BACKGROUND: Spectral computed tomography (CT) has the capability to resolve the energy levels of incident photons, which has the potential to distinguish different material compositions. Although material decomposition methods based on x-ray attenuation characteristics have good performance in dual-energy CT imaging, there are some limitations in terms of image contrast and noise levels. OBJECTIVE: This study focused on multi-material decomposition of spectral CT images based on a deep learning approach. METHODS: To classify and quantify different materials, we proposed a multi-material decomposition method via the improved Fully Convolutional DenseNets (FC-DenseNets). A mouse specimen was first scanned by spectral CT system based on a photon-counting detector with different energy ranges. We then constructed a training set from the reconstructed CT images for deep learning to decompose different materials. RESULTS: Experimental results demonstrated that the proposed multi-material decomposition method could more effectively identify bone, lung and soft tissue than the basis material decomposition based on post-reconstruction space in high noise levels. CONCLUSIONS:The new proposed approach yielded good performance on spectral CT material decomposition, which could establish guidelines for multi-material decomposition approaches based on the deep learning algorithm.

Excessive activation of the microglia in the brain is involved in the development of several neurodegenerative diseases. Previous studies have indicated that (-)-epigallocatechin gallate (EGCG), a major active constituent of green tea, exhibits potent suppressive effects on the activation of microglia. As the 67 kDa laminin receptor (67LR) is a key element in cellular activation and migration, we investigated the effect of EGCG on cell migration and 67LR in lipopolysaccharide (LPS)-activated macrophagic RAW264.7 cells. The presence of EGCG (1-25 μM) markedly attenuated LPS-induced cell migration in a dose-dependent manner. However, the total amount of 67LR protein in the RAW264.7 cells was unaffected by EGCG, as revealed by Western blot analysis. In addition, confocal immunofluorescence microscopy indicated that EGCG caused a marked membrane translocation of 67LR from the membrane surface towards the cytoplasm. Cell-surface biotinylation analysis confirmed that EGCG induced a significant internalization of 67LR by 24-68% in a dose-dependent manner. This study helps to explain the pharmacological action of EGCG on 67LR, suggesting its potential use in the treatment of diseases associated with macrophage/microglia activation, such as neurodegenerative diseases and cancer.

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The spectral computed tomography (CT) based on photon-counting detector performs energydependent image reconstruction of material attenuation coefficients, allowing for effective medical diagnosis and material discrimination. However, the spectral CT image quality is degraded in narrow energy bins as a consequence of low photon counts. Thus the edge information of some materials with similar attenuation cannot be well identified. To improve the accuracy of material discrimination of spectral CT, we proposed a deep-learning-based material discrimination method based on Fully Convolutional Pyramidal Residual Network (FC-PRNet). The FC-PRNet model can predict each pixel of spectral CT images and extract more edge information for different material components. We evaluated our method using mouse spectral CT data set, and experimental results demonstrated that the proposed method could efficiently discriminate different materials compared with traditional method based on post-reconstruction. Moreover, our algorithm has fewer parameters, faster convergent speed and higher accuracy, and achieves better quality of material discrimination than SegNet, FCN-8s and U-Net. INDEX TERMS Biomedical computing, artificial neural networks, multispectral imaging, image segmentation.