Wu Weiwen, Zhang Yanbo, Wang Qian, Liu Fenglin, Luo Fulin, Yu Hengyong
Key Lab of Optoelectronic Technology and Systems, Ministry of Education, Chongqing University, Chongqing 400044, China.
Department of Electrical and Computer Engineering, University of Massachusetts Lowell, Lowell, MA 01854, USA.
Inverse Probl. 2018 Oct;34(10). doi: 10.1088/1361-6420/aad67b. Epub 2018 Aug 14.
Spectral computed tomography (CT) reconstructs the same scanned object from projections of multiple narrow energy windows, and it can be used for material identification and decomposition. However, the multi-energy projection dataset has a lower signal-noise-ratio (SNR), resulting in poor reconstructed image quality. To address this thorny problem, we develop a spectral CT reconstruction method, namely spatial-spectral cube matching frame (SSCMF). This method is inspired by the following three facts: ) human body usually consists of two or three basic materials implying that the reconstructed spectral images have a strong sparsity; ) the same basic material component in a single channel image has similar intensity and structures in local regions. Different material components within the same energy channel share similar structural information; ) multi-energy projection datasets are collected from the subject by using different narrow energy windows, which means images reconstructed from different energy-channels share similar structures. To explore those information, we first establish a tensor cube matching frame (CMF) for a BM4D denoising procedure. Then, as a new regularizer, the CMF is introduced into a basic spectral CT reconstruction model, generating the SSCMF method. Because the SSCMF model contains an L-norm minimization of 4D transform coefficients, an effective strategy is employed for optimization. Both numerical simulations and realistic preclinical mouse studies are performed. The results show that the SSCMF method outperforms the state-of-the-art algorithms, including the simultaneous algebraic reconstruction technique, total variation minimization, total variation plus low rank, and tensor dictionary learning.
光谱计算机断层扫描（CT）通过多个窄能量窗口的投影来重建同一扫描对象，可用于物质识别和分解。然而，多能量投影数据集的信噪比（SNR）较低，导致重建图像质量较差。为解决这一棘手问题，我们开发了一种光谱CT重建方法，即空间光谱立方匹配框架（SSCMF）。该方法受到以下三个事实的启发：1）人体通常由两三种基本物质组成，这意味着重建的光谱图像具有很强的稀疏性；2）单通道图像中相同的基本物质成分在局部区域具有相似的强度和结构。同一能量通道内不同的物质成分共享相似的结构信息；3）多能量投影数据集是通过使用不同的窄能量窗口从对象采集的，这意味着从不同能量通道重建的图像具有相似的结构。为探索这些信息，我们首先为BM4D去噪过程建立了一个张量立方匹配框架（CMF）。然后，作为一种新的正则化器，将CMF引入基本的光谱CT重建模型，生成SSCMF方法。由于SSCMF模型包含4D变换系数的L范数最小化，因此采用了一种有效的优化策略。进行了数值模拟和实际的临床前小鼠研究。结果表明，SSCMF方法优于包括同步代数重建技术、总变差最小化、总变差加低秩以及张量字典学习在内的现有算法。
