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

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 Schr{ö}dinger 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 ($<$0.2 seconds 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-adaptive ultrasound visualization.