Model construction and software design of computed tomography radiation system based on visualization_Journal of Biomedical Engineering

Authors：

LIU Ying ¹ , MENG Ting ¹ ,  ZHANG Haowei ¹ , LU Heqing ²

1. School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, P. R. China;
2. Department of Medical Equipment, Shanghai First Maternity and Infant Hospital, School of Medicine, Tongji University, Shanghai 200092, P. R. China;

Corresponding?author：

ZHANG Haowei, Email: howeizh@aliyun.com

Keywords：

Radiation dose; Computed tomography radiation system geometric model; Monte Carlo N-Particle; Visual modeling; Constructive solid geometry

DOI：

10.7507/1001-5515.202201053

Video：

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The Monte Carlo N-Particle (MCNP) is often used to calculate the radiation dose during computed tomography (CT) scans. However, the physical calculation process of the model is complicated, the input file structure of the program is complex, and the three-dimensional (3D) display of the geometric model is not supported, so that the researchers cannot establish an accurate CT radiation system model, which affects the accuracy of the dose calculation results. Aiming at these two problems, this study designed a software that visualized CT modeling and automatically generated input files. In terms of model calculation, the theoretical basis was based on the integration of CT modeling improvement schemes of major researchers. For 3D model visualization, LabVIEW was used as the new development platform, constructive solid geometry (CSG) was used as the algorithm principle, and the introduction of editing of MCNP input files was used to visualize CT geometry modeling. Compared with a CT model established by a recent study, the root mean square error between the results simulated by this visual CT modeling software and the actual measurement was smaller. In conclusion, the proposed CT visualization modeling software can not only help researchers to obtain an accurate CT radiation system model, but also provide a new research idea for the geometric modeling visualization method of MCNP.

Citation： LIU Ying, MENG Ting, ZHANG Haowei, LU Heqing. Model construction and software design of computed tomography radiation system based on visualization. Journal of Biomedical Engineering, 2023, 40(5): 989-995. doi: 10.7507/1001-5515.202201053 Copy

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2.	林茂松, 蔡勇, 王金諾. 基于組件技術的MCNP三維可視化平臺模型研究. 計算機應用研究, 2007(2): 225-226.
3.	Hassan A I, Skalej M, Schlattl H, et al. Determination and verification of the x-ray spectrum of a CT scanner. J Med Imaging, 2018, 5(1): 1-15.
4.	Gu J, Bednarz B, Caracappa P F, et al. The development, validation and application of a multi-detector CT (MDCT) scanner model for assessing organ doses to the pregnant patient and the fetus using Monte Carlo simulations. Phys Med Biol, 2009, 54(9): 2699-2717.
5.	Pan Y, Qiu R, Gao L, et al. Development of 1-year-old computational phantom and calculation of organ doses during CT scans using Monte Carlo simulation. Phys Med Biol, 2014, 59(18): 5243-5260.
6.	Caon M, Bibbo G, Pattison J. An EGS4-ready tomographic computational model of a 14-year-old female torso for calculating organ doses from CT examinations. Phys Med Biol, 1999, 44(9): 2213-2225.
7.	Khursheed A, Hillier M C, Shrimpton P C, et al. Influence of patient age on normalized effective doses calculated for CT examinations. Br J Radiol, 2002, 75(898): 819-830.
8.	Jarry G, DeMarco J J, Beifuss U, et al. A Monte Carlo-based method to estimate radiation dose from spiral CT: from phantom testing to patient-specific models. Phys Med Biol, 2003, 48(16): 2645-2663.
9.	Turner A C, Zankl M, DeMarco J J, et al. The feasibility of a scanner-independent technique to estimate organ dose from MDCT scans: Using CTDI_vol to account for differences between scanners. Med Phys, 2010, 37(4): 1816-1825.
10.	Boone J M. Method for evaluating bow tie filter angle-dependent attenuation in CT: theory and simulation results. Med Phys, 2010, 37(1): 40-48.
11.	Schwarz R A, Carter L L. Current state of Monte Carlo visualization tools// Kling A, Bar?o F J C, Nakagawa M, et al. Advanced Monte Carlo for radiation physics, particle transport simulation and applications. Berlin, Heidelberg: Springer, 2000: 815-819.
12.	李春艷, 李君利, 程建平, 等. MCNP可視化輸入程序的開發. 清華大學學報(自然科學版), 2007(S1): 1089-1092.
13.	Theis C, Bucheger K, Brugger M, et al. Interactive three dimensional visualization and creation of geometries for Monter Carlo calculation. Nucl Instrum Methods, 2006, 562(2): 827-829.
14.	Theis C, Bucheger K, Feld G, et al. Simple Geo-New developments in the interactive creation and geometries for Monte Carlo simulations. Nucl Sci Tech, 2011, 2: 587-590.
15.	朱曉林, 蔡勇, 張建生. CSG模型到MCNP幾何模型轉換算法的研究與實現. 現代計算機, 2012, 8: 9-12.
16.	Li Y, Lu L, Ding A, et al. Benchmarking of MCAM 4. 0 with the ITER 3D model. Fusion Eng Des, 2007, 82(15-24): 2861-2866.
17.	羅月童, 樊曉. 基于面殼封閉的B-Rep到CSG轉換算法. 計算機輔助設計與圖形學學報, 2014, 26(10): 1673-1680.
18.	吳斌, 俞盛朋, 程夢云, 等. 反應堆中子學分析精準建模方法. 核科學與工程, 2016, 36(1): 72-76.
19.	趙瑛峰, 劉檢華, 武林林, 等. 基于特征分解的半空間構造實體幾何模型轉換算法. 計算機集成制造系統, 2021, 27(5): 1382-1389.
20.	孫文軍, 閻慧, 高永明. 基于MapleSim和LabVIEW的航天器姿態控制仿真研究. 計算機應用研究, 2011, 28(11): 4202-4205, 4218.
21.	X-5 Monte Carlo Team. MCNP―A general Monte Carlo N-Particle transport code, version 5, Volume II: User’s Guide. Los Alamos: Los Alamos National Lab, 2003.
22.	Rob R, Panoiu C. Using LabVIEW linx for creating 3d objects. IOP Conf Ser Mater Sci Eng, 2019, 477(1): 012027.
23.	劉鋒. 羅德里格斯旋轉公式的證明及應用. 江蘇科技信息, 2020, 37(28): 37-40.
24.	丁愛平, 李瑩, 盧磊, 等. 粒子輸運計算模型MCNP模型的可視化實現. 原子核物理評論, 2006(2): 130-133.
25.	Hubbell J H, Seltzer S M. Tables of X-ray mass attenuation coefficients and mass energy-absorption coefficients 1 keV to 20 MeV for elements Z= 1 to 92 and 48 additional substances of dosimetric interest. Gaithersburg: National Institute of Standards and Technology-PL, 1995.

1. Zheng J Z, Gao L F, Zhuo W H, et al. A trend study on radiodiagnosis and radiotherapy and radiological protection for medical exposure in Shanghai. Radiat Prot, 2014, 34(5): 265-273.
2. 林茂松, 蔡勇, 王金諾. 基于組件技術的MCNP三維可視化平臺模型研究. 計算機應用研究, 2007(2): 225-226.
3. Hassan A I, Skalej M, Schlattl H, et al. Determination and verification of the x-ray spectrum of a CT scanner. J Med Imaging, 2018, 5(1): 1-15.
4. Gu J, Bednarz B, Caracappa P F, et al. The development, validation and application of a multi-detector CT (MDCT) scanner model for assessing organ doses to the pregnant patient and the fetus using Monte Carlo simulations. Phys Med Biol, 2009, 54(9): 2699-2717.
5. Pan Y, Qiu R, Gao L, et al. Development of 1-year-old computational phantom and calculation of organ doses during CT scans using Monte Carlo simulation. Phys Med Biol, 2014, 59(18): 5243-5260.
6. Caon M, Bibbo G, Pattison J. An EGS4-ready tomographic computational model of a 14-year-old female torso for calculating organ doses from CT examinations. Phys Med Biol, 1999, 44(9): 2213-2225.
7. Khursheed A, Hillier M C, Shrimpton P C, et al. Influence of patient age on normalized effective doses calculated for CT examinations. Br J Radiol, 2002, 75(898): 819-830.
8. Jarry G, DeMarco J J, Beifuss U, et al. A Monte Carlo-based method to estimate radiation dose from spiral CT: from phantom testing to patient-specific models. Phys Med Biol, 2003, 48(16): 2645-2663.
9. Turner A C, Zankl M, DeMarco J J, et al. The feasibility of a scanner-independent technique to estimate organ dose from MDCT scans: Using CTDI_vol to account for differences between scanners. Med Phys, 2010, 37(4): 1816-1825.
10. Boone J M. Method for evaluating bow tie filter angle-dependent attenuation in CT: theory and simulation results. Med Phys, 2010, 37(1): 40-48.
11. Schwarz R A, Carter L L. Current state of Monte Carlo visualization tools// Kling A, Bar?o F J C, Nakagawa M, et al. Advanced Monte Carlo for radiation physics, particle transport simulation and applications. Berlin, Heidelberg: Springer, 2000: 815-819.
12. 李春艷, 李君利, 程建平, 等. MCNP可視化輸入程序的開發. 清華大學學報(自然科學版), 2007(S1): 1089-1092.
13. Theis C, Bucheger K, Brugger M, et al. Interactive three dimensional visualization and creation of geometries for Monter Carlo calculation. Nucl Instrum Methods, 2006, 562(2): 827-829.
14. Theis C, Bucheger K, Feld G, et al. Simple Geo-New developments in the interactive creation and geometries for Monte Carlo simulations. Nucl Sci Tech, 2011, 2: 587-590.
15. 朱曉林, 蔡勇, 張建生. CSG模型到MCNP幾何模型轉換算法的研究與實現. 現代計算機, 2012, 8: 9-12.
16. Li Y, Lu L, Ding A, et al. Benchmarking of MCAM 4. 0 with the ITER 3D model. Fusion Eng Des, 2007, 82(15-24): 2861-2866.
17. 羅月童, 樊曉. 基于面殼封閉的B-Rep到CSG轉換算法. 計算機輔助設計與圖形學學報, 2014, 26(10): 1673-1680.
18. 吳斌, 俞盛朋, 程夢云, 等. 反應堆中子學分析精準建模方法. 核科學與工程, 2016, 36(1): 72-76.
19. 趙瑛峰, 劉檢華, 武林林, 等. 基于特征分解的半空間構造實體幾何模型轉換算法. 計算機集成制造系統, 2021, 27(5): 1382-1389.
20. 孫文軍, 閻慧, 高永明. 基于MapleSim和LabVIEW的航天器姿態控制仿真研究. 計算機應用研究, 2011, 28(11): 4202-4205, 4218.
21. X-5 Monte Carlo Team. MCNP―A general Monte Carlo N-Particle transport code, version 5, Volume II: User’s Guide. Los Alamos: Los Alamos National Lab, 2003.
22. Rob R, Panoiu C. Using LabVIEW linx for creating 3d objects. IOP Conf Ser Mater Sci Eng, 2019, 477(1): 012027.
23. 劉鋒. 羅德里格斯旋轉公式的證明及應用. 江蘇科技信息, 2020, 37(28): 37-40.
24. 丁愛平, 李瑩, 盧磊, 等. 粒子輸運計算模型MCNP模型的可視化實現. 原子核物理評論, 2006(2): 130-133.
25. Hubbell J H, Seltzer S M. Tables of X-ray mass attenuation coefficients and mass energy-absorption coefficients 1 keV to 20 MeV for elements Z= 1 to 92 and 48 additional substances of dosimetric interest. Gaithersburg: National Institute of Standards and Technology-PL, 1995.

Journal of Biomedical Engineering

Model construction and software design of computed tomography radiation system based on visualization

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