Robotic Ultrasound Makes CBCT Alive

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

cs.CVMar 10, 2026

Robotic Ultrasound Makes CBCT Alive

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

Organizations: Chair for Computer-Aided Medical Procedures and Augmented Reality, Technical University of Munich, Munich, Germany · Munich Center for Machine Learning, Munich, Germany · The University of Hong Kong, Hong Kong, China

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.

Figures & tables

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 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).

Method	MAE $\downarrow$	NCC $\uparrow$	FB $\downarrow$	Fold(% ) $\downarrow$	Dice(% ) $\uparrow$
Baseline
RAFT	$0.05\pm 0.02$	$0.85\pm 0.10$	$1.81\pm 1.22$	$0.24\pm 0.14$	$90.31\pm 4.14$
Ours
USCorUNet	$0.05\pm 0.02$	$0.85\pm 0.09$	$0.85\pm 0.57$	$0.13\pm 0.10$	$90.62\pm 3.76$
Ablation Study
w/o Corr.	$0.08\pm 0.03$	$0.61\pm 0.18$	$1.32\pm 3.14$	$0.56\pm 0.51$	$89.10\pm 3.33$

Table 1: Testset performance of the base model on Datasets A, averaged over both deformation directions (

I_{0}\!\rightarrow\!I_{1}

I_{1}\!\rightarrow\!I_{0}

) and reported as mean

\pm

standard deviation.

\downarrow/\uparrow

indicate lower/higher is better. Best results are in bold.

Method	1 N	2 N	3 N	4 N	5 N	6 N
DefCor-Net	$95.9\pm 3.3$	$92.4\pm 4.8$	$91.1\pm 7.5$	$87.8\pm 7.1$	$87.8\pm 12.2$	$82.6\pm 12.1$
USCorUNet	$95.4\pm 2.6$	$94.1\pm 2.4$	$94.0\pm 3.6$	$93.2\pm 1.8$	$89.8\pm 1.5$	$87.5\pm 1.2$

Table 2: Bone-mask Dice under the force-stratified protocol of DefCor-Net [ 9 ] on Dataset A. Dice is reported for the

I_{1}\!\rightarrow\!I_{0}

direction.

Motion	Method	MAE $\downarrow$	NCC $\uparrow$	FB $\downarrow$	Fold(% ) $\downarrow$
Probe- induced	RAFT	$0.03\pm 0.01$	$0.94\pm 0.03$	$1.03\pm 2.27$	$0.23\pm 0.14$
	Base model	$0.04\pm 0.01$	$0.84\pm 0.08$	$0.84\pm 0.65$	$0.15\pm 0.11$
	Probe-adapted	$0.03\pm 0.01$	$0.93\pm 0.03$	$0.33\pm 0.28$	$0.09\pm 0.10$
Externally induced	RAFT	$0.03\pm 0.01$	$0.93\pm 0.05$	$0.91\pm 1.35$	$0.14\pm 0.13$
	Base model	$0.05\pm 0.02$	$0.71\pm 0.17$	$1.28\pm 1.95$	$0.45\pm 0.55$
	External-adapted	$0.03\pm 0.01$	$0.92\pm 0.05$	$0.23\pm 0.24$	$0.07\pm 0.07$

Table 3: Test-set performance of fine-tuned models on Datasets B–D (probe-induced) and Dataset B (externally induced), averaged over both directions.

Figure 4: $CT_{1}^{\prime}$ 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.

Model	MAE $\downarrow$	SSIM $\uparrow$	Dice (%) $\uparrow$	Timing (ms) $\downarrow$
USCorUNet	0.33 $\pm$ 0.04	0.22 $\pm$ 0.02	82.22 $\pm$ 10.54	11.25 $\pm$ 0.44
RAFT	0.34 $\pm$ 0.04	0.21 $\pm$ 0.03	79.86 $\pm$ 12.31	56.24 $\pm$ 1.78
LC2-FFD	0.33 $\pm$ 0.05	0.22 $\pm$ 0.03	58.91 $\pm$ 8.77	5764.26 $\pm$ 510.63

Table 4: Quantitative results for CBCT updating on Dataset D using ultrasound-based deformation estimation, with all three methods evaluated under identical conditions and runtime reported end-to-end.

Cone-Beam CT

Cone-beam computed tomography (CBCT) reconstructs three-dimensional anatomy from X-ray projections for dental, interventional, and radiotherapy imaging.

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Deformable CT-US Registration via Anatomy-Aware Implicit Neural Representations

Agnieszka Lach, Magdalena Wysocki, Feng Li, Mohammad Farid Azampour+6

medPhoton GmbH, Salzburg, Austria · Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany

Medical Image Registration Ultrasound Imaging Cross-Modal Image Registration Deformable Image Registration

Open paper AskPDF arXiv

Slice-to-volume registration between ultrasound (US) and preoperative computed tomography (CT) imaging would enhance many minimally invasive interventions, for example by locating soft tissue structures intra-operatively that are discernible in CT. While optical tracking enables initial rigid registration, contact from the probe induces soft tissue deformations that inhibit accurate alignment. In this work, we introduce a deformable CT-ultrasound registration framework that incorporates anatomical priors derived from CT to improve registration under deformation. Rigid registration is first established using a robot-assisted optical tracking system, after which a deformable transformation is estimated using a sinusoidal implicit neural representation (SIREN) optimized per frame. Tissue stiffness is approximated from CT-based HU values and used as spatially varying regularization, suppressing deformation in rigid structures such as bone while allowing more flexibility in soft tissue. Two additional constraints capture the physics of probe contact: a contact-zone displacement prior that drives the displacement field to compress tissue below the probe face, and a fan-geometry regularization term based on beam direction and convex transducer field of view. Model parameters are optimized with a normalized gradient field (NGF). The proposed approach improves alignment over rigid initialisation by 17% and outperforms classical deformable baselines while maintaining near-zero topological folding.

Abstract

Cone-Beam CT

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