Zou Jing, Liu Jia, Choi Kup-Sze, Qin Jing
Center for Smart Health, School of Nursing, the Hong Kong Polytechnic University, Hong Kong, China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen 518055, China.
Bioengineering (Basel). 2023 May 8;10(5):562. doi: 10.3390/bioengineering10050562.
Deformable lung CT image registration is an essential task for computer-assisted interventions and other clinical applications, especially when organ motion is involved. While deep-learning-based image registration methods have recently achieved promising results by inferring deformation fields in an end-to-end manner, large and irregular deformations caused by organ motion still pose a significant challenge. In this paper, we present a method for registering lung CT images that is tailored to the specific patient being imaged. To address the challenge of large deformations between the source and target images, we break the deformation down into multiple continuous intermediate fields. These fields are then combined to create a spatio-temporal motion field. We further refine this field using a self-attention layer that aggregates information along motion trajectories. By leveraging temporal information from a respiratory cycle, our proposed methods can generate intermediate images that facilitate image-guided tumor tracking. We evaluated our approach extensively on a public dataset, and our numerical and visual results demonstrate the effectiveness of the proposed method.
可变形肺部CT图像配准是计算机辅助干预和其他临床应用中的一项重要任务，尤其是在涉及器官运动时。虽然基于深度学习的图像配准方法最近通过端到端推断变形场取得了有前景的结果，但器官运动引起的大的和不规则的变形仍然构成重大挑战。在本文中，我们提出了一种针对特定成像患者定制的肺部CT图像配准方法。为了解决源图像和目标图像之间大变形的挑战，我们将变形分解为多个连续的中间场。然后将这些场组合起来创建一个时空运动场。我们使用一个自注意力层沿着运动轨迹聚合信息来进一步细化这个场。通过利用呼吸周期的时间信息，我们提出的方法可以生成有助于图像引导肿瘤跟踪的中间图像。我们在一个公共数据集上对我们的方法进行了广泛评估，我们的数值和视觉结果证明了所提出方法的有效性。
