用可微分体素渲染提升少视角CBCT重建质量，减少辐射暴露。
Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstruction
- 直接优化体素网格，通过物理可微的X射线渲染实现自监督重建。
- 仅用少量视角输入时，重建精度超越传统算法和神经场方法。
- 适合需要低剂量成像的临床场景，如牙科与介入放射学。
我们提出DiffVox，一种基于物理可微X射线渲染的自监督锥束计算机断层扫描（CBCT）重建框架，直接优化体素网格表示。研究了渲染器中X射线成像模型的不同实现对3D重建和新视角合成质量的影响。结合正则化的体素学习框架，发现使用精确的离散比尔-朗伯定律实现射线衰减的渲染器优于广泛使用的迭代重建算法和现代神经场方法，尤其在输入视角极少时表现更优。由此可从更少的X射线中重建高保真三维CBCT体积，有望降低电离辐射暴露并提升诊断价值。代码已开源：https://github.com/hossein-momeni/DiffVox。
原文摘要 · Abstract (English)
We present DiffVox, a self-supervised framework for Cone-Beam Computed Tomography (CBCT) reconstruction by directly optimizing a voxelgrid representation using physics-based differentiable X-ray rendering. Further, we investigate how the different implementations of the X-ray image formation model in the renderer affect the quality of 3D reconstruction and novel view synthesis. When combined with our regularized voxel-based learning framework, we find that using an exact implementation of the discrete Beer-Lambert law for X-ray attenuation in the renderer outperforms both widely used iterative CBCT reconstruction algorithms and modern neural field approaches, particularly when given only a few input views. As a result, we reconstruct high-fidelity 3D CBCT volumes from fewer X-rays, potentially reducing ionizing radiation exposure and improving diagnostic utility. Our implementation is available at https://github.com/hossein-momeni/DiffVox.
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