Interactive X-ray and proton therapy training and simulation

An Erratum to this article was published on 05 August 2015



External beam X-ray therapy (XRT) and proton therapy (PT) are effective and widely accepted forms of treatment for many types of cancer. However, the procedures require extensive computerized planning. Current planning systems for both XRT and PT have insufficient visual aid to combine real patient data with the treatment device geometry to account for unforeseen collisions among system components and the patient.


The 3D surface representation (S-rep) is a widely used scheme to create 3D models of physical objects. 3D S-reps have been successfully used in CAD/CAM and, in conjunction with texture mapping, in the modern gaming industry to customize avatars and improve the gaming realism and sense of presence. We are proposing a cost-effective method to extract patient-specific S-reps in real time and combine them with the treatment system geometry to provide a comprehensive simulation of the XRT/PT treatment room.


The X3D standard is used to implement and deploy the simulator on the web, enabling its use not only for remote specialists’ collaboration, simulation, and training, but also for patient education.


An objective assessment of the accuracy of the S-reps obtained proves the potential of the simulator for clinical use.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10


  1. 1.

    Explanation of paradigms in radioSurgery—the external beam radiation therapy procedure. Accessed 9 Dec 2014

  2. 2.

    Stroud I (2006) Representations for solid modelling. In: Boundary representation modelling techniques. Springer, ISBN 1-84628-312-4

  3. 3.

    Hamza-Lup FG, Sopin I, Zeidan O (2008) Online external beam radiation treatment simulator. Int J Comput Assis Radiol Surg 3(4):275–281

    Article  Google Scholar 

  4. 4.

    Web3D Consortium (2015) What is X3D?. Accessed 10 Mar

  5. 5.

    Hamza-Lup FG, Sopin I, Zeidan O (2007) Towards 3D web-based simulation and training systems for radiation oncology. In: ADVANCE for imaging and oncology administrators, pp 64–68

  6. 6.

    Hua C, Chang J, Yenice K, Chan M, Amols H (2004) A practical approach to prevent gantry-couch collision for linac-based radiosurgery. Med Phys J 31(7):2128–2134

    Article  Google Scholar 

  7. 7.

    Beange I, Nisbet A (2000) Collision prevention software tool for complex three-dimensional isocentric set-ups. Br J Radiol 73(869):537–541

    CAS  Article  PubMed  Google Scholar 

  8. 8.

    Humm J, Pizzuto D, Fleischman E, Mohan R (1995) Collision detection and avoidance during treatment planning. Int J Radiat Oncol Biol Phys 33(5):1101–1108

    CAS  Article  PubMed  Google Scholar 

  9. 9.

    Purdy JA, Harms WB, Matthews JW, Drzymala R, Emami B, Simpson JR, Manolis J, Rosenberger FU (1993) Advances in 3-dimensional radiation treatment planning systems: room-view display with real time interactivity. Int J Radiat Oncol Biol Phys 27(4):933–944

    CAS  Article  PubMed  Google Scholar 

  10. 10.

    Tsiakalos MF, Scherebmann E, Theodorou K, Kappas C (2001) Graphical treatment simulation and automated collision detection for conformal and stereotactic radiotherapy treatment planning. Med Phys 28(7):1359–1363

  11. 11.

    Beavis A, Ward J, Bridge P, Appleyard R, Phillips R (2006) An immersive virtual environment for training of radiotherapy students and developing clinical experience. In: Proceedings of AAPM, Jul 30–Aug 3, Orlando, FL

  12. 12.

    NEMA (2015) Digital imaging and communications in medicine (DICOM) homepage. Accessed 12 Feb

  13. 13.

    Schroeder WJ, Martin KM, Lorensen WE (1996) The design and implementation of an object-oriented toolkit for 3D graphics and visualization. In: Proceedings of IEEE visualization ’96, San Francisco

  14. 14.

    Lorensen WE, Cline, HE. (1987) Marching cubes: a high resolution 3D surface construction algorithm. In: Proceedings of the 21th annual conference on computer graphics and interactive techniques, 21(4): 163–169

  15. 15.

    Microsoft developer network (2015) Kinect for windows sensor components and specifications. Accessed 20 Jan

  16. 16.

    Kahlesz F, Lilge C, Klein R (2007) Easy-to-use calibration of multiple-camera setups. In: Proceedings of the 5th international conference on computer vision systems

  17. 17.

    Microsoft download center (2015) Kinect for windows developer toolkit v1.8. Accessed 17 Jan

  18. 18.

    Kazhdan M, Hoppe H (2013) Screened poisson surface reconstruction. ACM Trans Graph 32(3):29

    Article  Google Scholar 

  19. 19.

    Garland M, Heckbert PS (1997) Surface simplification using quadric error metrics. In: proceedings of the 24th annual conference on computer graphics and interactive techniques. ACM Press/Addison-Wesley Publishing Co., New York, pp 209–216

  20. 20.

    W3 Foundation “Ajax tutorial and reference” (2015) Accessed 15 Mar

  21. 21.

    Laser Scanner Faro (2015) Accessed 10 Mar

  22. 22.

    Buzdar SA, Afzal M, Nazir A, Gadhi MA (2013) Accuracy requirements in radiotherapy treatment planning. J Coll Physicians Surg Pak 23(6):418–423

    PubMed  Google Scholar 

  23. 23.

    Thwaites D (2006) Accuracy required and achievable in radiotherapy dosimetry: have modern technology and techniques changed our views?. J Phys: conference series, IO Publishing, 06/2013, 444(1)

  24. 24.

    Queisner M, Friedrich K (2014) Automated killing and mediated caring. Symposium on machine ethics in the context of medical and care agents, annual convention of the society for the study of artificial intelligence and the simulation of behavior, April 4

  25. 25.

    Oracle (2015) Chapter 5: JavaServer Pages Technology. In: The Java EE 5 tutorial. Accessed 10 Mar

  26. 26.

    Konika-Minolta 3D scanner (2015) Accessed 5 Mar

  27. 27.

    Shin B, Venkatramani R, Borker P, Olch A, Grimm J, Wong K (2013) Spatial accuracy of a low cost high resolution 3D surface imaging device for medical applications. Int J Med Phys Clin Eng Radiat Oncol 2(2):45–51

    Article  Google Scholar 

  28. 28.

    Khoshelham K, Elberink SO (2012) Accuracy and resolution of kinect depth data for indoor mapping applications. Sensors 12(2):1437–1454

    PubMed Central  Article  PubMed  Google Scholar 

  29. 29.

    Cignoni P, Rocchini C, Scopigno P (1998) Metro: measuring error on simplified surfaces. Comput Graph Forum 17(2):167–174

    Article  Google Scholar 

Download references

Author information



Corresponding author

Correspondence to Felix G. Hamza-Lup.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Hamza-Lup, F.G., Farrar, S. & Leon, E. Interactive X-ray and proton therapy training and simulation. Int J CARS 10, 1675–1683 (2015).

Download citation


  • Proton therapy
  • X-ray therapy
  • E-learning
  • X3D
  • Radiation therapy