Development of Artificial Intelligence as a Conversion Tool for Cine Electronic Portal Imaging Device Images to Radiotherapy Dosimetry: Preliminary Study

Document Type : Original Paper


1 Department of Physics, Faculty of Mathematics and Natural Sciences, Universitas Indonesia

2 Department of Radiotherapy, Cipto Mangunkusumo General Hospital, Jakarta, 10430, Indonesia


Introduction: This research is a preliminary study of the development of Artificial Intelligence (AI) as a conversion tool from the pixel value of Cine a-Si 1000 Electronic Portal Imaging Device (EPID) images to dose. It also investigates the relationship between the Monitor Unit (MU), dose rate, number of frames, and beam profile of Electronic Portal Imaging Device (EPID) images to facilitate further mathematical correction that must be added to create accurate dosimetry by Cine EPID images.
Material and Methods: Homogeneous and inhomogeneous phantom was irradiated in a Linear Accelerator (Linac) 6 MV with different techniques, field size, and phantom thickness. The Cine a-Si 1000 EPID images were taken and compared to dose distribution data derived from the Eclipse treatment planning system (TPS) at Source Axis Distance 100 cm or isocenter field. The AI model training process begins with the augmentation of EPID and TPS images from homogeneous phantom so that 1152 images are obtained. These images are then split randomly into training and testing data 7:3, and validation is done using gamma index 3%/3mm.
Results: An AI model based on Convolutional Neural Network (CNN) with 6 layers has been successfully created that can convert EPID pixel values into dose distribution without any mathematical correction. The best results from validation with a gamma index of 3%/3mm compared to TPS calculations reached 92.40% ±28.14%.
Conclusion: An AI model has been successfully created that can convert EPID pixel values into dose distribution but need improvement by considering the characteristics contained in the EPID image and the number of datasets.


Main Subjects

  1. Tan YI. 2D transit dosimetry using electronic portal imaging device [Thesis]. University of Glasgow; 2016.
  2. Van Elmpt W, McDermott L, Nijsten S, Wendling M, Lambin P, Mijnheer B. A literature review of electronic portal imaging for radiotherapy dosimetry. Radiotherapy and Oncology. 2008;88(3):289–
  3. Yip S, Rottmann J, Berbeco R. The impact of cine EPID image acquisition frame rate on markerless soft-tissue tracking. Medical Physics. 2014;41(6):061702.
  4. Mans A, Wendling M, McDermott LN, Sonke JJ, Tielenburg R, Vijlbrief R, et al. Catching errors with in vivo EPID dosimetry. Medical Physics. 2010;37(6):2638–
  5. Tan YI, Metwaly M, Glegg M, Baggarley SP, Elliott A. A dual two dimensional electronic portal imaging device transit dosimetry model based on an empirical quadratic formalism. British Journal of Radiology. 2015;88(1051):20140645.
  6. Peca S, Brown DW. Two-dimensional in vivo dose verification using portal imaging and correlation ratios. Journal of Applied Clinical Medical Physics. 2014;15(4):117–
  7. Wendling M, McDermott LN, Mans A, Sonke JJ, van Herk M, Mijnheer BJ. A simple backprojection algorithm for 3D in vivo EPID dosimetry of IMRT treatments. Medical Physics. 2009;36(7):3310–
  8. Wendling M, Louwe RJW, McDermott LN, Sonke JJ, van Herk M, Mijnheer BJ. Accurate two-dimensional IMRT verification using a back-projection EPID dosimetry method. Medical Physics. 2006;33(2):259–
  9. Wendling M, Mcdermott LN, Mans A, Olaciregui-ruiz Í, Pecharromán-gallego R, Sonke J, et al. In aqua vivo EPID dosimetry In aqua vivo EPID dosimetry. 2012;367.
  10. Piermattei A, Fidanzio A, Stimato G, Azario L, Grimaldi L, D’Onofrio G, et al. In vivo dosimetry by an aSi-based EPID. Medical Physics. 2006;33(11):4414–
  11. Peca S, Brown DW, Smith WL. A Simple Method for 2-D In Vivo Dosimetry by Portal Imaging. Technology in Cancer Research and Treatment. 2017;16(6):944–
  12. Winkler P, Hefner A, Georg D. Dose-response characteristics of an amorphous silicon EPID. Medical Physics. 2005;32(10):3095–
  13. Mccurdy BMCC. Dosimetry in radiotherapy using a-Si EPIDs: Systems, methods, and applications focusing on 3D patient dose estimation. Journal of Physics: Conference Series. 2013;444(1).
  14. Bridge P, Bridge R. Artificial Intelligence in Radiotherapy : A Philosophical Perspective. Journal of Medical Imaging and Radiation Sciences. 2019;50(4):S27–
  15. Thompson RF, Valdes G, Fuller CD, Carpenter CM, Morin O, Aneja S, et al. Artificial intelligence in radiation oncology : A specialty-wide disruptive transformation ? Radiotherapy and Oncology. 2018;129(3):421–
  16. Poortmans PMP, Takanen S, Nader G, Meattini I. Winter is over : The use of Arti fi cial Intelligence to individualise radiation therapy for breast cancer *. The Breast. 2020;49:194–
  17. Jarrett D, Stride E, Vallis K, Gooding MJ. Applications and limitations of machine learning in radiation oncology. British Journal of Radiology. 2019;92(1100):1–
  18. Kalet AM, Luk SMH, Phillips MH. Radiation Therapy Quality Assurance Tasks and Tools: The Many Roles of Machine Learning. Medical Physics. 2020;47(5):e168–
  19. Luo Y, Chen S, Valdes G. Machine learning for radiation outcome modeling and prediction. Medical Physics. 2020;47(5):e178–
  20. Sadeghnejad Barkousaraie A, Ogunmolu O, Jiang S, Nguyen D. A fast deep learning approach for beam orientation optimization for prostate cancer treated with intensity-modulated radiation therapy. Medical Physics. 2020;47(3):880–
  21. Ronneberger O, Fischer P, Brox T. U-Net: Convolutional Networks for Biomedical Image Segmentation. 2015;1–
  22. Armanious K, Jiang C, Abdulatif S, Küstner T, Gatidis S, Yang B. Unsupervised medical image translation using Cycle-MeDGAN. European Signal Processing Conference. 2019;2019;1-5.
  23. Liu Z, Yan WQ, Yang ML. Image denoising based on a CNN model. In: Proceedings - 2018 4th International Conference on Control, Automation and Robotics, ICCAR 2018. 2018.
  24. Olaciregui-Ruiz I, Torres-Xirau I, Teuwen J, van der Heide UA, Mans A. A Deep Learning-based correction to EPID dosimetry for attenuation and scatter in the Unity MR-Linac system. Physica Medica. 2020;71:124–
  25. Podgorsak EB. Radiation Oncology Physics : A Handbook for Teachers and Students. Vienna; 2005.
  26. Mhatre V, Pilakkal S, Chadha P, Talpatra K. Dosimetric Comparison of a-Si 1200 and a-Si 1000 Electronic Portal Imager for Intensity Modulated Radiation Therapy ( IMRT ). Journal of Nuclear Medicine & Radiation Therapy. 2018;9(1):1–
  27. Greer PB. Correction of pixel sensitivity variation and off-axis response for amorphous silicon EPID dosimetry Correction of pixel sensitivity variation and off-axis response for amorphous silicon EPID dosimetry. 2014;3558(2005).
  28. Ding A, Xing L, Han B. Development of an accurate EPID-based output measurement and dosimetric verification tool for electron beam therapy. Medical Physics. 2015;42(7):4190–







Volume 19, Issue 5
September and October 2022
Pages 296-304
  • Receive Date: 11 October 2021
  • Revise Date: 13 February 2022
  • Accept Date: 18 February 2022
  • First Publish Date: 18 February 2022