Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT Reconstruction

Understand

Score-based generative models (SGMs) have gained prominence in sparse-view CT reconstruction for their precise sampling of complex distributions.

• In SGM-based reconstruction, data consistency in the score-based diffusion model ensures close adherence of generated samples to observed data distribution, crucial for improving image quality.
• Shortcomings in data consistency characterization manifest in three aspects.
• Firstly, data from the optimization process can lead to artifacts in reconstructed images.