Health physics最新文献

Nuclear medicine radiopharmaceutical therapies or theranostics procedures may be performed in a number of environments within medical facilities. Some examples are having a dedicated room within a Nuclear Medicine Department, using inpatient units, clinics, or via a theranostics center. All methods may be regulatory compliant, effective, and advantageous depending on the medical facility's current environment. Organizations may consider designing, constructing, and implementing a freestanding theranostic center because of the potential advantages it can offer. A dedicated theranostic center can improve patient safety, care, and experience along with accommodation of increasing patient volumes, fiscal realities, and addition of new theranostic services that may be clinical or research in nature. Organizations are unique and the plausible benefits and applicability may be variable for different healthcare facilities.

Magnetic resonance imaging (MRI) has revolutionized disease diagnosis and treatment. However, the technology poses safety risks, such as exposure to magnetic fields, RF pulses, and cryogens, necessitating strict adherence to safety protocols to protect patients and healthcare workers. This cross-sectional descriptive study assessed compliance with MRI safety standards in Khuzestan province, Iran) imaging centers, focusing on electromagnetic fields and other key safety domains. A 61-item researcher-developed checklist, based on international safety guidelines, was used to evaluate safety protocols in 11 MRI centers across seven domains, including facility design, equipment labeling, static magnetic and gradient fields, RF waves, cryogens, patient and staff protection, and infection control. MRI staff responded with yes/no answers. Responses to three additional questions also were collected. Data analysis was conducted using SPSS 26. A p-value < 0.05 was considered statistically significant. Overall, facility design scores ranged from 54.5% to 100%, but static magnetic field safety ratings were significantly lower (25% to 100%). Although safety equipment availability reached 100% in some centers, gaps were noted in labeling ferromagnetic devices. Infection control adherence was high, but only seven centers featured seamless flooring in the magnet room. Cryogen safety showed partial compliance with some centers lacking exhaust fans. Employee and patient safety measures were inconsistent, with one center scoring as low as 18%. While MRI centers demonstrated strengths in infection control and facility design, critical deficiencies in static magnetic field safety and emergency protocols highlight the need for targeted training, regular audits, and updated policies. Addressing these gaps is essential to enhancing MRI safety practices and aligning with international standards.

Lifetime risks are a useful tool in quantifying health risks related to radiation exposure and play an important role in the radiation detriment and, in the case of radon, for radon dose conversion. This study considers the lifetime risk of dying from lung cancer related to occupational radon exposure. For this purpose, in addition to other risk measures, the lifetime excess absolute risk (LEAR) is mainly examined. Uncertainty intervals for such lifetime risk estimates and corresponding statistical methods are rarely presented in the radon literature. Based on previous work on LEAR estimates, the objective of this article is to introduce and discuss novel methods to derive uncertainty intervals for lifetime risk estimates for lung cancer related to occupational radon exposure. Uncertainties of two main components of lifetime risk calculations are modeled: uncertainties of risk model parameter estimates describing the excess relative risk for lung cancer and of baseline mortality rates. Approximate normality assumption (ANA) methods derived from likelihood theory and Bayesian techniques are employed to quantify uncertainty in risk model parameters. The derived methods are applied to risk models from the German "Wismut" uranium miners cohort study (full Wismut cohort with follow-up up to 2018 and sub-cohort with miners first hired in 1960 or later, designated as "1960+ sub-cohort"). Mortality rate uncertainty is assessed based on information from the WHO mortality database. All uncertainty assessment methods are realized with Monte Carlo simulations. Resulting uncertainty intervals for different lifetime risk measures are compared. Uncertainty from risk model parameters imposes the largest uncertainty on lifetime risks but baseline lung cancer mortality rate uncertainty is also substantial. Using the ANA method accounting for uncertainty in risk model parameter estimates, the LEAR in % for the 1960+ sub-cohort risk model was 6.70 with a 95% uncertainty interval of [3.26; 12.28] for the exposure scenario of 2 Working Level Months from age 18-64 years, compared to the full cohort risk model with a LEAR in % of 3.43 and narrower 95% uncertainty interval [2.06; 4.84]. ANA methods and Bayesian techniques with a non-informative prior yield similar results, whenever comparable. There are only minor differences across different lifetime risk measures. Based on the present results, risk model parameter uncertainty accounts for a substantial share of lifetime risk uncertainty for radon protection. ANA methods are the most practicable and should be employed in the majority of cases. The explicit choice of lifetime risk measures is negligible. The derived uncertainty intervals are comparable to the range of lifetime risk estimates from uranium miners studies in the literature. These findings should be accounted for when developing radiation protection policies, which are based on lifetime risks.

Inhaled radioactive materials can pose a long-term health concern, as the material can be incorporated into the body's metabolic pathways and remain in organs and tissues for extended durations. During the retention period, the radioactive material may localize in a source organ and irradiate adjacent target organs and tissues. Distribution of these materials changes over time, requiring biokinetic modeling to evaluate their movement through various tissues and organs. The evolving distribution depends on multiple inputs characterizing the inhaled material, such as particle size and size distribution, particle density, aspect ratio, specific radionuclide, the chemical form, and solubility. In addition, biological parameters such as breathing rate, breathing type (nasal or nasal/oral), respiratory system morphometry, tidal volume, functional residual capacity, and anatomical dead space all influence material transport. These aerosol properties and physiological characteristics of the respiratory tract jointly define a range of initial conditions that influence the time-dependent distribution of radioactive material. To evaluate both uncertainty in the initial conditions of inhalation exposure and the final output (committed effective dose) from biokinetic models, a Python-based software tool, Radiological Exposure Dose Calculator (REDCAL), was developed to propagate uncertainty within the human respiratory tract model. Focusing on deposition fraction uncertainty, the primary objective was to characterize the initial activity distribution across respiratory regions as a function of anticipated particle sizes and distributions. The impact of the deposition fraction uncertainty was propagated to committed effective dose coefficients for selected radionuclides in a companion publication. For each particle size, a lognormal distribution, characterized by its geometric mean as defined within ICRP Publication 66, serves as the basis for introducing uncertainty into the physical processes governing deposition in various lung regions. This study addresses the deposition process and examines how uncertainty in deposition mechanisms affects activity distribution in the airways, ultimately presenting the expected range and standard deviation of deposited activity as a function of particle size.

Reference inhalation dose models rely on deterministic biokinetics and reference computational phantoms, limiting their applicability to the variability present in population-specific exposures encountered in emergency response scenarios. This study introduces REDCAL, a Python-based computational framework developed to propagate uncertainty in inhalation dose coefficients using the International Commission on Radiological Protection (ICRP) Publication 66 Human Respiratory Tract Model. REDCAL integrates ICRP deposition and clearance models, systemic biokinetics, and governing physics principles, and leverages Sandia National Laboratories' Dakota toolkit for uncertainty quantification via Latin Hypercube Sampling. REDCAL was validated against DCAL, with biokinetic retention results differing by less than 1% and effective dose coefficients by less than 2% across all tested radionuclides. Stochastic sampling introduced variability in dose coefficients, with geometric standard deviations (GSD) in committed effective dose coefficients (CEDC) ranging from 1.0 to 1.5, based on lognormal distribution fits. Analysis demonstrated that variations in the activity median aerodynamic diameter (AMAD) notably influenced the computed CEDC values. Smaller particles (<1 µm) increased doses by 20-30% due to deeper lung deposition and prolonged retention for alpha emitting radionuclides, such as 241Am and 239Pu. Radionuclides with fast clearance, such as 133I, demonstrated a dose reduction exceeding 50%, as AMAD increased beyond 5 µm due to upper airway deposition and rapid mucociliary clearance. The greatest GSD among the radionuclides reported in this study was for 241Am. In most cases, the largest GSDs in the CEDC were associated with larger particle sizes, an expected outcome, as ICRP Publication 66 defines GSD in particle size as a function of AMAD, resulting in an extended tail of the lognormal distribution. The findings support improved inhalation dose assessments and enhance consequence management strategies for the U.S. Federal Radiological Monitoring and Assessment Center by quantifying uncertainty in dose coefficients and strengthening decision-making for emergency response scenarios.