We developed computational methods and tools to assess organ doses for pediatric and adult patients undergoing computed tomography (CT) examinations. We used the International Commission on Radiological Protection (ICRP) reference pediatric and adult phantoms combined with the Monte Carlo simulation of a reference CT scanner to establish comprehensive organ dose coefficients (DC), organ absorbed dose per unit volumetric CT Dose Index (CTDIvol) (mGy/mGy). We also developed methods to estimate organ doses with tube current modulation techniques and size specific dose estimates. A graphical user interface was designed to obtain user input of patient- and scan-specific parameters, and to calculate and display organ doses. A batch calculation routine was also integrated into the program to automatically calculate organ doses for a large number of patients. We entitled the computer program, National Cancer Institute dosimetry system for CT(NCICT). We compared our dose coefficients with those from CT-Expo, and evaluated the performance of our program using CT patient data. Our pediatric DCs show good agreements of organ dose estimation with those from CT-Expo except for thyroid. Our results support that the adult phantom in CT-Expo seems to represent a pediatric individual between 10 and 15 years rather than an adult. The comparison of CTDIvol values between NCICT and dose pages from 10 selected CT scans shows good agreements less than 12% except for two cases (up to 20%). The organ dose comparison between mean and modulated mAs shows that mean mAs-based calculation significantly overestimates dose (up to 2.4-fold) to the organs in close proximity to lungs in chest and chest-abdomen-pelvis scans. Our program provides more realistic anatomy based on the ICRP reference phantoms, higher age resolution, the most up-to-date bone marrow dosimetry, and several convenient features compared to previous tools. The NCICT will be available for research purpose in the near future.

Deposition densities (Bq m -2 ) of all important dose-contributing radionuclides occurring in nuclear weapons testing fallout from tests conducted at Bikini and Enewetak Atolls (1946-1958) have been estimated on a test-specific basis for all the 31 atolls and separate reef islands of the Marshall Islands. A complete review of various historical and contemporary data, as well as meteorological analysis, was used to make judgments regarding which tests deposited fallout in the Marshall Islands and to estimate fallout deposition density. Our analysis suggested that only 20 of the 66 nuclear tests conducted in or near the Marshall Islands resulted in substantial fallout deposition on any of the 25 inhabited atolls. This analysis was confirmed by the fact that the sum of our estimates of 137 Cs deposition from these 20 tests at each atoll is in good agreement with the total 137 Cs deposited as estimated from contemporary soil sample analyses. The monitoring data and meteorological analyses were used to quantitatively estimate the deposition density of 63 activation and fission products for each nuclear test, plus the cumulative deposition of 239+240 Pu at each atoll. Estimates of the degree of fractionation of fallout from each test at each atoll, as well as of the fallout transit times from the test sites to the atolls were used in this analysis. The estimates of radionuclide deposition density, fractionation, and transit times reported here are the most complete available anywhere and are suitable for estimations of both external and internal dose to representative persons as described in companion papers.

Dosimetic uncertainties, particularly those that are shared among subgroups of a study population, can bias, distort or reduce the slope or significance of a dose response. Exposure estimates in studies of health risks from environmental radiation exposures are generally highly uncertain and thus, susceptible to these methodological limitations. An analysis was published in 2008 concerning radiation-related thyroid nodule prevalence in a study population of 2,994 villagers under the age of 21 years old between August 1949 and September 1962 and who lived downwind from the Semi-palatinsk Nuclear Test Site in Kazakhstan. This dose-response analysis identified a statistically significant association between thyroid nodule prevalence and reconstructed doses of fallout-related internal and external radiation to the thyroid gland; however, the effects of dosimetric uncertainty were not evaluated since the doses were simple point “best estimates”. In this work, we revised the 2008 study by a comprehensive treatment of dosimetric uncertainties. Our present analysis improves upon the previous study, specifically by accounting for shared and unshared uncertainties in dose estimation and risk analysis, and differs from the 2008 analysis in the following ways: 1. The study population size was reduced from 2,994 to 2,376 subjects, removing 618 persons with uncertain residence histories; 2. Simulation of multiple population dose sets (vectors) was performed using a two-dimensional Monte Carlo dose estimation method; and 3. A Bayesian model averaging approach was employed for evaluating the dose response, explicitly accounting for large and complex uncertainty in dose estimation. The results were compared against conventional regression techniques. The Bayesian approach utilizes 5,000 independent realizations of population dose vectors, each of which corresponds to a set of conditional individual median internal and external doses for the 2,376 subjects. These 5,000 population dose vectors reflect uncertainties in dosimetric parameters, partly shared and partly independent, among individual members of the study population. Risk estimates for thyroid nodules from internal irradiation were higher than those published in 2008, which results, to the best of our knowledge, from explicitly accounting for dose uncertainty. In contrast to earlier findings, the use of Bayesian methods led to the conclusion that the biological effectiveness for internal and external dose was similar. Estimates of excess relative risk per unit dose (ERR/Gy) for males (177 thyroid nodule cases) were almost 30 times those for females (571 cases) and were similar to those reported for thyroid cancers related to childhood exposures to external and internal sources in other studies. For confirmed cases of papillary thyroid cancers (3 in males, 18 in females), the ERR/Gy was also comparable to risk estimates from other studies, but not significantly different from zero. These findings represent the first reported dose response for a radiation epidemiologic stu...

dose-response analysis for epidemiological studies with complex uncertainty in dose estimation. AbstractMost conventional risk analysis methods rely on a single best estimate of exposure per person which does not allow for adjustment for exposure-related uncertainty. Here, we propose a Bayesian model averaging method to properly quantify the relationship between radiation dose and disease outcomes by accounting for shared and unshared uncertainty in estimated dose. Our Bayesian risk analysis method utilizes multiple realizations of sets (vectors) of doses generated by a two-dimensional Monte Carlo simulation method that properly separates shared and unshared errors in dose estimation. The exposure model used in this work is taken from a study of the risk of thyroid nodules among a cohort of 2,376 subjects following exposure to fallout resulting from nuclear testing in Kazakhstan. We assessed the performance of our method through an extensive series of simulation tests and comparisons against conventional regression risk analysis methods. We conclude that when estimated doses contain relatively small amounts of uncertainty, the Bayesian method using multiple realizations of possibly true dose vectors gave similar results to the conventional regression-based methods of dose-response analysis.However, when large and complex mixtures of shared and unshared uncertainties are present, the Bayesian method using multiple dose vectors had significantly lower relative bias than conventional regression-based risk analysis methods as well as a markedly increased capability to include the pre-established "true" risk coefficient within the credible interval of the Bayesianbased risk estimate. An evaluation of the dose-response using our method is presented for an epidemiological study of thyroid disease following radiation exposure.3

We developed models of lymphatic nodes for 6 pediatric and 2 adult hybrid computational phantoms to calculate the lymphatic node dose estimates from external and internal radiation exposures. We derived the number of lymphatic nodes from the recommendations in International Commission on Radiological Protection (ICRP) Publications 23 and 89 at 16 cluster locations for the lymphatic nodes: extrathoracic, cervical, thoracic (upper and lower), breast (left and right), mesentery (left and right), axillary (left and right), cubital (left and right), inguinal (left and right), and popliteal (left and right), for different ages (newborn, 1-, 5-, 10-, 15-year-old, and adult). We modeled each lymphatic node within the voxel format of the hybrid phantoms by assuming that all nodes have identical size derived from published data except narrow cluster sites. The lymph nodes were generated by the following algorithm: (1) selection of the lymph node site among the 16 cluster sites; (2) random sampling of the location of the lymph node within a spherical space centered at the chosen cluster site; (3) creation of the sphere or ovoid of tissue representing the node based on lymphatic node characteristics defined in ICRP Publications 23 and 89. We created lymph nodes until the pre-defined number of lymphatic nodes at the selected cluster site was reached. This algorithm was applied to pediatric (newborn, 1-, 5-, and 10-year-old male, and 15-year-old males) and adult male and female ICRP-compliant hybrid phantoms after voxelization. To assess the performance of our models for internal dosimetry, we calculated dose conversion coefficients, called S values, for selected organs and tissues with Iodine-131 distributed in 6 lymphatic node cluster sites using MCNPX2.6, a well validated Monte Carlo radiation transport code. Our analysis of the calculations indicates that the S values were significantly affected by the location of the lymph node clusters and that the values increased for smaller phantoms due to the shorter inter-organ distances compared to the bigger phantoms. By testing sensitivity of S values to random sampling and voxel resolution, we confirmed that the lymph node model is reasonably stable and consistent for different random samplings and voxel resolutions.

In epidemiological studies, exposures of interest are often measured with uncertainties, which may be independent or correlated. Independent errors can often be characterized relatively easily while correlated measurement errors have shared and hierarchical components that complicate the description of their structure. For some important studies, Monte Carlo dosimetry systems that provide multiple realizations of exposure estimates have been used to represent such complex error structures. While the effects of independent measurement errors on parameter estimation and methods to correct these effects have been studied comprehensively in the epidemiological literature, the literature on the effects of correlated errors, and associated correction methods is much more sparse. In this paper, we implement a novel method that calculates corrected confidence intervals based on the approximate asymptotic distribution of parameter estimates in linear excess relative risk (ERR) models. These models are widely used in survival analysis, particularly in radiation epidemiology. Specifically, for the dose effect estimate of interest (increase in relative risk per unit dose), a mixture distribution consisting of a normal and a lognormal component is applied. This choice of asymptotic approximation guarantees that corrected confidence intervals will always be bounded, a result which does not hold under a normal approximation. A simulation study was conducted to evaluate the proposed method in survival analysis using a realistic ERR model. We used both simulated Monte Carlo dosimetry systems (MCDS) and actual dose histories from the Mayak Worker Dosimetry System 2013, a MCDS for plutonium exposures in the Mayak Worker Cohort. Results show our proposed methods provide much improved coverage probabilities for the dose effect parameter, and noticeable improvements for other model parameters.

The NOAA Hybrid Single-Particle Lagrangian Integrated Trajectory Model (HYSPLIT) was evaluated as a research tool to simulate the dispersion and deposition of radioactive fallout from nuclear tests. Model-based estimates of fallout can be valuable for use in the reconstruction of past exposures from nuclear testing, particularly, where little historical fallout monitoring data is available. The ability to make reliable predictions about fallout deposition could also have significant importance for nuclear events in the future. We evaluated the accuracy of the HYSPLIT-predicted geographic patterns of deposition by comparing those predictions against known deposition patterns following specific nuclear tests with an emphasis on nuclear weapons tests conducted in the Marshall Islands. We evaluated the ability of the computer code to quantitatively predict the proportion of fallout particles of specific sizes deposited at specific locations as well as their time of transport. In our simulations of fallout from past nuclear tests, historical meteorological data were used from a reanalysis conducted jointly by the National Centers for Environmental Prediction (NCEP) and the National Center for Atmospheric Research (NCAR). We used a systematic approach in testing the HYSPLIT model by simulating the release of a range of particles sizes from a range of altitudes and evaluating the number and location of particles deposited. Our findings suggest that the quantity and quality of meteorological data are the most important factors for accurate fallout predictions and that when satisfactory meteorological input data are used, HYSPLIT can produce relatively accurate deposition patterns and fallout arrival times. Furthermore, when no other measurement data are available, HYSPLIT can be used to indicate whether or not fallout might have occurred at a given location and provide, at minimum, crude quantitative estimates of the magnitude of the deposited activity. A variety of simulations of the deposition of fallout from atmospheric nuclear tests conducted in the Marshall Islands, at the Nevada Test Site (USA), and at the Semipalatinsk Nuclear Test Site (Kazakhstan) were performed using reanalysis data composed of historic meteorological observations. The results of the Marshall Islands simulations were used in a limited fashion to support the dose reconstruction described in companion papers within this volume.

This study provides a retrospective assessment of doses to 13 organs for the most common radiographic examinations conducted between the 1930s and 2010, taking into account typical technical parameters used for radiography during those years. This study is intended to be a resource on changes in medical diagnostic radiation exposure over time with a specific purpose of supporting retrospective epidemiological studies of radiation health risks. The authors derived organ doses to the brain, esophagus, thyroid, red bone marrow, lungs, breast, heart, stomach, liver, colon, urinary bladder, ovaries, and testes based on 14 common radiographic procedures and compared, when possible, with doses reported in the literature. These dose estimates were based on radiographic exposure parameters described in textbooks widely used by radiologic technologists in training from 1939 to 2010. The derived estimated doses presented here are believed to be representative of typical organs for an average-size adult who might be considered to be similar to the reference person. There were large variations in organ doses noted among the different types of radiographic examinations. Doses were highest in organs within the area imaged and next highest in organs in close proximity to the area imaged. Estimated organ doses have declined substantially [overall 22-fold (±38)] over time as a consequence of changes in technology, imaging protocols and protective measures. For some examinations, only slight differences were observed in doses for the decades of the 1960s, 1970s, and 1980s due to minor changes in technical parameters. Substantial dose reductions were observed in the 1990s and 2000s.