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Spectral computed tomography based on a photon-counting detector (PCD)attracts more and more attentions since it has the capability to provide moreaccurate identification and quantitative analysis for biomedical materials. Thelimited number of photons within narrow energy bins leads to imaging results oflow signal-noise ratio. The existing supervised deep reconstruction networksfor CT reconstruction are difficult to address these challenges because it isusually impossible to acquire noise-free clinical images with clear structuresas references. In this paper, we propose an iterative deep reconstructionnetwork to synergize unsupervised method and data priors into a unifiedframework, named as Spectral2Spectral. Our Spectral2Spectral employs anunsupervised deep training strategy to obtain high-quality images from noisydata in an end-to-end fashion. The structural similarity prior withinimage-spectral domain is refined as a regularization term to further constrainthe network training. The weights of neural network are automatically updatedto capture image features and structures within the iterative process. Threelarge-scale preclinical datasets experiments demonstrate that theSpectral2spectral reconstructs better image quality than other thestate-of-the-art methods.

Spectral2Spectral: Image-spectral Similarity Assisted Spectral CT Deep Reconstruction without Reference