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

Image reconstruction of proton computed tomography modelled by Geant4 Monte Carlo toolkit

Document Type : Research Paper

Authors

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

1 Physics and Accelerators Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box:14155-1339, Tehran-Iran

2 Reactor and Nuclear Safety Research School, Nuclear Science and Technology Research Institute, P.O.Box: 14155-1339, Tehran -Iran

3 Radiation Application Research School, Nuclear Science and Technology Research Institute, AEOI, P.O.Box: 14155-1339, Tehran –Iran

https://doi.org/10.24200/nst.2021.1309
Abstract
Accuracy in the treatment planning of proton therapy depends on the accuracy of the information used to calculate the relative stopping power of tissues in the patient's body. This information is obtained from x-ray computed tomography images using a calibration curve to convert Hansfield numbers to relative stopping power values. Using x-ray computed tomography images leads to errors in estimating the proton range and the proton dose distribution in the treatment plan program. But applying the proton computed tomography eliminates this error and directly calculates the relative stopping power map of the tissues. In the present study, a modern proton computed tomography imaging system was simulated using the Monte Carlo Geant4 toolkit by tracing particle-to-particle trajectory. The purpose of this simulation was the improvement of density resolution of tissue without dose increment. The standard CIRS 062M phantom was irradiated with a 300 MeV proton beam. The energy, position, and direction of particle movement values before and after the phantom were stored in the root file by nuclear detectors. The image matrix phantom was reconstructed as a relative stopping power map using three radon analytical algorithms. The comparison was made regarding dose, density resolution, and RMSE concerning real phantom image data. The proposed algorithm improved the density resolution from 9.1% to 4.3% and RMSE from 26.43% to 6.81% by correcting the angles of the projections at the same dose level.

Keywords

pCT
Electron density phantom
CIRS062M
Reconstruction
FBP
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4.   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).

 

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

 

7.      https://www.ptcog.ch.

 

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9.    U. Schneider et al. Secondary neutron dose during proton therapy using spot scanning, Int J Radiation Oncology Biol Phys. 53, 244–251 (2002).

 

10.  M. Prall et al. High-energy proton imaging for biomedical applications,‌ scientific reports 6. 27651 (2016). ‏

 

11.  G. Poludniowski, N.M. Allinson, and P.M. Evans, Proton radiography and tomography with application to proton therapy, The British journal of radiology. 88.1053, (2015). ‏

 

12.  T. Li, and J.Z. Liang, Reconstruction with most likely trajectory for proton computed tomography, Medical Imaging, Image Processing. 5370, (2004). ‏

 

13.  M. Bucciantonio, and F. Sauli, Proton computed tomography, Modern Physics Letters A. 30.17, (2015). ‏

 

14.  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).

 

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

 

16.   H. Paganetti, Range uncertainties in proton therapy and the role of Monte Carlo simulations, Physics in Medicine and Biology. 57.11, 99 (2012).

 

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

 

18. F. Ulrich-Pur et al, Imaging with protons at MedAustron, Nuclear Inst. And Methods in Physics Research, A 978, 164407 (2020). ‏

 

19.  T. Li et al., Reconstruction for proton computed tomography: A Monte Carlo study, IEEE Medical Imaging Conference. (2003).

 

20.   G. Poludniowa, G., Allinson, and N., Evans, Proton radiography and tomography with application to proton therap, The British journal of radiology. (2015).

 

21.   E. Schnell, S. Ahmad and T. de la Fuente Herman, Commissioning of a Relative Stopping Power to Hounsfield Unit Calibration Curve for a Mevion Proton Radiation Treatment Unit, University of Oklahoma Health. (2016).

 

22.  C.B. Saw et al, Determination of CT-to-density conversion relationship for image-based treatment planning systems, Medical Dosimetry 30.3,‌ 145-1483 (2005).

 

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).
 
2.   F.M. Khan and B.J. Gerbi, Treatment planning in radiation oncology, Wolters Kluwer Health/Lippincott Williams & Wilkins‌ (2012). ‏
 
3.    W.P.  Levin, et al.  Proton beam therapy, British J Cancer. 93, 849–854 (2005).
 
4.   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).
 
5.   O. Jakel, State of the art in hadron therapy, AIP Conference Proceedings. 95, 70-77 (2007).
 
6.    K.W.D. Ledingham et al.  Towards Laser Driven Hadron Cancer Radiotherapy, A Review of Progress. Med Phys. (2014).
 
7.      https://www.ptcog.ch.
 
8.     M.W. McDonald and M.M. Fitzek, Proton Therapy, Curr Probl Cancer. 34, 257-296 (2010).
 
9.    U. Schneider et al. Secondary neutron dose during proton therapy using spot scanning, Int J Radiation Oncology Biol Phys. 53, 244–251 (2002).
 
10.  M. Prall et al. High-energy proton imaging for biomedical applications,‌ scientific reports 6. 27651 (2016). ‏
 
11.  G. Poludniowski, N.M. Allinson, and P.M. Evans, Proton radiography and tomography with application to proton therapy, The British journal of radiology. 88.1053, (2015). ‏
 
12.  T. Li, and J.Z. Liang, Reconstruction with most likely trajectory for proton computed tomography, Medical Imaging, Image Processing. 5370, (2004). ‏
 
13.  M. Bucciantonio, and F. Sauli, Proton computed tomography, Modern Physics Letters A. 30.17, (2015). ‏
 
14.  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).
 
15.  C. Zeng et al. Proton Treatment Planning, Target Volume Delineation and Treatment Planning for Particle Therapy. Springer, Cham. 45-105 (2018).‏
 
16.   H. Paganetti, Range uncertainties in proton therapy and the role of Monte Carlo simulations, Physics in Medicine and Biology. 57.11, 99 (2012).
 
17.   C.T. Quinones, Proton computed tomography, Diss. Université de Lyon. (2016). ‏
 
18. F. Ulrich-Pur et al, Imaging with protons at MedAustron, Nuclear Inst. And Methods in Physics Research, A 978, 164407 (2020). ‏
 
19.  T. Li et al., Reconstruction for proton computed tomography: A Monte Carlo study, IEEE Medical Imaging Conference. (2003).
 
20.   G. Poludniowa, G., Allinson, and N., Evans, Proton radiography and tomography with application to proton therap, The British journal of radiology. (2015).
 
21.   E. Schnell, S. Ahmad and T. de la Fuente Herman, Commissioning of a Relative Stopping Power to Hounsfield Unit Calibration Curve for a Mevion Proton Radiation Treatment Unit, University of Oklahoma Health. (2016).
 
22.  C.B. Saw et al, Determination of CT-to-density conversion relationship for image-based treatment planning systems, Medical Dosimetry 30.3,‌ 145-1483 (2005).
 

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