A Novel Method for Predicting Pixel Value Distribution Non-uniformity Due to Heel Effect of X-ray Tube in Industrial Digital Radiography Using Artificial Neural Network

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

doi:10.1007/s10921-018-0542-9

Cited by 16 publications

(2 citation statements)

References 17 publications

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“…In recent years, researchers have used mathematical models called artificial neural networks to help them understand how radiation interacts with tissue [ 18 , 19 , 20 , 21 , 22 , 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 ]. Moreover, the strong mathematical tool of numerical computing [ 32 , 33 , 34 , 35 , 36 , 37 , 38 ] has been employed to solve various engineering challenges, most notably in the field of artificial networks [ 39 , 40 , 41 , 42 , 43 , 44 ].…”

Section: Methodsmentioning

confidence: 99%

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

Zhang

Wang

et al. 2023

Diagnostics

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The air kerma, which is the amount of energy given off by a radioactive substance, is essential for medical specialists who use radiation to diagnose cancer problems. The amount of energy that a photon has when it hits something can be described as the air kerma (the amount of energy that was deposited in the air when the photon passed through it). Radiation beam intensity is represented by this value. Hospital X-ray equipment has to account for the heel effect, which means that the borders of the picture obtain a lesser radiation dosage than the center, and that air kerma is not symmetrical. The voltage of the X-ray machine can also affect the uniformity of the radiation. This work presents a model-based approach to predict air kerma at various locations inside the radiation field of medical imaging instruments, making use of just a small number of measurements. Group Method of Data Handling (GMDH) neural networks are suggested for this purpose. Firstly, a medical X-ray tube was modeled using Monte Carlo N Particle (MCNP) code simulation algorithm. X-ray tubes and detectors make up medical X-ray CT imaging systems. An X-ray tube’s electron filament, thin wire, and metal target produce a picture of the electrons’ target. A small rectangular electron source modeled electron filaments. An electron source target was a thin, 19,290 kg/m3 tungsten cube in a tubular hoover chamber. The electron source–object axis of the simulation object is 20° from the vertical. For most medical X-ray imaging applications, the kerma of the air was calculated at a variety of discrete locations within the conical X-ray beam, providing an accurate data set for network training. Various locations were taken into account in the aforementioned voltages inside the radiation field as the input of the GMDH network. For diagnostic radiology applications, the trained GMDH model could determine the air kerma at any location in the X-ray field of view and for a wide range of X-ray tube voltages with a Mean Relative Error (MRE) of less than 0.25%. This study yielded the following results: (1) The heel effect is included when calculating air kerma. (2) Computing the air kerma using an artificial neural network trained with minimal data. (3) An artificial neural network quickly and reliably calculated air kerma. (4) Figuring out the air kerma for the operating voltage of medical tubes. The high accuracy of the trained neural network in determining air kerma guarantees the usability of the presented method in operational conditions.

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Section: Methodsmentioning

confidence: 99%

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

Zhang

Wang

et al. 2023

Diagnostics

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“…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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A novel approach to enhance defect detection in wire arc additive manufacturing parts using radiographic testing without surface milling

El Mountassir,

Flotte,

Yaacoubi

et al. 2024

J Intell Manuf

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A Novel Method for Predicting Pixel Value Distribution Non-uniformity Due to Heel Effect of X-ray Tube in Industrial Digital Radiography Using Artificial Neural Network

Cited by 16 publications

References 17 publications

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

Air Kerma Calculation in Diagnostic Medical Imaging Devices Using Group Method of Data Handling Network

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

A novel approach to enhance defect detection in wire arc additive manufacturing parts using radiographic testing without surface milling

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