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：超声成像是无创临床诊断的基石，但其有限的视野为新视角合成带来了挑战。我们提出了UltraGS，一个实时框架，通过将显式辐射场与轻量级、受物理启发的声学建模相结合，将高斯溅射技术应用于无传感器超声成像。UltraGS采用具有可学习视野的深度感知高斯基元，以改善无约束探头运动下的几何一致性，并引入了PD渲染，这是一种可微分的声学算子，它将低阶球谐函数与一阶波动效应相结合，以实现高效的强度合成。我们还提供了一个在真实世界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在性能与效率之间建立了新的前沿，在PSNR（高达29.55）和SSIM（高达0.89）方面取得了最先进的结果，同时在单个GPU上实现了64.69 fps的实时合成。代码和数据集已在以下地址开源：https://github.com/Bean-Young/UltraGS。