Kundu Bipasha, Yang Zixin, Simon Richard, Linte Cristian
Center for Imaging Science, Rochester Institute of Technology, Rochester, NY 14623, USA.
Department of Biomedical Engineering, Rochester Institute of Technology, Rochester, NY 14623, USA.
Proc SPIE Int Soc Opt Eng. 2024 Feb;12928. doi: 10.1117/12.3008594. Epub 2024 Mar 29.
Non-rigid surface-based soft tissue registration is crucial for surgical navigation systems, but its adoption still faces several challenges due to the large number of degrees of freedom and the continuously varying and complex surface structures present in the intra-operative data. By employing non-rigid registration, surgeons can integrate the pre-operative images into the intra-operative guidance environment, providing real-time visualization of the patient's complex pre- and intra-operative anatomy in a common coordinate system to improve navigation accuracy. However, many of the existing registration methods, including those for liver applications, are inaccessible to the broader community. To address this limitation, we present a comparative analysis of several open-source, non-rigid surface-based liver registration algorithms, with the overall goal of contrasting their strength and weaknesses and identifying an optimal solution. We compared the robustness of three optimization-based and one data-driven nonrigid registration algorithms in response to a reduced visibility ratio (reduced partial views of the surface) and to an increasing deformation level (mean displacement), reported as the root mean square error (RMSE) between the pre-and intra-operative liver surface meshed following registration. Our results indicate that the Gaussian Mixture Model - Finite Element Model (GMM-FEM) method consistently yields a lower post-registration error than the other three tested methods in the presence of both reduced visibility ratio and increased intra-operative surface displacement, therefore offering a potentially promising solution for pre- to intra-operative nonrigid liver surface registration.
基于非刚性表面的软组织配准对于手术导航系统至关重要，但由于术中数据存在大量自由度以及不断变化且复杂的表面结构，其应用仍面临诸多挑战。通过采用非刚性配准，外科医生可以将术前图像整合到术中引导环境中，在一个公共坐标系中实时可视化患者复杂的术前和术中解剖结构，以提高导航准确性。然而，包括肝脏应用的那些方法在内，许多现有的配准方法广大群体难以获取。为解决这一局限性，我们对几种基于非刚性表面的开源肝脏配准算法进行了比较分析，总体目标是对比它们的优缺点并确定最优解决方案。我们比较了三种基于优化的和一种数据驱动的非刚性配准算法在可见度比率降低（表面局部视图减少）和变形程度增加（平均位移）情况下的鲁棒性，将其报告为配准后术前和术中肝脏表面网格之间的均方根误差（RMSE）。我们的结果表明，在可见度比率降低和术中表面位移增加的情况下，高斯混合模型 - 有限元模型（GMM - FEM）方法始终比其他三种测试方法产生更低的配准后误差，因此为术前到术中的非刚性肝脏表面配准提供了一个潜在的有前景的解决方案。
