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

Nazemi, Ehsan; Rokrok, B.; Amir, Mohammad; Dastjerdi, M.H. Choopan

doi:10.1016/j.apradiso.2018.05.017

Cited by 16 publications

(3 citation statements)

References 14 publications

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“…Some X-ray spectra, such as those produced from an aluminum filter with a tube voltage of 20 kV and from a beryllium filter with a tube voltage of 50 kV, are not predictable, according to their findings utilizing the RBF neural network. Referenced research [33] explored the use of…”

Section: Plos Onementioning

confidence: 99%

“…There were 99 computed X-ray spectra used to train the neural network, each calculated under a unique set of parameters such as a distinct tube voltage (20, 30, 40, 50, 60, 80, 100, 130, and 150kV), filter material (no filter, beryllium, and aluminum), and filter thickness (0.4, 0.8, 1.2, 1.6, and 2mm). In the previous research [33], the X-ray spectra obtained from the MCNP code were compared with the X-ray spectra obtained from the experimental structure and also with other software packages. It was observed the results were in good agreement with each other.…”

Section: Plos Onementioning

confidence: 99%

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

He,

Zhanjian,

Zheng

et al. 2023

PLoS ONE

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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.

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Section: Plos Onementioning

confidence: 99%

Section: Plos Onementioning

confidence: 99%

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

He,

Zhanjian,

Zheng

et al. 2023

PLoS ONE

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show abstract

“…MCNP is a Monte Carlo N-particle code that can be used for neutron, photon, electron, or coupled neutron/photon/electron transport (Briesmeister 2000). In recent years, it has been shown tlhat MCNP code has a wide applications in various subjects of nuclear engineering such as nondestructive testing (Nazemi et al 2018(Nazemi et al , 2019, radiation shielding (Yadollahi et al 2016a, b), nuclear gauges (Roshani Fig. 1 The schematic view of simulated setup configuration in MCNPX code: a three-dimensional view, b twodimensional view without particle tracking, and c two-dimensional view with particle tracking et al 2017b, 2018c, d), etc.…”

Section: Monte Carlo Simulation: Process and Datamentioning

confidence: 99%

An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

2019

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The problem of how to accurately measure the volume fractions of oil-gas-water mixtures in a pipeline remains as one of the key challenges in the petroleum industry. The current research highlights the capability of a hybrid system of the Jaya optimization algorithm and the adaptive neuro-fuzzy inference system (ANFIS), to model the stratified three-phase flow of gas-oil-water. As a matter of fact, the present study devotes to forecast the volume fractions in the stratified three-phase flow, on the basis of a dual-energy metering system, including the 152 Eu and 137 Cs and one NaI detector, using the aforementioned hybrid model. Since the summation of volume fractions are constant (equal to 100%), a constraint modelling problem exists, meaning that the hybrid model must forecast only two volume fractions. In this paper, three main hybrid models are employed. The first network is applied to forecast the gas and water volume fractions, the next one to forecast the water and oil volume fractions, and the last one to forecast the oil and gas volume fractions. For the next step, the hybrid models are trained based on numerically obtained data from the MCNP-X code.

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Utilizing Features Extracted from Registered 60Co Gamma-Ray Spectrum in One Detector as Inputs of Artificial Neural Network for Independent Flow Regime Void Fraction Prediction

Roshani

Nazemi

Shama

2019

MAPAN

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

Cited by 16 publications

References 14 publications

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

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

An intelligent integrated approach of Jaya optimization algorithm and neuro-fuzzy network to model the stratified three-phase flow of gas–oil–water

Utilizing Features Extracted from Registered 60Co Gamma-Ray Spectrum in One Detector as Inputs of Artificial Neural Network for Independent Flow Regime Void Fraction Prediction

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