The 3D/2D registration for 3D pre-operative images (computed tomography, CT) and 2D intra-operative images (X-ray) plays an important role in image-guided spine surgeries. Conventional iterative-based approaches suffer from time-consuming processes. Existing learning-based approaches require high computational costs and face poor performance on large misalignment because of projection-induced losses or ill-posed reconstruction. In this paper, we propose a Progressive 3D/2D rigid Registration network with the guidance of Single-view Cycle Synthesis, named PRSCS-Net. Specifically, we first introduce the differentiable backward/forward projection operator into the single-view cycle synthesis network, which reconstructs corresponding 3D geometry features from two 2D intra-operative view images (one from the input, and the other from the synthesis). In this way, the problem of limited views during reconstruction can be solved. Subsequently, we employ a self-reconstruction path to extract latent representation from pre-operative 3D CT images. The following pose estimation process will be performed in the 3D geometry feature space, which can solve the dimensional gap, greatly reduce the computational complexity, and ensure that the features extracted from pre-operative and intra-operative images are as relevant as possible to pose estimation. Furthermore, to enhance the ability of our model for handling large misalignment, we develop a progressive registration path, including two sub-registration networks, aiming to estimate the pose parameters via two-step warping volume features. Finally, our proposed method has been evaluated on a public dataset CTSpine1k and an in-house dataset C-ArmLSpine for 3D/2D registration. Results demonstrate that PRSCS-Net achieves state-of-the-art registration performance in terms of registration accuracy, robustness, and generalizability compared with existing methods. Thus, PRSCS-Net has potential for clinical spinal disease surgical planning and surgical navigation systems.

中文翻译：

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PRSCS-Net：单视图循环合成指导下的渐进式 3D/2D 刚性配准网络


3D 术前图像（计算机断层扫描、CT）和 2D 术中图像（X 射线）的 3D/2D 配准在图像引导脊柱手术中发挥着重要作用。传统的基于迭代的方法存在耗时的过程。现有的基于学习的方法需要很高的计算成本，并且由于投影引起的损失或不适定重建而在大偏差上面临性能不佳的问题。在本文中，我们提出了一种在单视图循环合成指导下的渐进式 3D/2D 刚性配准网络，称为 PRSCS-Net。具体来说，我们首先将可微分后向/前向投影算子引入到单视图循环合成网络中，该网络从两个 2D 术中视图图像（一个来自输入，另一个来自合成）重建相应的 3D 几何特征。这样就可以解决重建时视野有限的问题。随后，我们采用自重建路径从术前 3D CT 图像中提取潜在表示。接下来的位姿估计过程将在3D几何特征空间中进行，可以解决维度差距，大大降低计算复杂度，并确保从术前和术中图像中提取的特征尽可能与位姿相关估计。此外，为了增强模型处理大偏差的能力，我们开发了一种渐进式配准路径，包括两个子配准网络，旨在通过两步扭曲体积特征来估计姿态参数。最后，我们提出的方法已在公共数据集 CTSpine1k 和用于 3D/2D 配准的内部数据集 C-ArmLSpine 上进行了评估。 结果表明，与现有方法相比，PRSCS-Net 在配准精度、鲁棒性和通用性方面实现了最先进的配准性能。因此，PRSCS-Net 在临床脊柱疾病手术规划和手术导航系统方面具有潜力。