Neural radiance field (NeRF) is a promising approach for reconstruction and new view synthesis. However, previous NeRF-based reconstruction methods overlook the critical role of acoustic impedance in ultrasound imaging. Localization methods face challenges related to local minima due to the selection of initial poses. In this study, we design a robotic ultrasound system (RUSS) with an acoustic-impedance-aware ultrasound NeRF (AIA-UltraNeRF) to decouple the scanning and diagnostic processes. Specifically, AIA-UltraNeRF models a continuous function of hash-encoded spatial coordinates for the 3D ultrasound map, allowing for the storage of acoustic impedance without dense sampling. This approach accelerates both reconstruction and inference speeds. We then propose a dual-supervised network that leverages teacher and student models to hash-encode the rendered ultrasound images from the reconstructed map. AIA-UltraNeRF retrieves the most similar hash values without the need to render images again, providing an offline initial image position for localization. Moreover, we develop a RUSS with a spherical remote center of motion mechanism to hold the probe, implementing operator-independent scanning modes that separate image acquisition from diagnostic workflows. Experimental results on a phantom and human subjects demonstrate the effectiveness of acoustic impedance in implicitly characterizing the color of ultrasound images. AIAUltraNeRF achieves both reconstruction and localization with inference speeds that are 9.9 faster than those of vanilla NeRF.

翻译：神经辐射场（NeRF）是一种用于三维重建与新视角合成的有前景的方法。然而，以往基于NeRF的重建方法忽视了声阻抗在超声成像中的关键作用。定位方法则因初始位姿的选择而面临局部极小值的挑战。本研究设计了一种机器人超声系统（RUSS），结合声阻抗感知的超声NeRF（AIA-UltraNeRF），以解耦扫描与诊断过程。具体而言，AIA-UltraNeRF通过哈希编码的空间坐标建模三维超声图的连续函数，使得无需密集采样即可存储声阻抗信息，从而加速重建与推理速度。我们进一步提出一种双监督网络，利用教师与学生模型对重建图谱渲染的超声图像进行哈希编码。AIA-UltraNeRF无需重新渲染图像，即可检索最相似的哈希值，为定位提供离线的初始图像位置。此外，我们开发了一种配备球形远程运动中心机构的RUSS来固定探头，实现操作者无关的扫描模式，将图像采集与诊断工作流分离。在仿体与人体受试者上的实验结果表明，声阻抗能有效隐式表征超声图像的颜色特征。AIA-UltraNeRF在实现重建与定位的同时，其推理速度比原始NeRF快9.9倍。