用多变量高斯神经辐射场重建超声宽视场图像，减少伪影并支持任意视角生成。
Multivariate Gaussian NeRF for Wide Field-of-View Ultrasound Reconstruction
- 基于多变量3D高斯建模声束几何，自适应处理深度依赖分辨率变化。
- 在模拟与猪心脏数据上验证，显著扩展术中导航所需的空间上下文。
- 适合需要高保真三维超声重建的临床影像与手术导航研究者。
宽视场（WFoV）重建通过提供重要的解剖上下文，提升3D超声成像质量，尤其适用于分割模型和可视化。临床超声主要使用凸阵探头，其发出发散声束以最大化解剖覆盖范围。传统拼接方法因深度相关的分辨率变化，易引入累积伪影和混叠。本文提出Ultra-Wide-NeRF，一种基于多变量3D高斯（MVG）的神经辐射场（NeRF）方法，通过距离依赖的凸形体采样和各向异性3D高斯，显式建模复杂声束几何，天然缓解伪影并实现抗混叠。该方法不仅重建静态3D网格，更提供连续神经表示，可合成任意虚拟轨迹下的高保真新视角。我们在模拟数据与猪心脏数据集上对经心腔超声成像进行了验证，结果表明该方法有效扩展了术中导航所需的空间上下文。代码将在发表后开源。
原文摘要 · Abstract (English)
Wide Field-of-View (WFoV) reconstruction enhances 3D ultrasound imaging by providing valuable anatomical context for segmentation models and visualization. Clinical ultrasound volumes are predominantly acquired using convex probes, which generate expanding, diverging acoustic beams to maximize anatomical coverage. Stitching these sweeps together traditionally introduces significant compounding artifacts and aliasing due to depth-dependent resolution changes. Here, we introduce Ultra-Wide-NeRF, a Multivariate 3D Gaussian (MVG) NeRF-based method for WFoV ultrasound reconstruction. By explicitly modeling the complex beam geometry using distance-dependent convex volumetric sampling and anisotropic 3D Gaussians, our method inherently mitigates these compounding artifacts and provides anti-aliasing. Beyond simply reconstructing a static 3D grid, our NeRF-based approach yields a continuous neural representation of the tissue, enabling the synthesis of high-fidelity novel views from arbitrary virtual trajectories. We validate Ultra-Wide-NeRF for intracardiac echocardiography on phantom and porcine datasets, demonstrating that our method expands the spatial context important in intraoperative navigation. Code will be open-sourced upon publication.
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