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:2608.24422v1 Announce Type: new Abstract: Recovering the full 3D spine anatomy from intraoperative ultrasound is an ill-posed inverse problem, as the complete structure must be inferred from in...
Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting remains insufficiently studied. We investigate completion through continuous-time generative modeling and introduce PCFM, a PTv3-backed flow matching approach for medical point cloud completion.
arXiv:2606. 24433v1 Announce Type: cross Abstract: Medical point cloud completion is important for anatomical reconstruction and downstream clinical workflows, yet generative modeling in this setting remains insufficiently studied.
arXiv:2610.09253v1 Announce Type: new Abstract: Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric info...
C3VDReg is a benchmark for local-to-local colonoscopic registration that uses the Colonoscopy 3D Video Dataset (C3VD) to generate 10,015 partial-to-partial point cloud pairs, with 2,088 held‑out test pairs. Each pair consists of a source point cloud from depth reprojection and a target point cloud from CT mesh raycasting, evaluated under a standardized protocol of 8,192 points per cloud and fixed pose conventions. Experiments show that high geometric overlap does not guarantee reliable pose recovery, revealing translation ambiguity along repetitive tubular anatomy as a key failure mode.
Reconstructing 3D Computed Tomography (CT) images from a few X-ray projections is a highly ill-posed inverse problem due to the loss of volumetric information. We propose PhyDiCT, a training-free fram...
arXiv:2610.09785v1 Announce Type: new Abstract: World models can enable autonomous ultrasound scanning by predicting the outcomes of probe motions from local observations. Learning this action--obser...
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...
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.
The paper introduces a high‑resolution Rectified Flow Matching model trained on 256×256 chest CT images to serve as a generative prior for CT reconstruction. A two‑stage training strategy—initial strong anatomically informed augmentation followed by fine‑tuning—helps mitigate overfitting and improve structural fidelity. When evaluated on various CT inverse problems, Flow Matching‑based reconstruction methods outperform diffusion‑based algorithms in PSNR, SSIM, and perceptual quality while requiring fewer sampling steps.
arXiv:2603.26509v2 Announce Type: replace Abstract: Computed tomography (CT) provides rich 3D anatomical detail but is often constrained by high radiation exposure, substantial costs, and limited ava...
The paper introduces a one‑pass conditional 3D rectified flow (3D Flow) framework for denoising whole‑body PET images, employing an optimized non‑uniform sampling strategy and a linear‑interpolant velocity‑matching objective. It reconstructs a full 3D volume in about 30 seconds, dramatically faster than multi‑hour 3D diffusion models, while maintaining high global image quality and lesion conspicuity even at ultra‑low doses (down to 1/100 of standard). Zero‑shot transfer tests on independent clinical data demonstrate robust performance across datasets and unseen dose levels.