Multi-material decomposition of spectral CT images via Fully Convolutional DenseNets.

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.

Attenuated migration by green tea extract (-)-epigallocatechin gallate (EGCG): involvement of 67 kDa laminin receptor internalization in macrophagic cells.

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.