Computed tomography (CT) is widely used in screening, diagnosis, and image-guided therapy for both clinical and research purposes. Since CT involves ionizing radiation, an overarching thrust of related technical research is development of novel methods enabling ultrahigh quality imaging with fine structural details while reducing the X-ray radiation. In this paper, we present a semi-supervised deep learning approach to accurately recover high-resolution (HR) CT images from lowresolution (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 deep imaging 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 CT imaging performance, which limit its real-world applications by imposing considerable computational and memory overheads, we apply a parallel 1 × 1 1 × 1 1 × 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. Quantitative and qualitative evaluations demonstrate that our proposed model is accurate, efficient and robust for superresolution (SR) image restoration from noisy LR input images. In particular, we validate our composite SR networks on three largescale CT datasets, and obtain promising results as compared to the other state-of-the-art methods.

Previous research has shown that type 2 diabetes mellitus (T2DM) is associated with an increased risk of cognitive impairment. Patients with impaired cognition often show decreased spontaneous brain activity on resting-state functional magnetic resonance imaging (rs-fMRI). This study used rs-fMRI to investigate changes in spontaneous brain activity among patients with T2DM and to determine the relationship of these changes with cognitive impairment. T2DM patients (n = 29) and age-, sex-, and education-matched healthy control subjects (n = 27) were included in this study. Amplitude of lowfrequency fluctuation (ALFF) and regional homogeneity (ReHo) values were calculated to represent spontaneous brain activity. Brain volume and cognition were also evaluated among these participants. Compared with healthy control subjects, patients with T2DM had significantly decreased ALFF and ReHo values in the occipital lobe and postcentral gyrus. Patients performed worse on several cognitive tests; this impaired cognitive performance was correlated with decreased activity in the cuneus and lingual gyrus in the occipital lobe. Brain volume did not differ between the two groups. The abnormalities of spontaneous brain activity reflected by ALFF and ReHo measurements in the absence of structural changes in T2DM patients may provide insights into the neurological pathophysiology underlying diabetes-associated cognitive decline.

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.

The extremely low permeability of the blood-brain barrier (BBB) poses the greatest impediment in the treatment of central nervous system (CNS) diseases. Recent work indicated that BBB permeability can be up-regulated by activating A2A adenosine receptor (AR), which temporarily increases intercellular spaces between the brain capillary endothelial cells. However, due to transient circulation lifetime of adenosine-based agonists, their capability to enhance brain delivery of drugs, especially macromolecular drugs, is limited. In this work, a series of nanoagonists (NAs) were developed by labeling different copies of A2A AR activating ligands on dendrimers. In vitro transendothelial electrical resistance measurements demonstrated that the NAs increased permeability of the endothelial cell monolayer by compromising the tightness of tight junctions, the key structure that restricts the entry of blood-borne molecules into the brain. In vivo imaging studies indicated the remarkably up-regulated brain uptake of a macromolecular model drug (45 kDa) after intravenous injection of NAs. Autoradiographic imaging showed that the BBB opening time-window can be tuned in a range of 0.5-2.0 h by the NAs labeled with different numbers of AR-activating ligands. By choosing a suitable NA, it is possible to maximize brain drug delivery and minimize the uncontrollable BBB leakage by matching the BBB opening time-window with the pharmacokinetics of a therapeutic agent. The NA-mediated brain drug delivery strategy holds promise for the treatment of CNS diseases with improved therapeutic efficiency and reduced side-effects.

medRxiv preprint 6 datasets. The predictive performance was further evaluated in test dataset on lung lobe-and patients-level. Main outcomesShort-term hospital stay (≤10 days) and long-term hospital stay (>10 days). ResultsThe CT radiomics models based on 6 second-order features were effective in discriminating short-and long-term hospital stay in patients with pneumonia associated with SARS-CoV-2 infection, with areas under the curves of 0.97 (95%CI 0.83-1.0) and 0.92 (95%CI 0.67-1.0) by LR and RF, respectively, in the test dataset. The LR model showed a sensitivity and specificity of 1.0 and 0.89, and the RF model showed similar performance with sensitivity and specificity of 0.75 and 1.0 in test dataset. ConclusionsThe machine learning-based CT radiomics models showed feasibility and accuracy for predicting hospital stay in patients with pneumonia associated with SARS-CoV-2 infection.All rights reserved. No reuse allowed without permission.author/funder, who has granted medRxiv a license to display the preprint in perpetuity. Results Patient characteristicsA total of 52 patients with laboratory-confirmed SARS-CoV-2 infection and initial CT images were enrolled from 5 designated hospitals in Ankang, Lishui, Zhenjiang, Lanzhou, and Linxia, China. As of February 20, 14 patients were still hospitalized, and 7 patients had non-findings in CT images. Therefore, 31 patients with 72 lesion segments were included in the final analysis. The training and inter-validation cohort comprised 26 patients (12 from Ankang, 8 from Lishui, 4 from Lanzhou, and 2 from Linxia) with 59 lesion segments, and test cohort comprised 5 patients from Zhenjiang with 13 lesion segments. The median age was 38.00 (interquartile range, 26.00-47.00) years and 17 (57%) were male. Comorbidities, symptoms and laboratory findings at admission were summarized in Table 1. Performance of CT radiomics modelThe CT radiomics model, based on 6 features (supplementary Table1), showed the highest AUC on the training and inter-validation dataset. The performance of modeling using LR and RF methods was shown in Figure 2. On lung lobe-level, models using LR method significantly distinguished short-and long-term hospital stay (In training and inter-validation datasets, cut-off value 0.31, AUC 0.94 (95%CI 0.92-0.97), sensitivity 1.0, specificity 0.87, NPV 1.0, and PPV 0.88; In test dataset, AUC 0.97 (95%CI 0.83-1.0), sensitivity 1.0, specificity 0.89, NPV 1.0, and PPV 0.8). Besides, models using RF method obtained satisfied results (In training and inter-validation datasets, cut-off value 0.68, AUC 1.0 (95%CI 1.0-1.0), All rights reserved. No reuse allowed without permission.author/funder, who has granted medRxiv a license to display the preprint in perpetuity.