Volumetric ultrasound has the potential to significantly improve diagnostic accuracy and clinical decision-making, yet its widespread adoption remains limited by dependence on specialized hardware and restrictive acquisition protocols. In this work, we present a novel unsupervised framework for reconstructing 3D anatomical structures from freehand 2D transvaginal ultrasound sweeps, without requiring external tracking or learned pose estimators. Our method, TVGS, adapts the principles of Gaussian Splatting to the domain of ultrasound, introducing a slice-aware, differentiable rasterizer tailored to the unique physics and geometry of ultrasound imaging. We model anatomy as a collection of anisotropic 3D Gaussians and optimize their parameters directly from image-level supervision. To ensure robustness against irregular probe motion, we introduce a joint optimization scheme that refines slice poses alongside anatomical structure. The result is a compact, flexible, and memory-efficient volumetric representation that captures anatomical detail with high spatial fidelity. This work demonstrates that accurate 3D reconstruction from 2D ultrasound images can be achieved through purely computational means, offering a scalable alternative to conventional 3D systems and enabling new opportunities for AI-assisted analysis and diagnosis.

翻译：容积超声有潜力显著提升诊断准确性与临床决策水平，但其广泛应用仍受限于对专用硬件和严格采集协议的依赖。本研究提出一种新颖的无监督框架，可从自由手扫的二维经阴道超声序列重建三维解剖结构，无需外部跟踪或学习式位姿估计器。我们的方法TVGS将高斯泼溅原理适配至超声领域，引入一种针对超声成像独特物理与几何特性设计的切面感知可微光栅化器。我们将解剖结构建模为一组各向异性三维高斯分布，并直接从图像级监督优化其参数。为确保对不规则探头运动的鲁棒性，我们提出一种联合优化方案，在优化解剖结构的同时精修切面位姿。最终获得一种紧凑、灵活且内存高效的容积表征，能以高空间保真度捕捉解剖细节。本研究表明，仅通过计算手段即可实现从二维超声图像到精确三维重建的目标，为传统三维系统提供了可扩展的替代方案，并为人工智能辅助分析与诊断开辟了新机遇。