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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

Abstract

Objective

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.

Methods

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.

Results

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.

Conclusion

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.

Keywords

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

Notes

Acknowledgments

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

Funding

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.

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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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