A Mechanics-Based Nonrigid Registration Method for Liver Surgery Using Sparse Intraoperative Data.
A Mechanics-Based Nonrigid Registration Method for Liver Surgery Using Sparse Intraoperative Data.
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DOI：
10.1109/tmi.2013.2283016
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发表时间：
2014-01
影响因子：
10.6
通讯作者：
Miga MI
中科院分区：
文献类型：
--
作者：
Rucker DC;Wu Y;Clements LW;Ondrake JE;Pheiffer TS;Simpson AL;Jarnagin WR;Miga MI
在开放式腹部图像引导肝脏手术中，器官表面的稀疏测量可以在术中通过激光范围扫描设备或跟踪触针进行，对手术工作流程的影响相对较小。我们提出了一种新的非刚性配准方法，它使用稀疏的表面数据来重建术前CT体积和术中患者空间之间的映射。使用组织力学模型生成映射，该模型受到与肝切除治疗期间的手术支持性填塞一致的边界条件的影响。我们的方法迭代地选择定义这些边界条件的参数，使得变形的组织模型最适合术中表面数据。使用两个肝脏模型，我们收集了总共五个变形数据集，其条件与开放手术相当。所提出的非刚性方法对于分散在整个体模体积中的目标实现了3.3 mm的平均目标配准误差（TRE），使用有限区域的表面数据来驱动非刚性配准算法，而刚性配准导致平均TRE为9.5 mm。此外，我们研究了表面数据范围的影响，包括地下数据，使用非线性组织模型的权衡、对刚性未对准的鲁棒性以及在五个临床数据集中的可行性。
In open abdominal image-guided liver surgery, sparse measurements of the organ surface can be taken intraoperatively via a laser-range scanning device or a tracked stylus with relatively little impact on surgical workflow. We propose a novel nonrigid registration method which uses sparse surface data to reconstruct a mapping between the preoperative CT volume and the intraoperative patient space. The mapping is generated using a tissue mechanics model subject to boundary conditions consistent with surgical supportive packing during liver resection therapy. Our approach iteratively chooses parameters which define these boundary conditions such that the deformed tissue model best fits the intraoperative surface data. Using two liver phantoms, we gathered a total of five deformation datasets with conditions comparable to open surgery. The proposed nonrigid method achieved a mean target registration error (TRE) of 3.3 mm for targets dispersed throughout the phantom volume, using a limited region of surface data to drive the nonrigid registration algorithm, while rigid registration resulted in a mean TRE of 9.5 mm. In addition, we studied the effect of surface data extent, the inclusion of subsurface data, the trade-offs of using a nonlinear tissue model, robustness to rigid misalignments, and the feasibility in five clinical datasets.