Robotic Ultrasound Makes CBCT Alive
MCML Authors
Zhongliang Jiang
Dr.
Nassir Navab
Prof. Dr.
Principal Investigator
Abstract
Zhongliang Jiang
Dr.
Nassir Navab
Prof. Dr.
Principal Investigator
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.
misc LLJ+26
Preprint
Mar. 2026Authors
F. Li • Z. Li • Z. Jiang • N. Navab • Y. BiLinks
arXiv GitHubResearch Area
BibTeXKey: LLJ+26