UltraGS: Gaussian Splatting for Ultrasound Novel View Synthesis

• URL: http://arxiv.org/abs/2511.07743v1
• Date: Wed, 12 Nov 2025 01:14:28 GMT
• Title: UltraGS: Gaussian Splatting for Ultrasound Novel View Synthesis
• Authors: Yuezhe Yang, Wenjie Cai, Dexin Yang, Yufang Dong, Xingbo Dong, Zhe Jin,
• Abstract summary: We propose textbfUltraGS, a Gaussian Splatting framework optimized for ultrasound imaging.<n>First, we introduce a depth-aware Gaussian splatting strategy, where each Gaussian is assigned a learnable field of view.<n>Second, we design SH-DARS, a lightweight rendering function combining low-order spherical harmonics with ultrasound-specific wave physics.
• Score: 11.32869758723533
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Ultrasound imaging is a cornerstone of non-invasive clinical diagnostics, yet its limited field of view complicates novel view synthesis. We propose \textbf{UltraGS}, a Gaussian Splatting framework optimized for ultrasound imaging. First, we introduce a depth-aware Gaussian splatting strategy, where each Gaussian is assigned a learnable field of view, enabling accurate depth prediction and precise structural representation. Second, we design SH-DARS, a lightweight rendering function combining low-order spherical harmonics with ultrasound-specific wave physics, including depth attenuation, reflection, and scattering, to model tissue intensity accurately. Third, we contribute the Clinical Ultrasound Examination Dataset, a benchmark capturing diverse anatomical scans under real-world clinical protocols. Extensive experiments on three datasets demonstrate UltraGS's superiority, achieving state-of-the-art results in PSNR (up to 29.55), SSIM (up to 0.89), and MSE (as low as 0.002) while enabling real-time synthesis at 64.69 fps. The code and dataset are open-sourced at: https://github.com/Bean-Young/UltraGS.

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