A method of lung puncture path planning based on multi-level constraint_Journal of Biomedical Engineering

Authors：

SUN Fenghui ¹ , PEI Hongliang ¹ , YANG Yifei ¹ ,  FAN Qingwen ¹ ,  LI Xiao’ou ²

1. School of Mechanical Engineering, Sichuan University, Chengdu 610065, P. R. China;
2. Department of Respiratory and Critical Care Medicine, West China Hospital, Sichuan University, Chengdu 610041, P. R. China;

Corresponding author：

FAN Qingwen, Email: fanqingwen@scu.edu.cn; LI Xiao’ou, Email: lixiaoou86@163.com

Keywords：

Lung puncture trajectory planning; Fibonacci lattice; Multi-level constraint; Oriented bounding box tree; Pareto optimization

DOI：

10.7507/1001-5515.202112029

Video：

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Percutaneous pulmonary puncture guided by computed tomography (CT) is one of the most effective tools for obtaining lung tissue and diagnosing lung cancer. Path planning is an important procedure to avoid puncture complications and reduce patient pain and puncture mortality. In this work, a path planning method for lung puncture is proposed based on multi-level constraints. A digital model of the chest is firstly established using patient's CT image. A Fibonacci lattice sampling is secondly conducted on an ideal sphere centered on the tumor lesion in order to obtain a set of candidate paths. Finally, by considering clinical puncture guidelines, an optimal path can be obtained by a proposed multi-level constraint strategy, which is combined with oriented bounding box tree (OBBTree) algorithm and Pareto optimization algorithm. Results of simulation experiments demonstrated the effectiveness of the proposed method, which has good performance for avoiding physical and physiological barriers. Hence, the method could be used as an aid for physicians to select the puncture path.

Citation： SUN Fenghui, PEI Hongliang, YANG Yifei, FAN Qingwen, LI Xiao’ou. A method of lung puncture path planning based on multi-level constraint. Journal of Biomedical Engineering, 2022, 39(3): 462-470. doi: 10.7507/1001-5515.202112029 Copy

1.	韩雪娇, 李雪梅, 龚小清, 等. 肿瘤领域全球研究热点——基于ESI热点论文的计量分析. 生物医学工程学杂志, 2020, 37(6): 1012-1024.
2.	Ferlay J, Colombet M, Soerjomataram I, et al. Cancer statistics for the year 2020: an overview. Int J Cancer, 2021, 149(4): 778-789.
3.	刘宗超, 李哲轩, 张阳, 等. 2020全球癌症统计报告解读. 肿瘤综合治疗电子杂志, 2021, 7(2): 1-13.
4.	杨雪玲, 于海鹏, 司同国. 胸部肿瘤经皮穿刺活检中国专家共识(2020版)[J]. 中华介入放射学电子杂志, 2021, 9(2): 117-126.
5.	Heerink W J, de Bock G H, de Jonge G J, et al. Complication rates of CT-guided transthoracic lung biopsy: meta-analysis. Eur Radiol, 2017, 27(1): 138-148.
6.	李纳. CT引导下肺穿刺活检技术的临床应用价值. 长沙: 湖南师范大学, 2020.
7.	Huang W M, Lin H C, Chen C H,, et al. Massive hemothorax after computed tomography-guided lung tumor biopsy: an unusual but disastrous complication. Thorac Cancer, 2018, 9(7): 892-896.
8.	戴一帆, 吕群, 阮肇扬, 等. CT引导下经皮肺穿刺活检380例临床分析. 中国实用内科杂志, 2018, 38(11): 1096-1099.
9.	王立学, 董鸿鹏, 白博锋, 等. CT引导下经皮肺穿刺活检对不同大小肺结节的诊断效能及并发症相关因素分析. 放射学实践, 2020, 35(11): 1409-1414.
10.	段星光, 温浩, 何睿, 等. 胸腹腔经皮穿刺机器人研究进展及关键技术分析. 机器人, 2021, 43(5): 567-584.
11.	Kimura T, Naka N, Minato Y, et al. Oblique approach of computed tomography guided needle biopsy using multiplanar reconstruction image by multidetector-row CT in lung cancer. Eur J Radiol, 2004, 52(2): 206-211.
12.	Villard C, Soler L, Papier N, et al. RF-Sim: a treatment planning tool for radiofrequency ablation of hepatic tumors//Proceedings on Seventh International Conference on Information Visualization, London: Institute of Electrical and Electronics Engineers, 2003: 561-566.
13.	Villard C, Baegert C, Schreck P, et al. Optimal trajectories computation within regions of interest for hepatic RFA planning//International Conference on Medical Image Computing and Computer-Assisted Intervention, Palm Springs: Springer Berlin Heidelberg, 2005(Ⅱ): 49-56.
14.	Baegert C, Villard C, Schreck P, et al. Trajectory optimization for the planning of percutaneous radiofrequency ablation of hepatic tumors. Comput Aided Surg, 2007, 12(2): 82-90.
15.	Baegert C, Villard C, Schreck P, et al. Precise determination of regions of interest for hepatic RFA planning. Stud Health Technol Inform, 2007, 125: 31-36.
16.	Baegert C, Villard C, Schreck P, et al. Multi-criteria trajectory planning for hepatic radiofrequency ablation//International Conference on Medical Image Computing and Computer-Assisted Intervention, Brisbane: Springer Berlin Heidelberg, 2007(Ⅱ): 676-684.
17.	Seitel A, Engel M, Sommer C M, et al. Computer-assisted trajectory planning for percutaneous needle insertions. Med Phys, 2011, 38(6): 3246-3259.
18.	Schumann C, Bieberstein J, Trumm C, et al. Fast automatic path proposal computation for hepatic needle placement. International Society for Optics and Photonics, 2010, 7625: 76251J.
19.	Schumann C, Bieberstein J, Braunewell S, et al. Visualization support for the planning of hepatic needle placement. Int J Comput Assist Radiol Surg, 2012, 7(2): 191-197.
20.	Schumann C, Rieder C, Haase S, et al. Interactive access path exploration for planning of needle-based interventions. Roboter-Assistenten Werden Sensitiv, 2013, 2013: 103-106.
21.	Schumann C, Rieder C, Haase S, et al. Interactive multi-criteria planning for radiofrequency ablation. Int J Comput Assist Radiol Surg, 2015, 10(6): 879-889.
22.	张睿, 周著黄, 吴薇薇, 等. CT引导肝肿瘤热消融治疗穿刺路径规划方法研究. 医疗卫生装备, 2020, 41(4): 27-34.
23.	Gao Chaowei, Chen Li, Hou Bochao, et al. Precise and semi-automatic puncture trajectory planning in craniofacial surgery: a prototype study//2014 7th International Conference on Biomedical Engineering and Informatics, Dalian: IEEE, 2014: 617-622.
24.	González Á. Measurement of areas on a sphere using Fibonacci and latitude–longitude lattices. Math Geosci, 2009, 42(1): 49-64.
25.	Mojsilovic A, Soljanin E. Color quantization and processing by Fibonacci lattices. IEEE Trans Image Process, 2001, 10(11): 1712-1725.
26.	温剑, 阳昆, 姚亚利, 等. K频段斐波那契网格稀疏阵列分析与设计. 电讯技术, 2021, 61(4): 488-495.
27.	尹中元. CT引导下经皮肺穿刺活检实操手册. 北京: 科学出版社, 2019: 21-29.
28.	鲍楠. CT引导的肺穿刺路径规划与手术导航关键技术研究. 沈阳: 东北大学, 2017.
29.	Gass S I, FU Mc. Encyclopedia of operations research and management science. Boston: Springer, 2013: 1112.
30.	Dong X, Lei Y, Wang T, et al. Automatic multiorgan segmentation in thorax CT images using U-net-GAN. Med Phys, 2019, 46(5): 2157-2168.
31.	Fan Q W, Pei H L, Luo F M, et al. Extraction of pulmonary Trachea by dynamic tubular edge contour algorithm. Ann Transl Med, 2020, 8(24): 1636.
32.	张琳. 基于自监督迁移学习的肺部气管和血管分割算法研究. 杭州: 浙江大学, 2021.
33.	Armato S G, Mclennan G, Bidaut L, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys, 2011, 38(2): 915-931.
34.	Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging, 2012, 30(9): 1323-1341.
35.	廖如云, 江宇, 李晓兰, 等. 对比CT引导下不同进针路径经皮肺穿刺活检效果. 中国介入影像与治疗学, 2021, 18(6): 335-339.
36.	Gottschalk S, Lin M C, Manocha D. OBBTree: a hierarchical structure for rapid interference detection//Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, New York: Association for Computing Machinery, 1996: 171-180.

1. 韩雪娇, 李雪梅, 龚小清, 等. 肿瘤领域全球研究热点——基于ESI热点论文的计量分析. 生物医学工程学杂志, 2020, 37(6): 1012-1024.
2. Ferlay J, Colombet M, Soerjomataram I, et al. Cancer statistics for the year 2020: an overview. Int J Cancer, 2021, 149(4): 778-789.
3. 刘宗超, 李哲轩, 张阳, 等. 2020全球癌症统计报告解读. 肿瘤综合治疗电子杂志, 2021, 7(2): 1-13.
4. 杨雪玲, 于海鹏, 司同国. 胸部肿瘤经皮穿刺活检中国专家共识(2020版)[J]. 中华介入放射学电子杂志, 2021, 9(2): 117-126.
5. Heerink W J, de Bock G H, de Jonge G J, et al. Complication rates of CT-guided transthoracic lung biopsy: meta-analysis. Eur Radiol, 2017, 27(1): 138-148.
6. 李纳. CT引导下肺穿刺活检技术的临床应用价值. 长沙: 湖南师范大学, 2020.
7. Huang W M, Lin H C, Chen C H,, et al. Massive hemothorax after computed tomography-guided lung tumor biopsy: an unusual but disastrous complication. Thorac Cancer, 2018, 9(7): 892-896.
8. 戴一帆, 吕群, 阮肇扬, 等. CT引导下经皮肺穿刺活检380例临床分析. 中国实用内科杂志, 2018, 38(11): 1096-1099.
9. 王立学, 董鸿鹏, 白博锋, 等. CT引导下经皮肺穿刺活检对不同大小肺结节的诊断效能及并发症相关因素分析. 放射学实践, 2020, 35(11): 1409-1414.
10. 段星光, 温浩, 何睿, 等. 胸腹腔经皮穿刺机器人研究进展及关键技术分析. 机器人, 2021, 43(5): 567-584.
11. Kimura T, Naka N, Minato Y, et al. Oblique approach of computed tomography guided needle biopsy using multiplanar reconstruction image by multidetector-row CT in lung cancer. Eur J Radiol, 2004, 52(2): 206-211.
12. Villard C, Soler L, Papier N, et al. RF-Sim: a treatment planning tool for radiofrequency ablation of hepatic tumors//Proceedings on Seventh International Conference on Information Visualization, London: Institute of Electrical and Electronics Engineers, 2003: 561-566.
13. Villard C, Baegert C, Schreck P, et al. Optimal trajectories computation within regions of interest for hepatic RFA planning//International Conference on Medical Image Computing and Computer-Assisted Intervention, Palm Springs: Springer Berlin Heidelberg, 2005(Ⅱ): 49-56.
14. Baegert C, Villard C, Schreck P, et al. Trajectory optimization for the planning of percutaneous radiofrequency ablation of hepatic tumors. Comput Aided Surg, 2007, 12(2): 82-90.
15. Baegert C, Villard C, Schreck P, et al. Precise determination of regions of interest for hepatic RFA planning. Stud Health Technol Inform, 2007, 125: 31-36.
16. Baegert C, Villard C, Schreck P, et al. Multi-criteria trajectory planning for hepatic radiofrequency ablation//International Conference on Medical Image Computing and Computer-Assisted Intervention, Brisbane: Springer Berlin Heidelberg, 2007(Ⅱ): 676-684.
17. Seitel A, Engel M, Sommer C M, et al. Computer-assisted trajectory planning for percutaneous needle insertions. Med Phys, 2011, 38(6): 3246-3259.
18. Schumann C, Bieberstein J, Trumm C, et al. Fast automatic path proposal computation for hepatic needle placement. International Society for Optics and Photonics, 2010, 7625: 76251J.
19. Schumann C, Bieberstein J, Braunewell S, et al. Visualization support for the planning of hepatic needle placement. Int J Comput Assist Radiol Surg, 2012, 7(2): 191-197.
20. Schumann C, Rieder C, Haase S, et al. Interactive access path exploration for planning of needle-based interventions. Roboter-Assistenten Werden Sensitiv, 2013, 2013: 103-106.
21. Schumann C, Rieder C, Haase S, et al. Interactive multi-criteria planning for radiofrequency ablation. Int J Comput Assist Radiol Surg, 2015, 10(6): 879-889.
22. 张睿, 周著黄, 吴薇薇, 等. CT引导肝肿瘤热消融治疗穿刺路径规划方法研究. 医疗卫生装备, 2020, 41(4): 27-34.
23. Gao Chaowei, Chen Li, Hou Bochao, et al. Precise and semi-automatic puncture trajectory planning in craniofacial surgery: a prototype study//2014 7th International Conference on Biomedical Engineering and Informatics, Dalian: IEEE, 2014: 617-622.
24. González Á. Measurement of areas on a sphere using Fibonacci and latitude–longitude lattices. Math Geosci, 2009, 42(1): 49-64.
25. Mojsilovic A, Soljanin E. Color quantization and processing by Fibonacci lattices. IEEE Trans Image Process, 2001, 10(11): 1712-1725.
26. 温剑, 阳昆, 姚亚利, 等. K频段斐波那契网格稀疏阵列分析与设计. 电讯技术, 2021, 61(4): 488-495.
27. 尹中元. CT引导下经皮肺穿刺活检实操手册. 北京: 科学出版社, 2019: 21-29.
28. 鲍楠. CT引导的肺穿刺路径规划与手术导航关键技术研究. 沈阳: 东北大学, 2017.
29. Gass S I, FU Mc. Encyclopedia of operations research and management science. Boston: Springer, 2013: 1112.
30. Dong X, Lei Y, Wang T, et al. Automatic multiorgan segmentation in thorax CT images using U-net-GAN. Med Phys, 2019, 46(5): 2157-2168.
31. Fan Q W, Pei H L, Luo F M, et al. Extraction of pulmonary Trachea by dynamic tubular edge contour algorithm. Ann Transl Med, 2020, 8(24): 1636.
32. 张琳. 基于自监督迁移学习的肺部气管和血管分割算法研究. 杭州: 浙江大学, 2021.
33. Armato S G, Mclennan G, Bidaut L, et al. The lung image database consortium (LIDC) and image database resource initiative (IDRI): a completed reference database of lung nodules on CT scans. Med Phys, 2011, 38(2): 915-931.
34. Fedorov A, Beichel R, Kalpathy-Cramer J, et al. 3D slicer as an image computing platform for the quantitative imaging network. Magn Reson Imaging, 2012, 30(9): 1323-1341.
35. 廖如云, 江宇, 李晓兰, 等. 对比CT引导下不同进针路径经皮肺穿刺活检效果. 中国介入影像与治疗学, 2021, 18(6): 335-339.
36. Gottschalk S, Lin M C, Manocha D. OBBTree: a hierarchical structure for rapid interference detection//Proceedings of the 23rd annual conference on Computer graphics and interactive techniques, New York: Association for Computing Machinery, 1996: 171-180.

Journal of Biomedical Engineering

A method of lung puncture path planning based on multi-level constraint

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