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Determination of water equivalent ratio for some dosimetric materials in proton therapy using MNCPX simulation tool

ACCELERATOR, RAY TECHNOLOGY AND APPLICATIONS

Determination of water equivalent ratio for some dosimetric materials in proton therapy using MNCPX simulation tool

Reza Bagheri
Alireza Khorrami Moghaddam
Bakhtiar Azadbakht
Mahmoud Reza Akbari
Seyed Pezhman Shirmardi
Nuclear Science and TechniquesVol.30, No.2Article number 31Published in print 01 Feb 2019Available online 24 Jan 2019
39900

The water equivalent ratio (WER) was calculated for polypropylene (PP), paraffin (PA), polyethylene (PE), polystyrene (PS), polymethyl methacrylate (PMMA), and polycarbonate (PC) materials with potential applications in dosimetry and medical physics. This was performed using the Monte Carlo simulation code, MCNPX, at different proton energies. The calculated WER values were compared with National Institute of Standards and Technology (NIST) data, available experimental and analytical results, as well as the FLUKA, SRIM, and SEICS codes. PP and PMMA were associated with the minimum and maximum WER values, respectively. Good agreement was observed between the MCNPX and NIST data. The biggest difference was 0.71% for PS at 150 MeV proton energy. In addition, a relatively large positive correlation between the WER values and the electron density of the dosimetric materials was observed. Finally, it was noted that PE presented the most analogous depth dose characteristics to liquid water.

Water equivalent ratioProton therapyDosimetric materialsMCNPX code.

1 Introduction

Proton radiotherapy is developing into an established approach for the treatment of localized tumors adjacent to critical organs, due to the superior conformity of the dose within the target volume associated with this technique [1,2]. Protons interact with the electrons and the nuclei of the medium through Coulomb forces. Inelastic collisions with the former result in the absorbed dose. Rare collisions with the nuclei cause nuclear reactions. Bremsstrahlung interactions with the nuclei are also possible, but are rare, and hence, negligible [3].

For a monoenergetic proton beam, there is an initial slow increase in dose with depth, followed by a sharp increase near the end of the range, called the Bragg peak [3,4]. The depth of the Bragg peak can be adjusted according to the depth and extent of the tumor region by selecting the appropriate proton beam energies [5,6].

The application of proton radiation therapy to medical practice is predicated on appropriate calibration to simulate human organs and tissues. It is rarely possible to directly measure the dose distribution in patients treated with any type of radiation.

The data on the dose distribution are almost entirely derived from measurements in phantoms made from tissue equivalent materials [7-9]. In clinical proton dosimetry, the measured dose distribution data are usually acquired in a liquid water phantom, which closely approximates the properties of soft tissue. However, water phantom has several disadvantages when used in conjunction with detectors. Numerous water equivalent solid phantoms have been developed as substitutes for water in the measurement of the absorbed dose [10-12]. For a given material to be tissue or water equivalent, it must have the same mass stopping power and secondary particle production, effective atomic number, electron density, and mass density as water [13,14].

A proton range, which was measured for the selected material and liquid water, is used as an equivalence estimator. For this purpose, the water equivalent ratio (WER) parameter was considered for comparing the dosimetric equivalency of the materials with water. Various experimental and theoretical works have been performed to facilitate the calculation of this quantity in a wide range of materials. Zhang and Newhauser calculated WER values for some materials using the numerical method [NM] presented by Newhauser et al. [15,16]. Also, in another work, Zhang et al. calculated analytical values of the WER for some selected materials using the Bragg-Kleeman [BK] rule [17]. This method has also been used by Zhang and Newhauser [15]. The BK rule determines the range of the proton beam in a given material using two material and energy dependent constants. Extensive energy-range tables were prepared by Janni for protons with energy in the range of 1 keV to 10 GeV for all the elements and many compounds and mixtures, using the Bethe equation with all the necessary corrections [18]. WER values are easily calculated thanks to these tables.

Furthermore, computerized codes were utilized to for obtain WER values. Akbari et al. calculated WER values for liquid water, PMMA, PS, and Al using FLUKA code and the SRIM program [19]. The WER values for these materials and graphite, Cu, Ti and Au were calculated according to the SEICS Monte Carlo code as well as by de Vera et al. [20]. Konefał et al. studied the influence of the energy spectrum and the spatial spread of 50–70 MeV proton beams applied to the treatment of eye tumors, on the depth-dose characteristics in water using the GEANT4 Monte Carlo simulation tool kit [21].

In addition, Moyers et al. measured the relative linear stopping powers of 21 different tissue substitutes for proton beams accelerated to the energies of 155, 200, and 250 MeV with the LLUPTF synchrotron, and compared their results with calculated and literature values [22]. Typically, the difference between the measured and the literature or calculated values was generally less than 1.5% and larger than the derived 2σuncertainty in the measurement.

In the present work, a Monte Carlo evaluation of the water equivalent ratio of six phantom materials originally developed for conventional radiation therapy, were studied using the MCNPX code in the range of 100 to 225 MeV proton energies with 25 MeV energy steps (usually applied in proton therapy). The depth dose profiles, range, penumbra width, electron density (ρe), effective atomic number (Zeff) and the relative electron density (RED) values of the phantom materials were also calculated.

In order to verify and validate the results from the simulation, the acquired values were compared with theoretical values from the National Institute of Standards and Technology (NIST). Furthermore, the simulated and theoretical values of WER were compared with available experimental and analytical data as well as the FLUKA, SRIM, and SEICS codes values for some investigated materials.

2 Materials and methods

2.1 Simulation

Polypropylene (PP), paraffin (PA), polyethylene (PE), polystyrene (PS), polymethyl methacrylate (PMMA), and polycarbonate (PC), were considered as the phantom materials. The MCNPX code (version 2.6.0) was used for Monte Carlo evaluation of the water equivalent of these materials. This code is a general-purpose Monte Carlo radiation transport algorithm for modeling the interaction of radiation with matter [23,24]. Since this code considers all interactions that affect the dose distribution within the material, the obtained results were found to be accurate and reliable [20,25].

For this purpose, cylindrical geometries with a height of 100 cm and a 15 cm radius were employed for modeling the phantom materials. A collimated monoenergetic proton beam with a 1 cm diameter that emitted protons perpendicular to the front face of the phantom materials was considered. A disc source was defined in the MCNP data card with ERG, PAR, POS, and DIR commands for energy, type of particle, position and direction of the proton beam respectively. In order to obtain the WER values, cylindrical detectors with a 2 cm height and 1 mm diameter were simulated along the beam’s trajectory in the phantom materials. To pave the way for this process, a cylindrical mesh tally was utilized to obtain the MCNPX simulation data. A third type of mesh tally was also included to record the energy deposition data per unit volume of material for all the tracked particles. The elemental composition and densities of the studied dosimetric materials were adopted from the National Institute of Standards and Technology (NIST) data using ICRU results. Simulations were performed with 5 to 50 million histories depending on the phantom material and the proton beam energy. All the results generated by the MCNPX code were reported with less than 0.02% statistical uncertainty. In order to reduce the relative error of a tally, the model geometry and the physics of the problem were simplified, and the technique of geometry splitting (using imp card) was used for the transport of particles in the cells of interest.

In addition, the NIST extracted data were processed using the PSTAR program, which calculates the stopping power and the range of protons in various materials according to the methods described in the ICRU reports 37 and 49 [12,26].

The comparison between the calculated and experimental data of WER values were evaluated according to relative deviation (RD) values.

2.2 Theory

The following formula was applied for calculating WER values [20]:

WER=d80, waterd80, material.    , (1)

where d80,water and d80,material are the ranges of the proton in water and the phantom material, respectively [9,27,28]. A range is defined as the distance between the entrance surface of the beam and the distal point of the 80% maximum dose. Likewise, the penumbra width is defined as the distance between the points of 80% and 20% absorbed dose in the distal rion of the Bragg curve. The curve fitting program, OriginPro 9.1, was used to fit an asymmetric Gaussian curve on the Bragg curves and to extract specific parameters.

The electron density (ρe, electron/cm3) of the phantom material was calculated from its mass density (ρm) and its atomic composition according to the formula [3]:

ρe=ρm×NA×ZA, (2)

where

ZA. =i=1nai×ZiAi, (3)

where NA is the Avogadro constant and ai is the fraction by weight of the ith element with Zi and Ai atomic number and weight respectively. Also, the effective atomic numbers (Zeff) of the phantom materials were derived bthe following equation [3]:

Zeff=(i=1nai×Zi2.94)12.94, (4)

where ai is the fractional contribution of the ith element to the total number of the electrons in the mixture. The relative electron density (RED) values were calculated as the ratio of the electron density of the phantom material to the water phantom.

We determined the rate of the energy loss or the linear stopping power for the charged particles (such as protons) in a given material using the Bethe formula [29]:

dEdx=4πe4z2m0v2×NZ [ln2m0v2Iln(1v2c2)v2c2], (5)

where v and z are the velocity and charge of the incident particle, respectively; N and Z are the atom number density and atomic number of the material, respectively; m0 is the electron rest mass; e is the electronic charge; and I represents the average excitation and ionization potential of the material. The first expression is related to the characteristics of the incident particle, which was identical for all the materials used in this research (z = 1 for proton) and the second expression represents the features of the absorbing material.

Table 1 presents the properties of the phantom materials. The effective atomic numbers and electron densities of the investigated dosimetric materials were calculated according to Eqs. (2) to (4). Moreover, the weights which were used in the material card of the MCNP code are given in percentage of each element in the materials and are presented in Table 2.

Table 1
Chemical formula, density (ρm, g cm-3), electron density (ρe × 1023 electron cm-3), effective atomic number (Zeff) and relative electron density (RED) for materials discussed in this work.
Material Chemical formula ρm ρe Zeff RED
Polypropylene (PP) (C3H6)n 0.90 3.10 5.44 0.926
Paraffin (PA) CnH2n +2 0.93 3.22 5.43 0.961
Polyethylene (PE) (CH2)n 0.94 3.24 5.44 0.967
Water (W) H2O 1.00 3.35 7.42 1.000
Polystyrene (PS) (C8H8)n 1.06 3.44 5.70 1.027
Polymethyl methacrylate (PMMA) (C5H8O2)n 1.19 3.87 6.47 1.156
Polycarbonate (PC) C16H14O3 1.20 3.81 6.26 1.139
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Table 2
The percentage of atomic composition for studied materials [12].
Element Atomic number Material
PP PA PE W PS PMMA PC
Hydrogen 1 14.37 14.86 14.37 11.19 7.74 8.05 5.55
Carbon 6 85.63 85.14 85.63 --- 92.26 59.99 75.57
Nitrogen 7 --- --- --- --- --- --- ---
Oxygen 8 --- --- --- 88.81 --- 31.96 18.88
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3 Results and discussion

The calculated depth dose profiles for the materials are shown in Fig 1. It was found that the relative dose ratio of the plateau region to the Bragg peak increases with an increase in the proton energy and likewise, the Bragg peaks broaden with an increase in energy.

Fig. 1
(Color online) Calculated depth dose profiles for the studied materials for selected proton energies. Data are normalized to the dose of the Bragg peak.
pic

In order to compare the Bragg peak position of the investigated materials for identical proton energy, the depth dose profiles are shown in Fig. 2 for 225 MeV proton energy.

Fig. 2
(Color online) Depth dose profiles of the studied materials for 225 MeV proton energy as a function of depth.
pic

It can be clearly seen from this figure that for a given proton energy, the largest differences in the Bragg peak position with respect to liquid water are observed in polymethyl methacrylate (PMMA) and polypropylene (PP) in two sides of the water Bragg peak. In addition, the results for polyethylene (PE) and paraffin (PA) present depth dose characteristics comparable to that of liquid water. These solid plastics exhibit the most analogous depth-dose characteristics to liquid water as an accessible phantom which closely approximates the radiation absorption and scattering properties of muscle and other soft tissue.

As shown in Figs. 1 and 2, unexpected wavelets are observed in the region before the Bragg peak. This is due to the 1 mm thick layers in the mesh tally. As previously indicated, cylindrical detectors with a 2 cm height and 1 mm diameter were utilized for calculation of the WER values. The MCNPX code calculates the average deposited energy at each cell volume with a 1 mm thickness. By reducing the thickness of the cells, the observed fluctuations (wavelets) will be removed in the plateau regions of the depth dose profiles. Since the Bragg peak region is required to calculate the quantities of interest in this research, an attempt was made to choose an optimum thickness values to minimize the wavelets without increasing relative errors and computational time.

The range values of the studied materials obtained using the MCNPX code and NIST data (using PSTAR program) are presented in Table 3 for the selected proton energies.

Table 3
MCNPX and NIST data of proton range (mm) for studied materials at different proton energies.
Material 100 MeV 125 MeV 150 MeV 175 MeV 200 MeV 225 MeV
MCNPX NIST MCNPX NIST MCNPX NIST MCNPX NIST MCNPX NIST MCNPX NIST
PP 80.59 80.30 119.72 119.33 164.78 164.44 215.45 215.11 271.23 271.00 331.69 331.44
PA 77.56 77.27 115.20 114.84 158.57 158.28 207.37 207.10 261.08 260.75 319.23 319.03
PE 77.32 77.56 114.63 115.20 157.79 158.57 206.30 207.37 259.68 261.08 317.56 319.23
W 77.50 77.18 114.97 114.60 158.16 157.60 206.58 206.00 259.92 259.60 317.69 317.40
PS 74.26 74.25 110.03 110.28 151.35 151.89 197.82 198.58 249.00 250.00 304.38 305.75
PC 67.80 67.50 100.55 100.17 138.27 138.00 180.65 180.42 227.31 227.08 277.88 277.75
PMMA 66.47 66.61 98.63 98.91 135.69 136.13 177.33 178.07 223.10 224.12 272.70 274.30
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Figure 3 shows the difference between the ranges of proton in water and the investigated dosimetric materials versus the proton energy using the results of Table 3. This difference become larger as the energy of the proton increases. Generally, polyethylene (PE) and polymethyl methacrylate (PMMA) have the smallest and largest range difference relative to water (45.00 and 0.10 mm for PMMA and PE respectively, at 225 MeV proton energy).

Fig. 3
(Color online) Differences between proton range in water (Rw) and studied dosimetric materials (Rm) versus proton energy.
pic

Calculated WER values for the investigated materials using the MCNPX code and the PSTAR program (NIST data) for different proton energies as well as the acquired relative deviation (RD) values are shown in Figs. 4 and 5 respectively. The WER values were calculated using equation 4 and Table 3 data.

Fig. 4
(Color online) WER values for different proton energies in the studied materials.
pic
Fig. 5
(Color online) Difference (%) between the MCNPX and NIST data of WER values in the studied materials.
pic

The RD difference between the MCNPX and NIST data of WER values at selected proton energies were calculated according to Eq. (6):

RD= {MCNPX - NIST} × 100/NIST. (6)

From Figs. 4 and 5, the MCNPX and the NIST data are in good agreement with each other. Polypropylene (PP) and Poly-methyl methacrylate (PMMA) have the minimum and maximum WER values respectively. WER values were approximately constant for all proton energies for all the investigated materials.

The RD values which ranged from –0.06% to 0.72% were found to be less than 0.8% for all the phantom materials. An average difference of 0.27% was obtained for the MCNPX and NIST data of WER values. The biggest observed difference was 0.71% for polystyrene at 150 MeV. In the case of polycarbonate, good agreement with the NIST data was observed (RD value less than 0.05%). In addition, it was determined that the RD values in Fig. 5 are energy independence.

The correlation of the WER values to the effective electron densities and the RED values of the dosimetric materials are shown in Fig. 6. The average WER value for the investigated range of proton energies (100 to 225 MeV) was considered for each material.

Fig. 6
A strong correlation of WER values to the RED values and the electron densities of the dosimetric materials (the correlation coefficient = + 0.999).
pic

A strong correlation of the WER values to the RED values and the electron densities of the dosimetric materials was observed (the correlation coefficient = +0.999). As previously indicated, for a given incident particle (proton in this research), the second expression in Equation 5 describes the characteristics of the absorbing material. A comparison of different materials revealed that the rate of proton energy loss and therefore its range, (integrating the reciprocal of the energy loss) and in turn the WER value, depends mainly on the product NZ. This product represents the electron density of the material. High electron density materials (or RED value) will therefore result in a high linear stopping power, short range and high WER values (water range per material range). As shown in Table 1 and Fig. 6, PMMA has the greatest electron density value (or RED value) and WER value. Fig. 6 indicates that the WER value is directly proportional to the electron density magnitude (or RED value) of the studied materials with high accuracy (the correlation coefficient = +0.999).

The penumbra width was calculated using MCNPX which is given as the distance between points 80% and 20% of the absorbed dose in the distal region of the Bragg curves was calculated using MCNPX is shown in Table 4 for selected proton energies.

Table 4
MCNPX results of penumbra width (mm) for studied materials at different proton energies.
Material Proton energy (MeV)
100 125 150 175 200 225
PP 1.36 1.83 2.36 2.95 3.68 4.40
PA 1.34 1.71 2.30 2.89 3.52 4.24
PE 1.32 1.78 2.23 2.87 3.52 4.24
W 1.36 1.75 2.30 2.94 3.59 4.30
PS 1.30 1.68 2.26 2.80 3.37 4.12
PC 1.17 1.56 2.09 2.61 3.20 3.80
PMMA 1.30 1.60 1.59 2.52 3.09 3.75
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It can be clearly seen from Table 4 that the penumbra width increases as the proton energy increases. In addition, the results indicate that for a given proton beams energy, a higher material density generally results in a wider penumbra.

Finally, the MCNPX and NIST results of WER values are compared with available experimental and analytical data as well as with the FLUKA, SRIM and SEICS code generated values. The WER values of PMMA, PS, PE, and PC solid plastics for the proton energy range of 100 to 225 MeV were considered using the available literature data.

In addition, Fig. 7 indicates the difference between the calculated (simulated and theoretical) and experimental data for WER values at 135, 175 and 225 MeV proton energies. The relative deviation (RD) values were calculated using the following equation:

Fig. 7
(Color online) Difference (%) between calculated and experimental results of WER values at 135, 175 and 225 MeV proton energies.
pic
RD= {Calculation – Experiment} × 100/ Experiment (7)

As seen in Fig. 7 and Table 5, in general, a good agreement is observed between the calculated and the experimental values. Moreover, the difference between the two datasets are in the range of the reported experimental error, which was less than 3% [21].

Table 5
Simulated, theoretical, and experimental values of water equivalent ratio for various incident proton energies for investigated materials. MCNPX, NIST and SRIM data were analyzed in this research.
Dosimetric material Method Density (g cm-3) Proton energy (MeV)
100 125 135 150 175 200 225
PMMA MCNPX 1.19 1.166 1.166 1.165 1.166 1.165 1.165 1.165
NIST 1.19 1.159 1.159 1.159 1.158 1.157 1.158 1.158
FLUKA [19] 1.188 1.155 1.176 1.174 1.174 1.172 1.173 1.173
SRIM 1.20 1.171 1.171 1.171 1.171 1.170 1.170 1.170
SEICS [20] 1.188 1.174 1.173 1.173 1.173 1.173 1.172 1.172
Zhang and Newhauser, 2009 1.19 1.158 --- --- 1.158 --- 1.157 ---
Zhang et al., 2010 1.185 --- --- 1.158 --- 1.158 --- 1.158
Janni, 1982 1.200 1.192 1.191 --- 1.191 1.190 1.190 1.189
Moyers et al., 2010 1.185 --- --- 1.170 --- 1.162 --- 1.167
PS MCNPX 1.06 1.044 1.045 1.044 1.045 1.044 1.044 1.044
NIST 1.06 1.040 1.039 1.039 1.038 1.037 1.038 1.038
FLUKA [19] 1.06 1.043 1.040 1.038 1.038 1.041 1.037 1.041
SRIM 1.06 1.039 1.038 1.038 1.038 1.037 1.037 1.037
SEICS [20] 1.06 1.042 1.041 --- 1.040 1.040 1.040 ---
Janni, 1982 1.06 1.068 1.066 --- 1.065 1.065 1.064 1.064
Moyers, et al., 2010 1.048 --- --- 1.034 --- 1.039 --- 1.042
PE MCNPX 0.94 1.002 1.003 1.002 1.002 1.001 1.001 1.000
NIST 0.94 1.002 1.001 1.000 0.999 0.998 0.999 0.998
SRIM 0.93 0.986 0.985 0.985 0.984 0.984 0.983 0.983
Zhang et al., 2010 0.964 --- --- 1.023 --- 1.022 --- 1.021
Janni, 1982 0.92 1.001 0.999 --- 0.998 0.997 0.996 0.996
Moyers et al., 2010 0.964 --- --- 1.036 --- 1.031 --- 1.035
PC MCNPX 1.20 1.143 1.143 1.143 1.144 1.144 1.143 1.143
NIST 1.20 1.143 1.144 1.143 1.142 1.142 1.143 1.143
SRIM 1.20 1.139 1.138 1.138 1.138 1.138 1.138 1.138
Janni, 1982 1.200 1.164 1.163 --- 1.163 1.157 1.162 1.161
Moyers et al., 2010 1.214 --- --- 1.145 --- 1.141 --- 1.15
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The RD values, which ranged from –3.77% to 5.29%, were found to be less than ±6% for all the phantom materials and methods. Polyethylene (PE) exhibited the greatest relative deviation values. High-density polyethylene (HDPE) with a density of 0.964 g cm-3 was investigated by Moyers et al. [17] however, the application of computational methods resulted in a lower value being assigned to this material. In addition, the RD values are the greatest when the difference between the density values are the largest, as indicated by the results and demonstrated by Janni [18].

The result of the application of Monte Carlo codes (MCNPX, FLUKA, SEICS and SRIM) and the analyzed NIST data produced the lowest RD values. Likewise, the analytical data suggested by Zhang et al. produced acceptable RD values less than ±1.35% [17].

The observed differences in the WER values may be due to differences in the selected values of the variables used in the calculations such as: the density and elemental compositions of the studied materials, the energy of the protons which enter the phantoms or slabs in the experiment, differences in the proton beam characteristics, errors in the different techniques and methods applied in the analytical procedures and simulations of the physical and mathematical models, uncertainties in the nuclear/atomic data, improper modeling of the source energy and actual geometry, etc. It should be noted that the model used in this simulation was ideal and simple. The suggested model estimates the depth dose characteristics of the studied materials to an acceptable extent and compares them with each other.

The experimental results indicate that the energy of the proton beam and the electron density (or RED value) of the irradiated material strongly affect the depth dose profiles including the intensity and depth of the Bragg peak, range and penumbra width.

4 Conclusion

In the present work, the Bragg curves and their related parameters (range, WER, and penumbra width) were calculated using MCNPX code for proton beams with energies from 100 to 225 MeV for several materials of interest (PP, PA, PE, liquid water, PS, PMMA, and PC). The results were compared with reported theoretical and experimental results. We determined that the results from NIST, MCNPX code, our experiment, and other Monte Carlo simulations are in good agreement with one another.

The calculated WER values using the MCNPX code suggest that the simulation geometry method can be utilized to determine these values and Bragg curves characteristics. This approach is particularly useful for materials for which experimental values are unavailable. It is concluded that polyethylene and paraffin have the most similar dose characteristics to liquid water.

References
1. G. Kraft,

Tumor therapy with heavy charged particles

. Prog. Part. Nucl. Phys. 45, S473-S544 (2000). doi: 10.1016/S0146-6410(00)00112-5
Baidu ScholarGoogle Scholar
2. W.D. Newhauser, R. Zhang,

The physics of proton therapy

. Phys. Med. Biol. 60, R155-R209 (2015). doi: 10.1088/0031-9155/60/8/R155
Baidu ScholarGoogle Scholar
3. F.M. Khan, J.P. Gibbons, Khan's the physics of radiation therapy, 5th edn. (Lippincott Williams & Wilkins, Philadelphia, 2014).
4. S. Shirmardi, E. Saniei, M. Erfani, et al.,

Tissue inhomogeneity in proton therapy and investigation of its effects on BRAGG peak by using MCNPX code

. Int. J. Radiat. Res. 12, 335-341 (2014).
Baidu ScholarGoogle Scholar
5. H Paganetti,,

Monte Carlo method to study the proton fluence for treatment planning

. Med. Phys. 25, 2370-2375 (1998). doi: 10.1118/1.598447
Baidu ScholarGoogle Scholar
6. W. Levin, H. Kooy, J. Loeffler et al.,

Proton beam therapy

. Br. J. Cancer 93, 849 (2005). doi: 10.1038/sj.bjc.6602754
Baidu ScholarGoogle Scholar
7. V.P. Singh, N.M. Badiger, N. Kucuk,

Assessment of methods for estimation of effective atomic numbers of common human organ and tissue substitutes: Waxes, plastics and polymers

. Radioprotection, 49, 115-121 (2014). doi: 10.1051/radiopro/2013090
Baidu ScholarGoogle Scholar
8. V.P. Singh, N.M. Badiger,

Effective atomic numbers, electron densities, and tissue equivalence of some gases and mixtures for dosimetry of radiation detectors

. Nucl. Technol. Radiat. 27, 117-124 (2012). doi: 10.2298/NTRP1202117S
Baidu ScholarGoogle Scholar
9. L. Al-Sulaiti, D. Shipley, R. Thomas et al.,

Water equivalence of various materials for clinical proton dosimetry by experiment and Monte Carlo simulation

. Nucl. Instrum. Methotds Phys. Res. A 619, 344-347 (2010). doi: 10.1016/j.nima.2010.01.026
Baidu ScholarGoogle Scholar
10. U. Schneider, P. Pemler, J. Besserer et al.,

The water equivalence of solid materials used for dosimetry with small proton beams

. Med. Phys. 29, 2946-2951 (2002). doi: 10.1118/1.1523408
Baidu ScholarGoogle Scholar
11.

ICRU S (1989) Tissue substitutes in radiation dosimetry and measurement, report 44

. International Commission on Radiation Units and Measurements, Bethesda, MD, USA.
Baidu ScholarGoogle Scholar
12.

ICRU S (1993) powers and ranges for protons and alpha particles, report 49

. International Commission on Radiation Units and Measurements, Bethesda, MD, USA. 23.
Baidu ScholarGoogle Scholar
13. V.P. Singh, M.E. Medhat,

Mass attenuation coefficients of composite materials by Geant4, XCOM and experimental data: comparative study

. Nucl. Technol. Radiat. 169, 800-807 (2014). doi: 10.1080/10420150.2014.950264
Baidu ScholarGoogle Scholar
14. V.P. Singh, S.P. Shirmardi, M.E. Medhat et al.,

Determination of mass attenuation coefficient for some polymers using Monte Carlo simulation

. Vacum 119, 284-288 (2015). doi: 10.1016/j.vacuum.2015.06.006
Baidu ScholarGoogle Scholar
15. R. Zhang, W.D. Newhauser,

Calculation of water equivalent thickness of materials of arbitrary density, elemental composition and thickness in proton beam irradiation

. Phys. Med. Biol. 54, 1383-1395 (2009). doi: 10.1088/0031-9155/54/6/001
Baidu ScholarGoogle Scholar
16. W. Newhauser, J. Fontenot, N. Koch et al.,

Monte Carlo simulations of the dosimetric impact of radiopaque fiducial markers for proton radiotherapy of the prostate

. Phys. Med. Biol. 52, 2937-2952 (2007). doi: 10.1088/0031-9155/52/11/001
Baidu ScholarGoogle Scholar
17. R. Zhang, P.J. Taddei, M.M. Fitzek et al.,

Water equivalent thickness values of materials used in beams of protons, helium, carbon and iron ions

. Phys. Med. Biol. 55, 2481-2493 (2010). doi: 10.1088/0031-9155/55/9/004
Baidu ScholarGoogle Scholar
18. J.F. Janni,

Proton Range-Energy Tables, 1 keV-10 GeV, Energy Loss, Range, Path Length, Time-of-Flight, Straggling, Multiple Scattering, and Nuclear Interaction Probability. Part I. For 63 Compounds

. At. Data Nucl. Data 27, 147-339 (1982). doi: 10.1088/0031-9155/55/9/004
Baidu ScholarGoogle Scholar
19. M.R. Akbari, H. Yousefnia, E. Mirrezaei,

Calculation of water equivalent ratio of several dosimetric materials in proton therapy using FLUKA code and SRIM program

. Appl. Radiat. Isotopes 90, 89-93 (2014). doi: 10.1016/j.apradiso.2014.03.012
Baidu ScholarGoogle Scholar
20. P. de Vera, I. Abril, R. Garcia-Molina,

Water equivalent properties of materials commonly used in proton dosimetry

. Appl. Radiat. Isotopes 83, 122-127 (2014). doi: 10.1016/j.apradiso.2013.01.023
Baidu ScholarGoogle Scholar
21. A. Konefał, P. Szaflik, W. Zipper,

Influence of the energy spectrum and spatial spread of proton beams used in eye tumor treatment on the depth-dose characteristics

. NUKLEONIKA 55, 313-316 (2010).
Baidu ScholarGoogle Scholar
22. M.F. Moyers, M. Sardesai, S. Sun et al.,

Ion stopping powers and CT numbers

. Med. Dosim. 35, 179-194 (2010). doi: 10.1016/j.meddos.2009.05.004
Baidu ScholarGoogle Scholar
23. D. Pellowitz,

MCNPX User's Manual, version 2.6. 0. Los Alamos Report No LA CP 2:408

(2007).
Baidu ScholarGoogle Scholar
24. R. Bagheri, A. Khorrami Moghaddam, A. Yousefi,

Gamma-ray shielding study of light to heavyweight concretes using MCNP-4C code

. Nucl. Sci. Tech. 15, 2-7 (2017). doi: 10.1007/s41365-016-0167-6
Baidu ScholarGoogle Scholar
25. H. Paganetti,

Dose to water versus dose to medium in proton beam therapy

. Phy. Med. Biol. 54, 4399-4421 (2009). doi: 10.1088/0031-9155/54/14/004
Baidu ScholarGoogle Scholar
26.

ICRU S (1984) Stopping powers for electrons and positrons, report 37

. International Commission on Radiation Units and Measurements, Bethesda, MD, USA.
Baidu ScholarGoogle Scholar
27. S. Rossomme, H. Palmans, D. Shipley et al.,

Conversion from dose-to-graphite to dose-to-water in an 80 MeV/A carbon ion beam

. Phys. Med. Biol. 58, 5363-5380 (2013). doi: 10.1088/0031-9155/58/16/5363
Baidu ScholarGoogle Scholar
28. H. Palmans, J.E. Symons, J.M. Denis et al.,

Fluence correction factors in plastic phantoms for clinical proton beams

. Phys. Med. Biol. 47, 3055-3071 (2002). doi: 10.1088/0031-9155/47/17/302
Baidu ScholarGoogle Scholar
29. G.F. Knoll, Radiation Detection and Measurement, 3rd edn. (Wiley, New York, 2000.