arXiv:2610.08419v1 Announce Type: new Abstract: Slice-to-volume registration between ultrasound (US) and preoperative computed tomography (CT) imaging would enhance many minimally invasive interventi...
arXiv:2607. 17810v1 Announce Type: cross Abstract: Accurate 3D--2D liver registration, which aligns preoperative 3D models to partial, view-dependent intraoperative surface observations, is critical for AR-guided laparoscopic surgery but remains challenging due to severe occlusion, limited visibility, and the lack of 3D ground-truth supervision.
arXiv:2608.28715v1 Announce Type: cross Abstract: Ultrasound-guided interventions can require localization of an untracked 2D frame within a 3D anatomical reference. Rigid slice-to-volume registratio...
arXiv:2606. 17379v1 Announce Type: cross Abstract: Accurate intraoperative liver registration is challenging due to substantial soft-tissue deformation yet sparse intraoperative measurements.
UBone3D is a new framework that completes 3D anatomical shapes from partial ultrasound point clouds using physics-rectified conditional flow matching. It models ultrasound artifacts with a simulated physics proxy and applies test-time physics rectification to guide the completion. The method combines a CT-trained generative shape prior and a physics consistency network, achieving higher reconstruction accuracy and anatomical fidelity than existing baselines.
arXiv:2607. 13475v1 Announce Type: cross Abstract: Surgical tissue retraction requires effective manipulation planning under partial and noisy perception.
CUFormer is a modality-aware registration framework that aligns intraoperative ultrasound (US) bone-surface point clouds with preoperative computed tomography (CT) models, addressing challenges from differing imaging physics and partial surface coverage. It introduces a Modality-Aware Transformer (MAT) that encodes modality through identity embeddings and uses modality-specific adapters within a shared geometric matcher, while modeling imaging visibility to weight correspondences. On two CT-US datasets (UltraBones100k and UltraBonesHip) and the CT-RGB-D SpineDepth dataset, CUFormer achieves the lowest mean registration errors, notably reducing RMSE by 65.8% on UltraBones100k compared to the best baseline.
UltraDiff is a modular framework that extends differentiable rendering to medical ultrasound by modeling image formation as a path‑space integral gated by travel time. It derives a Monte Carlo estimator for both the forward ultrasound model and its gradients, enabling gradient‑based optimization of scene parameters. The authors demonstrate the method on inverse geometry estimation, recovering vertebral surfaces from simulated and real B‑mode sweeps without requiring pre‑segmented images, achieving competitive geometric accuracy.
World models can enable autonomous ultrasound scanning by predicting the outcomes of probe motions from local observations. Learning this action--observation relationship typically relies on synchroni...
Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted modality-robust descriptors such as Modality-Inde...
The paper presents a clinically viable pipeline that uses routine intraoperative C‑arm fluoroscopy to derive per‑vertebra 6‑DoF poses, which are co‑registered to an RGB‑D camera via a multimodal fiducial array. This approach provides independent subsurface ground truth for validating markerless spinal tracking methods without adding radiation or altering standard surgical workflows. Ex vivo tests show an end‑to‑end accuracy of about 2 mm and 3.3°, and the framework is applied to evaluate two markerless registration strategies on real surgical data.
arXiv:2610.34600v1 Announce Type: new Abstract: Objective. Deformable image registration remains challenging because corresponding anatomy may exhibit different intensity relationships. Hand-crafted...