  Computed Tomography (CT) technology reduces radiation haz-ards to the human
body through sparse sampling, but fewer sampling angles pose challenges for
image reconstruction. Score-based generative models are widely used in
sparse-view CT re-construction, performance diminishes significantly with a
sharp reduction in projection angles. Therefore, we propose an ultra-sparse
view CT reconstruction method utilizing multi-scale dif-fusion models (MSDiff),
designed to concentrate on the global distribution of information and
facilitate the reconstruction of sparse views with local image characteristics.
Specifically, the proposed model ingeniously integrates information from both
comprehensive sampling and selectively sparse sampling tech-niques. Through
precise adjustments in diffusion model, it is capable of extracting diverse
noise distribution, furthering the understanding of the overall structure of
images, and aiding the fully sampled model in recovering image information more
effec-tively. By leveraging the inherent correlations within the projec-tion
data, we have designed an equidistant mask, enabling the model to focus its
attention more effectively. Experimental re-sults demonstrated that the
multi-scale model approach signifi-cantly improved the quality of image
reconstruction under ultra-sparse angles, with good generalization across
various datasets.
