Dose Verification in Lung Radiotherapy Using PET Imaging of Nanoparticle-Induced Positrons

Document Type : Original Paper

Authors

1 Department of Medical Physics, Medical School, Tabriz University of Medical Sciences, Tabriz, Iran.

2 Department of Medical Physics, Hamadan University of Medical Sciences, Hamadan, Iran

3 Student Research Committee, Tabriz University of Medical Sciences, Tabriz, Iran

10.22038/ijmp.2026.92153.2636

Abstract

Introduction: Accurate verification of radiation dose delivery remains a major challenge in radiotherapy. Positron emission tomography (PET) imaging of megavoltage (MV)-induced positrons (MVIPET) has recently emerged as a potential in vivo dosimetry technique. In this study, we investigated the feasibility of enhancing MVIPET signals using high-Z nanoparticles (NPs), specifically platinum (Pt) and silver (Ag), to enable real-time dose monitoring during lung radiotherapy.
Material and Methods: PET images arising from positrons induced by platinum and silver nanoparticles in a lung tumor were generated during radiotherapy with 6, 10, and 15 MV photon beams using the GATE Monte Carlo code. The resulting images were evaluated for both image quality and dose verification.
Results: Results showed that positron production, absorbed dose, and PET signal intensity increased with both photon beam energy and NP concentration, with PtNPs producing significantly higher enhancement than AgNPs. High-quality MVIPET images with acceptable SBR and CNR, and low RMSE, were obtained for PtNP concentrations ≥8 wt% at 10 MV and ≥4 wt% at 15 MV. In contrast, AgNPs required higher concentrations and only yielded reliable monitoring at 15 MV. At 6 MV, image quality and dose–image correlation were insufficient for clinical feasibility.
Conclusion: These findings demonstrate that MVIPET, particularly when combined with PtNPs and higher photon energies, is a promising strategy for real-time, non-invasive dose verification in lung radiotherapy.

Keywords

Main Subjects


  1. Li Y, Yan B, He S. Advances and challenges in the treatment of lung cancer. Biomed Pharmacother. 2023;169:115891.
  2. Ren XC, Liu YE, Li J, Lin Q. Progress in image-guided radiotherapy for the treatment of non-small cell lung cancer. World J Radiol. 2019;11(3):46-54.
  3. Hansen CR, Hussein M, Bernchou U, Zukauskaite R, Thwaites D. Plan quality in radiotherapy treatment planning - Review of the factors and challenges. J Med Imaging Radiat Oncol. 2022;66(2):267-78.
  4. Grégoire V, Guckenberger M, Haustermans K, Lagendijk JJW, Ménard C, Pötter R, et al. Image guidance in radiation therapy for better cure of cancer. Mol Oncol. 2020;14(7):1470-91.
  5. Sakai Y, Monzen H, Takei Y, Kosaka H, Nakamura K, Yanagi Y, et al. Evaluation of In-room Volumetric Imaging Doses for Image-guided Radiotherapy: A Multi-institutional Study. J Med Phys. 2023;48(2):189-94.
  6. Sun L, Gonzalez G, Pandey PK, Wang S, Kim K, Limoli C, et al. Towards quantitative in vivo dosimetry using x-ray acoustic computed tomography. Med Phys. 2023;50(11):6894-907.
  7. Spinelli AE, Boschi F. Novel biomedical applications of Cerenkov radiation and radioluminescence imaging. Phys Med. 2015;31(2):120-9.
  8. LaRochelle EPM, Shell JR, Gunn JR, Davis SC, Pogue BW. Signal intensity analysis and optimization for in vivo imaging of Cherenkov and excited luminescence. Phys Med Biol. 2018;63(8):085019.
  9. Samant P, Trevisi L, Ji X, Xiang L. X-ray induced acoustic computed tomography. Photoacoustics. 2020;19:100177.
  10. Kheruka SC, Jain A, Usmani MS, Al-Maymani N, Al-Makhmari N, Al-Saidi H, et al. Integrating Positron Emission Tomography Combined with Computed Tomography Imaging into Advanced Radiation Therapy Planning: Clinical Applications, Innovations, and Challenges. Journal of Medical Physics. 2025;50(2):198-206.
  11. Brivio D, Sajo E, Zygmanski P. Gold nanoparticle detection and quantification in therapeutic MV beams via pair production. Phys Med Biol. 2021;66(6):064004.
  12. Lyu Q, Neph R, Sheng K. Tomographic detection of photon pairs produced from high-energy X-rays for the monitoring of radiotherapy dosing. Nat Biomed Eng. 2023;7(3):323-34.
  13. Berg E, Cherry SR. Innovations in Instrumentation for Positron Emission Tomography. Semin Nucl Med. 2018;48(4):311-31.
  14. Kuncic Z, Lacombe S. Nanoparticle radio-enhancement: principles, progress and application to cancer treatment. Phys Med Biol. 2018;63(2):02tr1.
  15. Liu W, Chen B, Zheng H, Xing Y, Chen G, Zhou P, et al. Advances of Nanomedicine in Radiotherapy. Pharmaceutics. 2021;13(11).
  16. Jiang Z, Zhang M, Li P, Wang Y, Fu Q. Nanomaterial-based CT contrast agents and their applications in image-guided therapy. Theranostics. 2023;13(2):483-509.
  17. Kumar PPP, Mahajan R. Gold Polymer Nanomaterials: A Promising Approach for Enhanced Biomolecular Imaging. Nanotheranostics. 2024;8(1):64-89.
  18. Ahmad MY, Liu S, Tegafaw T, Saidi A, Zhao D, Liu Y, et al. Heavy Metal-Based Nanoparticles as High-Performance X-ray Computed Tomography Contrast Agents. Pharmaceuticals (Basel). 2023;16(10).
  19. Talebi AS, Mehnati P, Rajabi H, Rezaei H, Geramifar P. Precision individual dosimetry in Yttrium-90 transarterial radioembolization in the presence of Au nanoparticles. Radiation Physics and Chemistry. 2024;222:111888.
  20. McNutt TR, Mackie TR, Reckwerdt P, Paliwal BR. Modeling dose distributions from portal dose images using the convolution/superposition method. Med Phys. 1996;23(8):1381-92.
  21. Sarrut D, Arbor N, Baudier T, Borys D, Etxebeste A, Fuchs H, et al. The OpenGATE ecosystem for Monte Carlo simulation in medical physics. Phys Med Biol. 2022;67(18).
  22. Merlin T, Stute S, Benoit D, Bert J, Carlier T, Comtat C, et al. CASToR: a generic data organization and processing code framework for multi-modal and multi-dimensional tomographic reconstruction. Phys Med Biol. 2018;63(18):185005.
  23. Zhu YM. Ordered subset expectation maximization algorithm for positron emission tomographic image reconstruction using belief kernels. J Med Imaging (Bellingham). 2018;5(4):044005.
  24. Segars WP, Tsui BMW, Jing C, Fang-Fang Y, Fung GSK, Samei E. Application of the 4-D XCAT Phantoms in Biomedical Imaging and Beyond. IEEE Trans Med Imaging. 2018;37(3):680-92.
  25. Sheikh-Bagheri D, Rogers DW. Monte Carlo calculation of nine megavoltage photon beam spectra using the BEAM code. Med Phys. 2002;29(3):391-402.
  26. Levillain H, Burghelea M, Derijckere ID, Guiot T, Gulyban A, Vanderlinden B, et al. Combined quality and dose-volume histograms for assessing the predictive value of (99m)Tc-MAA SPECT/CT simulation for personalizing radioembolization treatment in liver metastatic colorectal cancer. EJNMMI Phys. 2020;7(1):75.
  27. Hosseinabadi RB, Rajabi H. Real-time dosimetry in lung cancer radiotherapy using PET imaging of positrons induced by gold nanoparticles. Journal of Radiation Research and Applied Sciences. 2025;18(2):101361.
  28. Siman W, Mawlawi OR, Mourtada F, Kappadath SC. Systematic and random errors of PET-based (90) Y 3D dose quantification. Med Phys. 2020;47(6):2441-9.
  29. Siman W, Mawlawi OR, Mikell JK, Mourtada F, Kappadath SC. Effects of image noise, respiratory motion, and motion compensation on 3D activity quantification in count-limited PET images. Phys Med Biol. 2017;62(2):448-64.
  30. Prasad SG, Parthasaradhi K, Bloomer WD. Effective atomic numbers of composite materials for total and partial interaction processes for photons, electrons, and protons. Med Phys. 1997;24(6):883-5.
  31. Hwang C, Kim JM, Kim J. Influence of concentration, nanoparticle size, beam energy, and material on dose enhancement in radiation therapy. J Radiat Res. 2017;58(4):405-11.
  32. Cheung JY, Ng BK, Yu KN. Dose enhancement close to platinum implants for the 4, 6, and 10 MV stereotactic radiosurgery. Med Phys. 2004;31(10):2787-91.
  33. Talebi AS, Rajabi H, Watabe H. Role of nanoparticles in transarterial radioembolization with glass microspheres. Ann Nucl Med. 2022;36(5):479-87.