Multidisciplinary Cancer Investigation
Sat, Dec 21, 2024
[Archive]
Volume 5, Issue 4 (Multidisciplinary Cancer Investigation 2021)
Multidiscip Cancer Investig 2021, 5(4): 1-8 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Amininejad M, Mahdavi S R, Saeedzadeh E. Dosimetry of the Electron Beams Generated by ‘LIAC’ for Intraoperative Radiation Therapy: Using GATE Monte Carlo Simulations. Multidiscip Cancer Investig 2021; 5 (4) :1-8
URL: http://mcijournal.com/article-1-321-en.html
URL: http://mcijournal.com/article-1-321-en.html
1- Department of Radiation Medical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran
2- Radiation Biology Research Center and Medical Physics Department, Iran University of Medical Sciences, Tehran, Iran
3- Department of Radiation Medical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran ,esaeedzadeh@gmail.com
2- Radiation Biology Research Center and Medical Physics Department, Iran University of Medical Sciences, Tehran, Iran
3- Department of Radiation Medical Engineering, Science and Research Branch, Islamic Azad University, Tehran, Iran ,
Abstract: (1619 Views)
The dose distribution in the tumor bed and the neighboring tissue is an important issue in intraoperative radiation therapy (IORT). In the current study, a new software tool was developed to calculate and visualize the 2D and 3D dose distributions of the electron beams from the light intraoperative accelerator (LIAC) and validate the software through experimental measurements.
Methods: The Monte Carlo code ‘GATE’ was used to simulate the LIAC. Percentage depth dose curves (PDD) and transverse dose profiles (TDP) were calculated for all nominal energies in the water phantom, for the reference applicator.
Results: The dose distribution was defined in the form of isodose curves in the water phantom to study the volumetric and superficial changes of absorbed dose. There weren’t significant differences between calculated and measured PDD curves and TDPs. R100, R50, R90, Rp, and Ds values obtained from simulation were in good agreement with measurement. The maximum relative error was 8.6% which was related to R100, due to the absence of charged particle equilibrium in the surface. As expected, the least error was related to R50; making it the most common parameter in electron dosimetry.
Conclusions: The developed software is a basis to assess the dosimetric characteristics of all applicators and energy levels of the LIAC accelerator by calculating the 2D and 3D dose distribution during a proper calculation time. It can perform as a treatment planning system for IORT to calculate the absorbed dose of the clinical target volume and adjacent normal tissues which is not directly possible.
Methods: The Monte Carlo code ‘GATE’ was used to simulate the LIAC. Percentage depth dose curves (PDD) and transverse dose profiles (TDP) were calculated for all nominal energies in the water phantom, for the reference applicator.
Results: The dose distribution was defined in the form of isodose curves in the water phantom to study the volumetric and superficial changes of absorbed dose. There weren’t significant differences between calculated and measured PDD curves and TDPs. R100, R50, R90, Rp, and Ds values obtained from simulation were in good agreement with measurement. The maximum relative error was 8.6% which was related to R100, due to the absence of charged particle equilibrium in the surface. As expected, the least error was related to R50; making it the most common parameter in electron dosimetry.
Conclusions: The developed software is a basis to assess the dosimetric characteristics of all applicators and energy levels of the LIAC accelerator by calculating the 2D and 3D dose distribution during a proper calculation time. It can perform as a treatment planning system for IORT to calculate the absorbed dose of the clinical target volume and adjacent normal tissues which is not directly possible.
Select article type: Original/Research Article |
Subject:
Health Services, Quality of Life and Outcomes
Received: 2021/05/19 | Accepted: 2021/10/9 | ePublished: 2021/12/29
Received: 2021/05/19 | Accepted: 2021/10/9 | ePublished: 2021/12/29
Send email to the article author
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |
