Data-iterative Optimization Score Model for Stable Ultra-Sparse-View CT
  Reconstruction
Weiwen Wu Yanyang Wang
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. Secondly, it often
neglects that the generation model and original data constraints are
independently completed, fragmenting unity. Thirdly, it predominantly focuses
on constraining intermediate results in the inverse sampling process, rather
than ideal real images. Thus, we propose an iterative optimization data scoring
model. This paper introduces the data-iterative optimization score-based model
(DOSM), integrating innovative data consistency into the Stochastic
Differential Equation, a valuable constraint for ultra-sparse-view CT
reconstruction. The novelty of this data consistency element lies in its sole
reliance on original measurement data to confine generation outcomes,
effectively balancing measurement data and generative model constraints.
Additionally, we pioneer an inference strategy that traces back from current
iteration results to ideal truth, enhancing reconstruction stability. We
leverage conventional iteration techniques to optimize DOSM updates.
Quantitative and qualitative results from 23 views of numerical and clinical
cardiac datasets demonstrate DOSM's superiority over other methods. Remarkably,
even with 10 views, our method achieves excellent performance.