinn, Brian
Ching, Hung
Dauer, Lawrence T
Estimating radiation effective doses from whole body computed tomography scans based on U.S. soldier patient height and weight
title Estimating radiation effective doses from whole body computed tomography scans based on U.S. soldier patient height and weight
title_full Estimating radiation effective doses from whole body computed tomography scans based on U.S. soldier patient height and weight
title_fullStr Estimating radiation effective doses from whole body computed tomography scans based on U.S. soldier patient height and weight
title_full_unstemmed Estimating radiation effective doses from whole body computed tomography scans based on U.S. soldier patient height and weight
title_short Estimating radiation effective doses from whole body computed tomography scans based on U.S. soldier patient height and weight
title_sort estimating radiation effective doses from whole body computed tomography scans based on u.s. soldier patient height and weight
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3224357/
https://www.ncbi.nlm.nih.gov/pubmed/22004072
http://dx.doi.org/10.1186/1471-2342-11-20
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