  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.
