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In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from low-resolution (LR) counterparts. Specifically, with the generative adversarial network (GAN) as the building block, we enforce the cycle-consistency in terms of the Wasserstein distance to establish a nonlinear end-to-end mapping from noisy LR input images to denoised and deblurred HR outputs. We also include the joint constraints in the loss function to facilitate structural preservation. In this process, we incorporate deep convolutional neural network (CNN), residual learning, and network in network techniques for feature extraction and restoration. In contrast to the current trend of increasing network depth and complexity to boost the imaging performance, we apply a parallel ${1}\times {1}$ CNN to compress the output of the hidden layer and optimize the number of layers and the number of filters for each convolutional layer. The quantitative and qualitative evaluative results demonstrate that our proposed model is accurate, efficient and robust for super-resolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three large-scale CT datasets, and obtain promising results as compared to the other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Background and purpose: Cerebral ischemia-reperfusion injury can activate signal transducers and activators of transcription (STAT). STAT1 initiates neuronal apoptosis following cerebral ischemia-reperfusion, while STAT3 is neuroprotective. Erythropoietin (EPO) promotes regeneration through STAT3 and facilitates neuronal survival following ischemia. However, there are few reports on the effects of EPO on phosphorylated STAT1 (P-STAT1) level following cerebral ischemia-reperfusion in rats, and there is no evidence on the simultaneous observation of the four kinds of protein:STAT1, P-STAT1, STAT3, and P-STAT3. Methods: We established a rat focal cerebral ischemia-reperfusion injury model, and used Western blot and immunohistochemical staining to assess the levels of STAT1 and STAT3 expression, and TdT-mediated dUTP-biotin nick end-labeling (TUNEL) was carried out to observe the number of apoptotic cells with or without EPO treatment. Results: Our findings show that EPO treatment had no significant effect on STAT1 and STAT3 expression, but P-STAT1 and P-STAT3 were slightly decreased and significantly increased, respectively, after EPO treatment. Neurologic deficits, the infarct volume, and the number of apoptotic cells were significantly decreased after EPO treatment. Conclusions: The results suggest that EPO exerts a neuroprotective effect by influencing STAT3 and STAll expression in the area injured by cerebral ischemia-reperfusion. [ABSTRACT FROM AUTHOR]

The purpose of this article is to describe a novel technique of multiphase fusion three-dimensional (3D) images in patients with malignant pancreatobiliary obstruction. Multiphase fusion 3D images of CT arteriography, portovenography and hepatic venography combined with negative-contrast CT cholangiopancreatography can be done with enhanced multiphase CT scan using intravenous contrast agent at once. This technique may be feasible for one-stop evaluation of malignant pancreatobiliary obstruction., Competing Interests: All authors declares that they have not an actual or potential conflict of interest including any financial, personal or other relationships with other people or organization, that can inappropriately influence the work., (© 2022 The Authors.)

Computed tomography (CT) is a popular medical imaging modality and enjoys wide clinical applications. At the same time, the x-ray radiation dose associated with CT scannings raises a public concern due to its potential risks to the patients. Over the past years, major efforts have been dedicated to the development of Low-Dose CT (LDCT) methods. However, the radiation dose reduction compromises the signal-to-noise ratio (SNR), leading to strong noise and artifacts that downgrade CT image quality. In this paper, we propose a novel 3D noise reduction method, called Structurally-sensitive Multi-scale Generative Adversarial Net (SMGAN), to improve the LDCT image quality. Specifically, we incorporate three-dimensional (3D) volumetric information to improve the image quality. Also, different loss functions for training denoising models are investigated. Experiments show that the proposed method can effectively preserve structural and textural information in reference to normal-dose CT (NDCT) images, and significantly suppress noise and artifacts. Qualitative visual assessments by three experienced radiologists demonstrate that the proposed method retrieves more information, and outperforms competing methods.