Intraoperative Correction of Liver Deformation Using Sparse Surface and Vascular Features via Linearized Iterative Boundary Reconstruction
Intraoperative Correction of Liver Deformation Using Sparse Surface and Vascular Features via Linearized Iterative Boundary Reconstruction
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DOI：
10.1109/tmi.2020.2967322
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发表时间：
2020-06-01
影响因子：
10.6
通讯作者：
Miga, Michael I.
中科院分区：
文献类型：
--
作者：
Heiselman, Jon S.;Jarnagin, William R.;Miga, Michael I.
在图像引导肝脏手术期间，当试图通过图像到物理配准实现手术解剖结构的准确定位时，软组织变形可能导致相当大的误差。本文提出了一种线性化的迭代边界重建技术来考虑这些变形。该方法利用边界条件的叠加公式来快速准确地估计应用于器官的术前模型的变形，给出表面和表面下特征的稀疏术中数据。使用这种方法，跟踪术中超声（iUS）作为一个潜在的数据源，用于增强配准精度超过传统的器官表面配准的能力进行了研究。在扩展的模拟数据集中，从iUS平面提取包括血管轮廓、血管中心线和后肝表面的特征。在不断增加的数据密度中比较配准精度，以确定如何最好地使用iUS来改善靶配准误差（TRE）。从仅使用稀疏表面数据的11.4 +/- 2.2 mm的基线平均TRE，结合来自三个iUS平面的额外稀疏特征将平均TRE提高到6.4 +/- 1.0 mm。此外，将稀疏覆盖范围增加到16个跟踪的iUS平面将平均TRE提高到3.9 +/- 0.7 mm，超过了基于可用的完整表面数据的配准的准确性，其中更繁琐的术中CT没有造影剂。此外，该方法应用于三个临床病例，其中当结合来自一个独立跟踪iUS平面的附加特征时，平均误差比刚性配准提高67%，比可变形表面配准提高56%。
During image guided liver surgery, soft tissue deformation can cause considerable error when attempting to achieve accurate localization of the surgical anatomy through image-to-physical registration. In this paper, a linearized iterative boundary reconstruction technique is proposed to account for these deformations. The approach leverages a superposed formulation of boundary conditions to rapidly and accurately estimate the deformation applied to a preoperative model of the organ given sparse intraoperative data of surface and subsurface features. With this method, tracked intraoperative ultrasound (iUS) is investigated as a potential data source for augmenting registration accuracy beyond the capacity of conventional organ surface registration. In an expansive simulated dataset, features including vessel contours, vessel centerlines, and the posterior liver surface are extracted from iUS planes. Registration accuracy is compared across increasing data density to establish how iUS can be best employed to improve target registration error (TRE). From a baseline average TRE of 11.4 +/- 2.2 mm using sparse surface data only, incorporating additional sparse features from three iUS planes improved average TRE to 6.4 +/- 1.0 mm. Furthermore, increasing the sparse coverage to 16 tracked iUS planes improved average TRE to 3.9 +/- 0.7 mm, exceeding the accuracy of registration based on complete surface data available with more cumbersome intraoperative CT without contrast. Additionally, the approach was applied to three clinical cases where on average error improved 67% over rigid registration and 56% over deformable surface registration when incorporating additional features from one independent tracked iUS plane.