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, 王金诺. 基于组件技术的MCNP三维可视化平台模型研究. 计算机应用研究, 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, Baräo 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.	李春艳, 李君利, 程建平, 等. MCNP可视化输入程序的开发. 清华大学学报(自然科学版), 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.	朱晓林, 蔡勇, 张建生. CSG模型到MCNP几何模型转换算法的研究与实现. 现代计算机, 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.	罗月童, 樊晓. 基于面壳封闭的B-Rep到CSG转换算法. 计算机辅助设计与图形学学报, 2014, 26(10): 1673-1680.
18.	吴斌, 俞盛朋, 程梦云, 等. 反应堆中子学分析精准建模方法. 核科学与工程, 2016, 36(1): 72-76.
19.	赵瑛峰, 刘检华, 武林林, 等. 基于特征分解的半空间构造实体几何模型转换算法. 计算机集成制造系统, 2021, 27(5): 1382-1389.
20.	孙文军, 阎慧, 高永明. 基于MapleSim和LabVIEW的航天器姿态控制仿真研究. 计算机应用研究, 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.	刘锋. 罗德里格斯旋转公式的证明及应用. 江苏科技信息, 2020, 37(28): 37-40.
24.	丁爱平, 李莹, 卢磊, 等. 粒子输运计算模型MCNP模型的可视化实现. 原子核物理评论, 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, 王金诺. 基于组件技术的MCNP三维可视化平台模型研究. 计算机应用研究, 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, Baräo 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. 李春艳, 李君利, 程建平, 等. MCNP可视化输入程序的开发. 清华大学学报(自然科学版), 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. 朱晓林, 蔡勇, 张建生. CSG模型到MCNP几何模型转换算法的研究与实现. 现代计算机, 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. 罗月童, 樊晓. 基于面壳封闭的B-Rep到CSG转换算法. 计算机辅助设计与图形学学报, 2014, 26(10): 1673-1680.
18. 吴斌, 俞盛朋, 程梦云, 等. 反应堆中子学分析精准建模方法. 核科学与工程, 2016, 36(1): 72-76.
19. 赵瑛峰, 刘检华, 武林林, 等. 基于特征分解的半空间构造实体几何模型转换算法. 计算机集成制造系统, 2021, 27(5): 1382-1389.
20. 孙文军, 阎慧, 高永明. 基于MapleSim和LabVIEW的航天器姿态控制仿真研究. 计算机应用研究, 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. 刘锋. 罗德里格斯旋转公式的证明及应用. 江苏科技信息, 2020, 37(28): 37-40.
24. 丁爱平, 李莹, 卢磊, 等. 粒子输运计算模型MCNP模型的可视化实现. 原子核物理评论, 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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