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.	韓雪嬌, 李雪梅, 龔小清, 等. 腫瘤領域全球研究熱點——基于ESI熱點論文的計量分析. 生物醫學工程學雜志, 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.	劉宗超, 李哲軒, 張陽, 等. 2020全球癌癥統計報告解讀. 腫瘤綜合治療電子雜志, 2021, 7(2): 1-13.
4.	楊雪玲, 于海鵬, 司同國. 胸部腫瘤經皮穿刺活檢中國專家共識(2020版)[J]. 中華介入放射學電子雜志, 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.	李納. CT引導下肺穿刺活檢技術的臨床應用價值. 長沙: 湖南師范大學, 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, 呂群, 阮肇揚, 等. CT引導下經皮肺穿刺活檢380例臨床分析. 中國實用內科雜志, 2018, 38(11): 1096-1099.
9.	王立學, 董鴻鵬, 白博鋒, 等. CT引導下經皮肺穿刺活檢對不同大小肺結節的診斷效能及并發癥相關因素分析. 放射學實踐, 2020, 35(11): 1409-1414.
10.	段星光, 溫浩, 何睿, 等. 胸腹腔經皮穿刺機器人研究進展及關鍵技術分析. 機器人, 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.	張睿, 周著黃, 吳薇薇, 等. CT引導肝腫瘤熱消融治療穿刺路徑規劃方法研究. 醫療衛生裝備, 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.	溫劍, 陽昆, 姚亞利, 等. K頻段斐波那契網格稀疏陣列分析與設計. 電訊技術, 2021, 61(4): 488-495.
27.	尹中元. CT引導下經皮肺穿刺活檢實操手冊. 北京: 科學出版社, 2019: 21-29.
28.	鮑楠. CT引導的肺穿刺路徑規劃與手術導航關鍵技術研究. 沈陽: 東北大學, 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.	張琳. 基于自監督遷移學習的肺部氣管和血管分割算法研究. 杭州: 浙江大學, 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, 李曉蘭, 等. 對比CT引導下不同進針路徑經皮肺穿刺活檢效果. 中國介入影像與治療學, 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. 韓雪嬌, 李雪梅, 龔小清, 等. 腫瘤領域全球研究熱點——基于ESI熱點論文的計量分析. 生物醫學工程學雜志, 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. 劉宗超, 李哲軒, 張陽, 等. 2020全球癌癥統計報告解讀. 腫瘤綜合治療電子雜志, 2021, 7(2): 1-13.
4. 楊雪玲, 于海鵬, 司同國. 胸部腫瘤經皮穿刺活檢中國專家共識(2020版)[J]. 中華介入放射學電子雜志, 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. 李納. CT引導下肺穿刺活檢技術的臨床應用價值. 長沙: 湖南師范大學, 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, 呂群, 阮肇揚, 等. CT引導下經皮肺穿刺活檢380例臨床分析. 中國實用內科雜志, 2018, 38(11): 1096-1099.
9. 王立學, 董鴻鵬, 白博鋒, 等. CT引導下經皮肺穿刺活檢對不同大小肺結節的診斷效能及并發癥相關因素分析. 放射學實踐, 2020, 35(11): 1409-1414.
10. 段星光, 溫浩, 何睿, 等. 胸腹腔經皮穿刺機器人研究進展及關鍵技術分析. 機器人, 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. 張睿, 周著黃, 吳薇薇, 等. CT引導肝腫瘤熱消融治療穿刺路徑規劃方法研究. 醫療衛生裝備, 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. 溫劍, 陽昆, 姚亞利, 等. K頻段斐波那契網格稀疏陣列分析與設計. 電訊技術, 2021, 61(4): 488-495.
27. 尹中元. CT引導下經皮肺穿刺活檢實操手冊. 北京: 科學出版社, 2019: 21-29.
28. 鮑楠. CT引導的肺穿刺路徑規劃與手術導航關鍵技術研究. 沈陽: 東北大學, 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. 張琳. 基于自監督遷移學習的肺部氣管和血管分割算法研究. 杭州: 浙江大學, 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, 李曉蘭, 等. 對比CT引導下不同進針路徑經皮肺穿刺活檢效果. 中國介入影像與治療學, 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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