Abstract
Intraoperative Cone Beam Computed Tomography (CBCT) provides a reliable 3D anatomical context essential for interventional planning. However, its static nature fails to provide continuous monitoring of soft-tissue deformations induced by respiration, probe pressure, and surgical manipulation, leading to navigation discrepancies. We propose a deformation-aware CBCT updating framework that leverages robotic ultrasound as a dynamic proxy to infer tissue motion and update static CBCT slices in real time. Starting from calibration-initialized alignment with linear correlation of linear combination (LC2)-based rigid refinement, our method establishes accurate multimodal correspondence. To capture intraoperative dynamics, we introduce the ultrasound correlation UNet (USCorUNet), a lightweight network trained with optical flow-guided supervision to learn deformation-aware correlation representations, enabling accurate, real-time dense deformation field estimation from ultrasound streams. The inferred deformation is spatially regularized and transferred to the CBCT reference to produce deformation-consistent visualizations without repeated radiation exposure. We validate the proposed approach through deformation estimation and ultrasound-guided CBCT updating experiments. Results demonstrate real-time end-to-end CBCT slice updating and physically plausible deformation estimation, enabling dynamic refinement of static CBCT guidance during robotic ultrasound-assisted interventions. The source code is publicly available at this https URL.
Abstract (translated)
术中锥形束计算机断层摄影（CBCT）提供了一种可靠的三维解剖学背景，这对于介入规划至关重要。然而，由于其静态特性，无法持续监测由呼吸、探针压力和手术操作引起的软组织变形，导致导航误差。我们提出了一种基于机器人超声的变形感知CBCT更新框架，利用动态代理来推断组织运动，并实时更新静态CBCT切片。从线性组合（LC2）基础刚体细化的校准初始化对齐开始，我们的方法建立了准确的多模式对应关系。为了捕捉术中动力学变化，我们引入了超声相关UNet (USCorUNet)，这是一个轻量级网络，在光流引导监督下训练，以学习感知变形的相关表示，能够从超声流中实现精确、实时密集变形场估计。推断出的变形经过空间正则化后被转移到CBCT参考上，从而生成不需重复辐射暴露的变形一致可视化图。我们通过变形估计和超声引导CBCT更新实验验证了该方法的有效性。结果表明，实现了端到端的实时CBCT切片更新，并且可以进行物理上合理的变形估算，使静态CBCT指导在机器人超声辅助介入过程中能够动态精炼化。源代码可在以下链接获取：[此URL]
URL
https://arxiv.org/abs/2603.10220
