UltraGS：实时物理解耦高斯点扩散用于超声新视角合成
UltraGS: Real-Time Physically-Decoupled Gaussian Splatting for Ultrasound Novel View Synthesis
AI总结：
UltraGS通过结合显式辐射场与轻量物理启发声学模型，实现了实时无传感器超声新视角合成，提升了几何一致性并达到新的性能效率前沿。
摘要：
超声成像是非侵入性临床诊断的基石，但其有限的视野对新视角合成构成挑战。本文提出UltraGS，一种实时框架，通过将高斯点扩散适应于无传感器超声成像，整合显式辐射场与轻量物理启发的声学模型。UltraGS采用具有可学习视野的深度感知高斯原语，以改进在不受约束探头运动下的几何一致性，并引入PD渲染，一种可微声学算子，结合低阶球谐函数与一阶波效应以实现高效的强度合成。我们进一步呈现了一个在真实世界扫描协议下获取的临床超声数据集。在三个数据集上的广泛评估表明，UltraGS在PSNR（最高29.55）和SSIM（最高0.89）方面达到最新水平，同时在单个GPU上以64.69 fps实现实时合成。代码和数据集在https://github.com/Bean-Young/UltraGS上开源。
英文摘要：
Ultrasound imaging is a cornerstone of non-invasive clinical diagnostics, yet its limited field of view poses challenges for novel view synthesis. We present UltraGS, a real-time framework that adapts Gaussian Splatting to sensorless ultrasound imaging by integrating explicit radiance fields with lightweight, physics-inspired acoustic modeling. UltraGS employs depth-aware Gaussian primitives with learnable fields of view to improve geometric consistency under unconstrained probe motion, and introduces PD Rendering, a differentiable acoustic operator that combines low-order spherical harmonics with first-order wave effects for efficient intensity synthesis. We further present a clinical ultrasound dataset acquired under real-world scanning protocols. Extensive evaluations across three datasets demonstrate that UltraGS establishes a new performance-efficiency frontier, achieving state-of-the-art results in PSNR (up to 29.55) and SSIM (up to 0.89) while achieving real-time synthesis at 64.69 fps on a single GPU. The code and dataset are open-sourced at: https://github.com/Bean-Young/UltraGS.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
