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.

翻译：超声成像是非侵入性临床诊断的基石，但其有限的视野给新视角合成带来了挑战。我们提出UltraGS，一个通过将显式辐射场与轻量级物理启发声学建模相结合，将高斯点画法适配于无传感器超声成像的实时框架。UltraGS采用具有可学习视野的深度感知高斯基元，以增强在无约束探头运动下的几何一致性，并引入PD渲染——一种结合低阶球谐函数与一阶波动效应的高效强度合成可微声学算子。进一步地，我们提供了一份在真实扫描协议下采集的临床超声数据集。在三个数据集上的广泛评估表明，UltraGS建立了新的性能效率边界，在单GPU上实现64.69帧/秒实时合成的同时，取得了PSNR（最高29.55）和SSIM（最高0.89）的领先成果。代码与数据集已开源：https://github.com/Bean-Young/UltraGS