Physics-based shape matching for intraoperative image guidance
Physics-based shape matching for intraoperative image guidance
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DOI：
10.1118/1.4896021
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发表时间：
2014-11-01
期刊：
影响因子：
3.8
通讯作者：
Speidel, Stefanie
中科院分区：
文献类型：
--
作者：
Suwelack, Stefan;Roehl, Sebastian;Speidel, Stefanie
目的：在计算机辅助介入治疗过程中，软组织变形会严重降低术前计划数据的有效性。术中成像，例如立体内窥镜、飞行时间或激光距离扫描仪数据，可以用于补偿这些运动。在这种情况下，术中表面必须与术前模型相匹配。形状匹配是特别具有挑战性的，在术中设置，由于嘈杂的传感器数据，只有部分可见的表面，模糊的形状描述符，和实时requirements.Methods：一种新的基于物理的形状匹配（PBSM）的方法注册术中获取的表面网格术前规划数据。该方法的关键思想是将非刚性配准过程描述为静电弹性问题，其中带电荷的弹性体（术前模型）滑入带相反电荷的刚性形状（术中表面）。结果表明，相应的能量泛函可以有效地解决使用有限元（FE）方法。它还演示了如何PBSM可以结合刚性注册计划的鲁棒非刚性注册任意对齐的表面。此外，它示出了如何的方法可以与地标为基础的方法相结合，并概述其在腹腔镜interventions.Results图像引导的应用程序：PBSM计划在硅片和幻影数据的基础上提出了深刻的分析。对几种肝脏模型的仿真研究表明，该方法对初始刚性配准和参数变化具有较好的鲁棒性。研究还表明，该方法实现了亚毫米级的配准精度（平均误差在0.32和0.46 mm之间）。该方法的未经优化的单核实现实现了接近实时的性能（2 TPS，7-19 s的总注册时间）。它在速度和准确性方面优于现有方法。此外，它表明，该方法是能够准确地匹配部分表面。最后，幻影实验演示了如何将该方法与立体内窥镜成像相结合，以提供非刚性注册在腹腔镜interventions.Conclusions：PBSM方法的表面匹配是快速，稳健，准确的。由于该技术是基于术前体积有限元模型，它自然恢复体积结构的位置（例如，例如，在一个实施例中，肿瘤和血管）。它不仅可以用于从术中表面模型中恢复软组织变形，还可以与来自体积成像的地标数据相结合。除了在腹腔镜手术中的应用外，该方法可能在需要从稀疏术中传感器数据进行软组织配准的其他领域（例如，例如，在一个实施例中，放射治疗）。(C)2014年美国医学物理学家协会。
Purpose: Soft-tissue deformations can severely degrade the validity of preoperative planning data during computer assisted interventions. Intraoperative imaging such as stereo endoscopic, time-of-flight or, laser range scanner data can be used to compensate these movements. In this context, the intraoperative surface has to be matched to the preoperative model. The shape matching is especially challenging in the intraoperative setting due to noisy sensor data, only partially visible surfaces, ambiguous shape descriptors, and real-time requirements.Methods: A novel physics-based shape matching (PBSM) approach to register intraoperatively acquired surface meshes to preoperative planning data is proposed. The key idea of the method is to describe the nonrigid registration process as an electrostatic-elastic problem, where an elastic body (preoperative model) that is electrically charged slides into an oppositely charged rigid shape (intraoperative surface). It is shown that the corresponding energy functional can be efficiently solved using the finite element (FE) method. It is also demonstrated how PBSM can be combined with rigid registration schemes for robust nonrigid registration of arbitrarily aligned surfaces. Furthermore, it is shown how the approach can be combined with landmark based methods and outline its application to image guidance in laparoscopic interventions.Results: A profound analysis of the PBSM scheme based on in silico and phantom data is presented. Simulation studies on several liver models show that the approach is robust to the initial rigid registration and to parameter variations. The studies also reveal that the method achieves submillimeter registration accuracy (mean error between 0.32 and 0.46 mm). An unoptimized, single core implementation of the approach achieves near real-time performance (2 TPS, 7-19 s total registration time). It outperforms established methods in terms of speed and accuracy. Furthermore, it is shown that the method is able to accurately match partial surfaces. Finally, a phantom experiment demonstrates how the method can be combined with stereo endoscopic imaging to provide nonrigid registration during laparoscopic interventions.Conclusions: The PBSM approach for surface matching is fast, robust, and accurate. As the technique is based on a preoperative volumetric FE model, it naturally recovers the position of volumetric structures (e. g., tumors and vessels). It cannot only be used to recover soft-tissue deformations from intraoperative surface models but can also be combined with landmark data from volumetric imaging. In addition to applications in laparoscopic surgery, the method might prove useful in other areas that require soft-tissue registration from sparse intraoperative sensor data (e. g., radiation therapy). (C) 2014 American Association of Physicists in Medicine.