A Feasibility Study to Reduce Misclassification Error in Occupational Dose Estimates for Epidemiological Studies Using Body Size-Dependent Computational Phantoms

Abstract

In the epidemiological study on the health effects of participants in the United States Radiologic Technologists (USRT) study, organ dosimetry was performed based on surveys and literature reviews. To convert dosimeter readings to organ doses, organ dose coefficients were adopted. However, the existing dose coefficients were derived from computational human phantoms with ICRP reference height and weight not accounting for the variation in body size. We first calculated preliminary body size-dependent organ dose coefficients using selected body size-dependent phantoms combined with Monte Carlo radiation transport method. We then tested the accuracy of these body-size dependent coefficients against the ICRP 74 reference size coefficients in comparison with five individual-specific organ dose coefficients computed from computed tomography (CT) image-based anatomical models of five adult males with different body sizes also using Monte Carlo methods. The reference size dose coefficients overall underestimate the patient-specific dose coefficients by up to 51%. Body size-dependent phantoms overall provided more accurate organ dose coefficients for the five patients. In case of the esophagus, the dose underestimation of 51% in the comparison with the reference phantom was reduced to 7%. The results confirm that potential dosimetric misclassification caused by using reference size phantom-based dose coefficients can be resolved by using the body size-dependent dose coefficients.

In silico simulation of hepatic arteries: An open‐source algorithm for efficient synthetic data generation

“…1,2 Many CHPs incorporate detailed anatomy and physiological functions, such as respiratory and cardiac motion and find application in com-puted tomography (CT), magnetic resonance imaging (MRI), and nuclear medicine. Uses of these phantoms include internal and external dosimetry, [2][3][4][5][6][7][8][9] the development and testing of novel image reconstruction algorithms (e.g., motion compensation, artifact suppression, sparse reconstruction), 1,[10][11][12][13][14][15] image acquisition techniques (e.g., artifact avoidance, collimator and detector optimization), 1,16,17 post-processing techniques (e.g., noise reduction), 1,18,19 and more. 20 Furthermore, the Quantitative Imaging Biomarker Alliance (QIBA) has used the term digital reference object (DRO) for the use of CHPs and other phantoms to establish a minimum performance requirement for quantitative imaging algorithms and reduce inter-scanner variability.…”