Numerous dual-energy CT (DECT) techniques have been developed in the past few decades. Dual-energy CT (DECT) statistical iterative reconstruction (SIR) has demonstrated its potential for reducing noise and increasing accuracy. Our lab proposed a joint statistical DECT algorithm for stopping power estimation and showed that it outperforms competing image-based material-decomposition methods. However, due to its slow convergence and the high computational cost of projections, the elapsed time of 3D DECT SIR is often not clinically acceptable. Therefore, to improve its convergence, we have embedded DECT SIR into a deep learning model-based unrolled network for 3D DECT reconstruction (MB-DECTNet) that can be trained in an end-to-end fashion. This deep learning-based method is trained to learn the shortcuts between the initial conditions and the stationary points of iterative algorithms while preserving the unbiased estimation property of model-based algorithms. MB-DECTNet is formed by stacking multiple update blocks, each of which consists of a data consistency layer (DC) and a spatial mixer layer, where the spatial mixer layer is the shrunken U-Net, and the DC layer is a one-step update of an arbitrary traditional iterative method. Although the proposed network can be combined with numerous iterative DECT algorithms, we demonstrate its performance with the dual-energy alternating minimization (DEAM). The qualitative result shows that MB-DECTNet with DEAM significantly reduces noise while increasing the resolution of the test image. The quantitative result shows that MB-DECTNet has the potential to estimate attenuation coefficients accurately as traditional statistical algorithms but with a much lower computational cost.

翻译：过去几十年中，双能CT（DECT）技术得到了广泛发展。双能CT统计迭代重建（SIR）在降低噪声和提高精度方面展现出潜力。本实验室提出了一种用于阻止能力估计的联合统计DECT算法，并证明其优于基于图像的材料分解竞争方法。然而，由于收敛速度慢且投影计算成本高，三维DECT SIR的运行时间往往难以满足临床需求。因此，为改善其收敛性，我们将DECT SIR嵌入到基于深度学习模型展开的三维DECT重建网络（MB-DECTNet）中，该网络可进行端到端训练。这种深度学习方法在保持基于模型算法无偏估计特性的同时，被训练用于学习初始条件与迭代算法驻点之间的捷径。MB-DECTNet通过堆叠多个更新模块构成，每个模块包含数据一致性层（DC）和空间混合层；其中空间混合层采用精简U-Net结构，而DC层则是对任意传统迭代方法进行单步更新。尽管该网络可与多种迭代DECT算法结合，我们通过双能交替最小化（DEAM）方法验证其性能。定性结果表明，基于DEAM的MB-DECTNet在显著降低噪声的同时提升了测试图像的分辨率。定量结果则显示，MB-DECTNet能够以远低于传统统计算法的计算成本，实现与之相当的衰减系数精确估计。