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.

翻译：计算机断层扫描（CT）技术通过稀疏采样降低对人体辐射危害，但采样角度的减少给图像重建带来了挑战。基于分数的生成模型广泛应用于稀疏视角CT重建，但当投影角度急剧减少时，其性能显著下降。为此，我们提出一种利用多尺度扩散模型（MSDiff）的超稀疏视角CT重建方法，该方法专注于信息的全局分布，并借助局部图像特征促进稀疏视角的重建。具体而言，所提模型巧妙融合了全面采样与选择性稀疏采样技术的信息。通过在扩散模型中进行精确调整，它能够提取多样化的噪声分布，从而加深对图像整体结构的理解，并帮助全采样模型更有效地恢复图像信息。利用投影数据内在的相关性，我们设计了一种等距掩码，使模型能够更高效地集中注意力。实验结果表明，多尺度模型方法显著提升了超稀疏角度下的图像重建质量，并在不同数据集上展现出良好的泛化能力。