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Values of E can be estimated utilizing patient-specific and procedure-specific parameters. The strong inverse correlation of E/P(KA) with patient weight allows simple estimation of E from P(KA) and patient weight. There is a wide variation in effective dose in oncologic hepatic embolizations with doses up to an order of magnitude higher than diagnostic imaging of the abdomen by CT radiology. Variation is likely due to patient geometry, clinical technique factors, and procedure complexity.

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BackgroundThe 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.MethodsOne 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.ResultsEstimates 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.ConclusionsA 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.