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MSDiff: multi-scale diffusion model for ultra-sparse view CT reconstruction
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DOI：10.1088/1361-6560/ae2fa7.png)
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Computed tomography (CT) technology reduces radiation exposure to the human body through sparse sampling, but fewer sampling angles pose challenges for image reconstruction. When the projection angles are significantly reduced, the quality of image reconstruction deteriorates. To improve the quality of image reconstruction under sparse angles, an ultra-sparse view CT reconstruction method utilizing multi-scale diffusion models is proposed. This method aims to focus on the global distribution of information while facilitating the reconstruction of local image features in sparse views. Specifically, the proposed model ingeniously combines information from both comprehensive sampling and selective sparse sampling techniques. By precisely adjusting the diffusion model, diverse noise distributions are extracted, enhancing the understanding of the overall image structure and assisting the fully sampled model in recovering image information more effectively. By leveraging the inherent correlations within the projection data, an equidistant mask is designed according to the principles of CT imaging, allowing the model to focus attention more efficiently. Experimental results demonstrate that the multi-scale model approach significantly improves image reconstruction quality under ultra-sparse views and exhibits good generalization across multiple datasets.
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DREAM-Net: Deep Residual Error Iterative Minimization Network for Sparse-View CT ReconstructionDREAM-Net: 稀疏视图CT重建的深度残差迭代最小化网络
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分享收藏分享收藏One-Sample Diffusion Modeling in Projection Domain for Low-Dose CT Imaging黄, B., 陆, S., 张, L., 林, B., 吴, W., 刘, Q., 2024b. 投影域单样本扩散建模用于低剂量CT成像. 电气与电子工程师协会（IEEE）辐射与等离子体医学科学汇刊 8, 902–915. https://doi.org/10.1109/TRPMS.2024.3392248.
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