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Robotic Ultrasound Makes CBCT Alive

Feng Li, Ziyuan Li, Zhongliang Jiang, Nassir Navab, Yuan Bi

TL;DR

The ultrasound correlation UNet (USCorUNet) is introduced, 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.

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.

Robotic Ultrasound Makes CBCT Alive

TL;DR

The ultrasound correlation UNet (USCorUNet) is introduced, 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.

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.
Paper Structure (13 sections, 4 figures, 4 tables)

This paper contains 13 sections, 4 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Methods
  3. Calibration and Registration
  4. Deformation Field Acquisition
  5. Ultrasound Guided CBCT Updating
  6. Experiments and Results
  7. Setup, Datasets, and Experimental Details
  8. Metrics, Baselines, and Ablations
  9. Results of Deformation Field Acquisition
  10. Base model.
  11. Fine-tuned models.
  12. Results of Ultrasound Guided CBCT Updating
  13. Discussion and Conclusion

Figures (4)

  • Figure 1: System overview. (a) Rigid calibration between robotic ultrasound and CBCT. (b) Image-based registration refinement. (c) Deformation estimation with USCorUNet. (d) Deformation transfer for CBCT slice updating. Ultrasound examples from the in vivo arm dataset (c) and the CT-mapped phantom dataset (b,d) illustrate cross-domain applicability. Conf. map denotes confidence map.
  • Figure 2: Architecture of USCorUNet.
  • Figure 3: Bidirectional deformation estimation results. (a,b) In vivo arm examples (Dataset A); (c,d) probe-induced motion (Datasets B and D); (e) externally induced motion (Dataset B). Orange/yellow dashed lines indicate visual alignment guides for $I_0$, $I_1$. The color bar indicates flow direction (x/y).
  • Figure 4: $CT_1'$ update results on two representative abdominal phantom cases (Dataset D) using USCorUNet, RAFT, and LC2-FFD ($CT_0$: source; $CT_1$: target). Red/orange boxes indicate structural artifacts/severe deformation regions.