Real-Time Novel-View Freehand Ultrasound Imaging via Point-Cloud Rendering and Diffusion-Bridge Completion

Hanrui Shi, Zheyuan Zhang, Boris Mailhe, Yikang Liu, Xiao Chen, Ankush Mukherjee, Terrence Chen, Shanhui Sun

Keywords: ultrasound imaging, novel-view synthesis, diffusion bridge, point-cloud reconstruction, real-time imaging

Abstract: Freehand ultrasound imaging is limited by sparse sampling and restricted probe coverage, which prevent consistent visualization of unseen planes and oblique orientations. We propose a real-time framework for novel-view ultrasound imaging that combines point-cloud rendering with diffusion-bridge completion. Given a sequence of 2D B-mode images and tracked probe poses, each novel view is first rendered as a partially observed slice from the reconstructed point cloud geometry, then completed by an Image-to-Image Schrödinger Bridge (I$^2$SB) model to synthesize anatomically coherent textures. The diffusion-bridge formulation accelerates convergence by conditioning on visible regions instead of noise, enabling stochastic yet efficient generation. A latent I$^2$SB variant further improves computational efficiency for high-resolution ultrasound data. Experiments on an abdominal dataset demonstrate realistic novel-view synthesis with fine structural continuity and real-time inference ($<$0.2s per view), outperforming standard diffusion inpainting baselines in both speed and visual fidelity. The proposed method provides an efficient generative approach for interactive and view-adapt ultrasound visualization.

Primary Subject Area: Image Acquisition and Reconstruction

Secondary Subject Area: Application: Radiology

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Submission Number: 184