Measurements of air kerma index in computed tomography: A comparison among methodologies

Alonso, Thêssa Cristina; Filho, Arnaldo Prata Mourão; Silva, Teógenes A. da

doi:10.1016/j.apradiso.2017.10.008

Cited by 7 publications

(4 citation statements)

References 7 publications

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“…A radiochromic film sheet was cut in a circular shape with 16 cm in diameter. This film sheet was placed between the standard head phantom and the the Development Center of Nuclear Technology (CDTN/ CNEN) [12][13][14].…”

Section: Methodsmentioning

confidence: 99%

Axial Dose Profile in Head CT Scan Using a 140 kV X-Ray Beam

Prata¹,

Stephanie²

2023

Int J Radiol Imaging Technol

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Section: Methodsmentioning

confidence: 99%

Axial Dose Profile in Head CT Scan Using a 140 kV X-Ray Beam

Prata¹,

Stephanie²

2023

Int J Radiol Imaging Technol

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“…Metrological reliability of the radiochromic films was demonstrated through homogeneity and repeatability tests and by calibrating it in a reference radiation for CT that were reproduced in the Calibration Laboratory of the Development Center of Nuclear Technology (CDTN/ CNEN) [12][13][14].…”

Section: A Radiochromic Film Sheet Was Cut In a Circularmentioning

confidence: 99%

Axial Dose Profile in Head CT Scan Using a 140 kV X-Ray Beam

Prata¹,

Stephanie²

2022

Int J Radiol Imaging Technol

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“…According to the findings of this study, air tension falls as tube voltage rises and exposure falls. Air kerma has been measured and reported on by researchers in the medical and industrial sectors [ 7 , 8 , 9 , 10 , 11 , 12 , 13 , 14 , 15 ]. These analyses did not include the rest of the X-ray tube’s radiation field in their estimation of air kerma at the tube’s core.…”

Section: Introductionmentioning

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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Measurements of air kerma index in computed tomography: A comparison among methodologies

Cited by 7 publications

References 7 publications

Axial Dose Profile in Head CT Scan Using a 140 kV X-Ray Beam

Axial Dose Profile in Head CT Scan Using a 140 kV X-Ray Beam

Axial Dose Profile in Head CT Scan Using a 140 kV X-Ray Beam

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

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