Digitally reconstructed radiographs (DRRs) are simulated 2D X-ray images
generated from 3D CT volumes, widely used in preoperative settings but limited
in intraoperative applications due to computational bottlenecks, especially for
accurate but heavy physics-based Monte Carlo methods. While analytical DRR
renderers offer greater efficiency, they overlook anisotropic X-ray image
formation phenomena, such as Compton scattering. We present a novel approach
that marries realistic physics-inspired X-ray simulation with efficient,
differentiable DRR generation using 3D Gaussian splatting (3DGS). Our
direction-disentangled 3DGS (DDGS) method separates the radiosity contribution
into isotropic and direction-dependent components, approximating complex
anisotropic interactions without intricate runtime simulations. Additionally,
we adapt the 3DGS initialization to account for tomography data properties,
enhancing accuracy and efficiency. Our method outperforms state-of-the-art
techniques in image accuracy. Furthermore, our DDGS shows promise for
intraoperative applications and inverse problems such as pose registration,
delivering superior registration accuracy and runtime performance compared to
analytical DRR methods.

数字重建射线照片（DRRs）在术前应用中得到广泛使用，但由于计算瓶颈在术中应用受到限制，特别是对于准确但计算密集型的基于蒙特卡罗方法。本文提出了一种新方法，将真实的受物理启发的 X 射线模拟与使用三维高斯平铺（3DGS）进行高效、可微分的 DRR 生成相结合。我们的方向分立的 3DGS（DDGS）方法将辐射通量贡献分解为各向同性和方向相关的部分，以近似复杂的各向异性相互作用而无需耗时的模拟。此外，我们适应了三维高斯平铺的初始化过程以适应层析数据的特性，提高了精度和效率。我们的方法在图像准确度方面优于最先进的技术。此外，我们的 DDGS 在术中应用和姿态配准等逆问题方面表现出色，并比解析 DRR 方法提供更高的配准准确度和运行时性能。