Validation of the spectral reconstruction o fan electron beam by comparison with spectrum from Monte Carlo
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Inverse reconstruction, one of the ways of calculating the energy spectrum of the central axis, has shown good results in various studies. Its validation, in the absence of the real spectrum and practicality, is usually done by comparing the PDP measured in the clinic, using the gamma index. The objective of the work is to validate the spectral reconstruction of a 6MeV electron beam from a Sinergy linear accelerator, by comparison with the spectrum derived from the Monte Carlo simulation of the accelerator head, as well as to carry out an analysis of its relationship with the validation by means of the clinically accepted gamma index criterion (>95% within 3% dose difference/3 mm in distance to agreement). The inverse reconstruction used Tikhonov regularization and generalized simulated annealing. Excellent agreement was observed between the PDP and the reconstructed dose profile (obtained from the reconstructed spectrum) and the measured and simulated PDPs (from the head simulation spectrum), as passage was >95% within of 1%/1mm and 2%/2mm for the PDPs and the dose profile, respectively. The simulated and reconstructed spectra presented similar shapes, coinciding in most probable energy and much of the low energy region. Despite serious discrepancies in the peak region, this was not reflected in clinically observable differences. In conclusion, it was verified that the reconstructed spectrum is close to one simulated by Monte Carlo, making it appropriate for clinical and research use.
Apaza Veliz D, Wilches Visbal J, Abrego F, Vega Ramírez J. Monte carlo calculation of the energy spectrum of a 6 MeV electron beam using penetration and energy loss of positrons and electrons code. J Med Phys 2020;45(2):116–22.
Zhu TC, Das IJ, Bjärngard BE. Characteristics of bremsstrahlung in electron beams. Med Phys 2001;28(7):1352–8. Disponible en: http://doi.wiley.com/10.1118/1.1382608
Zhengming L, Jette D. On the possibility of determining an effective energy spectrum of clinical electron beams from percentage depth dose (PDD) data of broad beams. Phys Med Biol 1999;44(8):177–82. Disponible en: https://pubmed.ncbi.nlm.nih.gov/10473217/
Brahme A, Svensson H. Radiation beam characteristics of a 22 mev microtron. Acta Oncol (Madr) 1979;18(3):244–72. Disponible en: https://pubmed.ncbi.nlm.nih.gov/118642/
Rogers DWO, Faddegon BA, Ding GX, Ma CM, We J, Mackie TR. BEAM: A Monte Carlo code to simulate radiotherapy treatment units. Med Phys 1995;22(5):503–24. Disponible en: https://pubmed.ncbi.nlm.nih.gov/7643786/
Gui L, Hui L, Ai-Dong W, Gang S, Yi-Can W. Realization and Comparison of Several Regression Algorithms for Electron Energy Spectrum Reconstruction. Chinese Phys Lett 2008;25(7):2710–3. Disponible en: https://iopscience.iop.org/article/10.1088/0256-307X/25/7/104
Li G, Wu A, Lin H, Wu Y. Electron spectrum reconstruction as nonlinear programming model using micro-adjusting algorithm [Internet]. En: IFMBE Proceedings. 2008. página 451–4.Disponible en: https://www.springerprofessional.de/electron-spectrum-reconstruction-as-nonlinear-programming-model-/2956546
Wilches-Visbal JH, Apaza-Veliz DG, Nicolucci P. Spectral reconstruction of kilovoltage photon beams using generalized simulated annealing. Uniciencia 2022;36(1):1–14. Disponible en: https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/16520
Andreo P, Brahme A, Nahum A, Mattsson O. Influence of energy and angular spread on stopping-power ratios for electron beams. Phys Med Biol 1989;34(6):751–68. Disponible en: https://iopscience.iop.org/article/10.1088/0031-9155/34/6/010
Dina GX, Rogers DWO, Mackie TR. Calculation of stopping-power ratios using realistic clinical electron beams. Med Phys 1995;22(5):489–501. Disponible en: https://pubmed.ncbi.nlm.nih.gov/7643785/
Carletti C, Meoli P, Cravero WR. A modified simulated annealing algorithm for parameter determination for a hybrid virtual model. Phys Med Biol 2006;51(16):3941–52. Disponible en: https://pubmed.ncbi.nlm.nih.gov/16885616/
Faddegon BA, Blevis I. Electron spectra derived from depth dose distributions. Med Phys 2000;27(3):514–26. Disponible en: https://aapm.onlinelibrary.wiley.com/doi/abs/10.1118/1.598919
Deng J, Jiang SB, Pawlicki T, Li J, Ma C-M. Derivation of electron and photon energy spectra from electron beam central axis depth dose curves. Phys Med Biol 2001;46(5):1429–49. Disponible en: https://iopscience.iop.org/article/10.1088/0031-9155/46/5/308
Visbal JHW, Costa AM. Inverse reconstruction of energy spectra of clinical electron beams using the generalized simulated annealing method. Radiat Phys Chem 2019;162:31–8. Disponible en: https://linkinghub.elsevier.com/retrieve/pii/S0969806X18310387
Wilches Visbal JH, Nicolucci P. Improved reconstruction methodology of clinical electron energy spectra based on Tikhonov regularization and generalized simulated annealing. J Appl Res Technol 2021;19(6):622–32. Disponible en: https://jart.icat.unam.mx/index.php/jart/article/view/1213
Chvetsov A V., Sandison GA. Reconstruction of electron spectra using singular component decomposition. Med Phys 2002;29(4):578–91. Disponible en: http://doi.wiley.com/10.1118/1.1461840
Wilches Visbal JH, Martins Da Costa A. Determinação da Dose dos Fótons Contaminantes de Feixes de Elétrons Clínicos usando o Método de Recozimento Simulado Generalizado. Rev Bras Física Médica 2017;11(2):2. Disponible en: http://www.rbfm.org.br/rbfm/article/view/409
Zhengming L. A numerical method for solving the Fredholm integral equation of the first kind and its application to restore the folded radiation spectrum. Nucl Inst Methods Phys Res A 1987;255(1–2):152–5. Disponible en: http://a.xueshu.baidu.com/usercenter/paper/show?paperid=7d22324b6bdaaf91bcb454f2bece26fd
Hansen PC. Regularization Tools version 4.0 for Matlab 7.3. Numer Algorithms 2007;46(2):189–94. Disponible en: http://link.springer.com/10.1007/s11075-007-9136-9
Wilches Visbal JH, Da Costa AM. Algoritmo de recocido simulado generalizado para Matlab. Ing y Cienc 2019;15(30):117–40. Disponible en: http://publicaciones.eafit.edu.co/index.php/ingciencia/article/view/5564
Salvat F, Fernández-Varea J, Sempau J. PENELOPE – A Code System for Monte Carlo Simulation of Electron and Photon Transport A Code System for Monte Carlo [Internet]. 2008. Disponible en: https://www.oecd-nea.org/jcms/pl_14442/penelope-2008-a-code-system-for-monte-carlo-simulation-of-electron-and-photon-transport
Gerbi BJ, Antolak JA, Deibel FC, Followill DS, Herman MG, Higgins PD, et al. Recommendations for clinical electron beam dosimetry: Supplement to the recommendations of Task Group 25. Med Phys 2009;36(7):3239–79. Disponible en: http://doi.wiley.com/10.1118/1.3125820
Geurts M. 1D, 2D or 3D gamma index computation in Matlab. [Internet]. Github2018 [citado 2021 feb 5];1. Disponible en: https://github.com/mwgeurts/gamma/blob/master/CalcGamma.m
Querebalú Garcia C, Carrasco Solis E, Valladolid Salazar JV, Centeno Ramos J. Evaluation of the impact of parameters on the gamma index for breast cancer treatments. Rev Mex Física 2021;67(6):1–6. Disponible en: https://rmf.smf.mx/ojs/index.php/rmf/article/view/5795
Wilches JH, Apaza DG, Da Costa AM. Sobre la posibilidad de usar el método de regularización de Tikhonov para reconstruir el espectro de energía de un haz clínico de electrones. MOMENTO 2020;(60):1–17. Disponible en: https://revistas.unal.edu.co/index.php/momento/article/view/75527
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