Atomic mean excitation energies for stopping powers from local plasma oscillator strengths

Wilson, J. Warren; Xu, Y. J.; Chang, C. K.; Kamaratos, Efstathios

doi:10.1063/1.334023

Cited by 4 publications

(3 citation statements)

References 14 publications

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“…I-values for some elements can be determined accurately using experimental data on the interactions of photons with matter. For gases it can be derived from the knowledge of oscillator-strength distributions (Zeiss et al 1977, Inokuti et al 1981, Wilson et al 1984, Kamakura et al 2006) and for condensed materials it can be obtained from dielectric response functions (Fano 1956, Fano 1963, Shiles et al 1980, Emfietzoglou et al 2009). Additionally, ab initio calculations have been done for some atomic gases (ICRU 1984).…”

Section: Introductionmentioning

confidence: 99%

The clinical impact of uncertainties in the mean excitation energy of human tissues during proton therapy

2013

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Uncertainties in the estimated mean excitation energies (I-values) needed for calculating proton stopping powers can be in the order of 10–15%, which introduces a fundamental limitation in the accuracy of proton range determination. Previous efforts have quantified shifts in proton depth dose distributions due to I-value uncertainties in water and homogenous tissue phantoms. This study is the first to quantify the clinical impact of I-value uncertainties on proton dose distributions within patient geometries. A previously developed Geant4 based Monte Carlo code was used to simulate a proton treatment plan for three patients (prostate, pancreases, and liver) with varying tissue I-values. A uniform variation study was conducted in which the tissue I-values were varied by ±5% and ±10% of the nominal values as well as a probabilistic variation study in which the I-values were randomly sampled according to a normal distribution with the mean equal to the nominal I-value and a standard deviation of 5 and 10% of the nominal values. Modification of tissue I-values impacted both the proton range and SOBP width. R90 range shifts up to 7.7 mm (4.4.%) and R80 range shifts up to 4.8 mm (1.9%) from the nominal range were recorded. Modulating the tissue I-values by 10% the nominal value resulted in up to a 3.5% difference mean dose in the target volumes and organs at risk (OARs) compared to the nominal case. The range and dose differences were the largest for the deeper-seated prostate and pancreas cases. The treatments that were simulated with randomly sampled I-values resulted in range and dose differences that were generally within the upper and lower bounds set by the 10% uniform variations. This study demonstrated the impact of I-value uncertainties on patient dose distributions. Clearly, sub-millimeter precision in proton therapy would necessitate a reduction in I-value uncertainties to ensure an efficacious clinical outcome.

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Section: Introductionmentioning

confidence: 99%

The clinical impact of uncertainties in the mean excitation energy of human tissues during proton therapy

2013

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“…During the beam modelling processes performed here, different values for the ionisation potential of water were inadvertently defined in the two systems. In the PSI system, the Geant4 default value for water of 78 eV was used, whereas in the independently tuned The Christie model, this was calculated internally using the Bragg additivity rule, 32 leading to an ionisation potential of 69 eV. Only after retuning of one system to be based on the same ionisation potential of water did both simulation algorithms agree well (1%, 1 mm γ agreement >93% for all fields).…”

Section: Discussionmentioning

confidence: 99%

Pitfalls in the beam modelling process of Monte Carlo calculations for proton pencil beam scanning

Winterhalter

Aitkenhead

Oxley

et al. 2020

The British Journal of Radiology

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Objective: Monte Carlo (MC) simulations substantially improve the accuracy of predicted doses. This study aims to determine and quantify the uncertainties of setting up such a MC system. Methods: Doses simulated with two Geant4-based MC calculation codes, but independently tuned to the same beam data, have been compared. Different methods of MC modelling of a pre-absorber have been employed, either modifying the beam source parameters (descriptive) or adding the pre-absorber as a physical component (physical). Results: After the independent beam modelling of both systems in water (resulting in excellent range agreement) range differences of up to 3.6/4.8 mm (1.5% of total range) in bone/brain-like tissues were found, which resulted from the use of different mean water ionisation potentials during the energy tuning process. When repeating using a common definition of water, ranges in bone/brain agreed within 0.1 mm and gamma-analysis (global 1%,1mm) showed excellent agreement (>93%) for all patient fields. However, due to a lack of modelling of proton fluence loss in the descriptive pre-absorber, differences of 7% in absolute dose between the pre-absorber definitions were found. Conclusion: This study quantifies the influence of using different water ionisation potentials during the MC beam modelling process. Furthermore, when using a descriptive pre-absorber model, additional Faraday cup or ionisation chamber measurements with pre-absorber are necessary. Advances in knowledge: This is the first study quantifying the uncertainties caused by the MC beam modelling process for proton pencil beam scanning, and a more detailed beam modelling process for MC simulations is proposed to minimise the influence of critical parameters.

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“…Here, n is the total electron density in graphene, which is 2.2 × 10 16 m −2 , E is the incident electron energy, E g is the material bandgap, e is the electron charge, k e is the electrostatic constant, I is the mean excitation potential, which is 78 eV for carbon atoms [38], J is the electron beam current, S is the area of the electron beam spot. For the given values of E = 200 keV , J = 1.2 nA (the experiments were performed using transmission electron microscope JEM-2100F, Jeol, Japan), we estimate that at the beam size of 1 µm the generated charge carriers density should be n g ≈ 10 12 cm −2 .…”

Section: Accepted Manuscriptmentioning

confidence: 99%

Influence of charge carriers on corrugation of suspended graphene

Kirilenko

Gorodetsky

Байдакова

2018

Solid State Communications

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Electronic degrees of freedom are predicted to play a significant role in mechanics of two-dimensional crystalline membranes. Here we show that appearance of charge carriers may cause a considerable impact on suspended graphene corrugation, thus leading to additional mechanism resulting in charge carriers mobility variation with their density. This finding may account for some details of suspended graphene conductivity dependence on its doping level and suggests that proper modeling of suspended graphene-based device properties must include the influence of charge carriers on its surface corrugation.

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Atomic mean excitation energies for stopping powers from local plasma oscillator strengths

Abstract: Mean excitation energies for stopping by isolated atoms are accurately predicted by the plasma absorption spectrum associated with the atomic orbitals when the plasma frequency shift due to individual electron motion proposed by Pines [Phys. Rev. 92, 626 (1953)] is incorporated.

Cited by 4 publications

References 14 publications

The clinical impact of uncertainties in the mean excitation energy of human tissues during proton therapy

The clinical impact of uncertainties in the mean excitation energy of human tissues during proton therapy

Pitfalls in the beam modelling process of Monte Carlo calculations for proton pencil beam scanning

Influence of charge carriers on corrugation of suspended graphene

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