  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.
