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UltraWorld: Learning Interactive Ultrasound World Models fro

1 reports1 reporting sourcesUpdated 11 days ago

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Source roundup from published reports. Claims below are attributed to their publishers, not independently verified. arXiv Robotics — research abstracts: UltraWorld: Learning Interactive Ultrasound World Models from Untracked Clinical Videos with Acoustic Sampling Map. UltraWorld is a self-distillation approach that transfers priors from clinical ultrasound videos into interactive world models without real action annotations. It adapts a video foundation model into an ultrasound genera…

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Environment: simulation
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“and imaging settings as pixel-wise 3D sampling positions, beam directions, and depths. Experiments demonstrate improved prediction fidelity and action following. Across nine simulated closed-loop local planning episodes, UltraWorld reduces the mean final distance to the goal and orientation error by 29\% and 38\%, respectively, compared with visual servoing. Project Page: https://ultraworld-project.github.io/.”

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10/8
  1. arXiv Robotics — research abstracts
    UltraWorld: Learning Interactive Ultrasound World Models from Untracked Clinical Videos with Acoustic Sampling Map

    UltraWorld is a self-distillation approach that transfers priors from clinical ultrasound videos into interactive world models without real action annotations. It adapts a video foundation model into an ultrasound generator conditioned on reference images and anatomical masks, using programmable trajectories through 3D anatomy to synthesize action-video pairs. The Acoustic Sampling Map (AsMap) represents probe poses and imaging settings as pixel-wise 3D sampling positions, beam directions, and depths. Experiments show improved prediction fidelity and action following, with reductions in mean final distance to the goal and orientation error compared to visual servoing.

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