Multi-energy computed tomography (MCT) has a great potential in material decomposition, tissue characterization, lesion detection, and other applications. However, the severe noise that exists within projections makes it difficult to obtain high-quality MCT images. To overcome this limitation, we propose a method termed Spectral-Image Similarity-based Tensor with Enhanced-sparsity Reconstruction (SISTER) method. SISTER utilizes the non-local feature similarity in the spatial-spectral domain by clustering similar spatial-spectral patches within non-local window to a 4th-order tensor group. Compared with the image gradient L0-norm with tensor dictionary learning (L0TDL) method, by adopting tensor decomposition rather than tensor dictionary learning, SISTER overcomes the instability of tensor dictionary. Besides, in our SISTER method the weight coefficients update strategy is also optimized. Both numerical simulation and preclinical dataset were performed to evaluate and validate the performance of SISTER. Qualitative and quantitative results show that the proposed method can lead to a promising improvement of edge preservation, finer feature recovery, and noise suppression.

中文翻译：

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SISTER：基于光谱图像相似性的张量与稀疏视图多能量 CT 的增强稀疏重建

多能量计算机断层扫描 (MCT) 在材料分解、组织表征、病变检测和其他应用方面具有巨大潜力。然而，投影中存在的严重噪声使得难以获得高质量的 MCT 图像。为了克服这个限制，我们提出了一种称为增强稀疏重建（SISTER）方法的基于光谱图像相似性的张量方法。SISTER 通过将非局部窗口内的相似空间光谱块聚类到四阶张量组来利用空间光谱域中的非局部特征相似性。与采用张量字典学习（L0TDL）方法的图像梯度L0-norm相比，SISTER通过采用张量分解而不是张量字典学习，克服了张量字典的不稳定性。除了，在我们的 SISTER 方法中，权重系数更新策略也得到了优化。进行了数值模拟和临床前数据集，以评估和验证 SISTER 的性能。定性和定量结果表明，所提出的方法可以在边缘保留、更精细的特征恢复和噪声抑制方面带来有希望的改进。