Publications (18)15.13 Total impact

Article: Methods used to calculate doses resulting from inhalation of Capstone depleted uranium aerosols.
[Show abstract] [Hide abstract]
ABSTRACT: The methods used to calculate radiological and toxicological doses to hypothetical persons inside either a U.S. Army Abrams tank or Bradley Fighting Vehicle that has been perforated by depleted uranium munitions are described. Data from time and particlesizeresolved measurements of depleted uranium aerosol as well as particlesizeresolved measurements of aerosol solubility in lung fluids for aerosol produced in the breathing zones of the hypothetical occupants were used. The aerosol was approximated as a mixture of nine monodisperse (single particle size) components corresponding to particle size increments measured by the eight stages plus the backup filter of the cascade impactors used. A Markov Chain Monte Carlo Bayesian analysis technique was employed, which straightforwardly calculates the uncertainties in doses. Extensive quality control checking of the various computer codes used is described.Health physics 04/2009; 96(3):30627. DOI:10.1097/01.HP.0000313340.22887.7d · 1.27 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Bayesian hypothesis testing may be used to qualitatively interpret a dataset as indicating something "detected" or not. Hypothesis testing is shown to be equivalent to testing the posterior distribution for positive true amounts by redefining the prior to be a mixture of the original prior and a deltafunction component at 0 representing the null hypothesis that nothing is truly present. The hypothesistesting interpretation of the data is based on the posterior probability of the usual modeling hypothesis relative to the null hypothesis. Real numerical examples are given and discussed, including the distribution of the nonnull hypothesis probability over 4,000 internal dosimetry cases. Currently used comparable methods based on classical statistics are discussed.Health Physics 04/2008; 94(3):24854. DOI:10.1097/01.HP.0000290624.35701.00 · 1.27 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Simulateddata internal dosimetry cases for use in intercomparison exercises or as a software verification and validation tool have been published on the internet (www.lanl.gov/bayesian/software Bayesian software package II). A user may validate their internal dosimetry code or method using this simulated bioassay data. Or, the user may choose to try out the Los Alamos National Laboratory codes ID and UF, which are also supplied. A Poissonlognormal model of data uncertainty is assumed. A collection of different possible models for each nuclide (e.g. solubility types and particle sizes) are used. For example, for 238Pu, 14 different biokinetic models or types (8 inhalation, 4 wound and 2 ingestion) are assumed. Simulated data are generated for all the assumed biokinetic models, both for incidents, where the time of intake is known, and for nonincidents, where it is not. For the dose calculations, the route of intake, but not the biokinetic model, is considered to be known. The object is to correctly calculate the known true dose from simulated data covering a period of time. A 'correct' result has been defined in two ways: (1) that the credible limits of the calculated dose include the correct dose and (2) that the calculated dose is within a factor of 2 of the correct dose.Radiation Protection Dosimetry 02/2007; 127(14):3619. DOI:10.1093/rpd/ncm470 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Several approaches are available for bioassay interpretation when assigning Pu doses to Mayak workers. First, a conventional approach is to apply ICRP models per se. An alternative method involves individualised fitting of bioassay data using Bayesian statistical methods. A third approach is to develop an independent dosimetry system for Mayak workers by adapting ICRP models using a dataset of available bioassay measurements for this population. Thus, a dataset of 42 former Mayak workers, who died of nonradiation effects, with both urine bioassay and postmortem tissue data was used to test these three approaches. All three approaches proved to be adequate for bioassay and tissue interpretation, and thus for Pu dose reconstruction purposes. However, large discrepancies are observed in the resulting quantitative dose estimates. These discrepancies can, in large part, be explained by differences in the interpretation of Pu behaviour in the lungs in the context of ICRP lung model. Thus, a careful validation of Pu lung dosimetry model is needed in Mayak worker dosimetry systems.Radiation Protection Dosimetry 02/2007; 127(14):48690. DOI:10.1093/rpd/ncm415 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Workers are routinely monitored by urinalysis for exposure to uranium at Los Alamos National Laboratory. Urine samples are analysed by alpha spectroscopy for 234U, 235U and 238U. Interpretation of the data is complicated by the presence of natural uranium in the workers' drinking water and diet. Earlier methods used drinking water samples to estimate the dietary component in urine excretion. However, there proved to be insufficient correlation between drinking water concentration and excretion rate. Instead, an iterative calculation is used to identify a typical excretion rate for each individual in the absence of occupational intakes. Bayesian doseassessment methods, first developed for plutonium bioassay at Los Alamos, have been adapted for uranium. These methods, coupled with an algorithm for identifying each individual's baseline level of uranium from environmental sources, yield improved reliability in the identification of occupational intakes.Radiation Protection Dosimetry 02/2007; 127(14):3338. DOI:10.1093/rpd/ncm352 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: One of the challenges to the dose assessment team in response to an inhalation incident in the workplace is to provide the occupational physicians, operational radiation protection personnel and line managers with early estimates of radionuclide intakes so that appropriate consequence management and mitigation can be done. For radionuclides such as Pu, where in vivo counting is not adequately sensitive, other techniques such as the measurement of removable radionuclide from the nasal airway passages can be used. At Los Alamos National Laboratory (LANL), nose swabs of the ET1 region have been used routinely as a first response to airborne Pu releases in the workplace, as well as for other radionuclides. This paper presents the results of analysing over 15 years of nose swab data, comparing these with dose assessments performed using the Bayesian methods developed at LANL. The results provide empirical support for using nose swab data for early dose assessments. For Pu, a rule of thumb is a dose factor of 0.8 mSv Bq(1), assuming a linear relationship between nasal swab activity and committed effective dose equivalent. However, this value is specific to the methods and models used at LANL, and should not be applied directly without considering possible differences in measurement and calculation methods.Radiation Protection Dosimetry 02/2007; 127(14):35660. DOI:10.1093/rpd/ncm354 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: This paper describes the design and implementation of the Los Alamos National Laboratory (LANL) dose assessment (DA) data system. Dose calculations for the most important radionuclides at LANL, namely plutonium, americium, uranium and tritium, are performed through the Microsoft Access DA database. DA includes specially developed forms and macros that perform a variety of tasks, such as retrieving bioassay data, launching the FORTRAN internal dosimetry applications and displaying dose results in the form of text summaries and plots. The DA software involves the following major processes: (1) downloading of bioassay data from a remote data source, (2) editing local and remote databases, (3) setting up and carrying out internal dose calculations using the UF code or the ID code, (3) importing results of the dose calculations into local results databases, (4) producing a secondary database of 'official results' and (5) automatically creating and emailing reports. The software also provides summary status and reports of the pending DAs, which are useful for managing the cases in process.Radiation Protection Dosimetry 02/2007; 127(14):3479. DOI:10.1093/rpd/ncm287 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Biokinetic models are the scientific underpinning of internal dosimetry and depend, ultimately, for their scientific validation on comparisons with human bioassay data. Three significant plutonium/americium bioassay databases, known to the authors, are described: (1) Sellafield, (2) Los Alamos and (3) the United States Transuranium Registry. A case is made for a uniform standard for database format, and the XML standard is discussed.Radiation Protection Dosimetry 02/2007; 125(14):5317. DOI:10.1093/rpd/ncm164 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: The methods used at Los Alamos for calculating internal dose for plutonium and americium are described. The main method, the ID code, is a straightforward use of Bayes' theorem, evaluated using Markov Chain Monte Carlo. A supercomputer cluster is used to do mass calculations on many cases at once. As an alternative, a single workstation continually does calculations as new bioassay data comes in, spreading out the calculation load over the year. Both methods are being used successfully.Applied Modeling and Computations in Nuclear Science, Edited by Thomas M' semkowo, 01/2005: chapter 7: pages 93103; American Chemical Society.  [Show abstract] [Hide abstract]
ABSTRACT: The inverse problem of internal dosimetry is naturally posed as a problem of Bayesian inference. The Bayesian approach is of practical importance in three areas: (1) avoiding false positives in the detection of rare events, (2) the calculation of uncertainties, and (3) the calculation of multiple intakes, all of which are important for internal dosimetry. In this paper, the Bayesian approach to the interpretation of measurements is first reviewed using a simple conceptual example. Then, a simple 239Pu case using IMBA expert is discussed, and finally a current cuttingedge example is discussed involving real 238Pu data calculated with a Markov Chain Monte Carlo algorithm and with exact calculation of poisson likelihood functions.Radiation Protection Dosimetry 02/2003; 105(14):3338. DOI:10.1093/oxfordjournals.rpd.a006252 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: A group of personnel at Los Alamos National Laboratory is routinely monitored for the presence of uranium isotopes by urine bioassay. Samples are analysed by alpha spectroscopy, and the results are examined for evidence of an intake of uranium. Because the measurement uncertainties are often comparable to the quantities of material we wish to detect, statistical considerations are crucial for the proper interpretation of the data. The problem is further complicated by the significant, but highly nonuniform, presence of uranium in local drinking water and, in some cases, food supply. Software originally developed for internal dosimetry of plutonium has been adapted to the problem of uranium dosimetry. The software uses an unfolding algorithm to calculate an approximate Bayesian solution to the problem of characterising any intakes which may have occurred, given the history of urine bioassay results for each individual in the monitored population. The program uses biokinetic models from ICRP Publications 68 and later, and a prior probability distribution derived empirically from the body of uranium bioassay data collected at Los Alamos over the operating history of the laboratory. For each individual, the software creates a posterior probability distribution of intake quantity and solubility type as a function of time. From this distribution, estimates are made of the cumulative committed dose (CEDE) to each individual. Results of the method are compared with those obtained using an earlier classical (nonBayesian) algorithm for uranium dosimetry. We also discuss the problem of distinguishing occupational intakes from intake of environmental uranium, within a Bayesian framework.Radiation Protection Dosimetry 02/2003; 105(14):4136. DOI:10.1093/oxfordjournals.rpd.a006271 · 0.91 Impact Factor 
Article: Using exact Poisson likelihood functions in Bayesian interpretation of counting measurements.
[Show abstract] [Hide abstract]
ABSTRACT: A technique for computing the exact marginalized (integrated) Poisson likelihood function for counting measurement processes involving a background subtraction is described. An empirical Bayesian method for determining the prior probability distribution of background count rates from population data is recommended and would seem to have important practical advantages. The exact marginalized Poisson likelihood function may be used instead of the commonly used Gaussian approximation. Differences occur in some cases of small numbers of measured counts, which are discussed. Optional use of exact likelihood functions in our Bayesian internal dosimetry codes has been implemented using an interpolationtable approach, which means that there is no computation time penalty except for the initial setup of the interpolation tables.Health Physics 11/2002; 83(4):5128. DOI:10.1097/0000403220021000000009 · 1.27 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: A new numerical method for solving the inverse problem of internal dosimetry is described. The new method uses Markov Chain Monte Carlo and the Metropolis algorithm. Multiple intake amounts, biokinetic types, and times of intake are determined from bioassay data by integrating over the Bayesian posterior distribution. The method appears definitive, but its application requires a large amount of computing time.Radiation Protection Dosimetry 02/2002; 98(2):1918. DOI:10.1093/oxfordjournals.rpd.a006709 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: Metal tritides with low dissolution rates may have residence times in the lungs which are considerably longer than the biological halftime normally associated with tritium in body water, resulting in longterm irradiation of the lungs by low energy beta particles and bremsstrahlung X rays. Samples of hafnium tritide were placed in a lung simulant fluid to determine approximate lung dissolution rates. Hafnium hydride samples were analysed for particle size distribution with a scanning electron microscope. Lung simulant data indicated a biological dissolution halftime for hafnium tritide on the order of 10(5) d. Hafnium hydride particle sizes ranged between 2 and 10 microns, corresponding to activity median aerodynamic diameters of 5 to 25 microns. Review of in vitro dissolution data, development of a biokinetic model, and determination of secondary limits for 1 micron AMAD particles are presented and discussed.Radiation Protection Dosimetry 02/2001; 93(1):5560. DOI:10.1093/oxfordjournals.rpd.a006413 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: The problem of choosing a prior distribution for the Bayesian interpretation of measurements (specifically internal dosimetry measurements) is considered using a theoretical analysis and by examining historical tritium and plutonium urine bioassay data from Los Alamos. Two models for the prior probability distribution are proposed: (1) the lognormal distribution, when there is some additional information to determine the scale of the true result, and (2) the 'alpha' distribution (a simplified variant of the gamma distribution) when there is not. These models have been incorporated into version 3 of the Bayesian internal dosimetry code in use at Los Alamos (downloadable from our web site). Plutonium internal dosimetry at Los Alamos is now being done using prior probability distribution parameters determined selfconsistently from population averages of Los Alamos data.Radiation Protection Dosimetry 02/2001; 94(4):34752. DOI:10.1093/oxfordjournals.rpd.a006509 · 0.91 Impact Factor  [Show abstract] [Hide abstract]
ABSTRACT: The classical statistics approach used in health physics for the interpretation of measurements is deficient in that it does not take into account "needle in a haystack" effects, that is, correct identification of events that are rare in a population. This is often the case in health physics measurements, and the false positive fraction (the fraction of results measuring positive that are actually zero) is often very large using the prescriptions of classical statistics. Bayesian statistics provides a methodology to minimize the number of incorrect decisions (wrong calls): false positives and false negatives. We present the basic method and a heuristic discussion. Examples are given using numerically generated and real bioassay data for tritium. Various analytical models are used to fit the prior probability distribution in order to test the sensitivity to choice of model. Parametric studies show that for typical situations involving rare events the normalized Bayesian decision level k(alpha) = Lc/sigma0, where sigma0 is the measurement uncertainty for zero true amount, is in the range of 3 to 5 depending on the true positive rate. Four times sigma0 rather than approximately two times sigma0, as in classical statistics, would seem a better choice for the decision level in these situations.Health Physics 07/2000; 78(6):598613. DOI:10.2172/676932 · 1.27 Impact Factor 
 [Show abstract] [Hide abstract]
ABSTRACT: SUMMARY A new numerical method for solving the inverse problem of in ternal dosimetry is described. The new method uses Markov Chain Monte Carlo and the Metropolis algorithm. Multiple intake amounts, biokinetic types, and times of intake are determined from bioas say data by integrating over the Bayesian posterior distribution. The method appears definitive, but its application requires a large amount of computing time.
Publication Stats
122  Citations  
15.13  Total Impact Points  
Top Journals
 Radiation Protection Dosimetry (11)
 Health Physics (3)
 Health physics (1)
Institutions

20002009

Los Alamos National Laboratory
ЛосАламос, California, United States
