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...
By Keke Yang, Erqi Wang, Sainan Guan, Hongliang Ren
UltraPIPS introduces domain‑specific foundation models for measuring perceptual similarity in B‑mode ultrasound images. The study shows that ultrasound‑trained LPIPS backbones better correlate with downstream tasks such as classification, segmentation, and reconstruction than natural‑image or general medical models. Optimizing LPIPS loss with an ultrasound backbone yields a strong balance between reconstruction quality and realism, and the authors provide an open‑source library for these metrics.
By Tal Grutman, Tali Ilovitsh
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.
By Weiying Chen, Yuchong Gao, Siyuan Li, Marek Reformat, Rui Zheng, Edmond Lou
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...
By Miruna-Alexandra Gafencu, Vlad Bratulescu, Yordanka Velikova, Mohammad Farid Azampour, Nassir Navab
RFlash is a physics‑informed post‑processing technique that removes acoustic shadows in ultrasound by decomposing beamformed images into attenuation and scatter‑intensity maps using a differentiable radiance‑field model. It re‑renders images to eliminate depth‑dependent signal loss, effectively simulating a virtual transducer advance. Across thousands of fetal brain, abdominal, and liver scans, RFlash outperforms traditional attenuation correction, reduces prediction error by 40% in fetal brain imaging, and provides shadow‑confidence maps that enhance bone‑shadow segmentation.
By Valentin Bacher (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom), Pak Hei Yeung (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom, Quantitative Healthcare Analysis), Bernhard Kainz (Friedrich-Alexander-Universit\"at Erlangen-N\"urnberg, Germany, Imperial College London, United Kingdom), Madeleine K. Wyburd (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom, Department of Computer Science, University of Copenhagen, Denmark), Nicola K. Dinsdale (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom), Michael Gray (Institute of Biomedical Engineering, University of Oxford, United Kingdom), Ana I. L. Namburete (Oxford Machine Learning in NeuroImaging Lab, University of Oxford, United Kingdom)
arXiv:2412.11325v2 Announce Type: replace
Abstract: 3D human mesh reconstruction (HMR) from RGB images often degrades under poor illumination, occlusion, and non-line-of-sight conditions. Acoustic se...
By Xiaoxuan Liang, Hong Zhou, Zhaolong Wei, Yansong Li, Shujian Yu, Jeremy Gummeson