Liu Jihao, Zheng Guoyan, Yan Weixin
State Key Laboratory of Ocean Engineering, School of Ocean and Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Institute of Medical Robotics, School of Medical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China.
Sensors (Basel). 2025 Jan 3;25(1):238. doi: 10.3390/s25010238.
It is a great challenge for a safe surgery to localize the cutting tip during laminar grinding. To address this problem, we develop a framework of state estimation based on the CT image-force model. For the proposed framework, the pre-operative CT image and intra-operative milling force signal work as source inputs. In the framework, a bone milling force prediction model is built, and the surgical planned paths can be transformed into the prediction sequences of milling force. The intra-operative milling force signal is segmented by the tumbling window algorithm. Then, the similarity between the prediction sequences and the segmented milling signal is derived by the dynamic time warping (DTW) algorithm. The derived similarity indicates the position of the cutting tip. Finally, to overcome influences of some factors, we used the random sample consensus (RANSAC). The code of the functional simulations has be opened.
在层流磨削过程中定位切割尖端对于安全手术来说是一项巨大挑战。为解决这一问题，我们开发了一种基于CT图像-力模型的状态估计框架。对于所提出的框架，术前CT图像和术中铣削力信号作为源输入。在该框架中，建立了骨铣削力预测模型，手术规划路径可转换为铣削力的预测序列。术中铣削力信号通过滚动窗口算法进行分段。然后，通过动态时间规整（DTW）算法得出预测序列与分段铣削信号之间的相似度。得出的相似度表明切割尖端的位置。最后，为克服一些因素的影响，我们使用了随机抽样一致性（RANSAC）。功能模拟代码已公开。
