نوع مقاله : مقاله پژوهشی

نویسندگان

1 پژوهشکده فیزیک و شتابگرها، پژوهشگاه علوم و فنون هسته‌ای، سازمان انرژی اتمی ایران، صندوق پستی:1339-14155، تهران- ایران

2 پژوهشکده رآکتور و ایمنی هسته‌ای، پژوهشگاه علوم و فنون هسته‌ای‌، سازمان انرژی اتمی ایران، صندوق پستی 14155-1339‌، تهران- ایران

3 پژوهشکده کاربرد پرتوها، پژوهشگاه علوم و فنون هسته‌ای، سازمان انرژی اتمی، صندوق پستی: 1339-14155، تهران- ایران

10.24200/nst.2021.1309

چکیده

دقت در طراحی‌ درمان پروتون‌درمانی بستگی به صحت اطلاعاتی دارد که برای محاسبه RSP بافت‌ها در بدن بیمار به‌کار می‌رود. این اطلاعات از تصاویر xCT و با استفاده از منحنی کالیبراسیون مورد نیاز برای تبدیل اعداد هانسفیلد به مقادیر RSP حاصل می‌شود. استفاده از‌ xCT منجر به ایجاد خطا، در تخمین برد و محاسبه دز پروتون در طراحی ‌درمان می‌شود. ولی با به‌کارگیری pCT در مد انتگرال نهشت انرژی، این خطا حذف شده و نقشه RSP بافت‌ها به‌طور مستقیم محاسبه می‌شود. در این مطالعه، یک سیستم مدرن تصویربرداری pCT با قابلیت ردیابی ذره به ذره، با استفاده از ابزار مونت‌کارلو Geant4 شبیه‌‏سازی شد. هدف از این شبیه‌‏سازی، بهبود رزولوشن‌چگالی تصاویر بافت‏‌ها بدون افزایش دز می‌‏باشد. فانتوم استاندارد CIRS062M با انرژی پروتون MeV 300 پرتودهی شد و مقادیر انرژی، موقعیت و جهت ‌حرکت ذرات قبل و بعد از فانتوم توسط آشکارسازهای هسته‏‌ای در فایل root ذخیره شدند. ماتریس تصویر فانتوم به‌صورت نقشه RSP بافت‌‏ها با استفاده از سه الگوریتم تحلیلی رادون بازسازی و نتایج از نظر مقدار دز، رزولوشن‌ چگالی و RMSE نسبت به داده‌های تصویر فانتوم مقایسه شد. الگوریتم پیشنهادی با اعمال تصحیح زاویه افکنده‌ها در سطح دز برابر، منجر به بهبود رزولوشن‌ چگالی از 1/9% به 3/4% و RMSE از 43/26% به 81/6% شد.

کلیدواژه‌ها

عنوان مقاله [English]

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

نویسندگان [English]

  • 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

چکیده [English]

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.

کلیدواژه‌ها [English]

  • pCT
  • Electron Density Phantom
  • CIRS062M
  • Reconstruction
  • FBP
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 systemsMedical Dosimetry 30.3,‌ 145-1483 (2005).

 

1.   C.J. Wong et al. High-resolution measurements of small field beams using polymer gelsApplied Radiation and Isotopes. 65.10, 1160-1164 (2007).
 
2.   F.M. Khan and B.J. Gerbi, Treatment planning in radiation oncologyWolters Kluwer Health/Lippincott Williams & Wilkins‌ (2012). ‏
 
3.    W.P.  Levin, et al.  Proton beam therapyBritish 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 EnergyPhys Med. 24, 102–106 (2008).
 
5.   O. Jakel, State of the art in hadron therapyAIP Conference Proceedings. 95, 70-77 (2007).
 
6.    K.W.D. Ledingham et al.  Towards Laser Driven Hadron Cancer RadiotherapyA 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 scanningInt 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 therapyThe British journal of radiology. 88.1053, (2015). ‏
 
12.  T. Li, and J.Z. Liang, Reconstruction with most likely trajectory for proton computed tomographyMedical Imaging, Image Processing. 5370, (2004). ‏
 
13.  M. Bucciantonio, and F. Sauli, Proton computed tomographyModern 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 calibrationPhysics in Medicine and Biology. 57.13, 4095–4115 (2012).
 
15.  C. Zeng et al. Proton Treatment PlanningTarget Volume Delineation and Treatment Planning for Particle TherapySpringer, 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 tomographyDiss. Université de Lyon. (2016). ‏
 
18. F. Ulrich-Pur et al, Imaging with protons at MedAustronNuclear Inst. And Methods in Physics Research, A 978, 164407 (2020). ‏
 
19.  T. Li et al., Reconstruction for proton computed tomography: A Monte Carlo studyIEEE Medical Imaging Conference. (2003).
 
20.   G. Poludniowa, G., Allinson, and N., Evans, Proton radiography and tomography with application to proton therapThe 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 UnitUniversity of Oklahoma Health. (2016).
 
22.  C.B. Saw et al, Determination of CT-to-density conversion relationship for image-based treatment planning systemsMedical Dosimetry 30.3,‌ 145-1483 (2005).