Accelerating direct material decomposition via diffusion probabilistic model for Sparse-view spectral computed tomography

Accurate material decomposition constitutes the foundation of Spectral Computed Tomography (Spectral CT) applications across diverse domains. Nevertheless, conventional model-based material decomposition methods face significant limitations including sparse-view sampling artifacts, slow convergence rates, noise amplification, and inherent ill-posedness-challenges that are particularly pronounced in geometrically inconsistent imaging. To overcome these constraints, we propose an unsupervised deep learning framework that synergistically optimizes virtual monochromatic images (VMIs) through the probabilistic diffusion model for direct material decomposition in sparse-view spectral CT. The proposed methodology introduces VMIs as critical differentiation enhancers for polychromatic projections, effectively addressing convergence limitations in iterative reconstruction algorithms. By incorporating probabilistic diffusion priors into the optimization process, we achieve superior refinement of material-specific representations. Our framework systematically enforces dual constraint: 1) data fidelity term ensuring measurement consistency, and 2) probabilistic regularization suppressing unwanted structures, thereby guaranteeing anatomically plausible material image reconstruction. Comprehensive validation on preclinical data demonstrates that our method achieves a 10 dB improvement in the peak-signal-to-noise ratio (PSNR) and a 4.31% increase in structural similarity (SSIM) for soft-tissue reconstructions compared to the optimal comparison algorithm with 90 projections. Experimental results confirm the algorithm's robustness under challenging conditions, maintaining reconstruction fidelity even with geometric inconsistency and sparse sampling.