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X线图（DRRs）是从3D CT体积生成的模拟2D X线图像，在术前设置中广泛使用，但由于计算瓶颈，尤其是对于精确但计算量大的基于物理的蒙特卡罗方法，在术中应用中受到限制。虽然分析型DRR渲染器提供了更高的效率，但它们忽略了各向异性X线图像形成现象，如康普顿散射。我们提出了一种新方法，将真实的物理启发型X线模拟与高效的可微分DRR生成结合起来，使用3D高斯涂抹（3DGS）。我们的方向解耦3DGS（DDGS）方法将辐射度贡献分为各向同性和方向依赖的组分，无需复杂的运行时模拟即可近似复杂的各向异性相互作用。此外，我们调整了3DGS的初始化，以考虑层析数据属性，提高了精度和效率。我们的方法在图像精度方面超越了最先进的技术。此外，我们的DDGS对于术中应用和逆问题（如姿态注册）显示出前景，与分析型DRR方法相比，提供了更优的注册精度和运行时性能。