Abstract
Craniotomy is a prerequisite for many neuro-surgeries such as intracranial tumor resection and decompression of cerebral hemorrhage. However, existing manual craniotomy methods are time-consuming and labor-intensive, presenting low efficiency and security. Robots have shown great potential for safe, precise and efficient craniotomy by introducing accurate positioning and stable motion control. We proposed a method that automatically computes operation path for robotic craniotomy and adapt to the irregular geometric change of the skull, based on the surgeon’s surgical plan from computed tomography (CT) images. The drilling path is generated from intersection between the skull entity and surgeon-input drilling intents. A virtual-center (VC) method is developed to adaptively compute an initial milling path through the generated drilling path, which is then improved under both clinical and skull-cutting constrains. The results show that our method works effectively to generate the operation path adapting to the unstructured skull. This research highlights the potential for optimizing current craniotomy procedures by employing the robot, laying the foundation for future autonomous robot-based craniotomy.
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References
Johns, U.S.: Hopkins medicine, craniotomy (2023). https://www.hopkinsmedicine.org/health/treatment-tests-and-therapies/craniotomy
Bian, G.B., Wei, B.T., Li, Z., et al.: Robotic automatic drilling for craniotomy: algorithms and in vitro animal experiments. IEEE/ASME Trans. Mechatron. (2023)
Caird, J.D., Choudhari, K.A.: Plunging’during burr hole craniostomy: a persistent problem amongst neurosurgeons in Britain and Ireland. Br. J. Neurosurg. 17(6), 509–512 (2003)
Chiang, H.Y., Kamath, A.S., Pottinger, J.M., et al.: Risk factors and outcomes associated with surgical site infections after craniotomy or craniectomy. J. Neurosurg. 120(2), 509–521 (2014)
Kwoh, Y.S., Hou, J., Jonckheere, E.A., et al.: A robot with improved absolute positioning accuracy for CT guided stereotactic brain surgery. IEEE Trans. Biomed. Eng. 35(2), 153–160 (1988)
Xu, C., Lin, L., Aung, Z.M., et al.: A preliminary study on animal experiments of robot-assisted craniotomy. World Neurosurg. 149, e748–e757 (2021)
Duan, X., Tian, H., Li, C., et al.: Virtual-fixture based drilling control for robot-assisted craniotomy: learning from demonstration. IEEE Robot. Autom. Lett. 6(2), 2327–2334 (2021)
Bian, G.B., Qian, C., Li, Z., et al.: Automatic robotic cranium-milling: a motion control study of in vitro animal experiments. IEEE Trans. Autom. Sci. Eng. (2024)
Pak, N., Siegle, J.H., Kinney, J.P., et al.: Closed-loop, ultraprecise, automated craniotomies. J. Neurophysiol. 113(10), 3943–3953 (2015)
Grygoryev, K., Komolibus, K., Gunther, J., et al.: Cranial perforation using an optically-enhanced surgical drill. IEEE Trans. Biomed. Eng. 67(12), 3474–3482 (2020)
Zakeri, V., Hodgson, A.J.: Automatic identification of hard and soft bone tissues by analyzing drilling sounds. IEEE/ACM Trans. Audio Speech Lang. Process. 27(2), 404–414 (2018)
Liang, L., Cool, D., Kakani, N., et al.: Automatic radiofrequency ablation planning for liver tumors with multiple constraints based on set covering. IEEE Trans. Med. Imaging 39(5), 1459–1471 (2019)
Zhang, Q., Li, M., Qi, X., et al.: 3D path planning for anterior spinal surgery based on CT images and reinforcement learning. In: IEEE International Conference on Cyborg and Bionic Systems (CBS), pp. 317–321. IEEE (2018)
Qi, X., Meng, J., Li, M., et al.: An automatic path planning method of pedicle screw placement based on preoperative CT images. IEEE Trans. Med. Robot. Bionics 4(2), 403–413 (2022)
Henrich, D.: Robotergestütztes fräsen an der lateralen schädelbasis: kraft-basierte lokale navigation bei der implantatbettanlage (2002)
Stolka, P.J., Henrich, D., Tretbar, S.H., et al.: First 3D ultrasound scanning, planning, and execution of CT-free milling interventions with a surgical robot. In: 30th Annual International Conference of the IEEE Engineering in Medicine and Biology Society, pp. 5605–5610. IEEE (2008)
Waringo, M., Henrich, D.: 3-Dimensionale schichtweise Bahnplanung fur any-time-frasanwendungen. Vdi Ber. 1841, 781–788 (2004)
Waringo, M., Henrich, D.: Smoothing of piecewise linear paths. Int. J. Adv. Rob. Syst. 5(3), 24 (2008)
Petermann, J., Kober, R., Heinze, R., et al.: Computer-assisted planning and robot-assisted surgery in anterior cruciate ligament reconstruction. Oper. Tech. Orthop. 10(1), 50–55 (2000)
Fuchs, K.H.: Minimally invasive surgery. Endoscopy 34(02), 154−159 (2002)
Otsu, N.: A threshold selection method from gray-level histograms. IEEE Trans. Syst. Man Cybern. 9(1), 62–66 (1979)
Kikinis, R., Pieper, S.D., Vosburgh, K.G.: 3D slicer: a platform for subject-specific image analysis, visualization, and clinical support. Intraoperative imaging and image-guided therapy, pp. 277–289. New York, NY: Springer New York (2013)
Drouin, S., Collins, D.L.: PRISM: an open source framework for the interactive design of GPU volume rendering shaders. PLoS ONE 13(3), e0193636 (2018)
Ran, L., Wanggen, W., Yiyuan, Z., et al.: Normal estimation algorithm for point cloud using KD-Tree (2013)
Kennedy, J., Eberhart, R.: Particle swarm optimization. In: Proceedings of ICNN′95-International Conference on Neural Networks, vol. 4, pp. 1942–1948. IEEE (1995)
Kurasov, D.A.: Computer-aided manufacturing: industry 4.0. In: IOP Conference Series: Materials Science and Engineering, vol. 1047, no. 1, p. 012153. IOP Publishing (2021)
Yang, G.Z., Cambias, J., Cleary, K., et al.: Dancewu. Sci. Robot. 2(4), eaam8638 (2017)
Acknowledgements
This work was supported in part by the Beijing Natural Science Foundation-Haidian Original Innovation Joint Fund Project under Grant L212048, and the State Key Laboratory of Tribology in Advanced Equipment of China under Grant SKLT2022B08.
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Li, Z., Hong, S., Ma, X., Wu, D. (2025). Adaptive Path Planning Method for Robot-Assisted Craniotomy. In: Lan, X., Mei, X., Jiang, C., Zhao, F., Tian, Z. (eds) Intelligent Robotics and Applications. ICIRA 2024. Lecture Notes in Computer Science(), vol 15205. Springer, Singapore. https://doi.org/10.1007/978-981-96-0777-8_10
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