Motion estimation is a fundamental step in dynamic medical image processing for the assessment of target organ anatomy and function. However, existing image-based motion estimation methods, which optimize the motion field by evaluating the local image similarity, are prone to produce implausible estimation, especially in the presence of large motion. In addition, the correct anatomical topology is difficult to be preserved as the image global context is not well incorporated into motion estimation. In this study, we provide a novel motion estimation framework of dense-sparse-dense (DSD), which comprises two stages. In the first stage, we process the raw dense image to extract sparse landmarks to represent the target organ’s anatomical topology, and discard the redundant information that is unnecessary for motion estimation. For this purpose, we introduce an unsupervised 3-D landmark detection network to extract spatially sparse but representative landmarks for the target organ’s motion estimation. In the second stage, we derive the sparse motion displacement from the extracted sparse landmarks of two images of different time points. Then, we present a motion reconstruction network to construct the motion field by projecting the sparse landmarks’ displacement back into the dense image domain. Furthermore, we employ the estimated motion field from our two-stage DSD framework as initialization and boost the motion estimation quality in light-weight yet effective iterative optimization. We evaluate our method on two dynamic medical imaging tasks to model cardiac motion and lung respiratory motion, respectively. Our method has produced superior motion estimation accuracy compared to the existing comparative methods. Besides, the extensive experimental results demonstrate that our solution can extract well-representative anatomical landmarks without any requirement of manual annotation. Our code is publicly available online: https://github.com/yyguo-sjtu/DSD-3D-Unsupervised-Landmark-Detection-Based-Motion-Estimation .

中文翻译：

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基于无监督地标检测的 4-D 动态医学图像时空运动估计

运动估计是动态医学图像处理中用于评估目标器官解剖结构和功能的基本步骤。然而，现有的基于图像的运动估计方法通过评估局部图像相似性来优化运动场，容易产生不可信的估计，尤其是在存在大运动的情况下。此外，由于图像全局上下文没有很好地结合到运动估计中，因此很难保留正确的解剖拓扑结构。在这项研究中，我们提供了一种新颖的密集-稀疏-密集（DSD）运动估计框架，它包括两个阶段。在第一阶段，我们处理原始密集图像以提取稀疏标志来表示目标器官的解剖拓扑结构，并丢弃对运动估计不必要的冗余信息。以此目的，我们引入了一个无监督的 3-D 地标检测网络来提取空间稀疏但具有代表性的地标，用于目标器官的运动估计。在第二阶段，我们从不同时间点的两幅图像的提取稀疏地标中导出稀疏运动位移。然后，我们提出了一个运动重建网络，通过将稀疏地标的位移投影回密集图像域来构建运动场。此外，我们使用两阶段 DSD 框架的估计运动场作为初始化，并在轻量级但有效的迭代优化中提高运动估计质量。我们在两个动态医学成像任务上评估我们的方法，分别模拟心脏运动和肺呼吸运动。与现有的比较方法相比，我们的方法产生了卓越的运动估计精度。此外，广泛的实验结果表明，我们的解决方案可以在不需要任何手动注释的情况下提取具有良好代表性的解剖标志。我们的代码可在线公开获取：https://github.com/yyguo-sjtu/DSD-3D-Unsupervised-Landmark-Detection-Based-Motion-Estimation .