用虚拟掩码增强双能CT稀疏采样重建，减少伪影提升质量。
Virtual-mask Informed Prior for Sparse-view Dual-Energy CT Reconstruction
- 设计虚拟掩码对高低能数据做扰动，构建扩散模型先验
- 在投影域与小波域协同优化全局结构与局部细节
- 多数据集验证效果显著，适合低剂量CT重建场景
双能计算机断层成像（DECT）中稀疏视图采样可显著降低辐射剂量并提高成像速度，但极易产生伪影。尽管扩散模型在处理不完整数据方面展现潜力，现有方法多集中于图像域且缺乏全局约束，导致重建质量不足。本研究提出一种双域虚拟掩码引导的扩散模型，利用DECT中通道间的高相关性。具体而言，设计虚拟掩码对高能和低能数据进行扰动操作，构建高维张量作为扩散模型的先验信息。同时，采用双域协同策略，将小波域中随机选择的高频成分与投影域信息融合，以优化全局结构与局部细节。实验结果表明，该方法在多个数据集上均表现优异。
原文摘要 · Abstract (English)
Sparse-view sampling in dual-energy computed tomography (DECT) significantly reduces radiation dose and increases imaging speed, yet is highly prone to artifacts. Although diffusion models have demonstrated potential in effectively handling incomplete data, most existing methods in this field focus on the image do-main and lack global constraints, which consequently leads to insufficient reconstruction quality. In this study, we propose a dual-domain virtual-mask in-formed diffusion model for sparse-view reconstruction by leveraging the high inter-channel correlation in DECT. Specifically, the study designs a virtual mask and applies it to the high-energy and low-energy data to perform perturbation operations, thus constructing high-dimensional tensors that serve as the prior information of the diffusion model. In addition, a dual-domain collaboration strategy is adopted to integrate the information of the randomly selected high-frequency components in the wavelet domain with the information in the projection domain, for the purpose of optimizing the global struc-tures and local details. Experimental results indicated that the present method exhibits excellent performance across multiple datasets.
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