Spectral-computed tomography (CT) has been demonstrating its great advantages in lesion detection, tissue characterization, and material decomposition. However, the quality of images is often significantly corrupted with various noises, which brings a great challenge for its applications. Because the channel-wise images from different energy interval share similar structure and physical message, the spatial sparsity, global correlation across the spectrum (GCS), and nonlocal self-similarity (NSS) as three important characteristics are employed to spectral CT reconstruction. In this study, we propose a spectral-image decomposition with energy-fusion sensing (SIDES) reconstruction method, which encourages to obtain better quality spectral images and material decomposition results by establishing a unified tensor decomposition model. First, considering the noise distribution in channel-wise and the difference of linear attenuation coefficients within channel-cross, an adaptive weighted full-spectrum prior image as additional supervised information is incorporated to formulate a new weighted prior image-based tensor. Cooperating with original image tensor, they fully explore the spatial sparsity, GCS, and NSS properties. Then, we formulate nonlocal similar patch-based tensor groups to encode the NSS property from image-domain and residual-image-domain (which is expanded by prior-image and image-self). Next, low-rank regularized Tucker tensor decomposition is employed to fully explore the intrinsic knowledge with the help of prior-image supervision. Finally, the relaxed convex optimization model is optimized by dividing reconstruction model into several subproblem using split-Bregman method. Numerical simulations and real experiments are designed to validate and evaluate the SIDES method and the results demonstrate that the SIDES reconstruction outperforms the state-of-the-art.

中文翻译：

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用于光谱 CT 重建的能量融合传感光谱图像分解

光谱计算机断层扫描 (CT) 已证明其在病变检测、组织表征和材料分解方面的巨大优势。然而，图像质量往往受到各种噪声的严重影响，这给其应用带来了巨大挑战。由于来自不同能量区间的通道图像具有相似的结构和物理信息，因此将空间稀疏性、光谱全局相关性 (GCS) 和非局部自相似性 (NSS) 作为三个重要特征用于光谱 CT 重建。在这项研究中，我们提出了一种能量融合传感（SIDES）重建方法的光谱图像分解，该方法通过建立统一的张量分解模型来鼓励获得更好质量的光谱图像和材料分解结果。第一的，考虑到通道方向的噪声分布和通道交叉内线性衰减系数的差异，将自适应加权全谱先验图像作为附加监督信息结合起来，以制定新的基于加权先验图像的张量。他们与原始图像张量合作，充分探索了空间稀疏性、GCS 和 NSS 特性。然后，我们制定非局部相似的基于补丁的张量组来编码来自图像域和残差图像域（由先验图像和图像自身扩展）的 NSS 属性。接下来，在先验图像监督的帮助下，采用低秩正则化 Tucker 张量分解来充分探索内在知识。最后，利用split-Bregman方法将重构模型划分为若干子问题，对松弛凸优化模型进行优化。数值模拟和真实实验旨在验证和评估 SIDES 方法，结果表明 SIDES 重建优于最先进的技术。