Downsampled Imaging Geometric Modeling for Accurate CT Reconstruction via Deep Learning.

X-ray computed tomography (CT) is widely used clinically to diagnose a variety of diseases by reconstructing the tomographic images of a living subject using penetrating X-rays. For accurate CT image reconstruction, a precise imaging geometric model for the radiation attenuation process is usually required to solve the inversion problem of CT scanning, which encodes the subject into a set of intermediate representations in different angular positions. Here, we show that accurate CT image reconstruction can be subsequently achieved by downsampled imaging geometric modeling via deep-learning techniques. Specifically, we first propose a downsampled imaging geometric modeling approach for the data acquisition process and then incorporate it into a hierarchical neural network, which simultaneously combines both geometric modeling knowledge of the CT imaging system and prior knowledge gained from a data-driven training process for accurate CT image reconstruction. The proposed neural network is denoted as DSigNet, i.e., downsampled-imaging-geometry-based network for CT image reconstruction. We demonstrate the feasibility of the proposed DSigNet for accurate CT image reconstruction with clinical patient data. In addition to improving the CT image quality, the proposed DSigNet might help reduce the computational complexity and accelerate the reconstruction speed for modern CT imaging systems.

Efficacy and safety of traditional Chinese herbal formula combined with western medicine for gastroesophageal reflux disease: A protocol for systematic review and meta-analysis.

BACKGROUND: The combined therapy of Chinese herbal formula and western medicine against gastroesophageal reflux disease (GERD) could significantly improve the clinical effect, reduce the recurrence rate and the side effects of western medicine, and even reduce the dosage and course of treatment of western medicine. This study tried to systematically evaluate the efficacy and safety traditional Chinese herbal formula combined with western medicine in the treatment of GERD. METHODS: Randomized controlled trials of traditional Chinese herbal formula combined with western medicine for GERD patients will be systematically searched using the PubMed, Embase, Medline, Cochrane Library, China National Knowledge Infrastructure (CNKI), Wanfang database, Chongqing VIP Chinese Science and Technology Periodical Database, and Chinese Biological and Medical database (CMB) until Aug. 28, 2020. Two researchers will perform data extraction and risk of bias assessment independently. Statistical analysis will be conducted in RevMan 5.3. RESULTS: This study will summarize the present evidence by exploring the efficacy and safety of traditional Chinese herbal formula combined with western medicine in the treatment of GERD. CONCLUSIONS: The findings of the study will help to determine potential benefits of traditional Chinese herbal formula combined with western medicine against GERD. ETHICS AND DISSEMINATION: The private information from individuals will not be published. This systematic review also will not involve endangering participant rights. Ethical approval is not required. The results may be published in a peer-reviewed journal or disseminated in relevant conferences. OSF REGISTRATION NUMBER: DOI 10.17605/OSF.IO/RSAVF.

Major depressive disorder identification by referenced multiset canonical correlation analysis with clinical scores.

A novel method based on multiset canonical correlation analysis (mCCA) and linear discriminant analysis (LDA) is presented to identify the major depressive disorder (MDD). The new method comprises two parts, namely, the mCCA-rreg and sparse LDA models. The mCCA-rreg model extends the classical canonical correlation model to calculate functional connections by restricting the references to a reference space and adding a spatial regularization term. The reference space is used to ensure that the model extracts important components first from several datasets simultaneously by decreasing the importance of the components in which we are uninterested. The spatial regularization term helps in avoiding the multicollinearity and overfitting problems under the low signal-to-noise ratio circumstance. The sparse LDA model extends the classical LDA model to extract a small subset of discriminative classification features by fusing clinical scores. In the real data experiment, we extract two functional connection modes from 45 subjects by the mCCA-rreg model. Then, we construct classifiers to identify the patients with MDD based on the connections selected by the sparse LDA model. The best accuracy is higher than 95%. The results show that the mCCA-rreg model can retrieve the important components characterized by a preassigned reference space and exclude the noise or components of no interest. The sparse LDA model can extract discriminative classification features related to clinical scores.

Aberrant cortical thickness in neurologically asymptomatic patients with end-stage renal disease.

PURPOSE: The aim of this study is to investigate the morphology of cortical gray matter in patients with end-stage renal disease (ESRD) and the relationship between cortical thickness and kidney function. PATIENTS AND METHODS: Three-dimensional high-resolution brain structural magnetic resonance imaging data were collected from 35 patients with ESRD (28 men, 18-61 years old) and 40 age- and gender-matched healthy controls (HCs, 32 men, 22-58 years old). Vertex-wise analysis was then performed to compare the brains of the patients with ESRD with those of HCs to identify abnormalities in the brains of the former. Multiple biochemical measures of renal metabolin, vascular risk factors, general cognitive ability, and dialysis duration were correlated with brain morphometry alterations for the patients. RESULTS: Patients with ESRD showed lesser cortical thickness than the HCs. The most significant cluster with decreased cortical thickness was found in the right prefrontal cortex (P<0.05, random-field theory correction). In addition, the four local peak vertices in the prefrontal cluster were lateral prefrontal cortex (Peaks 1 and 2), medial prefrontal cortex (Peak 3), and ventral prefrontal cortex (Peak 4). Significant negative correlations were observed between the cortical thicknesses of all four peak vertices and blood urea nitrogen; a negative correlation, between the cortical thickness in three of four peaks and serum creatinine; and a positive correlation, between cortical thickness in the medial prefrontal cortex (Peak 3) and hemoglobin. CONCLUSION: These results provided compelling evidence for cortical abnormality of ESRD patients and suggested that kidney function may be the key factor for predicting changes of brain tissue structure.

A CCA and ICA-Based Mixture Model for Identifying Major Depression Disorder.

The fMRI signals are usually filtered before processing and analyzing. This process can result in the loss of information carried by the higher frequency in the low frequency fluctuation. ICA and CCA are two classical methods in fMRI. ICA finds the statistically independent components of the observed data, however these components are usually physiologically uninterpretable without auxiliary procedures. CCA decomposes two sets of data into component pairs in some order, however these components may be mixtures of real signals and noise. In order to obtain statistically independent components and avoid the loss of information in the process of filtering, we propose a mixed model based on ICA and CCA, which does not need to filter the data. It is shown by the experiments that the new model has some advantages compared with the classical ICA and CCA. The components obtained by the new model is statistically independent. The useful information included in the low frequency fluctuation can be preserved. Experiments on synthetic data show satisfying results. As an application, this new model is used to design an algorithm to discriminate the major depressions from normal controls, with encouraging experimental results.

Decreased functional connectivity in the language regions in bipolar patients during depressive episodes but not remission.

BACKGROUND: Retardation of thought is a crucial clinical feature in patients with bipolar depression, characterized by dysfunctional semantic processing and language communication. However, the underlying neuropathological mechanisms remain largely unknown. The objective of this study was to evaluate the disruption in resting-state functional connectivity in 90 different brain regions during the depressive episodes of bipolar disorder and during disease remission. METHODS: Applying the whole brain and language regions of interest methods to the resting-state functional magnetic resonance imaging data, we explored the discrepancies in 90 brain regions' functional connectivity in 42 patients with bipolar disorder - 23 experiencing a depressive episode and 19 in remission - and 28 healthy controls matched for gender, age, and education. RESULTS: Bipolar depressive patients had significantly reduced connectivity strength in the language regions relative to healthy controls. Specifically, the affected regions included the left triangular part of the inferior frontal gyrus, left opercular part of the inferior frontal gyrus, left middle temporal gyrus, and left angular gyrus. However, no significant differences in these regions were observed between bipolar patients in remission and healthy controls. Furthermore, the decreased connectivity strength between the left middle temporal gyrus and right lingual gyrus showed significant positive correlation with the scores on the Hamilton Depression Rating Scale. LIMITATIONS: Bipolar depressive patients received treatment of benzodiazepines, which may confound the findings. CONCLUSIONS: Our results illustrated that connectivity disturbances in the language regions may change depending on the disease phase of bipolar disorder.