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Open Access Highly Accessed Research article

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

Robert D Prins12*, Raymond H Thornton1, C Ross Schmidtlein1, Brian Quinn1, Hung Ching1 and Lawrence T Dauer1

Author Affiliations

1 Department of Medical Physics, Memorial Sloan-Kettering Cancer Center, 1275 York Ave. New York, NY 10021, USA

2 Department of Environmental Health Sciences, Mailman School of Public Health, Columbia University, 722 West 168th Street, New York, NY 10032, USA

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BMC Medical Imaging 2011, 11:20  doi:10.1186/1471-2342-11-20

Published: 17 October 2011



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.


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.


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.


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.