Published

2026-01-20

RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING

RECONSTRUCCIÓN DEL ESPECTRO DE ENERGÍA DE FOTONES A PARTIR DE CURVAS CLÍNICAS DE DOSIS PORCENTUAL EN PROFUNDIDAD MEDIANTE REGULARIZACIÓN DE TIKHONOV Y GENERALIZED SIMULATED ANNEALING

DOI:

https://doi.org/10.15446/mo.n72.119862

Keywords:

spectral reconstruction, photon beams, radiotherapy, Monte Carlo simulation (en)
reconstrucción espectral, haces de fotones, radioterapia, simulación de Monte Carlo (es)

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Authors

  • Jorge H. Wilches Visbal University of Magdalena https://orcid.org/0000-0003-3649-5079
  • Giulianne R. Rodrigues Zaratim Universidade de Brasília
  • Ana Quevedo Oeste Paulista University – UNOESTE https://orcid.org/0000-0003-2726-691X
  • Luciana Cardoso Matsushima Instituto de Física da Universidade Federal de Goiás- Campus Samambaia
  • Glauco Rogério Veneziani Dosemed Soluções em radioterapia
  • Iury Mergen Knoll Federal University of Health Sciences of Porto Alegre-UFCSPA
  • Alexandre Bonatto Federal University of Health Sciences of Porto Alegre-UFCSPA https://orcid.org/0000-0003-1992-7116
  • Mirko Alva Sánchez Federal University of Health Sciences of Porto Alegre-UFCSPA https://orcid.org/0000-0003-3936-3629

The primary objective of this study is to reconstruct the energy spectrum from three linear accelerators (LINACs) using experimental measurements of the percentage depth dose (PDD) curve. The experimentally obtained percentage depth dose curves were used to solve the Fredholm integral equation. The photon beam spectra are related to radiation doses through a Fredholm integral equation, utilizing the generalized simulated annealing optimization method. The resulting spectrum was used to simulate an irradiation reference condition as recommended by TRS-398. The Monte Carlo codes PENELOPE and TOPAS were employed to create the simulation scenario under reference conditions (10 x 10 cm² field size, 100 cm SSD, and 30 x 30 x 30 cm³ water phantom) for 6 MV photon beams.  The calculated spectra from the three LINACs demonstrated a remarkable level of concordance, achieving up to 99% agreement. The validation of the reconstructed spectrum was carried out by comparing it with the PDD and beam profile curves, revealing a highly favorable correspondence in their behavior. A comprehensive analysis compared the experimentally acquired PDDs with those simulated using the reconstructed spectrum. Parameters such as the entrance dose and   were derived from the PDD curves for evaluation. Upon conducting a thorough comparison of these parameters with the experimental dataset, noticeable deviations of 10 % (entrance dose), and 3 % ( ).  Beam profile comparisons across field size dimensions revealed differences ranging from 0.5% to 5.3%. The present study encompassed the reconstruction of the photon beam spectrum originating from LINACs, revealing a noteworthy level of agreement among them. The validation of the Fredholm integral equation by utilizing two simulation codes, as facilitated by the analysis of the PDD and beam profile curves, revealed substantial disparities within the region leading up to the build-up point. This reconstructed spectrum holds considerable potential for simulation scenarios within radiotherapy applications. This significance is particularly underscored by the challenges associated with acquiring comprehensive data from manufacturers of LINACs, which impedes access to crucial information regarding the constituents of these accelerators.

El objetivo principal de este estudio es reconstruir el espectro de energía de tres aceleradores lineales (LINACs) utilizando mediciones experimentales de la curva de dosis en profundidad porcentual (PDD). Las curvas del porcentaje de dosis en profundidad obtenidas experimentalmente se utilizaron para resolver la ecuación integral de Fredholm. Los espectros de los haces de fotones están relacionados con las dosis de radiación a través de una ecuación integral de Fredholm, utilizando el método de optimización de recocido simulado generalizado. El espectro resultante se empleó para simular una condición de irradiación de referencia según lo recomendado por el TRS-398. Los códigos de Monte Carlo PENELOPE y TOPAS se utilizaron para crear el escenario de simulación en condiciones de referencia (tamaño de campo de 10 x 10 cm², SSD de 100 cm y un fantoma de agua de 30 x 30 x 30 cm³) para haces de fotones de 6 MV. Los espectros calculados de los tres LINACs demostraron un notable nivel de concordancia, alcanzando hasta un 99% de acuerdo. La validación del espectro reconstruido se llevó a cabo comparándolo con las curvas de PDD y los perfiles del haz, revelando una correspondencia altamente favorable en su comportamiento. Se realizó un análisis comparativo entre las PDD adquiridas experimentalmente y aquellas simuladas con el espectro reconstruido. Se derivaron parámetros como la dosis de entrada y el  a partir de las curvas de PDD para su evaluación. Al comparar estos parámetros con los datos experimentales, se observaron desviaciones notables del 10% (dosis de entrada) y del 3% ( ). Las comparaciones de perfiles del haz en diferentes tamaños de campo revelaron diferencias que oscilaron entre el 0.5% y el 5.3%. Este estudio abarcó la reconstrucción del espectro del haz de fotones originado en LINACs, mostrando un nivel significativo de concordancia entre ellos. La validación de la ecuación integral de Fredholm mediante la utilización de dos códigos de simulación, a través del análisis de las curvas de PDD y del perfil del haz, reveló discrepancias sustanciales en la región build-up. El espectro reconstruido tiene un considerable potencial para la simulación de escenarios en aplicaciones de radioterapia. Esta relevancia se ve particularmente resaltada por los desafíos asociados con la obtención de datos detallados de los fabricantes de LINACs, lo que dificulta el acceso a información crucial sobre los componentes de estos aceleradores.

References

S. Espenel, J.-C. Trone, and et al., Bull. Cancer 102, 105 (2015). https://www.sciencedirect.com/science/article/abs/pii/S0007455114000022?via%3Dihub

J. Wong, T. Schultheiss, and et al., Advances in Radiation Oncology (Springer, 2017). https://link.springer.com/book/10.1007/978-3-319-53235-6

ICRU, Prescribing, Recording and Reporting Photon Beam Therapy (Supplement to ICRU Report 50), Report 62 (U.S.A., 1999). https://www.xatrivietnam.vn/wp-content/uploads/2015/10/ICRU-62-Prescribing-Recording-and-Reporting-Photon-Beam-Therapy-Supp-to-ICRU-50.pdf

I. Das, C. Cheng, and et al., Med. Phys. 35, 4186 (2008). https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.2969070

G. Kayal, M. Chauvin, and et al., Phys. Med. 85, 24 (2021). https://www.physicamedica.com/article/S1120-1797(21)00158-7/fulltext

J. Spiga, P. Pellicioli, and et al., Phys. Med. 66, 45 (2019). https://www.physicamedica.com/article/S1120-1797(19)30278-9/abstract

D. Sarrut and et al., Med. Phys. 41, 064301 (2014). https://aapm.onlinelibrary.wiley.com/doi/full/10.1118/1.4871617

A. Bennett and et al., Ann. Nucl. Energy 96, 1 (2016). https://www.sciencedirect.com/science/article/abs/pii/S0306454916302596?via%3Dihub

S. Taneja, L. Bartol, and et al., Int. J. Med. Phys. Clin. Eng. Radiat. Oncol. 9, 186 (2020). https://www.scirp.org/journal/paperinformation?paperid=103495

F. Salvat and et al., PENELOPE-2008: A Code System for Monte Carlo Simulation of Electron and Photon Transport, NEA No. 6416 (OECD Nuclear Energy Agency, 2009). https://www.oecd-nea.org/upload/docs/application/pdf/2019-12/nea6416-penelope.pdf

J. Sempau and et al., NIMB 207, 107 (2003). https://www.sciencedirect.com/science/article/abs/pii/S0168583X03004531?via%3Dihub

B. Faddegon, J. Ramos-Méndez, and et al., Phys. Med. 72, 114 (2020). https://www.physicamedica.com/article/S1120-1797(20)30071-5/fulltext

J. Perl, J. Shin, and et al., Med. Phys. 39, 6818 (2012). https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.4758060

G. García Gómez-Tejedor and M. C. Fuss, eds., Radiation Damage in Biomolecular Systems (Springer, 2012). https://link.springer.com/book/10.1007/978-94-007-2564-5

L. Brualla, M. Rodriguez, and et al., Radiat. Oncol. 14, 6 (2019). https://link.springer.com/article/10.1186/s13014-018-1186-8

L. de Sousa and et al., Braz. J. Radiat. Sci. 9, 1639 (2019). https://www.bjrs.org.br/revista/index.php/REVISTA/es/article/view/1639

A. Abou Jaoudé, ed., The Monte Carlo Methods –Recent Advances, New Perspectives and Applications (IntechOpen, London, United Kingdom, 2022). https://www.intechopen.com/books/11066

M. Alva-Sánchez and T. A. Pianoschi, Radiat. Phys. Chem. 167, 108428 (2020). https://www.sciencedirect.com/science/article/abs/pii/S0969806X18306716?via%3Dihub

N. Souza Neto and et al., Braz. J. Radiat. Sci. 10, 2049 (2022). https://doi.org/https://doi.org/10.15392/bjrs.v10i3.2049

D. Sheikh-Bagheri and D. Rogers, Med. Phys. 29, 391 (2002). https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.1445413

W. T. Jalbout and N. M. Spyrou, Phys. Med. Biol. 51, 2211 (2006). doi.org/https://doi.org/10.1088/0031-9155/51/9/007

J. Deng, S. B. Jiang, and et al., Phys. Med. Biol. 46, 1429 (2001). https://iopscience.iop.org/article/10.1088/0031-9155/46/5/308

J. P. O. Manrique, Nucl. Instrum. Methods Phys. Res. A 985, 164684 (2021). https://www.sciencedirect.com/science/article/pii/S0168900220310810?via%3Dihub

J. Torres Díaz and et al., Phys. Med. 96, 81 (2022). https://www.physicamedica.com/article/S1120-1797(22)01431-4/abstract

H. J. Choi, H. Park, and et al., Med. Phys. 46, 3285 (2019). https://aapm.onlinelibrary.wiley.com/doi/10.1002/mp.13569

Z. Aboulbanine and K. Bahhous, Radiat. Phys. Chem. 201, 110451 (2022). https://www.sciencedirect.com/science/article/abs/pii/S0969806X22004911?via%3Dihub

J. H. Wilches Visbal and A. M. Costa, Radiat. Phys. Chem. 162, 31 (2019). https://jart.icat.unam.mx/index.php/jart/article/view/1213

J. Wei, G. A. Sandison, and et al., Med. Phys. 33, 354 (2006). https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.2161404

J. H. Wilches Visbal and P. Nicolucci, J. Appl. Res. Technol. 19, 622 (2021). https://jart.icat.unam.mx/index.php/jart/article/view/1213

L. Zhengming and D. Jette, Phys. Med. Biol. 44, N177 (1999). https://iopscience.iop.org/article/10.1088/0031-9155/44/8/401

S. I. Kabanikhin, J. Inverse Ill-Posed Probl. 16, 317 (2008). https://www.degruyterbrill.com/document/doi/10.1515/JIIP.2008.019/html

A.-M. Wazwaz, Linear and Nonlinear Integral Equations: Methods and Applications (Springer Berlin Heidelberg, 2011). https://link.springer.com/book/10.1007/978-3-642-21449-3

L. Zhengming, Nucl. Instrum. Methods Phys. Res. A 255, 152 (1987). https://www.sciencedirect.com/science/article/abs/pii/0168900287910916?via%3Dihub

P. C. Hansen, Inverse Problems 8, 849 (1992). https://iopscience.iop.org/article/10.1088/0266-5611/8/6/005

D. D. Bui and P. Nelson, Inverse Problems 8, 821 (1992). https://iopscience.iop.org/article/10.1088/0266-5611/8/6/003

Y. Xu, Y. Pei, and et al., Radiat. Phys. Chem. 50, 1 (2016). https://www.sciencedirect.com/science/article/abs/pii/S0955598616300334?via%3Dihub

P. C. Hansen, SIAM Rev. 34, 561 (1992). https://epubs.siam.org/doi/10.1137/1034115

J. Wilches-Visbal and D. Apaza-Veliz, Ing. y Competitividad 25, e21112051 (2023). https://revistaingenieria.univalle.edu.co/index.php/ingenieria_y_competitividad/article/view/12051

T. Schanze, Comput. Phys. Commun. 175, 708 (2006). https://www.sciencedirect.com/science/article/abs/pii/S0010465506002980?via%3Dihub

J. H. Wilches-Visbal, D. Apaza-Veliz, and et al., Uniciencia 36, 1 (2022). https://www.revistas.una.ac.cr/index.php/uniciencia/article/view/16520

V. Mariotti and et al., Radiat. Phys. Chem. 190, 109782 (2022). https://www.sciencedirect.com/science/article/abs/pii/S0969806X21004321?via%3Dihub

D. Sheikh-Bagheri and D. W. O. Rogers, Med. Phys. 29, 391 (2002). https://aapm.onlinelibrary.wiley.com/doi/10.1118/1.1445413

A. Baumgartner and et al., Appl. Radiat. Isot. 67, 2007 (2009). https://www.sciencedirect.com/science/article/abs/pii/S0969804309004667?via%3Dihub

A. Konefa l, M. Cygan-Bakoniak, and et al., Radiat. Meas. 72, 12 (2015). https://www.sciencedirect.com/science/article/abs/pii/S1350448714003187?via%3Dihub

M. S. Alva-Sanchez and et al., Radiat. Meas. 176, 107221 (2024). https://www.sciencedirect.com/science/article/abs/pii/S1350448724001690

G. Ding, D. Rogers, and et al., Energy spectra, angular spread and dose distributions of electron beams from various accelerators used in radiotherapy, Tech. Rep. (Institute for National Measurement Standards, Ottawa, 1995). https://people.physics.carleton.ca/%7Edrogers/pubs/papers/pirs439/pirs439.html

L. Apipunyasopon and et al., J. Radiat. Res. 54, 374 (2013). https://academic.oup.com/jrr/article/54/2/374/949934

S. Alashrah, S. Kandaiya, and et al., Radiat. Prot. Dosim. 162, 338 (2014). https://academic.oup.com/rpd/article-abstract/162/3/338/1610139?redirectedFrom=fulltext

M. Castro and et al., Radiat. Phys. Chem. 188, 109748 (2021). https://www.sciencedirect.com/science/article/abs/pii/S0969806X21003984?via%3Dihub

J. Torres-Díaz and et al., Phys. Med. 96, 81 (2022). https://www.physicamedica.com/article/S1120-1797(22)01431-4/abstract

B. Bednarz, C. Hancox, and et al., Phys. Med. Biol. 54, 5271 (2009). https://iopscience.iop.org/article/10.1088/0031-9155/54/17/013

H. Aamri, A. Fielding, and et al., Radiat. Phys. Chem. 178, 109013 (2021). https://www.sciencedirect.com/science/article/abs/pii/S0969806X20302188?via%3Dihub

M. Assalmi and et al., Rep. Pract. Oncol. Radiother. 25, 1001 (2020). https://www.sciencedirect.com/science/article/pii/S1507136720301346?via%3Dihub

G. Silva, V. Botelho, and et al., Applied Radiation and Isotopes 225, 112087 (2025). https://www.sciencedirect.com/science/article/abs/pii/S0969804325004324

How to Cite

APA

Wilches Visbal, J. H., Rodrigues Zaratim, G. R., Quevedo, A., Cardoso Matsushima, L., Veneziani, G. R., Mergen Knoll, I., Bonatto, A. & Alva Sánchez, M. (2026). RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING. MOMENTO, (72), 98–115. https://doi.org/10.15446/mo.n72.119862

ACM

[1]
Wilches Visbal, J.H., Rodrigues Zaratim, G.R., Quevedo, A., Cardoso Matsushima, L., Veneziani, G.R., Mergen Knoll, I., Bonatto, A. and Alva Sánchez, M. 2026. RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING. MOMENTO. 72 (Jan. 2026), 98–115. DOI:https://doi.org/10.15446/mo.n72.119862.

ACS

(1)
Wilches Visbal, J. H.; Rodrigues Zaratim, G. R.; Quevedo, A.; Cardoso Matsushima, L.; Veneziani, G. R.; Mergen Knoll, I.; Bonatto, A.; Alva Sánchez, M. RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING. Momento 2026, 98-115.

ABNT

WILCHES VISBAL, J. H.; RODRIGUES ZARATIM, G. R.; QUEVEDO, A.; CARDOSO MATSUSHIMA, L.; VENEZIANI, G. R.; MERGEN KNOLL, I.; BONATTO, A.; ALVA SÁNCHEZ, M. RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING. MOMENTO, [S. l.], n. 72, p. 98–115, 2026. DOI: 10.15446/mo.n72.119862. Disponível em: https://revistas.unal.edu.co/index.php/momento/article/view/119862. Acesso em: 22 jan. 2026.

Chicago

Wilches Visbal, Jorge H., Giulianne R. Rodrigues Zaratim, Ana Quevedo, Luciana Cardoso Matsushima, Glauco Rogério Veneziani, Iury Mergen Knoll, Alexandre Bonatto, and Mirko Alva Sánchez. 2026. “RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING”. MOMENTO, no. 72 (January):98-115. https://doi.org/10.15446/mo.n72.119862.

Harvard

Wilches Visbal, J. H., Rodrigues Zaratim, G. R., Quevedo, A., Cardoso Matsushima, L., Veneziani, G. R., Mergen Knoll, I., Bonatto, A. and Alva Sánchez, M. (2026) “RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING”, MOMENTO, (72), pp. 98–115. doi: 10.15446/mo.n72.119862.

IEEE

[1]
J. H. Wilches Visbal, “RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING”, Momento, no. 72, pp. 98–115, Jan. 2026.

MLA

Wilches Visbal, J. H., G. R. Rodrigues Zaratim, A. Quevedo, L. Cardoso Matsushima, G. R. Veneziani, I. Mergen Knoll, A. Bonatto, and M. Alva Sánchez. “RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING”. MOMENTO, no. 72, Jan. 2026, pp. 98-115, doi:10.15446/mo.n72.119862.

Turabian

Wilches Visbal, Jorge H., Giulianne R. Rodrigues Zaratim, Ana Quevedo, Luciana Cardoso Matsushima, Glauco Rogério Veneziani, Iury Mergen Knoll, Alexandre Bonatto, and Mirko Alva Sánchez. “RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING”. MOMENTO, no. 72 (January 20, 2026): 98–115. Accessed January 22, 2026. https://revistas.unal.edu.co/index.php/momento/article/view/119862.

Vancouver

1.
Wilches Visbal JH, Rodrigues Zaratim GR, Quevedo A, Cardoso Matsushima L, Veneziani GR, Mergen Knoll I, Bonatto A, Alva Sánchez M. RECONSTRUCTION OF PHOTON ENERGY SPECTRA FROM CLINICAL PERCENTAGE DEPTH-DOSE CURVES USING TIKHONOV REGULARIZATION AND GENERALIZED SIMULATED ANNEALING. Momento [Internet]. 2026 Jan. 20 [cited 2026 Jan. 22];(72):98-115. Available from: https://revistas.unal.edu.co/index.php/momento/article/view/119862

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