Determination of HPGe detector response using MCNP5 for 20–150keV X-rays

Salgado, César Marques; Conti, C.C.; Becker, Paulo H.B.

doi:10.1016/j.apradiso.2005.12.011

Cited by 46 publications

(14 citation statements)

References 8 publications

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“…The agreement amongst the four codes used here indicates the level of accuracy to be expected as these codes have all been benchmarked against experimental data in different situations. [2][3][4][5][6][7][8][9][10][11] It is worthwhile to emphasize that as our results were not based on experimental measurements, the validation achieved through this benchmarking is only relative to these calculations with widely used codes and not directly to measurements. But the concordance between the four Monte Carlo results provides strong confidence for the value and the validity of this exercise.…”

Section: Introductionmentioning

confidence: 99%

Monte Carlo reference data sets for imaging research: Executive summary of the report of AAPM Research Committee Task Group 195

et al. 2015

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The use of Monte Carlo simulations in diagnostic medical imaging research is widespread due to its flexibility and ability to estimate quantities that are challenging to measure empirically. However, any new Monte Carlo simulation code needs to be validated before it can be used reliably. The type and degree of validation required depends on the goals of the research project, but, typically, such validation involves either comparison of simulation results to physical measurements or to previously published results obtained with established Monte Carlo codes. The former is complicated due to nuances of experimental conditions and uncertainty, while the latter is challenging due to typical graphical presentation and lack of simulation details in previous publications. In addition, entering the field of Monte Carlo simulations in general involves a steep learning curve. It is not a simple task to learn how to program and interpret a Monte Carlo simulation, even when using one of the publicly available code packages. This Task Group report provides a common reference for benchmarking Monte Carlo simulations across a range of Monte Carlo codes and simulation scenarios. In the report, all simulation conditions are provided for six different Monte Carlo simulation cases that involve common x-ray based imaging research areas. The results obtained for the six cases using four publicly available Monte Carlo software packages are included in tabular form. In addition to a full description of all simulation conditions and results, a discussion and comparison of results among the Monte Carlo packages and the lessons learned during the compilation of these results are included. This abridged version of the report includes only an introductory description of the six cases and a brief example of the results of one of the cases. This work provides an investigator the necessary information to benchmark his/her Monte Carlo simulation software against the reference cases included here before performing his/her own novel research. In addition, an investigator entering the field of Monte Carlo simulations can use these descriptions and results as a self-teaching tool to ensure that he/she is able to perform a specific simulation correctly. Finally, educators can assign these cases as learning projects as part of course objectives or training programs.

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Section: Introductionmentioning

confidence: 99%

Monte Carlo reference data sets for imaging research: Executive summary of the report of AAPM Research Committee Task Group 195

et al. 2015

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“…Thus, for this simulation we used a general ratio of low energy and high-energy spectral features to find a normalization factor. A similar method is used in other studies using MCNP (Salgado et al 2006). The main interest for our simulation is the shape of the spectral features, which confirm the x-ray production, filtration, and attenuation of the beam.…”

Section: Methodsmentioning

confidence: 99%

Theoretical modeling of a portable x-ray tube based KXRF system to measure lead in bone

2017

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Objective K-shell x-ray fluorescence (KXRF) techniques have been used to identify health effects resulting from exposure to metals for decades, but the equipment is bulky and requires significant maintenance and licensing procedures. A portable x-ray fluorescence (XRF) device was developed to overcome these disadvantages, but introduced a measurement dependency on soft tissue thickness. With recent advances to detector technology, an XRF device utilizing the advantages of both systems should be feasible. Approach In this study, we used Monte Carlo simulations to test the feasibility of an XRF device with a high-energy x-ray tube and detector operable at room temperature. Main Results We first validated the use of Monte Carlo N-particle transport code (MCNP) for x-ray tube simulations, and found good agreement between experimental and simulated results. Then, we optimized x-ray tube settings and found the detection limit of the high-energy x-ray tube based XRF device for bone lead measurements to be 6.91 μg g−1 bone mineral using a cadmium zinc telluride detector. Significance In conclusion, this study validated the use of MCNP in simulations of x-ray tube physics and XRF applications, and demonstrated the feasibility of a high-energy x-ray tube based XRF for metal exposure assessment.

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“…Monte Carlo (MC) simulation technique, which is becoming progressively popular [7][8][9], has been used by many authors, many years ago, to simulate the process of gamma-ray detection. It was used to calculate the response characteristics of different germanium detectors at monoenergetic and different gamma-ray energy ranges [10][11][12][13][14][15][16][17][18]. It was also used for detectors calibration, either directly or through combination with experimental measurements [7,8].…”

Section: Introductionmentioning

confidence: 99%

Verification of 235u, 238u by Mathematical Calibration Curve for Point Sources by Using Monte Carlo

Makhlouf

2014

Al-Azhar Bulletin of Science

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The calibration of γ-ray spectrometer was performed by means of a set of certified standard point sources of energies that cover the energy range of interest. The value of the absolute full energy peak efficiency "εab " could be found by measuring "CR" experimentally and the use of the known value of " A ". Also, it is possible to calculate "εab" for a certain source to detector distances (D) by using Monte Carlo [MC] simulation method. The obtained results indicated that the fitting is identical to the experimental curve and MCNP calculations, that is both these two methods (experimental and MCNP) can be used to extend the curve to cover the high energy range. It is possible to drop any line energy inside the used energy range to estimate the absolute full energy peak efficiency, (for example, the energies 185.7, 1001 and 1275 keV corresponding to the sources 235U, 238U and 22Na). However, it can be estimated by using any method from the previous methods (experimental or MCNP in addition to the fitting equation).

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Determination of HPGe detector response using MCNP5 for 20–150keV X-rays

Cited by 46 publications

References 8 publications

Monte Carlo reference data sets for imaging research: Executive summary of the report of AAPM Research Committee Task Group 195

Monte Carlo reference data sets for imaging research: Executive summary of the report of AAPM Research Committee Task Group 195

Theoretical modeling of a portable x-ray tube based KXRF system to measure lead in bone

Verification of 235u, 238u by Mathematical Calibration Curve for Point Sources by Using Monte Carlo

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