The potential huge advantage of spectral computed tomography (CT) is that it can provide accurate material identification and quantitative tissue information by material decomposition. However, material decomposition is a typical inverse problem, where the noise can be magnified. To address this issue, we develop a dictionary learning based image-domain material decomposition (DLIMD) method for spectral CT to achieve accurate material components with better image quality. Specifically, a set of image patches are extracted from the mode-1 unfolding of normalized material images decomposed by direct inversion to train a unified dictionary using the K-SVD technique. Then, the DLIMD model is established to explore the redundant similarities of the material images, where the split-Bregman is employed to optimize the model. Finally, more constraints (i.e. volume conservation and the bounds of each pixel within material maps) are integrated into the DLIMD model. Numerical phantom, physical phantom and preclinical experiments are performed to evaluate the performance of the proposed DLIMD in material decomposition accuracy, material image edge preservation and feature recovery.

光谱计算机断层扫描（CT）的潜在巨大优势是它可以通过材料分解提供准确的材料识别和定量的组织信息。但是，材料分解是一个典型的反问题，可以放大噪声。为了解决这个问题，我们开发了一种基于字典学习的光谱CT图像域材料分解（DLIMD）方法，以实现具有更好图像质量的准确材料成分。具体而言，使用K-SVD技术从通过直接反演分解的归一化素材图像的模式1展开中提取一组图像块，以训练统一字典。然后，建立DLIMD模型以探索材料图像的冗余相似性，其中使用split-Bregman优化模型。最后，更多的约束条件（i。e。体积守恒和材料贴图中每个像素的边界）都集成到DLIMD模型中。进行了数值模型，物理模型和临床前实验，以评估所提出的DLIMD在材料分解精度，材料图像边缘保留和特征恢复方面的性能。