Simulation of a complete X-ray digital radiographic system for industrial applications

Application of MLP neural network to predict X-ray spectrum from tube voltage, filter material, and filter thickness used in medical imaging systems

The X-ray energy spectrum is crucial for image quality and dosage assessment in mammography, radiography, fluoroscopy, and CT which are frequently used for the diagnosis of many diseases including but not limited to patients with cardiovascular and cerebrovascular diseases. X-ray tubes have an electron filament (cathode), a tungsten/rubidium target (anode) oriented at an angle, and a metal filter (aluminum, beryllium, etc.) that may be placed in front of an exit window. When cathode electrons meet the anode, they generate X-rays with varied energies, creating a spectrum from zero to the electrons' greatest energy. In general, the energy spectrum of X-rays depends on the electron beam's energy (tube voltage), target angle, material, filter thickness, etc. Thus, each imaging system's X-ray energy spectrum is unique to its tubes. The primary goal of the current study is to develop a clever method for quickly estimating the X-ray energy spectrum for a variety of tube voltages, filter materials, and filter thickness using a small number of unique spectra. In this investigation, two distinct filters made of beryllium and aluminum with thicknesses of 0.4, 0.8, 1.2, 1.6, and 2 mm were employed to obtain certain limited X-ray spectra for tube voltages of 20, 30, 40, 50, 60, 80, 100, 130, and 150 kV. The three inputs of 150 Multilayer Perceptron (MLP) neural networks were tube voltage, filter type, and filter thickness to forecast the X-ray spectra point by point. After training, the MLP neural networks could predict the X-ray spectra for tubes with voltages between 20 and 150 kV and two distinct filters made of aluminum and beryllium with thicknesses between 0 and 2 mm. The presented methodology can be used as a suitable, fast, accurate and reliable alternative method for predicting X-ray spectrum in medical applications. Although a technique was put out in this work for a particular system that was the subject of Monte Carlo simulations, it may be applied to any genuine system.

Proposing an intelligent approach for measuring the thickness of metal sheets independent of alloy type

Radiation based gauges have been widely utilized as a nondestructive and robust tool for measuring the thickness of metal sheets in industry. The typical radiation thickness meter can just work accurately when the composition of the material is fixed during the measurement process. In conditions that material composition may differ substantially from the nominal composition, such as manufacturing rolled metals factories, the thickness measurements would be along with errors. The purpose of the present research is resolving the problem of measuring the thickness of metal sheets with various alloys. The aluminum is investigated in this work as a case study but the procedure can be applied for other types of metals. As the first step, the performance of various arrangements of two main detection techniques, named dual energy and dual modality, were investigated using MCNPX code to obtain optimum technique and arrangement. The simulation results indicated that a binary combination of ²⁴¹Am-⁶⁰Co isotopes as the source and one transmission detector in dual energy technique is the most appropriate choice. After then, an experimental setup based on the obtained optimal technique from simulation investigations was established. The aluminum sheets with 4 alloy types of 1050, 3105, 5052 and 6061 and thicknesses in the range of 0.2-4 cm with a step of 0.2 cm were tested and the obtained data were implemented for testing and training the artificial neural network (ANN). The proposed methodology could predict the thickness of aluminum sheet independent of its alloy type with an error of less than 0.04 cm in experiments.

Designing a new graphite illuminator for imaging facility of INUS to improve neutron beam uniformity and intensity

A cylindrical graphite illuminator with a thickness of 6.5 cm and diameter of 18 cm was installed inside the collimator of INUS (Instalatie de Neutronografie UScata) neutron imaging facility in the past. The graphite illuminator is usually utilized inside the collimator of neutron imaging facility to provide an intense and approximately uniform beam of neutrons at the outlet of collimator. With the mentioned existing illuminator in INUS imaging facility, the thermal neutron flux at the exit of collimator was measured 7.2 × 10⁴ n/cm²/s. Also the obtained neutron beam profile in this facility shows that it is not completely uniform at the imaging screen and the intensity of neutrons at the top and bottom of beam profile are not the same. Hence, in this paper a new graphite illuminator is proposed to improve the neutron beam characteristics in INUS imaging facility. Monte Carlo N-Particle (MCNP) code was implemented in this study for evaluating the proposed illuminator. The shape of proposed illuminator is a cylinder whose one side is inclined. Three quality factors of thermal neutron intensity, thermal neutron beam uniformity and gamma radiation dose rate were used to evaluate performance of the new illuminator. In order to obtain optimum illuminator shape, three effective parameters of thickness, angle of inclined side and position of the illuminator inside the collimator were investigated in this research. The investigation was carried out on thicknesses in the range of 5 to 25 cm with a step of 5 cm, angles in the range of 10 to 60° with a step of 10° and positions of -5, 0 and 5 cm with respect to center of reactor core. After investigating and interpolating the results, it was found that the proposed illuminator with a thickness of 10 cm, angle of 54.5° and position of 0 can produce a uniform beam profile, increase the thermal neutron intensity up to 7.1% and also decrease the neutron to gamma ratio up to 5% in comparison with the existing one.