Abstract
Theoretical predictions were made using TALYS-1.95(G) and EMPIRE 3.2 reaction-model codes for 69Ge, 67Ge, and medically used 68Ge, 67Ga, 68Ga, 65Zn, 64Cu radionuclides produced in the interaction of α-projectile with 66Zn-target at 10–40 MeV α-energies. Pearson’s statistical coefficients showed moderate to strong positive correlations between the theoretically predicted and experimentally measured production cross sections for radionuclides with practical medical applications. Furthermore, the present results indicated that a medium-sized cyclotron and a single α + 66Zn system (projectile + target system) might be an option for optimized production of 68Ge, 68Ga, 67Ga, 65Zn, and 64Cu radionuclides.
Acknowledgment
The author is grateful to Professor A. K. Chaubey for his helpful scientific discussions on the present work. However, all opinions and any errors are the author’s responsibility alone.
-
Author contributions: All the authors have accepted responsibility for the entire content of this submitted manuscript and approved submission.
-
Research funding: None declared.
-
Conflict of interest statement: The authors declare no conflicts of interest regarding this article.
References
1. Qaim, S. M. Radiochemical determination of nuclear data for theory and applications. J. Radional. Nucl. Chem. 2010, 284, 489–505; https://doi.org/10.1007/s10967-010-0460-5.Search in Google Scholar
2. Yigit, M., Tel, E. Alpha production cross sections for some target fusion structural materials up to 35 MeV. J. Fusion Energy 2013, 32, 442–450; https://doi.org/10.1007/s10894-012-9591-8.Search in Google Scholar
3. Qaim, S. M. Nuclear data relevant to cyclotron produced short-lived medical radioisotopes. Radiochim. Acta 1982, 30, 147–162.Search in Google Scholar
4. IAEA. Cyclotron-Produced Radionuclides: Physical Characteristics and Production Methods. IAEA Technical Report Series; International Atomic Energy Agency: Vienna, Vol. 468, 2009; p. 266.Search in Google Scholar
5. Tárkányi, F. T., Ignatyuk, A. V., Hermanne, A., Capote, R., Carlson, B. V., Engle, J. W., Kellett, M. A., Kibedi, T., Kim, G. N., Kondey, F. G., Hussain, M., Lebeda, O., Luca, A., Nagai, Y., Naik, H., Nichols, A. L., Nortier, F. M., Suryanarayana, S. V., Takacs, S., Verpelli, M. Recommended nuclear data for medical radioisotope production: diagnostic gamma emitters. J. Radioanal. Nucl. Chem. 2019, 319, 487–531; https://doi.org/10.1007/s10967-018-6142-4.Search in Google Scholar
6. Qaim, S. M., Spahn, I., Scholten, B., Neumaier, B. Uses of alpha particles, especially in nuclear reaction studies and medical radionuclide production. Radiochim. Acta 2016, 104, 601–624; https://doi.org/10.1515/ract-2015-2566.Search in Google Scholar
7. Qaim, S. M. The present and future of medical radionuclide production. Radiochim. Acta 2012, 100, 635; https://doi.org/10.1524/ract.2012.1966.Search in Google Scholar
8. Nayak, D., Lahiri, S. Application of radioisotopes in the field of nuclear medicine. J. Radioanal. Nucl. Chem. 1999, 242, 423–432; https://doi.org/10.1007/bf02345573.Search in Google Scholar
9. Balwinder, S., Jaspreet, S., Amritpal, K. Applications of radioisotopes in agriculture. Int. J. Biotechnol. Bioeng Res. 2013, 4, 167–174.Search in Google Scholar
10. JieHeng, L., Lin, L., QianQian, Q., Zeyuan, Y., ZeYong, L. A method of detecting level change of uranium fluorination mixture in the hopper by gamma-ray dose. Appl. Radiat. Isot. 2021, 174, 109712; https://doi.org/10.1016/j.apradiso.2021.109712.Search in Google Scholar PubMed
11. World Nuclear Association. Radioisotopes in Medicine, 2022. https://www.world-nuclear.org/information-library/non-power-nuclear-applications/radioisotopes-research/radioisotopes-in-medicine.aspx (accessed Jun 16, 2022).Search in Google Scholar
12. Nida, T. K. Radioisotopes and their biomedical applications. J. Biomol. Res. Therapeut. 2017, 6, 2; https://doi.org/10.4172/2167-7956.1000156.Search in Google Scholar
13. Tárkányi, F. T., Ignatyuk, A. V., Hermanne, A., Capote, R., Carlson, B. V., Engle, J. W., Kellett, M. A., Kibedi, T., Kim, G. N., Kondey, F. G., Hussain, M., Lebeda, O., Luca, A., Nagai, Y., Naik, H., Nichols, A. L., Nortier, F. M., Suryanarayana, S. V., Takacs, S., Verpelli, M. Recommended nuclear data for medical radioisotope production: diagnostic positron emitters. J. Radioanal. Nucl. Chem. 2019, 319, 533–666; https://doi.org/10.1007/s10967-018-6380-5.Search in Google Scholar
14. Kotani, K., Kawabe, J., Higashiyama, S., Yoshida, A., Shiomi, S. Diffuse gallium-67 accumulation in the left atrial wall detected using SPECT/CT fusion images. Case Rep. Radiol. 2016, 2016, 6374584; https://doi.org/10.1155/2016/6374584.Search in Google Scholar PubMed PubMed Central
15. Qaim, S. M. Nuclear data for production and medical application of radionuclides: present status and future needs. Nucl. Med. Biol. 2017, 44, 31–49; https://doi.org/10.1016/j.nucmedbio.2016.08.016.Search in Google Scholar PubMed
16. Qaim, S. M. Nuclear data for medical application: an overview. Radiochim. Acta 2001, 89, 189–196. https://doi.org/10.1524/ract.2001.89.4-5.189.Search in Google Scholar
17. Hermanne, A., Adam-Rebeles, R., Tárkányi, F., Takács, S. Alpha particle induced reactions on natCr up to 39 MeV: experimental cross-sections, comparison with theoretical calculations and thick target yields for medically relevant 52gFe production. Nucl. Instrum. Methods B 2015, 356, 28; https://doi.org/10.1016/j.nimb.2015.04.025.Search in Google Scholar
18. Tárkányi, F., Hermanne, A., Király, B., Takács, S., Ignatyuk, A. V. Study of excitation functions of alpha-particle induced nuclear reactions on holmium for 167Tm production. Appl. Radiat. Isot. 2010, 68, 404–4011; https://doi.org/10.1016/j.apradiso.2009.11.043.Search in Google Scholar PubMed
19. Aslam, M. N., Qaim, S. M. Nuclear model analysis of excitation functions of p, d and α- particle induced reactions on nickel isotopes for production of the medically interesting copper-61. Appl. Radiat. Isot. 2014, 89, 65–73; https://doi.org/10.1016/j.apradiso.2014.02.007.Search in Google Scholar PubMed
20. Király, B., Tárkányi, F., Takács, S., Hermanne, A., Kovalev, S. F., Ignatyuk, A. V. Excitation functions of alpha-particle induced nuclear reactions on natural ytterbium. Nucl. Instrum. Methods B 2008, 266, 3919; https://doi.org/10.1016/j.nimb.2008.07.002.Search in Google Scholar
21. Szkliniarz, K., Sitarz, M., Walczak, R., Jastrzebski, J., Bilewicz, A., Choinski, J., Jakubowski, A., Majkowska, A., Stolarz, A., Trzcinska, A., Zipper, W. Production of medical Sc radioisotopes with an alpha particle beam. Appl. Radiat. Isot. 2016, 118, 182–189; https://doi.org/10.1016/j.apradiso.2016.07.001.Search in Google Scholar PubMed
22. Amanuel, F. K. Nuclear model prediction for production of medical 22Na, 51Cr, 60Co, 61Cu, 64Cu, 65Zn, 67, 68Ga, 88Y and 99Mo radionuclides: comparison of experimental and theoretical data. Appl. Radiat. Isot. 2021, 172, 109674; https://doi.org/10.1016/j.apradiso.2021.109674.Search in Google Scholar PubMed
23. Uddin, M. S., Scholten, B., Hermanne, A., Sudar, S., Coenen, H. H., Qaim, S. M. Radiochemical determination of cross sections of α-particle induced reactions on 192Os for the production of the therapeutic radionuclide 193mPt. Appl. Radiat. Isot. 2010, 68, 2001–2006; https://doi.org/10.1016/j.apradiso.2010.05.002.Search in Google Scholar PubMed
24. Aslam, M. N., Zubia, K., Qaim, S. M. Nuclear model analysis of excitation functions of α- particle induced reactions on in and Cd up to 60 MeV with relevance to the production of high specific activity 117mSn. Appl. Radiat. Isot. 2018, 132, 181–188; https://doi.org/10.1016/j.apradiso.2017.12.002.Search in Google Scholar PubMed
25. Smith, S. V. Molecular imaging with copper-64. J. Inorg. Biochem. 2004, 98, 1874–1901; https://doi.org/10.1016/j.jinorgbio.2004.06.009.Search in Google Scholar PubMed
26. NIDC (National Isotopes Development Center). Medical isotopes. In The U.S. Department of Energy Isotope Program, 2022. https://www.isotopes.gov/outreach/med_isotopes.html1/5 (accessed Jun 23, 2022).Search in Google Scholar
27. Malinowska, E., Doboszyńska, A., Śliwińska, A., Buscombe, J. R., Kulesza, G., Moczulska, B., Ćwikła, J. B. The use of 67Ga scintigraphy in patients with Sarcoidosis. Nucl. Med. Rev 2018, 21, 59–65; https://doi.org/10.5603/NMR.a2018.0007.Search in Google Scholar PubMed
28. Irina, V. Positron emitting 68Ga-based imaging agents: chemistry and diversity. Med. Chem. 2011, 7, 345–379; https://doi.org/10.2174/157340611796799195.Search in Google Scholar PubMed
29. Wouter, A. P. B., Alfons, M. V. The 68Ge/68Ga generator has high potential, but when can we use 68Ga-labelled tracers in clinical routine? Eur. J. Nucl. Med. Mol. Imag. 2007, 34, 978–981; https://doi.org/10.1007/s00259-007-0387-4.Search in Google Scholar PubMed PubMed Central
30. Nagame, Y., Nakahara, H., Furukawa, M. Excitation functions for alpha and 3He particles induced reactions on zinc. Radiochim. Acta 1989, 46, 5; https://doi.org/10.1524/ract.1989.46.1.5.Search in Google Scholar
31. Levkovski, V. N. Cross Sections of Medium Mass Nuclide Activation (A=40-100) by Medium Energy Protons and Alpha-Particles (E=10-50 MeV); Inst. Yadernoi Fiziki: Almaty, Kazakhstan, 1991.Search in Google Scholar
32. AbuIssa, N. N., Antropov, A. E., Gusev, V. P., Zarubin, P. P., Kolozhvari, A. A., Smirnov, A. V. Analysis of the excitation functions of alpha-particles interactions with nuclei Zn-64, 66, 68, 70 at energy 14.8 – 24.4 MeV, 39. In Conf. Nucl. Spectrosc. and Nucl. Struct.: Tashkent, 1989; p. 350.Search in Google Scholar
33. Nikjou, A., Sadeghi, M. Overview and evaluation of different nuclear level density models for the 123I radionuclide production. Appl. Radiat. Isot. 2018, 136, 45–58; https://doi.org/10.1016/j.apradiso.2018.02.003.Search in Google Scholar PubMed
34. Demir, B., Kaplan, A., Capali, V., Sarpun, I. H., Aydin, A., Tel, E. Production cross section calculations of medical 32P, 117Sn, 153Sm and 186,188Re radionuclides used in bone pain palliation treatment. Kerntechnik 2015, 80, 58–65; https://doi.org/10.3139/124.110477.Search in Google Scholar
35. Weisskopf, V. F., Ewing, D. H. On the yield of nuclear reactions with heavy elements. Phys. Rev. 1940, 57, 472–485; https://doi.org/10.1103/PhysRev.57.472.Search in Google Scholar
36. Blann, M. Hybrid model for pre-equilibrium decay in nuclear reactions. Phys. Rev. Lett. 1971, 27, 337–340; https://doi.org/10.1103/physrevlett.27.1550.Search in Google Scholar
37. Blann, M. Importance of the nuclear density distribution on pre-equilibrium decay. Phys. Rev. Lett. 1972, 28, 757; https://doi.org/10.1103/PhysRevLett.28.757.Search in Google Scholar
38. Blann, M., Vonach, H. K. Global test of modified pre-compound decay models. Phys. Rev. C 1983, 28, 1475–1492; https://doi.org/10.1103/physrevc.28.1475.Search in Google Scholar
39. Herman, M., Capote, R., Sin, M., Trkov, A., Carlson, B. V., Oblozinsky, P., Mattoon, C. M., Wienket, H., Hoblit, S., Cho, Y., Nobre, G. P. A., Plujko, V. A., Zerkin, V. EMPIRE-3.2 Malta Modular System for Nuclear Reaction Calculations and Nuclear Data Evaluation User’s Manual; Brookhaven National Laboratory: Upton, NY, 2013.10.2172/1108585Search in Google Scholar
40. Hauser, W., Feshbach, H. The inelastic scattering of neutrons. Phys. Rev. C 1952, 87, 366–373; https://doi.org/10.1103/physrev.87.366.Search in Google Scholar
41. Cline, C. K., Blann, M. The pre-equilibrium statistical model: description of the nuclear equilibration process and parameterization of the model. Nucl. Phys. A 1971, 172, 225; https://doi.org/10.1016/0375-9474(71)90713-5.Search in Google Scholar
42. Cline, C. K. Extensions to the pre-equilibrium statistical model and a study of complex particle emission. Nucl. Phys. A 1972, 193, 417; https://doi.org/10.1016/0375-9474(72)90330-2.Search in Google Scholar
43. Ribansky, I., Oblozinsky, P., Betak, E. Pre-equilibrium decay and the exciton model. Nucl. Phys. A 1973, 205, 545; https://doi.org/10.1016/0375-9474(73)90705-7.Search in Google Scholar
44. Koning, A. J., Hilaire, S., Goriely, S. Computer Code TALYS, Version 1.95, 2019. https://tendl.web.psi.ch/tendl_2019/talys/talys1.95.tar.Search in Google Scholar
45. Konobeyev, A. Y., Fischer, U., Pereslavtsev, P. E. Implementation of the geometry dependent hybrid model in TALYS. J. Kor. Phys. Soc. 2011, 59, 935; https://doi.org/10.3938/jkps.59.935.Search in Google Scholar
46. Patrick, S. Correlation coefficients appropriate use and interpretation. Anesth. Analg. 2018, 126, 1763–1768; https://doi.org/10.1213/ANE.0000000000002864.Search in Google Scholar PubMed
47. Wang, J. On the relationship between Pearson correlation coefficient and Kendall’s Tau under bivariate homogeneous shock model. Int. Sch. Res. Network: ISRN Probab. Stat. 2012, 2012, 717839; https://doi.org/10.5402/2012/717839.Search in Google Scholar
48. Sedgwick, P. Pearson’s correlation coefficient. BMJ 2012, 345, e4448; https://doi.org/10.1136/bmj.e4483.Search in Google Scholar
© 2022 Walter de Gruyter GmbH, Berlin/Boston