Journal of Nuclear Science, Engineering and Technology (JONSAT)
In cooperation with the Iranian Nuclear Society

Evaluation of proton path estimators for spatial resolution modification in images obtained by Proton computed tomography

Document Type : Research Paper

Authors

E. Alibeigi 1
Z. Riazi 1
M. Askari 2
A. Movafeghi 3

1 Physics and Accelerators Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box:11365-8486, Tehran-Iran

2 Radiation Application Research School, Nuclear Science and Technology Research Institute, P. O. Box 11365-3486, Tehran - Iran

3 Leading Materials Organization, Nuclear Science and Technology Research Institute, AEOI, P.O.BOX: 14395-836, Tehran-Iran

https://doi.org/10.24200/nst.2022.1064.1719
Abstract
Proton computed tomography (pCT) can reduce proton therapy uncertainty by measuring Relative Stopping Power (RSP) directly. The spatial resolution of the pCT images decreases due to the multi colomb scattering (MCS) of protons inside the phantom. This reduction of image quality can be compensated by using the most probable proton path in the reconstruction algorithm. In this study, a pCT system was simulated by particle-to-particle tracking of protons using the Geant4 toolkit. This simulation improves the spatial resolution of images obtained from applying different estimators of the proton path, including straight line path (SLP), cubic spline path (CSP), and most likely path (MLP). The Catphan528 phantom was irradiated with 200MeV protons. The energy, position, and direction of the particle were recorded before and after the phantom. The RSP image matrix was modified by weigthing factors obtaind using SLP, CSP, and MLP path esitimators and image was reconstructed using FBP. Spatial resolution and root mean square errors (RMSE) were compared to phantom image data. According to the results, the MLP method is less error-prone and more accurate than other methods in resolving spatial resolutions. For 100,000 protons, with image resolution ranging from 1 to 0.1 mm, the spatial resolution increased from 3 to 9 line pairs/cm, while the RMSE increased from 8.11% to 14.97%.

Highlights

  1. C.J. Wong, et al., High-resolution measurements of small field beams using polymer gels, Applied Radiation and Isotopes., 65.10, 1160-1164 (2007).

 

  1. F.M. Khan, B.J. Gerbi, Treatment planning in radiation oncology, Wolters Kluwer Health/Lippincott Williams & Wilkins (2012).

 

  1. W.P. Levin, et al., Proton beam therapy, British. J. Cancer, 93, 849–854 (2005).

 

  1. F. Attanasi, et al., Experimental Validation of the Filtering Approach for Dose Monitoring in Proton Therapy at Low Energy, Phys Med., 24, 102–106 (2008).

 

  1. O. Jakel, State of the art in hadron therapy, AIP Conference Proceedings, 95, 70-77 (2007).

 

  1. K.W.D. Ledingham, et al., Towards Laser Driven Hadron Cancer Radiotherapy, A Review of Progress Med Phys, (2014).

 

  1. M. Prall, et al., High-energy proton imaging for biomedical applications, Scientific Reports, 6, 27651 (2016).

 

  1. G. Poludniowski, N.M. Allinson, P.M. Evans, Proton radiography and tomography with application to proton therapy, The British Journal of Radiology, 88, 1053 (2015).

 

  1. Li, Tianfang, et al., Reconstruction for proton computed tomography by tracing proton trajectories: A Monte Carlo study, Medical Physics, 33, 3 (2006).

 

  1. M. Bucciantonio, F. Sauli, Proton computed tomography, Modern Physics Letters A. 30, 17 (2015).

 

  1. M. Yang, et al., Comprehensive analysis of proton range uncertainties related to patient stoppingpower- ratio estimation using the stoichiometric calibration, Physics in Medicine and Biology, 57(13), 4095–4115 (2012).

 

  1. C. Zeng, et al., Proton Treatment Planning, Target Volume Delineation and Treatment Planning for Particle Therapy, Springer, Cham, 45-105 (2018).

 

  1. H. Paganetti, Range uncertainties in proton therapy and the role of Monte Carlo simulations, Physics in Medicine and Biology, 57(11), 99 (2012).

 

  1. C.T. Quinones, Proton computed tomography, Diss. Université de Lyon, (2016).

 

  1. M. Prall, et al., High-energy proton imaging for biomedical applications, Scientific Reports, 6, 27651 (2016).

 

  1. Schulte, Reinhard, et al., Conceptual design of a proton computed tomography system for applications in proton radiation therapy, IEEE Transactions on Nuclear Science, 51, 3 (2004).

 

  1. Li, Tianfang, Jerome Zhengrong Liang, Reconstruction with most likely trajectory for proton computed tomography, Medical Imaging 2004: Image Processing. Vol. 5370. International Society for Optics and Photonics, (2004).

 

  1. Williams, David C, The most likely path of an energetic charged particle through a uniform medium, Physics in Medicine & Biology, 49, 13 (2004).

 

  1. R.W. Schulte, et al., A maximum likelihood proton path formalism for application in proton computed tomography, Medical Physics, 35, 11 (2008).

 

  1. B. Erdelyi, A comprehensive study of the most likely path formalism for proton-computed tomography, Physics in Medicine & Biology, 54, 20 (2009).

 

  1. Koehler, Proton radiography, Science (New York, N.Y.). 3825, 303–304 (1968).

 

  1. G. Poludniowski, G. Allinson, N. Evans, Proton radiography and tomography with application to proton therap, The British Journal of Radiology, (2015).

 

  1. C.T. Quiñones, J.M. Létang, S. Rit, Filtered back-projection reconstruction for attenuation proton CT along most likely paths, Physics in Medicine & Biology, 61, 9 (2016).

 

  1. Khellaf, Feriel, et al., A comparison of direct reconstruction algorithms in proton computed tomography, Physics in Medicine & Biology, 65, 10 (2020).

 

  1. Collins-Fekete, Charles-Antoine, et al., A theoretical framework to predict the most likely ion path in particle imaging, Physics in Medicine & Biology, 62, 5 (2017).

 

  1. Rit, Simon, et al., Distance-driven binning for proton CT filtered backprojection along most likely paths, Second International Conference on Image Formation in X-Ray Computed Tomography, Conference Paper, (2010).

Keywords

Proton computed tomography
Image reconstruction
Monte Carlo simulation
Proton path estimator
  1. C.J. Wong, et al., High-resolution measurements of small field beams using polymer gels, Applied Radiation and Isotopes., 65.10, 1160-1164 (2007).

 

  1. F.M. Khan, B.J. Gerbi, Treatment planning in radiation oncology, Wolters Kluwer Health/Lippincott Williams & Wilkins (2012).

 

  1. W.P. Levin, et al., Proton beam therapy, British. J. Cancer, 93, 849–854 (2005).

 

  1. F. Attanasi, et al., Experimental Validation of the Filtering Approach for Dose Monitoring in Proton Therapy at Low Energy, Phys Med., 24, 102–106 (2008).

 

  1. O. Jakel, State of the art in hadron therapy, AIP Conference Proceedings, 95, 70-77 (2007).

 

  1. K.W.D. Ledingham, et al., Towards Laser Driven Hadron Cancer Radiotherapy, A Review of Progress Med Phys, (2014).

 

  1. M. Prall, et al., High-energy proton imaging for biomedical applications, Scientific Reports, 6, 27651 (2016).

 

  1. G. Poludniowski, N.M. Allinson, P.M. Evans, Proton radiography and tomography with application to proton therapy, The British Journal of Radiology, 88, 1053 (2015).

 

  1. Li, Tianfang, et al., Reconstruction for proton computed tomography by tracing proton trajectories: A Monte Carlo study, Medical Physics, 33, 3 (2006).

 

  1. M. Bucciantonio, F. Sauli, Proton computed tomography, Modern Physics Letters A. 30, 17 (2015).

 

  1. M. Yang, et al., Comprehensive analysis of proton range uncertainties related to patient stoppingpower- ratio estimation using the stoichiometric calibration, Physics in Medicine and Biology, 57(13), 4095–4115 (2012).

 

  1. C. Zeng, et al., Proton Treatment Planning, Target Volume Delineation and Treatment Planning for Particle Therapy, Springer, Cham, 45-105 (2018).

 

  1. H. Paganetti, Range uncertainties in proton therapy and the role of Monte Carlo simulations, Physics in Medicine and Biology, 57(11), 99 (2012).

 

  1. C.T. Quinones, Proton computed tomography, Diss. Université de Lyon, (2016).

 

  1. M. Prall, et al., High-energy proton imaging for biomedical applications, Scientific Reports, 6, 27651 (2016).

 

  1. Schulte, Reinhard, et al., Conceptual design of a proton computed tomography system for applications in proton radiation therapy, IEEE Transactions on Nuclear Science, 51, 3 (2004).

 

  1. Li, Tianfang, Jerome Zhengrong Liang, Reconstruction with most likely trajectory for proton computed tomography, Medical Imaging 2004: Image Processing. Vol. 5370. International Society for Optics and Photonics, (2004).

 

  1. Williams, David C, The most likely path of an energetic charged particle through a uniform medium, Physics in Medicine & Biology, 49, 13 (2004).

 

  1. R.W. Schulte, et al., A maximum likelihood proton path formalism for application in proton computed tomography, Medical Physics, 35, 11 (2008).

 

  1. B. Erdelyi, A comprehensive study of the most likely path formalism for proton-computed tomography, Physics in Medicine & Biology, 54, 20 (2009).

 

  1. Koehler, Proton radiography, Science (New York, N.Y.). 3825, 303–304 (1968).

 

  1. G. Poludniowski, G. Allinson, N. Evans, Proton radiography and tomography with application to proton therap, The British Journal of Radiology, (2015).

 

  1. C.T. Quiñones, J.M. Létang, S. Rit, Filtered back-projection reconstruction for attenuation proton CT along most likely paths, Physics in Medicine & Biology, 61, 9 (2016).

 

  1. Khellaf, Feriel, et al., A comparison of direct reconstruction algorithms in proton computed tomography, Physics in Medicine & Biology, 65, 10 (2020).

 

  1. Collins-Fekete, Charles-Antoine, et al., A theoretical framework to predict the most likely ion path in particle imaging, Physics in Medicine & Biology, 62, 5 (2017).

 

  1. Rit, Simon, et al., Distance-driven binning for proton CT filtered backprojection along most likely paths, Second International Conference on Image Formation in X-Ray Computed Tomography, Conference Paper, (2010).

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