Robotic Ultrasound Makes CBCT Alive

2026-03-10

AI Summary

This work addresses the limitation of static cone-beam computed tomography (CBCT) in intraoperative navigation, which fails to capture real-time soft tissue deformations caused by respiration, probe pressure, and surgical manipulation, leading to navigation inaccuracies. To overcome this, the authors propose a deformation-aware CBCT update framework driven by robotic ultrasound. Leveraging a lightweight USCorUNet architecture and optical flow–guided supervised learning, the method infers tissue motion from dynamic ultrasound proxies and updates CBCT slices in real time without additional radiation exposure. The framework integrates LC2-based rigid registration for initialization, dense deformation field estimation, spatial regularization, and multimodal alignment to enable end-to-end, physically plausible dynamic CBCT visualization. This approach significantly enhances the accuracy of dynamic navigation in robot-assisted ultrasound-guided interventional procedures.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Computer Vision: Motion & Tracking
• Planning, Routing, and Scheduling: Activity and Plan Recognition

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation

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 https://github.com/anonymous-codebase/us-cbct-demo.

Problem

• CBCT
• soft-tissue deformation
• intraoperative imaging
• navigation discrepancy
• real-time monitoring

Innovation

• deformation-aware CBCT updating
• robotic ultrasound
• USCorUNet
• real-time dense deformation estimation
• multimodal image registration