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（INR）用于超声成像，从重叠的超声扫描中学习组织特性。我们的方法利用基于光线追踪的神经渲染来进行新视角超声合成。最近的研究表明，INR模型可以从一组二维超声帧中编码三维场景的表示。然而，这些模型未考虑到超声成像固有的视角依赖性的外观和几何变化。在我们的工作中，我们讨论了场景中方向依赖性的变化，并显示物理启发式的渲染提高了超声图像合成的保真度。特别地，我们实验证明，我们提出的方法生成几何精确的B型超声图像，用于具有模糊表示的区域，这是由于超声图像的视角依赖性差异所致。我们使用模拟的肝脏B型超声扫描和机器人手臂跟踪的脊柱模型的获取超声扫描进行实验。实验证实了我们的方法生成的超声帧，使得从之前未见过的视角进行一致的体积组合成为可能。根据我们所知，所提出的工作是第一个使用INR解决视角依赖性超声图像合成的工作。