TH-D-BRB-02: Improving the Accuracy of Monte Carlo Spread-Out Bragg Peak Treatment Head Models Using Beam Current Modulation Optimization

2010 ◽  
Vol 37 (6Part28) ◽  
pp. 3466-3466
Author(s):  
B Bednarz ◽  
H Lu ◽  
M Engelsman ◽  
H Paganetti
Keyword(s):  
Monte Carlo ◽  
Bragg Peak ◽  
Beam Current ◽  
Current Modulation ◽  
Spread Out Bragg Peak

Sensitivities in the production of spread-out Bragg peak dose distributions by passive scattering with beam current modulation

2007 ◽  
Vol 34 (10) ◽  
pp. 3844-3853 ◽  
Author(s):  
Hsiao-Ming Lu ◽  
Robert Brett ◽  
Martijn Engelsman ◽  
Roelf Slopsema ◽  
Hanne Kooy ◽  
...  
Keyword(s):  
Bragg Peak ◽  
Beam Current ◽  
Dose Distributions ◽  
Current Modulation ◽  
Peak Dose ◽  
Passive Scattering ◽  
Spread Out Bragg Peak

scholarly journals An Iterating Method to Calculate the Geometry of Range Modulation Wheel in Passive Proton Therapy

2019 ◽  
pp. 1-11
Author(s):  
Zahra Sadat Tabatabaeian ◽  
Mahdi Sadeghi ◽  
Mohammad Reza Ghasemi
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Penetration Depth ◽  
Proton Beam ◽  
Proton Therapy ◽  
Bragg Peak ◽  
Particle Energy ◽  
Absorption Energy ◽  
Energy Proton ◽  
Spread Out Bragg Peak

In the passive method of proton therapy, range modulation wheel is used to scatter the single energy proton beam. It rounds and scatters the single energy proton beam to the spectrum of particles that covers cancerous tissue by a change in penetration depth. Geant4 is a Monte Carlo simulation platform for studying particles behaviour in a matter. We simulated proton therapy nozzle with Geant4. Geometric properties of this nozzle have some effects on this beam absorption plot. Concerning the relation between penetration depth and proton particle energy, we have designed a range modulation wheel to have an approximately flat plot of absorption energy. An iterative algorithm programming helped us to calculate the weight and thickness of each sector of range modulation wheel. Flatness and practical range are calculated for resulting spread-out Bragg peak.

Spread-out Bragg peak and monitor units calculation with the Monte Carlo Code MCNPX

2007 ◽  
Vol 34 (2) ◽  
pp. 680-688 ◽  
Author(s):  
J. Hérault ◽  
N. Iborra ◽  
B. Serrano ◽  
P. Chauvel
Keyword(s):  
Monte Carlo ◽  
Bragg Peak ◽  
Monte Carlo Code ◽  
Spread Out Bragg Peak

In vitro modified microdosimetric kinetic model-based predictions for B14-150 cells survival in 450 MeV/u carbon ion beam with aluminum ridge filter for biologically optimized spread-out Bragg peak

2021 ◽  
Author(s):  
Aleksei Solovev ◽  
Marina Troshina ◽  
Pikalov Vladimir ◽  
Vyacheslav Saburov ◽  
Aleksandr Chernukha ◽  
...  
Keyword(s):  
Monte Carlo ◽  
Kinetic Model ◽  
Cell Line ◽  
Bragg Peak ◽  
Clonogenic Assay ◽  
Survival Curve ◽  
Carbon Ion ◽  
Ridge Filter ◽  
Spread Out Bragg Peak

Abstract The relative biological efficiency of particle irradiation could be predicted with a wide variety of radiobiological models for various end-points. We validate the forecast of modified Microdosimetric Kinetic Model in vitro using combined data of reference Co-60 radiation and carbon ion plateau data for specific cell line to optimize the survival function in spread-out Bragg Peak obtained with an especially designed ridge filter. We used Geant4 Monte-Carlo software to simulate the fragment contribution along Bragg curve inside water phantom, open-source toolkit Survival to predict the expected linear-quadratic model parameters for each fragment, and in-house software to form the total survival curve in spread-out Bragg Peak. The irradiation was performed at U-70 synchrotron with an especially designed Aluminum ridge filter under the control of PTW and in-house ionization chambers. The cell clonogenic assay was conducted with the B14-150 cell line. The data analysis was accomplished using scipy and CERN ROOT. The clonogenic assay represents the survival in spread-out Bragg Peak at different points and qualitatively follows the modeled survival curve very well. The quantitative difference is within 3σ, and the deviation might be explained by the uncertainties of physical modeling using Monte-Carlo methods. Overall, the obtained results are promising for further usage in radiobiological studies or carbon ion radiotherapy. Shaping the survival curve in the region of interest (i.e., spread-out Bragg Peak) is a comprehensive task that requires high-performance computing approaches. Nevertheless, the method's potential application is related to the development of next-generation treatment planning systems for ion beams. This can open a wide range of improvements in patient treatment outcome, provide new optimized fractionation regimes or optimized dose delivery schemes, and serve as an entrance point to the translational science approach.

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