SU-E-T-121: Analyzing the Broadening Effect On the Bragg Peak Due to Heterogeneous Geometries and Implementing User-Routines in the Monte-Carlo Code FLUKA in Order to Reduce Computation Time

2015 ◽  
Vol 42 (6Part13) ◽  
pp. 3359-3359
Author(s):  
K Baumann ◽  
U Weber ◽  
Y Simeonov ◽  
K Zink
2019 ◽  
Vol 54 (2) ◽  
pp. 125-132
Author(s):  
T. Deschler ◽  
N. Arbor ◽  
F. Carbillet ◽  
A. Nourreddine

Monte Carlo methods have become widespread in the field of radiation protection and in particular in medical physics where the use of voxelized volumes for the reconstruction of dosimetric quantities is increasing. Changing the resolution of a dose map can be useful to compare dosimetric results coming from voxelized volumes with different resolutions, or to reduce computation time. This can be done by superimposing a dosel grid with a different resolution than that of the voxelized volume. In this case, each dosel will cover several voxels, leading the Monte Carlo code to calculate the dose in heterogeneous volumes. Two algorithms are available in GATE to perform these calculations, the Volume-Weighting (V-W) and the Mass-Weighting (M-W) algorithms, the latter being the subject of this work. In a general way, the M-W algorithm tends to reconstruct a higher dose than that the V-W one. In dosels involving heavy and lightweight materials (air-skin, bone-tissue), the M-W reconstructed dose is better estimated than the V-W one (up to 10% better at the air-skin interface). Moreover, the statistical uncertainty of the M-W dose can be up to 80% lower than the V-W one at air-skin interfaces. These results show that the M-W algorithm is more suitable for radiological protection applications and must be preferentially used in GATE for dose calculations in heterogeneous volumes.


2007 ◽  
Vol 34 (2) ◽  
pp. 680-688 ◽  
Author(s):  
J. Hérault ◽  
N. Iborra ◽  
B. Serrano ◽  
P. Chauvel

2019 ◽  
Vol 19 (2) ◽  
pp. 173-181 ◽  
Author(s):  
Shiva Zarifi ◽  
Hadi Taleshi Ahangari ◽  
Sayyed Bijan Jia ◽  
Mohammad Ali Tajik-Mansoury ◽  
Milad Najafzadeh ◽  
...  

AbstractPurpose:To examine detail depth dose characteristics of ideal proton beams using the GATE Monte Carlo technique.Methods:In this study, in order to improve simulation efficiency, we used pencil beam geometry instead of parallel broad-field geometry. Depth dose distributions for beam energies from 5 to 250 MeV in a water phantom were obtained. This study used parameters named Rpeak, R90, R80, R73, R50, full width at half maximum (FWHM), width of 80–20% distal fall-off (W(80–20)) and peak-to-entrance ratio to represent Bragg peak characteristics. The obtained energy–range relationships were fitted into third-order polynomial formulae. The present study also used the GATE Monte Carlo code to calculate the stopping power of proton pencil beams in a water cubic phantom and compared results with the National Institute of Standards and Technology (NIST) standard reference database.Results:The study results revealed deeper penetration, broader FWHM and distal fall-off and decreased peak-to-entrance dose ratio with increasing beam energy. Study results for monoenergetic proton beams showed that R73 can be a good indicator to characterise a range of incident beams. These also suggest FWHM is more sensitive than W(80–20) distal fall-off in finding initial energy spread. Furthermore, the difference between the obtained stopping power from simulation and NIST data almost in all energies was lower than 1%.Conclusion:Detail depth dose characteristics for monoenergetic proton beams within therapeutic energy ranges were reported. These results can serve as a good reference for clinical practitioners in their daily practice.


Kerntechnik ◽  
2015 ◽  
Vol 80 (4) ◽  
pp. 394-401 ◽  
Author(s):  
S. S. Aleshin ◽  
S. S. Gorodkov ◽  
A. I. Shcherenko

2020 ◽  
Vol 1548 ◽  
pp. 012020
Author(s):  
M De Simoni ◽  
M Fischetti ◽  
E Gioscio ◽  
M Marafini ◽  
R Mirabelli ◽  
...  

2021 ◽  
Vol 154 ◽  
pp. 108099
Author(s):  
Guanlin Shi ◽  
Yuchuan Guo ◽  
Conglong Jia ◽  
Zhiyuan Feng ◽  
Kan Wang ◽  
...  

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