Journal of Radiation Oncology

, Volume 8, Issue 2, pp 185–198 | Cite as

Fast in situ image reconstruction for proton radiography

  • Caesar E. Ordoñez
  • Nicholas T. Karonis
  • Kirk L. Duffin
  • John R. Winans
  • Ethan A. DeJongh
  • Don F. DeJongh
  • George Coutrakon
  • Nicole F. Myers
  • Mark Pankuch
  • James S. WelshEmail author
Original Research



Proton beam therapy is an emerging modality for cancer treatment that, compared to X-ray radiation therapy, promises to provide better dose delivery to clinical targets with lower doses to normal tissues. Crucial to accurate treatment planning and dose delivery is knowledge of the water equivalent path length (WEPL) of each ray, or pencil beam, from the skin to every point in the target. For protons, this length is estimated from relative stopping power based on X-ray Hounsfield units. Unfortunately, such estimates lead to 3 to 4% uncertainties in the proton range prediction. Therefore, protons in the Bragg peak may overshoot (or undershoot) the desired stopping depth in the target causing tissue damage beyond the target volume. Recent studies indicate that tomographic imaging using protons has the potential to provide directly more accurate measurement of RSPs with significantly lower radiation dose than X-rays. We are currently working on a proton radiography system that promises to provide accurate two-dimensional (2D) images of WEPL values for protons that pass through the body. These will be suitable for positioning and range verification in daily treatments. In this study, we demonstrate that this system is capable of rapidly achieving such accurate images in clinically meaningful times.


We have developed a software platform to characterize the potential performance of the prototype proton radiography system. We use Geant4 to simulate raw data detected by the device. An especially written software — pRad — was written to process these data as they are received and uses iterative methods to generate radiographs. The software has been designed to generate a radiograph from a few million protons in under a minute after receiving the first proton from the device. We used a head phantom with known chemical compositions that could be modeled quite accurately in Geant4 simulations of proton radiographs. The radiographs are displayed as pixelated WEPL values displayed on a 2D gray scale image of WEPL values.


Rapid radiograph reconstruction of 3D phantoms using simulated proton pencil beams have been achieved with our software platform. On a modest desktop computer with a single central processing unit (CPU) and a single graphics processing unit (GPU), it takes about 11 s to reconstruct images using iterative linear algorithms to reconstruct a radiograph from 7.6 million protons. For the radiographic reconstructions of the head phantom described here, the mean WEPL errors, in the proton radiograph using a large majority of the pixels in the complete image were less than 1 mm when compared to images obtained without proton scattering and without detector resolution included.


We have demonstrated, through computer simulations of proton irradiation of a pediatric head phantom using the newly built pRad detector and image reconstruction software, that high-quality proton radiographs can be generated for patient alignment and verification of water equivalent thickness of the patient before each treatment.


Proton radiography Proton computed tomography Patient alignment Range verification Treatment planning Relative stopping power 



This work used resources of the Center for Research Computing and Data at Northern Illinois University and resources at the Northwestern Medicine Chicago Proton Center. The authors thank Reinhard Schulte from Loma Linda University for his collaboration, vision, and leadership in particle-based image reconstruction. We also thank Yair Censor from the University of Haifa, Scott Penfold from the Royal Adelaide Hospital, Keith Schubert and Blake Schultze from Baylor University, and Ernesto Gomez from California State University San Bernardino for our many collaborative discussions. We thank Nick Detrich from IBA for his cooperation to integrate information from the proton accelerator system into our radiography software and Christina Sarosiek from Northern Illinois University for her analyses of reconstructed images. We thank Niek Schreuder from Provision for organizing a beam test at the Knoxville ProNova facility. Finally, we especially thank Victor Rykalin and Igor Polnyi from ProtonVDA, Inc. for their work in designing and assembling the proton detector system and their strong support, insight, and collaboration to integrate the elements of their detector’s design into our radiography software.

Compliance with ethical standards


The National Cancer Institute of the National Institutes of Health contract number R44CA203499, the US Department of the Army contract number W81XWH-10-1-0170, and the US Department of Energy contract number DE-SC0005135 sponsored this work. The US Army Medical Research Acquisition Activity, 820 Chandler Street, Fort Detrick MD 21702-5014 is the awarding and administering acquisition office for contract number W81XWH-10-1-0170. The content in this article does not necessarily reflect the position or policy of the Government, and no official endorsement should be inferred.

Conflict of interest

The authors have intellectual property rights to the innovations described in this paper. James S. Welsh has served as a medical advisor to ProTom International. Don F. Dejongh is a co-owner of ProtonVDA Inc.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Statement of informed consent was not applicable since the manuscript does not contain any patient data.


  1. 1.
    Particle Therapy Co-operative Group, Particle therapy centers, facilities in operation. Accessed 16 April 2018
  2. 2.
    Seco J, Spadea MF (2015) Imaging in particle therapy: state of the art and future perspective. Acta Oncol 54:1254–1258CrossRefGoogle Scholar
  3. 3.
    Knopf AC, Lomax A (2013) In vivo proton range verification: a review. Phys Med Biol 58:R131–R160CrossRefGoogle Scholar
  4. 4.
    Bär E, Lalonde A, Royle G, Lu HM, Bouchard H (2017) The potential of dual-energy CT to reduce proton beam range uncertainties. Med Phys 44:2332–2344CrossRefGoogle Scholar
  5. 5.
    Xie Y, Bentefour EH, Janssens G, Smeets J, Vander Stappen F, Hotoiu L, Yin L, Dolney D, Avery S, O'Grady F, Prieels D, McDonough J, Solberg TD, Lustig RA, Lin A, Teo BKK (2017) Prompt gamma imaging for in vivo range verification of pencil beam scanning proton therapy. Int J Radiat Oncol Biol Phys 99:210–218CrossRefGoogle Scholar
  6. 6.
    Lehrack S, Assmann W, Bertrand D, Henrotin S, Herault J, Heymans V, Stappen FV, Thirolf PG, Vidal M, van de Walle J, Parodi K (2017) Submillimeter ionoacoustic range determination for protons in water at a clinical synchrocyclotron. Phys Med Biol 62:L20–L30CrossRefGoogle Scholar
  7. 7.
    Cambraia Lopes P, Bauer J, Salomon A, Rinaldi I, Tabacchini V, Tessonnier T, Crespo P, Parodi K, Schaart DR (2016) First in situ TOF-PET study using digital photon counters for proton range verification. Phys Med Biol 61:6203–6230CrossRefGoogle Scholar
  8. 8.
    Schulte RW, Bashkirov V, Klock MC, Li T, Wroe AJ, Evseev I, Williams DC, Satogata T (2005) Density resolution of proton computed tomography. Med Phys 32:1035–1046CrossRefGoogle Scholar
  9. 9.
    Pankuch M, DeJongh E, DeJongh F, et al. O115: a method to evaluate the clinical utility of proton radiography for geometric patient alignment. Proceedings of the 57th Annual Meeting of the Particle Therapy Cooperative Group (PTCOG) 21-26 May 2018. Int J Particle Ther 2018, 114. doi: Accessed 16 Apr 2019
  10. 10.
    Karonis NT, Duffin KL, Ordoñez CE, Erdelyi B, Uram TD, Olson EC, Coutrakon G, Papka ME (2013) Distributed and hardware accelerated computing for clinical medical imaging using proton computed tomography (pCT). J Parallel Distrib Comput 73:1605–1612CrossRefGoogle Scholar
  11. 11.
    Ordoñez CE, Karonis N, Duffin K, Coutrakon G, Schulte R, Johnson R, Pankuch M (2017) A real-time image reconstruction system for particle treatment planning using proton computed tomography (pCT). Phys Procedia 90:193–199CrossRefGoogle Scholar
  12. 12.
    Johnson RP, Bashkirov VA, Coutrakon G, Giacometti V, Karbasi P, Karonis NT, Ordoñez CE, Pankuch M, Sadrozinski HFW, Schubert KE, Sculte RW (2016) Results from a prototype proton-CT head scanner. Phys Procedia 90:209–214CrossRefGoogle Scholar
  13. 13.
    Garner LE (1981) An outline of projective geometry. North HollandGoogle Scholar
  14. 14.
    Duffin KL (1999) Image based modeling techniques for virtual environments, Appendix A. Brigham Young UniversityGoogle Scholar
  15. 15.
    Duffin KL, Barrett WA (2001) Fast focal length solution in partial panoramic image stitching. Proceedings of IEEE conference on computer vision and pattern recognition II:690–695Google Scholar
  16. 16.
    Shum H, Szeliski R (1998) Construction and refinement of panoramic mosaics with global and local alignment. In: International conference on computer vision pp 953–958Google Scholar
  17. 17.
    Nash JC (1990) Compact numerical methods for computers. Adam HilgeGoogle Scholar
  18. 18.
    Williams DC (2004) The most likely path of an energetic charged particle through a uniform medium. Phys Med Biol 49:2899–2911CrossRefGoogle Scholar
  19. 19.
    Schukte RE, Penfold SN, Tafas JT, Schubert KE (2008) A maximum likelihood proton path formalism for application in proton computed tomography. Med Phys 35:4849–4856CrossRefGoogle Scholar
  20. 20.
    Penfold SN (2010) Image reconstruction and Monte Carlo simulations in the development of proton computed tomography for applications in proton radiation therapy. PhD Thesis, University of WollongongGoogle Scholar
  21. 21.
    Schultze B, Witt M, Censor Y, Schulte RW, Schubert KE (2015) Performance of hull-detection algorithms for proton computed tomography reconstruction. Contemp Math 636:211–224CrossRefGoogle Scholar
  22. 22.
    Collins-Fekete CA, Brousmiche S, Portillo SKN, Beaulieu L, Seco J (2016) A maximum likelihood method for high resolution radiography/proton CT. Phys Med Biol 61:8232–8248CrossRefGoogle Scholar
  23. 23.
    Gordon R, Bender R, Herman GT (1970) Algebraic reconstruction techniques (ART) for three-dimensional electron microscopy and x-ray photography. J Theor Biol 29:471–481CrossRefGoogle Scholar
  24. 24.
    Censor Y, Elfving T, Herman GT, Nikazad T (2008) On diagonally relaxed orthogonal projection methods. SIAM J Sci Comput 30:473–504CrossRefGoogle Scholar
  25. 25.
    Gordon D, Gordon R (2005) Component-averaged row projections: a robust block-parallel scheme for sparse linear systems. SIAM J Sci Comput 27:1092–1117CrossRefGoogle Scholar
  26. 26.
    OpenMP Accessed 1 April 2018
  27. 27.
    Agostinelli S, Allison J, Amako K, Apostolakis J, Araujo H, Arce P, Asai M, Axen D, Banerjee S, Barrand G, Behner F, Bellagamba L, Boudreau J, Broglia L, Brunengo A, Burkhardt H, Chauvie S, Chuma J, Chytracek R, Cooperman G, Cosmo G, Degtyarenko P, Dell'Acqua A, Depaola G, Dietrich D, Enami R, Feliciello A, Ferguson C, Fesefeldt H, Folger G, Foppiano F, Forti A, Garelli S, Giani S, Giannitrapani R, Gibin D, Gómez Cadenas JJ, González I, Gracia Abril G, Greeniaus G, Greiner W, Grichine V, Grossheim A, Guatelli S, Gumplinger P, Hamatsu R, Hashimoto K, Hasui H, Heikkinen A, Howard A, Ivanchenko V, Johnson A, Jones FW, Kallenbach J, Kanaya N, Kawabata M, Kawabata Y, Kawaguti M, Kelner S, Kent P, Kimura A, Kodama T, Kokoulin R, Kossov M, Kurashige H, Lamanna E, Lampén T, Lara V, Lefebure V, Lei F, Liendl M, Lockman W, Longo F, Magni S, Maire M, Medernach E, Minamimoto K, Mora de Freitas P, Morita Y, Murakami K, Nagamatu M, Nartallo R, Nieminen P, Nishimura T, Ohtsubo K, Okamura M, O'Neale S, Oohata Y, Paech K, Perl J, Pfeiffer A, Pia MG, Ranjard F, Rybin A, Sadilov S, di Salvo E, Santin G, Sasaki T, Savvas N, Sawada Y, Scherer S, Sei S, Sirotenko V, Smith D, Starkov N, Stoecker H, Sulkimo J, Takahata M, Tanaka S, Tcherniaev E, Safai Tehrani E, Tropeano M, Truscott P, Uno H, Urban L, Urban P, Verderi M, Walkden A, Wander W, Weber H, Wellisch JP, Wenaus T, Williams DC, Wright D, Yamada T, Yoshida H, Zschiesche D (2003) GEANT4 – a simulation toolkit. Nucl Instr and Meth in Phys A 506:250–303CrossRefGoogle Scholar
  28. 28.
    Giacometti V, Bashkirov VA, Piersimoni P, Guatelli S, Plautz TE, Sadrozinski HFW, Johnson RP, Zatserklyaniy A, Tessonnier T, Parodi K, Rosenfeld AB, Schulte RW (2017) Software platform for simulation of a prototype proton CT scanner. Med Phys 44:1002–1016CrossRefGoogle Scholar
  29. 29.
    Miller C, Altoos B, DeJongh EA, Pankuch M, DeJongh DF, Rykalin V, Ordonez CE, Karonis NT, Winans JR, Coutrakon G, Welsh JS (2019) Reconstructed and real proton radiographs for image-guidance in ptoton beam therapy. J Radiat Oncol 8:97–101. CrossRefGoogle Scholar

Copyright information

© This is a U.S. government work and not under copyright protection in the U.S.; foreign copyright protection may apply 2019

Authors and Affiliations

  • Caesar E. Ordoñez
    • 1
  • Nicholas T. Karonis
    • 2
    • 3
  • Kirk L. Duffin
    • 2
  • John R. Winans
    • 1
  • Ethan A. DeJongh
    • 4
  • Don F. DeJongh
    • 4
  • George Coutrakon
    • 5
  • Nicole F. Myers
    • 2
  • Mark Pankuch
    • 6
  • James S. Welsh
    • 7
    • 8
    Email author
  1. 1.Center for Research Computing and DataNorthern Illinois UniversityDeKalbUSA
  2. 2.Computer Science DepartmentNorthern Illinois UniversityDeKalbUSA
  3. 3.Argonne National Laboratory, Data Science and Learning DivisionArgonneUSA
  4. 4.ProtonVDA, IncNapervilleUSA
  5. 5.Physics DepartmentNorthern Illinois UniversityDeKalbUSA
  6. 6.Northwestern Medicine Chicago Proton CenterWarrenvilleUSA
  7. 7.Edward Hines Jr VA Medical Center, Radiation Oncology ServiceHinesUSA
  8. 8.Department of Radiation OncologyLoyola University Stritch School of MedicineMaywoodUSA

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