Spectral computed tomography based on a photon-counting detector (PCD) attracts more and more attentions since it has the capability to provide more accurate identification and quantitative analysis for biomedical materials. The limited number of photons within narrow energy bins leads to imaging results of low signal-noise ratio. The existing supervised deep reconstruction networks for CT reconstruction are difficult to address these challenges because it is usually impossible to acquire noise-free clinical images with clear structures as references. In this paper, we propose an iterative deep reconstruction network to synergize unsupervised method and data priors into a unified framework, named as Spectral2Spectral. Our Spectral2Spectral employs an unsupervised deep training strategy to obtain high-quality images from noisy data in an end-to-end fashion. The structural similarity prior within image-spectral domain is refined as a regularization term to further constrain the network training. The weights of neural network are automatically updated to capture image features and structures within the iterative process. Three large-scale preclinical datasets experiments demonstrate that the Spectral2spectral reconstructs better image quality than other the state-of-the-art methods.

翻译：基于光子计数探测器（PCD）的光谱计算机断层扫描因其能够为生物医学材料提供更精确的识别和定量分析而受到越来越多的关注。然而，窄能量区间内有限的光子数量导致成像结果信噪比较低。现有用于CT重建的监督式深度重建网络难以应对这些挑战，因为通常无法获取结构清晰且无噪声的临床图像作为参考。本文提出一种迭代深度重建网络，将无监督方法与数据先验协同整合到统一框架中，命名为Spectral2Spectral。我们的Spectral2Spectral采用无监督深度训练策略，以端到端方式从含噪数据中获取高质量图像。图像-光谱域内的结构相似性先验被优化为正则化项，进一步约束网络训练。神经网络权重在迭代过程中自动更新以捕捉图像特征与结构。三个大规模临床前数据集实验表明，Spectral2Spectral重建的图像质量优于其他当前最优方法。