For orthopedic diagnostics, both 2D X-ray and 3D CT imaging play essential roles. X-ray imaging is widely accessible, clinically effective, easy to operate, and has lower radiation exposure than CT. However, its inherent 2D nature limits comprehensive visualization of skeletal structures, which 3D CT provides. To bridge this gap, we propose SkeDiff, an algorithm for reconstructing 3D CT images of the skeleton from orthogonal 2D X-ray projections. To fully leverage the information in X-ray images for guiding the diffusion process, we design a cross-dimensional conditional encoder, E\₂₎₍₃, to extract 2D priors for the 3D diffusion model, DM\₃₃₋. This encoder integrates a CNN-Mamba hybrid architecture to enhance feature extraction and nonlinear mapping. Additionally, we introduce a 3D UKAN diffusion backbone, which employs Kolmogorov-Arnold network (KAN) to improve feature representation through learnable nonlinear activations. Furthermore, we propose a diffusion-based scoliosis classifier, D\ₒ₂, enabling scoliosis classification during the 3D CT reconstruction process. Experiments show that SkeDiff outperforms recent algorithms on spine, hip, and knee datasets.
Gao et al. (Thu,) studied this question.