Image registration of liver dynamic contrast-enhanced computed tomography (DCE-CT) is crucial for diagnosis and image-guided surgical planning of liver cancer. However, intensity variations due to the flow of contrast agents combined with complex spatial motion induced by respiration brings great challenge to existing intensity-based registration methods. To address these problems, we propose a novel structure-aware registration method by incorporating structural information of related organs with segmentation-guided deep registration network. Existing segmentation-guided registration methods only focus on volumetric registration inside the paired organ segmentations, ignoring the inherent attributes of their anatomical structures. In addition, such paired organ segmentations are not always available in DCE-CT images due to the flow of contrast agents. Different from existing segmentation-guided registration methods, our proposed method extracts structural information in hierarchical geometric perspectives of line and surface. Then, according to the extracted structural information, structure-aware constraints are constructed and imposed on the forward and backward deformation field simultaneously. In this way, all available organ segmentations, including unpaired ones, can be fully utilized to avoid the side effect of contrast agent and preserve the topology of organs during registration. Extensive experiments on an in-house liver DCE-CT dataset and a public LiTS dataset show that our proposed method can achieve higher registration accuracy and preserve anatomical structure more effectively than state-of-the-art methods.

翻译：肝脏动态对比增强计算机断层扫描（DCE-CT）图像的配准对于肝癌诊断及图像引导下的手术规划至关重要。然而，对比剂流动导致灰度变化，加之呼吸引起的复杂空间运动，给现有基于灰度的配准方法带来了巨大挑战。为解决这些问题，我们提出了一种新颖的结构感知配准方法，通过将相关器官的结构信息与基于分割的深度配准网络相结合。现有基于分割的配准方法仅关注配对器官分割区域内的体积配准，忽略了其解剖结构的固有属性。此外，由于对比剂的流动，DCE-CT图像中此类配对器官分割并不总是可用。与现有基于分割的配准方法不同，我们的方法从线和面的分层几何视角提取结构信息。随后，根据提取的结构信息，构造结构感知约束并同时施加于正向和反向形变场。通过这种方式，所有可用的器官分割（包括非配对分割）均可被充分利用，以规避对比剂的副作用并保留配准过程中的器官拓扑结构。在内部肝脏DCE-CT数据集和公开LiTS数据集上进行的大量实验表明，与现有最优方法相比，我们的方法可实现更高的配准精度并更有效地保持解剖结构。