多模态图像与点云融合在膝关节置换中的空间曲面配准
Fusion of multimodality image and point cloud for spatial surface registration for knee arthroplasty.
机构信息
Digital Medical Research Center, School of Basic Medical Sciences, Fudan University, Shanghai, China.
Shanghai Key Laboratory of Medical Imaging Computing and Computer Assisted Intervention, Shanghai, China.
出版信息
Int J Med Robot. 2022 Oct;18(5):e2426. doi: 10.1002/rcs.2426. Epub 2022 Jun 8.
BACKGROUND
Image-guided computer-aided navigation system is an indispensable part of computer assisted orthopaedic surgery. However, the location and number of fiducial markers, the time required to localise fiducial markers in existing systems affect their effectiveness.
METHOD
The study proposed that spatial surface registration between the point cloud on the surface of the fusion model based on preoperative knee MRI and CT images and the point cloud on the cartilage surface captured by intraoperative laser scanner could solve the above limitations.
RESULTS
The experimental results show that the registration error of the method is less than 2 mm, but the total time from scanning the point cloud on patient's cartilage surface to registering it with the point cloud in preoperative image space is less than 2 min.
CONCLUSION
The method achieves the registration accuracy similar to existing methods without selecting anatomical corresponding points, which is of great help to the clinic.
背景
图像引导计算机辅助导航系统是计算机辅助骨科手术不可或缺的一部分。然而，现有系统中基准标记的位置和数量、定位基准标记所需的时间影响了它们的有效性。
方法
本研究提出，基于术前膝关节 MRI 和 CT 图像的融合模型表面上的点云与术中激光扫描仪获取的软骨表面点云之间的空间曲面配准可以解决上述限制。
结果
实验结果表明，该方法的配准误差小于 2mm，但从扫描患者软骨表面的点云到将其与术前图像空间中的点云配准的总时间不到 2 分钟。
结论
该方法在无需选择解剖对应点的情况下实现了与现有方法相似的配准精度，对临床具有重要意义。
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