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一个粗细的框架与曲率特征学习,用于脊柱外科导航中强大的点云注册
Lijing Zhang1, Wei Wang2, Tianbao Liu1
1School of Biomedical Engineering, Capital Medical University, Beijing 100069, China.
Bioengineering (Basel, Switzerland)
|October 29, 2025
概括
这项研究引入了外科导航的新粗细注册框架. 它提高了准确性和速度,使手术前和手术期间的点云对齐,以固定脚螺丝.
科学领域:
- 医疗成像医学成像
- 计算机辅助手术 计算机辅助手术
- 机器人技术 机器人技术 机器人技术
背景情况:
- 准确地记录手术前和手术期间的点云对于手术导航辅助脚螺丝固定至关重要.
- 经典的注册算法在最初的显著姿势差异,很少的重叠和噪音方面扎,影响准确性和性能.
研究的目的:
- 开发一个强大而准确的粗到细的注册框架,用于在外科导航中对准点云.
- 克服传统方法在处理噪音数据和大量初始姿势变化的局限性.
主要方法:
- 一个新的基于曲率特征学习的点匹配 (CFL-PM) 算法用于粗略的注册.
- 最远点采样 (FPS) 以确定重叠区域进行初始调整.
- 代式最接近点 (ICP) 算法用于微调注册准确度.
主要成果:
- 在无噪声条件下,实现了0.34°和0.27mm的平均旋转和转移误差.
- 在噪音条件下经过证明的稳定性,平均粗度记录误差为7.28° (旋转) 和9.08mm (转换).
- 在注册准确性,速度和在手术前和手术内数据集的稳定性方面超过了现有的算法.
结论:
- 拟议的粗细注册框架有效地解决了手术点云注册方面的挑战.
- 这种方法可以为手术导航辅助脚螺丝固定提供精确的对齐,从而提高手术结果.
- 与ICP相结合的CFL-PM算法在医疗图像记录准确性和效率方面取得了显著的进步.
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