Applied Radiation and Isotopes最新文献

In diagnostic radiology, the air kerma is an essential parameter. Radiologists consider the air kerma, when calculating organ doses and dangers to patients. The intensity of the radiation beam is represented by the air kerma, which is the value of energy wasted by a photon as it travels through air. Because of the heel effect in X-ray sources, air kerma varies throughout the field of medical imaging systems. One possible contributor to this discrepancy is the X-ray tube's voltage. In this study, an approach has been proposed for predicting the air kerma anywhere inside the field of X-ray beams utilized in medical diagnostic imaging systems. As a first step, a diagnostic imaging system was modelled using the Monte Carlo N-Particle platform. We used a tungsten target and aluminum and beryllium filters of varying thicknesses to recreate the X-ray tube. The air kerma has been measured in different parts of the conical X-ray beam that is working at 30, 50, 70, 90, 110, 130, and 150 kV. This gives enough data for training neural networks. The voltage of the X-ray tube, filter type, filter thickness, and the coordinates of each point used to calculate the air kerma were all inputs to the MLP neural network. The MLP architecture, known for its significant advancements in research and expanding applications, was trained to predict the quantity of air kerma as its output. Specifically, by considering X-ray tube filters of varying thicknesses, the trained MLP model demonstrated its capability to accurately predict the air kerma at every point within the X-ray field for a range of X-ray tube voltages typically used in medical diagnostic radiography (30–150 kV).

Isochronous Mass Spectrometry is a practical approach for studying decays of short-lived isomers. However, solely relying on the time stamps between the isomer and ground state does not provide clear sign of decay. To address this issue, we proposed a method for extracting decay time point by analyzing the residuals of time stamps within a window of ($20\mu s$, $180\mu s$) after the start of data acquisition. Decay events out of the window were disregarded due to poor accuracy of revolution time. In this paper, we propose a novel approach based on the discrete Fourier transform technique, which was tested by simulation data. We found that the accuracy of the decay time point can be improved, leading to an expanded window of ($15\mu s$, $185\mu s$). Furthermore, as the novel method was applied to experimental data, additional five decay events were identified. The newly determined half-life of ${}^{94m}$Ru${}^{44+}$ is consistent with the previous value.

In vivo treatment monitoring in ion therapy is one of the key issues for improving the treatment quality assurance procedures. Range verification is one of the most relevant and yet complex task used for in vivo treatment monitoring. In carbon ion therapy, positron emission tomography is the most widely used method. This technique exploits the ${\beta }^{+}$-activity of positron emitters created by nuclear interactions between the incoming beam and the irradiated tissue. Currently, high computational efforts and time-consuming Monte Carlo simulation platforms are typically used to predict positron emitter distributions. In order to avoid time-consuming simulations, an extended filtering approach was suggested to analytically predict positron emitter profiles from depth dose distributions in carbon ion therapy. The purpose of this work is to investigate such an analytical prediction model in patient anatomies of varying complexity, highlighting its potential and the need of further improvements, especially in highly heterogeneous anatomies where many air cavities are present in the beam path. The accuracy of range verification showed a mean relative error of $\sim 3\text{\%}$ and a deviation between the simulation and the prediction below $2\mathrm{mm}$ for the three patient cases analysed: a brain case and two head and neck cases. Additional investigations demonstrated the region of applicability of the method for cases of patient data. The analytical method enables range verification in carbon ion therapy by replacing computing-intensive Monte Carlo simulations and thus minimize the PET monitoring burden on the clinical workflow.

In this groundbreaking study, artificial neural networks (ANNs) are employed to predict the production cross-sections of crucial radioisotopes, namely ¹⁸O, ²⁰⁹Bi, ²³²Th, and ⁶⁸Zn, via the (p,n) reaction. We employed a comparative approach to validate the ANN model's predictions by comparing them to outputs generated by established nuclear reaction codes (TALYS 1.9, EMPIRE-3.2 (Malta)) and data from the authoritative source, the Experimental Nuclear Reaction Data (EXFOR).Motivated by the increasing demand for radioisotopes in precise medical diagnostics and successful therapies, this study focuses on investigating methods and new techniques for determining production cross-sections with high accuracy, which are crucial for the consistent supply of vital radioisotopes. In line with this objective, the ANN model demonstrated exceptional performance, achieving remarkably high correlation coefficients, exceeding 0.999 for training and all data, and reaching 0.98665 for testing. Supportive of this, the high correlation coefficients indicate that the ANN estimations effectively match experimental data. Significantly, our findings illustrate the potential of ANNs as a promising alternative for estimating the production cross-sections of ¹⁸O, ²⁰⁹Bi, ²³²Th, and ⁶⁸Zn, with the possibility of extending this application to other medically relevant radioisotopes.

Understanding the interaction between photons and matter is crucial for exploring essential questions in nuclear physics. The Giant Dipole Resonance (GDR) is the prevailing mechanism in photo-absorption cross-sections up to 30 MeV. Depending on whether the nucleus is spherical or deformed, the curve of the photo-absorption cross-section versus photon energy is characterized by one or several Lorentzian peaks. Theoretical calculations of photo-absorption cross-sections are largely centered on deducing GDR parameters. These parameters are used in theoretical reaction codes that aim to simulate photon-induced nuclear reactions accurately. In this study, the GDR parameters for the spherical isotopes ¹¹⁵In, ¹⁴⁴Sm, ¹⁴⁸Sm, ¹⁵⁰Sm, and for the deformed isotopes ¹⁵⁴Sm, ¹⁵³Eu, and ¹⁶⁰Gd were calculated by optimizing to the experimental data. The calculated GDR parameters were inputted into the TALYS 1.8 code to compute the photo-neutron cross sections, which were then compared with experimental results from the literature. It has been observed that the calculations performed with the obtained GDR parameters are consistent with the experimental data.

A prompt γ-ray neutron activation analysis system has recently been developed at China advanced research reactor (CARR), the 60 MW research reactor in China Institute of Atomic Energy (CIAE). The system is set at the cold neutron beam guide with a thermal equivalent neutron flux at the sample position of 1.0×10⁹ n·cm^-2·s^-1 with the power of 30 MW, and it is mainly composed of a neutron beam collimator, a sample chamber, a beam stopper, neutron and γ-ray shieldings and a detection system. The detection system can realize three modes of measurement: single, Compton suppression, and pair modes. The detection efficiency was calibrated up to 11 MeV using a set of radionuclides and the (n, γ) reactions of N and Cl. Boron, one of the most important elements in high-temperature alloy material studies, was analyzed in this work, as the first pilot experiment of the CARR-PGNAA system. The analytical sensitivity of 2000 cps/mg-B was obtained. The results verified the feasibility of the CARR-PGNAA system to measure boron in high-temperature alloys, and laid a foundation for the accurate quantification of boron in the next step.

In this paper, it is proposed to locate multiple unknown radioactive sources within a certain time limit through particle filtering and Voronoi partitioning. Firstly, with each robot as a Voronoi centroid, the entire area is partitioned. Then, the robots conduct source search concurrently through particle filtering. When all the robots complete the process of one-particle filtering, the iteration ends and the next one begins until the search for the radioactive source is terminated. Finally, experiment is conducted to demonstrate the efficiency and accuracy of the proposed method.

One of the most well-liked energizing drinks is now tea, which is primarily used in Malaysia. The natural radioactivity in the associated soils where tea plants are cultivated plays a major role in determining the presence of radionuclides in tea leaves. The present study assesses the transfer of radionuclides from soil-to-tea leaves and then estimates the committed effective doses through tea consumption. Tea leaves and the associated soils were obtained from the largest tea plantation area, which is located in the Cameron Highlands, Malaysia. The marketed tea leaves in powdered form were obtained from the supermarkets in Kuala Lumpur. HPGe gamma-ray spectrometry was used to determine the prevailing concentrations of long-lived radioactive materials in tea leaves. Activity concentrations of ²²⁶Ra, ²³²Th, and ⁴⁰K in tea soils ranged from 49 to 101.7 Bq kg⁻¹, 74.5–124.1 Bq kg⁻¹ and 79.6–423.2 Bq kg⁻¹, respectively, while the respective values in tea leaves are 14.4–23.8 Bq kg⁻¹, 12.9–29.5 Bq kg⁻¹ and 297–387.5 Bq kg⁻¹. Transfer factors of radionuclides showed typical values (<1.0) except for the ⁴⁰K. The threshold tea consumption rates suggest that one should not consume more than 67 g of tea leaves per day (around 4 g of tea leaves are needed for making 1 cup of tea, so 17 cups per day) to avoid negative health effects. Committed effective doses due to tea consumption are found to be lower (5.18–6.08 μSv y⁻¹) than the United Nations Scientific Committee on the Effects of Atomic Radiation (2000) reference dose guidance limit of 290 μSv y⁻¹ for foodstuffs; however, it should be noted that the guidance limit is recommended for all foodstuffs collectively. Providing data on natural radioactivity in tea leaves grown in Malaysia, this study may help people manage a healthy lifestyle.

This research focused on the determination of scatter radiation levels in x-ray rooms during chest radiography. 108 patients were examined. Four x-ray machines (A, B, C, and D) were used during the research from three centers. Three positions were considered in this study; position Q just beside the (Bucky stand), position R, which is 150 cm from the left of the Bucky stand towards the door and position T, 200 cm from the Bucky stand to the radiographer's protective screen respectively. Two machines (A and B) from center 1 and one machine from center 2 (C) and one machine from center 3 (D). The body mass index (BMI) of the participants ranged from 20 to 25 kgm⁻² with mean value of 23.97 kgm⁻². The background radiation level was read using Radalert 100 m before any exposure, and the mean background level was 0.298 mR/h. The mean of the scatter radiation doses obtained from positions Q with respect to the four machines A, B, C, and D, were 0.109, 0.201, 0.204, 0.200 mR/h (9.166, 16.903, 17.156, 16.819 mSv/yr) and their standard deviations were ±0.052, ±0.053, ±0.064, and ±0.081 respectively. The results were comparable with previous studies. The study recommends staff education and training in determination of radiation levels for enhanced work safety.

The study of complex phases in nuclear fuels is necessary to understand the physicochemical properties of the fuel. Na₆Mo₇O₂₄⋅14H₂O (1) was prepared via a simplified method and the crystal structure was improved. Upon thermal degradation, 1 decomposes into Na₂Mo₂O₇ and MoO₃. Additionally, novel Ba₃Mo₇O₂₄⋅12H₂O (2) was isolated via an aqueous synthetic route and characterized via FTIR and elemental analysis. PXRD pattern of 2 was determined. Thermal degradation of 2 indicates formation of BaMoO₄, BaMo₃O₁₀, MoO₃, and an unidentified phase.