Differentiable Voxel-based X-ray Rendering Improves Sparse-View 3D CBCT Reconstruction
This addresses the problem of reducing ionizing radiation exposure in medical imaging by improving 3D reconstruction from fewer X-rays, though it appears incremental as it builds on differentiable rendering and voxel-based approaches.
The authors tackled sparse-view 3D CBCT reconstruction by developing DiffVox, a self-supervised framework that optimizes a voxelgrid using differentiable X-ray rendering, finding that an exact implementation of the discrete Beer-Lambert law outperforms existing methods with few input views.
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.