Quantitative Elemental Analysis Using Whole Spectral Information (with GA and MLR Methods) of Proton Induced Prompt Gamma-Rays Simulated Using Geant4 Toolkit
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Abstract
Purpose: Online determination of the elemental composition of tissues near the Bragg peak is a challenge in proton therapy related studies. In the present work, an analysis method based on the whole spectral information is presented for the quantitative determination of the elemental composition (weight %) of an irradiated target from its emitted Prompt Gamma (PG) spectrum.
Materials and Methods: To address this issue, four test phantoms with different weights (%) of 12C, 16O, 20Ca, and 14N elements were considered. The simulated PG spectra were recorded using 3 × 3 inch NaI detectors. A library consisting of the spectra of single-element phantoms as well as the spectra of test-irradiated phantoms was produced for 30, 70, and 150 MeV incident protons using the Geant4 Monte Carlo toolkit. The elemental analysis was performed using the information of the whole spectrum by applying two methods, including the well-known Genetic Algorithm (GA) and Multiple Linear Regression (MLR).
Results: The results show that the proposed method estimates the oxygen concentration accurately. Furthermore, the estimated weights of other elements, with both methods, agree well with nominal values in each test phantom, for the considered energies.
Conclusion: The proposed quantitative elemental analysis of proton-bombarded phantoms using their induced PG spectrum is expected to be beneficial in treatment planning and treatment verification studies.
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23- Marica Baldoncini et al., "Investigating the potentialities of Monte Carlo simulation for assessing soil water content via proximal gamma-ray spectroscopy." J Journal of environmental radioactivity, Vol. 192pp. 105-16, (2018).
24- Sea Agostinelli et al., "GEANT4—a simulation toolkit." J Nuclear Instruments Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors Associated Equipment, Vol. 506 (No. 3), pp. 250-303, (2003).
25- H Shahabinejad and SAH Feghhi, "Design, optimization and performance of source and detector collimators for gamma-ray scanning of a lab-scale distillation column." Applied Radiation and Isotopes, Vol. 99pp. 25-34, (2015).
26- John Furey and Cliff Morgan, "Quartic collimator design for high-energy gamma rays." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol. 629 (No. 1), pp. 202-05, (2011).
27- Zanyar Movasaghi, Shazza Rehman, and Dr Ihtesham Jur Rehman, "Fourier transform infrared (FTIR) spectroscopy of biological tissues.", Applied Spectroscopy Reviews, Vol. 43 (No. 2), pp. 134-79, (2008).
28- Hashem Miri Hakimabad, Hamed Panjeh, and Alireza Vejdani-Noghreiyan, "Evaluation the nonlinear response function of a 3× 3 in NaI scintillation detector for PGNAA applications." J Applied radiation isotopes, Vol. 65 (No. 8), pp. 918-26, (2007).
29- Ilker Meric, Geir A Johansen, Marie B Holstad, Jiaxin Wang, and Robin P Gardner, "Produced water characterization by prompt gamma-ray neutron activation analysis." J Measurement Science Technology, Vol. 22 (No. 12), p. 125701, (2011).
30- Hadi Shahabinejad and Naser Vosoughi, "SGSD: A novel Sequential Gamma-ray Spectrum Deconvolution algorithm." Annals of Nuclear Energy, Vol. 132pp. 369-80, (2019).
31- Hadi Shahabinejad and Naser Vosoughi, "Analysis of complex gamma-ray spectra using particle swarm optimization." J Nuclear Instruments Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors Associated Equipment, Vol. 911pp. 123-30, (2018).
32- David E Goldberg, "Genetic algorithm." J Search, Optimization Machine Learning, pp. 343-49, (1989).
33- John Holland, "Adaptation in natural and artificial systems: an introductory analysis with application to biology." J Control artificial intelligence, (1975).
34- H Shahabinejad, SA Hosseini, and M Sohrabpour, "A new neutron energy spectrum unfolding code using a two steps genetic algorithm." J Nuclear Instruments Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors Associated Equipment, Vol. 811pp. 82-93, (2016).
35- Vitisha Suman and PK Sarkar, "Neutron spectrum unfolding using genetic algorithm in a Monte Carlo simulation." J Nuclear Instruments Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors, Associated Equipment, Vol. 737pp. 76-86, (2014).
36- Miltiadis Alamaniotis, John Mattingly, and Lefteri H Tsoukalas, "Pareto-Optimal gamma spectroscopic radionuclide identification using evolutionary computing." J IEEE Transactions on Nuclear Science, Vol. 60 (No. 3), pp. 2222-31, (2013).
37- Jeyasingam Peterson Jeyasugiththan, Stephen W, "Evaluation of proton inelastic reaction models in Geant4 for prompt gamma production during proton radiotherapy." in J Physics in Medicine Biology Vol. 60, ed, (2015), p. 7617.
38- F Saheli, Z Riazi, A Jokar, H Shahabinejad, N Vosoughi, and SA Ghasemi, "Evaluation the nonlinear response function of a HPGe detector for 59 keV to 10.7 MeV gamma-rays using a Monte Carlo simulation and comparison with experimental data.", Journal of Instrumentation, Vol. 16 (No. 07), p. P07003, (2021).
2- Harald Paganetti, "Range uncertainties in proton therapy and the role of Monte Carlo simulations." Physics in Medicine and Biology, Vol. 57 (No. 11), p. R99, (2012).
3- E Fiorina, INSIDE collaboration %J Nuclear Instruments, Spectrometers Methods in Physics Research Section A: Accelerators, Detectors, and Associated Equipment, "An integrated system for the online monitoring of particle therapy treatment accuracy." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 824pp. 198-201, (2016).
4- E Basile et al., "An online proton beam monitor for cancer therapy based on ionization chambers with micro pattern readout." Journal of Instrumentation, Vol. 7 (No. 03), p. C03020, (2012).
5- Niccolo' Camarlinghi et al., "An in-beam PET system for monitoring ion-beam therapy: test on phantoms using clinical 62 MeV protons." Journal of Instrumentation, Vol. 9 (No. 04), p. C04005, (2014).
6- S Muraro et al., "Proton therapy treatment monitoring with the DoPET system: Activity range, positron emitters evaluation and comparison with Monte Carlo predictions." Journal of Instrumentation, Vol. 12 (No. 12), p. C12026, (2017).
7- Valeria Rosso et al., "A new PET prototype for proton therapy: comparison of data and Monte Carlo simulations." Journal of Instrumentation, Vol. 8 (No. 03), p. C03021, (2013).
8- Metin Usta, Mustafa Çağatay Tufan, Güral Aydın, and Ahmet Bozkurt, "Stopping power and dose calculations with analytical and Monte Carlo methods for protons and prompt gamma range verification." Nuclear instruments methods in physics research section A: Accelerators, Spectrometers, Detectors Associated Equipment, Vol. 897pp. 106-13, (2018).
9- Veronica Ferrero et al., "Evaluation of in-beam PET treatment verification in proton therapy with different reconstruction methods." IEEE Transactions on Radiation and Plasma Medical Sciences, Vol. 4 (No. 2), pp. 202-11, (2019).
10- J Krimmer, D Dauvergne, JM Létang, and É Testa, "Prompt-gamma monitoring in hadrontherapy: A review." Nuclear Instruments and Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors and Associated Equipment, Vol. 878pp. 58-73, (2018).
11- F Stichelbaut and Y Jongen, "Verification of the proton beam position in the patient by the detection of prompt gamma-rays emission." in 39th PTCOG meeting, San Francisco, (2003).
12- Chul-Hee Min, Chan Hyeong Kim, Min-Young Youn, and Jong-Won Kim, "Prompt gamma measurements for locating the dose falloff region in the proton therapy." J Applied physics letters, Vol. 89 (No. 18), p. 183517, (2006).
13- E Testa et al., "Monitoring the Bragg peak location of 73 MeV∕ u carbon ions by means of prompt γ-ray measurements." J Applied physics letters, Vol. 93 (No. 9), p. 093506, (2008).
14- J Polf, S Peterson, G Ciangaru, M Gillin, and S Beddar, "Prompt gamma-ray emission from biological tissues during proton irradiation: a preliminary study." J Physics in Medicine Biology, Vol. 54 (No. 3), p. 731, (2009).
15- J Polf et al., "Measurement and calculation of characteristic prompt gamma ray spectra emitted during proton irradiation." J Physics in Medicine Biology, Vol. 54 (No. 22), p. N519, (2009).
16- Jerimy Polf et al., "Measurement of characteristic prompt gamma rays emitted from oxygen and carbon in tissue-equivalent samples during proton beam irradiation." J Physics in Medicine Biology, Vol. 58 (No. 17), p. 5821, (2013).
17- Joost M Verburg and Joao Seco, "Proton range verification through prompt gamma-ray spectroscopy." J Physics in Medicine Biology, Vol. 59 (No. 23), p. 7089, (2014).
18- Joost M Verburg, Helen A Shih, and Joao Seco, "Simulation of prompt gamma-ray emission during proton radiotherapy." J Physics in Medicine Biology, Vol. 57 (No. 17), p. 5459, (2012).
19- Fereshte Saheli et al., "Single peak analysis of proton induced prompt gamma counts." Nuclear Instruments and Methods in Physics Research B, Vol. 475pp. 63-70, (2020).
20- Robin P Gardner, A Sood, YY Wang, L Liu, P Guo, and RJ Gehrke, "Single peak versus library least-squares analysis methods for the PGNAA analysis of vitrified waste." J Applied radiation isotopes, Vol. 48 (No. 10-12), pp. 1331-35, (1997).
21- Miltiadis Alamaniotis and Tatjana Jevremovic, "Hybrid fuzzy-genetic approach integrating peak identification and spectrum fitting for complex gamma-ray spectra analysis." J IEEE Transactions on Nuclear Science, Vol. 62 (No. 3), pp. 1262-77, (2015).
22- L Salmon, "Analysis of gamma-ray scintillation spectra by the method of least squares." J Nuclear Instruments Methods in Physics Research Section B: Beam Interactions with Materials Atoms, Vol. 14pp. 193-99, (1961).
23- Marica Baldoncini et al., "Investigating the potentialities of Monte Carlo simulation for assessing soil water content via proximal gamma-ray spectroscopy." J Journal of environmental radioactivity, Vol. 192pp. 105-16, (2018).
24- Sea Agostinelli et al., "GEANT4—a simulation toolkit." J Nuclear Instruments Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors Associated Equipment, Vol. 506 (No. 3), pp. 250-303, (2003).
25- H Shahabinejad and SAH Feghhi, "Design, optimization and performance of source and detector collimators for gamma-ray scanning of a lab-scale distillation column." Applied Radiation and Isotopes, Vol. 99pp. 25-34, (2015).
26- John Furey and Cliff Morgan, "Quartic collimator design for high-energy gamma rays." Nuclear Instruments and Methods in Physics Research Section B: Beam Interactions with Materials and Atoms, Vol. 629 (No. 1), pp. 202-05, (2011).
27- Zanyar Movasaghi, Shazza Rehman, and Dr Ihtesham Jur Rehman, "Fourier transform infrared (FTIR) spectroscopy of biological tissues.", Applied Spectroscopy Reviews, Vol. 43 (No. 2), pp. 134-79, (2008).
28- Hashem Miri Hakimabad, Hamed Panjeh, and Alireza Vejdani-Noghreiyan, "Evaluation the nonlinear response function of a 3× 3 in NaI scintillation detector for PGNAA applications." J Applied radiation isotopes, Vol. 65 (No. 8), pp. 918-26, (2007).
29- Ilker Meric, Geir A Johansen, Marie B Holstad, Jiaxin Wang, and Robin P Gardner, "Produced water characterization by prompt gamma-ray neutron activation analysis." J Measurement Science Technology, Vol. 22 (No. 12), p. 125701, (2011).
30- Hadi Shahabinejad and Naser Vosoughi, "SGSD: A novel Sequential Gamma-ray Spectrum Deconvolution algorithm." Annals of Nuclear Energy, Vol. 132pp. 369-80, (2019).
31- Hadi Shahabinejad and Naser Vosoughi, "Analysis of complex gamma-ray spectra using particle swarm optimization." J Nuclear Instruments Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors Associated Equipment, Vol. 911pp. 123-30, (2018).
32- David E Goldberg, "Genetic algorithm." J Search, Optimization Machine Learning, pp. 343-49, (1989).
33- John Holland, "Adaptation in natural and artificial systems: an introductory analysis with application to biology." J Control artificial intelligence, (1975).
34- H Shahabinejad, SA Hosseini, and M Sohrabpour, "A new neutron energy spectrum unfolding code using a two steps genetic algorithm." J Nuclear Instruments Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors Associated Equipment, Vol. 811pp. 82-93, (2016).
35- Vitisha Suman and PK Sarkar, "Neutron spectrum unfolding using genetic algorithm in a Monte Carlo simulation." J Nuclear Instruments Methods in Physics Research Section A: Accelerators, Spectrometers, Detectors, Associated Equipment, Vol. 737pp. 76-86, (2014).
36- Miltiadis Alamaniotis, John Mattingly, and Lefteri H Tsoukalas, "Pareto-Optimal gamma spectroscopic radionuclide identification using evolutionary computing." J IEEE Transactions on Nuclear Science, Vol. 60 (No. 3), pp. 2222-31, (2013).
37- Jeyasingam Peterson Jeyasugiththan, Stephen W, "Evaluation of proton inelastic reaction models in Geant4 for prompt gamma production during proton radiotherapy." in J Physics in Medicine Biology Vol. 60, ed, (2015), p. 7617.
38- F Saheli, Z Riazi, A Jokar, H Shahabinejad, N Vosoughi, and SA Ghasemi, "Evaluation the nonlinear response function of a HPGe detector for 59 keV to 10.7 MeV gamma-rays using a Monte Carlo simulation and comparison with experimental data.", Journal of Instrumentation, Vol. 16 (No. 07), p. P07003, (2021).
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| Issue | Vol 10 No 1 (2023) | |
| Section | Original Article(s) | |
| DOI | https://doi.org/10.18502/fbt.v10i1.11515 | |
| Keywords | ||
| Proton Therapy Prompt Gamma-Ray Spectrum Whole Spectrum Analysis Genetic Algorithm Multiple Linear Regression | ||
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How to Cite
1.
Saheli F, Vosoughi N, Riazi Z, Rasouli FS, Jowkar A. Quantitative Elemental Analysis Using Whole Spectral Information (with GA and MLR Methods) of Proton Induced Prompt Gamma-Rays Simulated Using Geant4 Toolkit. Frontiers Biomed Technol. 2022;10(1):78-87.
