We present a physics-enhanced implicit neural representation (INR) for ultrasound (US) imaging that learns tissue properties from overlapping US sweeps. Our proposed method leverages a ray-tracing-based neural rendering for novel view US synthesis. Recent publications demonstrated that INR models could encode a representation of a three-dimensional scene from a set of two-dimensional US frames. However, these models fail to consider the view-dependent changes in appearance and geometry intrinsic to US imaging. In our work, we discuss direction-dependent changes in the scene and show that a physics-inspired rendering improves the fidelity of US image synthesis. In particular, we demonstrate experimentally that our proposed method generates geometrically accurate B-mode images for regions with ambiguous representation owing to view-dependent differences of the US images. We conduct our experiments using simulated B-mode US sweeps of the liver and acquired US sweeps of a spine phantom tracked with a robotic arm. The experiments corroborate that our method generates US frames that enable consistent volume compounding from previously unseen views. To the best of our knowledge, the presented work is the first to address view-dependent US image synthesis using INR.

翻译：我们提出了一种物理增强的隐式神经表示方法，用于超声成像中通过重叠超声扫描学习组织特性。该方法利用基于光线追踪的神经渲染技术实现新型视角超声图像合成。最新研究表明，隐式神经表示模型能够从二维超声帧集合中编码三维场景表征。然而，现有模型未能考虑超声成像中固有的外观与几何随视角依赖性变化。本文探讨场景的方向依赖性变化，并证明物理启发的渲染可提升超声图像合成的保真度。具体而言，我们通过实验证明，对于因超声图像视角依赖差异而导致表征模糊的区域，所提方法能生成几何精确的B模式图像。我们分别使用肝脏模拟B模式超声扫描数据和机器人臂追踪的脊柱体模采集数据进行实验验证。实验结果证实，该方法能够生成支持从先前未观测视角进行一致容积复合成的超声帧。据我们所知，本文首次采用隐式神经表示解决超声图像的视角依赖性合成问题。