Abstract
Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which is able to distinguish different material compositions. Nowadays, deep learning has generated widespread attention in CT imaging applications. In this paper, a method of material decomposition for spectral CT based on improved Fully Convolutional DenseNets (FC-DenseNets) was proposed. Spectral data were acquired by a photon-counting detector and reconstructed spectral CT images were used to construct a training dataset. Experimental results showed that the proposed method could effectively identify bone and different tissues in high noise levels. This work could establish guidelines for multi-material decomposition approaches with spectral CT.
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