MRI reconstruction with enhanced self-similarity using graph convolutional network.

BACKGROUND: Recent Convolutional Neural Networks (CNNs) perform low-error reconstruction in fast Magnetic Resonance Imaging (MRI). Most of them convolve the image with kernels and successfully explore the local information. Nonetheless, the non-local image information, which is embedded among image patches relatively far from each other, may be lost due to the limitation of the receptive field of the convolution kernel. We aim to incorporate a graph to represent non-local information and improve the reconstructed images by using the Graph Convolutional Enhanced Self-Similarity (GCESS) network. METHODS: First, the image is reconstructed into the graph to extract the non-local self-similarity in the image. Second, GCESS uses spatial convolution and graph convolution to process the information in the image, so that local and non-local information can be effectively utilized. The network strengthens the non-local similarity between similar image patches while reconstructing images, making the reconstruction of structure more reliable. RESULTS: Experimental results on in vivo knee and brain data demonstrate that the proposed method achieves better artifact suppression and detail preservation than state-of-the-art methods, both visually and quantitatively. Under 1D Cartesian sampling with 4 × acceleration (AF = 4), the PSNR of knee data reached 34.19 dB, 1.05 dB higher than that of the compared methods; the SSIM achieved 0.8994, 2% higher than the compared methods. Similar results were obtained for the reconstructed images under other sampling templates as demonstrated in our experiment. CONCLUSIONS: The proposed method successfully constructs a hybrid graph convolution and spatial convolution network to reconstruct images. This method, through its training process, amplifies the non-local self-similarities, significantly benefiting the structural integrity of the reconstructed images. Experiments demonstrate that the proposed method outperforms the state-of-the-art reconstruction method in suppressing artifacts, as well as in preserving image details.

[In vitro infection of human liver cancer cell line HepG2 with HCV].

BACKGROUND: To test susceptibility of human liver cell line Hep G2 to HCV in vitro. METHODS: Hep G2 was cultivated with the serum from a chronic hepatitis C patient. After inoculation, plus and minus strand of HCV RNA, the expression of HCV NS3 antigen and the location of HCV RNA in cell and/or supernatant were examined by RT-PCR, immunohistochemistry and in situ hybridization, respectively. RESULTS: HCV RNA could be detected from day 2 to day 40 post-inoculation in both cell and supernatant. HCV NS3 antigen could be expressed in infected cells and HCV RNA was mainly situated within cytoplasm. CONCLUSIONS: The results suggested that HepG2 cell line was not only susceptible to HCV but also could support its replication in vitro.

[Effects of polypeptides from HCV core region on the function of cytotoxic T cells].

OBJECTIVE: To investigate the pathogenesis of cytotoxic T cell (CTL) dysfunction in patients with HCV infection. METHODS: BALB/c mice were immunized by subcutaneous injection of polypeptides from HCV core region, and the CTL activity of mouse spleen cells was detected by the LDH release test. Two polypeptides which can enhance CTL function and two polypeptides which can inhibit CTL function were selected and cross-combined. BALB/c mice were immunized using the combined polypeptides and the CTL activities were detected afterwards. RESULTS: CTL activity was inhibited by CPA9 (39-74 amino acids), CPB7 (67-76 amino acids) and CPB8 (71-80 amino acids), and promoted by CPA10 (5-23 amino acids), CPB6 (63-72 amino acids) and CPB2 (131-140 amino acids). Using single factor analysis of variance, the CTL activity in the mice could be enhanced by polypeptides from the HCV core region, CPB2+CPB8, CPB6+CPB8, respectively. There was no obvious difference between CPB2+CPB7, CPB6+CPB7 and negative control. CONCLUSIONS: CPA9, CPB7, and CPB8, the 3 polypeptides from HCV core region play an inhibition role and CPA10, CPB6, and CPB2 play an enhancement role in CTL activity in mice. The inhibition and enhancement functions of the polypeptides from HCV core region interact each other.