Medicine-Engineering Interdisciplinary

Vascular Interventional Surgery Path Planning and 3D Visual Navigation

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  • 1. State Key Lab of Mechanical System and Vibration, Shanghai Jiao Tong University, Shanghai 200240, China; 2. School of Mechanical and Electrical Engineering, Guangdong University of Technology, Guangzhou 510006, China

Received date: 2023-03-14

  Accepted date: 2023-04-28

  Online published: 2025-06-06

Abstract

The introduction of path planning and visual navigation in vascular interventional surgery can provide an intuitive reference and guidance for doctors. In this study, based on the preprocessing results of vessel skeleton extraction and stenosis diagnosis in X-ray coronary angiography images, clustering is used to determine the connectivity of the intersection points, and then the improved Dijkstra algorithm is used to automatically plan the surgical path. On this basis, the intermediate point is introduced to piecewise correct the path and improve the accuracy of the system. Finally, the epipolar constrained inverse projection transformation is used to reconstruct the coronary artery 3D model, and the optimal path is marked to achieve a multi-angle 3D visual navigation. Clinical experimental results show that compared with the traditional Dijkstra algorithm, the improved method can reduce the need for intermediate points, which improves computational efficiency, and the average error of manual calibration path is reduced to 4% of that before overall optimization. The results of 3D reconstruction and reprojection further qualitatively and quantitatively verify the effectiveness of the whole scheme.

Cite this article

Fu Zeyu, Fu Zhuang, Guan Yisheng . Vascular Interventional Surgery Path Planning and 3D Visual Navigation[J]. Journal of Shanghai Jiaotong University(Science), 2025 , 30(3) : 472 -481 . DOI: 10.1007/s12204-023-2653-4

References

[1]   The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases in China 2021: An updated summary [J]. Chinese Circulation Journal, 2022, 37(6): 553-578 (in Chinese).

[2] LEVY R I, JESSE M J, MOCK M B. Position on percutaneous transluminal coronary angioplasty (PTCA) [J]. Circulation, 1979, 59(3): 613.

[3] LOWE H C, OESTERLE S N, KHACHIGIAN L M. Coronary in-stent restenosis: Current status and future strategies [J]. Journal of the American College of Cardiology, 2002, 39(2): 183-193.

[4] KIRIŞLI H A, SCHAAP M, METZ C T, et al. Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography [J]. Medical Image Analysis, 2013, 17(8): 859-876.

[5] LESSARD S, LAU C, CHAV R, et al. Guidewire tracking during endovascular neurosurgery [J]. Medical Engineering & Physics, 2010, 32(8): 813-821.

[6] GAO M K, CHEN Y M, ZHANG D H, et al. Path planning of vascular access surgery based on improved ant colony algorithm [J]. Journal of Shanghai University (Natural Science Edition), 2019, 25(2): 198-205 (in Chinese).

[7] AZIZI A, TREMBLAY C, MARTEL S. Trajectory planning for vascular navigation from 3D angiography images and vessel centerline data [C]//2017 International Conference on Manipulation, Automation and Robotics at Small Scales. Montreal: IEEE, 2017: 1-6.

[8] GUO J, SUN Y, GUO S X. A training system for vascular interventional surgeons based on local path planning [C]//2021 IEEE International Conference on Mechatronics and Automation. Takamatsu: IEEE, 2021: 1328-1333.

[9] FU Z Y, FU Z, LU C Z, et al. Robust implementation of foreground extraction and vessel segmentation for X-ray coronary angiography image sequence [DB/OL]. (2022-09-15). https://arxiv.org/abs/2209.07237

[10] ZHUANG Y, CHEN G B, FU Z. Segmentation and diagnosis of angiocardiography image [J]. Machinery & Electronics, 2018, 36(4): 16-19, 37 (in Chinese).

[11] SUI C X, FU Z, FU Z Y, et al. A novel method for vessel segmentation and automatic diagnosis of vascular stenosis [C]//2019 IEEE International Conference on Robotics and Biomimetics. Dali: IEEE, 2019: 918-923.

[12] CHERKASSKY B V, GOLDBERG A V, RADZIK T. Shortest paths algorithms: Theory and experimental evaluation [J]. Mathematical Programming, 1996, 73(2): 129-174.

[13] FANG H H, ZHU J J, AI D N, et al. Greedy soft matching for vascular tracking of coronary angiographic image sequences [J]. IEEE Transactions on Circuits and Systems for Video Technology, 2020, 30(5): 1466-1480.

[14] ÇIMEN S, GOOYA A, GRASS M, et al. Reconstruction of coronary arteries from X-ray angiography: A review [J]. Medical Image Analysis, 2016, 32: 46-68.

[15] BANERJEE A, GALASSI F, ZACUR E, et al. Point-cloud method for automated 3D coronary tree reconstruction from multiple non-simultaneous angiographic projections [J]. IEEE Transactions on Medical Imaging, 2019, 39(4): 1278-1290.

[16] JIA Y S, XIAO D Q, YAN Q, et al. A method for reconstructing 3D skeleton of coronary artery from 2D X-ray angiographic images [C]//IMIP 2022: 2022 4th International Conference on Intelligent Medicine and Image Processing. Tianjin: ACM, 2022: 70-75.

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