Detection method of viral pneumonia imaging features based on CT scan images in COVID-19 case study

This study aims to automatically analyze and extract abnormalities in the lung field due to Coronavirus Disease 2019 (COVID-19). Types of abnormalities that can be detected are Ground Glass Opacity (GGO) and consolidation. The proposed method can also identify the location of the abnormality in the lung field, that is, the central and peripheral lung area. The location and type of these abnormalities affect the severity and confidence level of a patient suffering from COVID-19. The detection results using the proposed method are compared with the results of manual detection by radiologists. From the experimental results, the proposed system can provide an average error of 0.059 for the severity score and 0.069 for the confidence level. This method has been implemented in a web-based application for general users.•A method to detect the appearance of viral pneumonia imaging features, namely Ground Glass Opacity (GGO) and consolidation on the chest Computed Tomography (CT) scan images.•This method can separate the lung field to the right lung and the left lung, and it also can identify the detected imaging feature's location in the central or peripheral of the lung field.•Severity level and confidence level of the patient's suffering are measured.

Centella asiatica ethanol extract increases hippocampal brain derived neurotrophic factor in male Wistar rats

BackgroundSynaptic plasticity, which primarily takes place in the hippocampus, is the molecular basis of long- term memory formation. Brain derived neurotrophic factor (BDNF), a member of the neurotrophin family, plays a significant role in synaptic plasticity and memory formation. When BDNF is released, it binds to its receptor and activates various intracellular signal transduction pathways leading to synaptic plasticity. Several methods to improve memory function in humans have been studied, one of which is the use of herbal compounds, such as Centella asiatica (CeA), an herbaceous plant that has been used for improving memory. This study aims to examine the effects of CeA ethanol extract on BDNF protein expression in the CA1 hippocampal region in adult male rats.MethodsA randomized experimental design was performed involving 18 adult male Wistar rats. The rats were randomized into three groups: one control/distilled water group and two groups treated with doses of CeA ethanol extract of 300 mg/kgBW (CeA300) and 600 mg/kgBW (CeA600), respectively. CeA ethanol extract was administered orally for 28 consecutive days with weekly weight-adjusted dose. After 28 days, the rats were decapitated, and the hippocampus was isolated from the brain. BDNF protein expression was assessed using immunohistochemistry. Data was analyzed using Kruskal-Wallis test and continued with post-hoc analysis. ResultsThere was a significant increase in BDNF protein expression in the CeA600 group compared to the control group (p<0.001). ConclusionAdministration of CeA ethanol extract increased BDNF protein expression in the CA1 hippocampal region of adult male rats.

Effect of Centella asiatica Ethanol Extract in Spatial Working Memory on Adult Male Rats

Cognitive decline can be started at early adult. It may be prevented with administration of Centella asiatica (CeA). CeA already known has some medicinal values for the brain such as to increase dendritic growth, to improve cognitive function and memory performance in rats after chronic stress. Objective: This study is aimed to investigate effect CeA ethanol extract on spatial working memory of normal adult male rats. Eighteen normal adult male Wistar rats were divided into three groups: control/aquadest group and two groups treated with different doses (mg/kg) of CeA: 300 (CeA300) and 600 (CeA600). Ethanol extract of CeA were administrated orally for 28 consecutive days with weekly weight-adjusted dose. Memory performance was tested using Y-Maze before, on 14th days of treatment and after treatment. Data were analyzed using Kruskal-Wallis test and continued with Mann-Whitney test. Result: Treatment groups showed a better spatial working memory performance than control group, but there were no significant result between CeA300 and CeA600 groups (p < 0.05). Ethanol extract of CeA prevents spatial working memory decline on normal male adult Wistar rats. The optimum dosage of CeA might be 300 mg/kg of body weight.

Contrast-optimized composite image derived from multigradient echo cardiac magnetic resonance imaging improves reproducibility of myocardial contours and T2* measurement

Objectives

Reproducibility of myocardial contour determination in cardiac magnetic resonance imaging is important, especially when determining T2* values per myocardial segment as a prognostic factor of heart failure or thalassemia. A method creating a composite image with contrasts optimized for drawing myocardial contours is introduced and compared with the standard method on a single image.

Materials and methods

A total of 36 short-axis slices from bright-blood multigradient echo (MGE) T2* scans of 21 patients were acquired at eight echo times. Four observers drew free-hand myocardial contours on one manually selected T2* image (method 1) and on one image composed by blending three images acquired at TEs providing optimum contrast-to-noise ratio between the myocardium and its surrounding regions (method 2).

Results

Myocardial contouring by method 2 met higher interobserver reproducibility than method 1 (P < 0.001) with smaller Coefficient of variance (CoV) of T2* values in the presence of myocardial iron accumulation (9.79 vs. 15.91 %) and in both global myocardial and mid-ventricular septum regions (12.29 vs. 16.88 and 5.76 vs. 8.16 %, respectively).

Conclusion

The use of contrast-optimized composite images in MGE data analysis improves reproducibility of myocardial contour determination, leading to increased consistency in the calculated T2* values enhancing the diagnostic impact of this measure of iron overload.

Clinical implications of non-steatotic hepatic fat fractions on quantitative diffusion-weighted imaging of the liver

Diffusion-weighted imaging (DWI) is an important diagnostic tool in the assessment of focal liver lesions and diffuse liver diseases such as cirrhosis and fibrosis. Quantitative DWI parameters such as molecular diffusion, microperfusion and their fractions, are known to be affected when hepatic fat fractions (HFF) are higher than 5.5% (steatosis). However, less is known about the effect on DWI for HFF in the normal non-steatotic range below 5.5%, which can be found in a large part of the population. The aim of this study was therefore to evaluate the diagnostic implications of non-steatotic HFF on quantitative DWI parameters in eight liver segments. For this purpose, eleven healthy volunteers (2 men, mean-age 31.0) were prospectively examined with DWI and three series of in-/out-of-phase dual-echo spoiled gradient-recalled MRI sequences to obtain the HFF and T₂*. DWI data were analyzed using the intravoxel incoherent motion (IVIM) model. Four circular regions (ø22.3 mm) were drawn in each of eight liver segments and averaged. Measurements were divided in group 1 (HFF≤2.75%), group 2 (2.75< HFF ≤5.5%) and group 3 (HFF>5.5%). DWI parameters and T₂* were compared between the three groups and between the segments. It was observed that the molecular diffusion (0.85, 0.72 and 0.49 ×10⁻³ mm²/s) and T₂* (32.2, 27.2 and 21.0 ms) differed significantly between the three groups of increasing HFF (2.18, 3.50 and 19.91%). Microperfusion and its fraction remained similar for different HFF. Correlations with HFF were observed for the molecular diffusion (r = −0.514, p<0.001) and T₂* (−0.714, p<0.001). Similar results were obtained for the majority of individual liver segments. It was concluded that fat significantly decreases molecular diffusion in the liver, also in absence of steatosis (HFF≤5.5%). Also, it was confirmed that fat influences T₂*. Determination of HFF prior to quantitative DWI is therefore crucial.

Blood Vessel Segmentation in Complex-Valued Magnetic Resonance Images with Snake Active Contour Model

Accurate blood vessel segmentation plays a crucial role in non-invasive blood flow velocity measurement based on complex-valued magnetic resonance images. We propose a specific snake active contour model-based blood vessel segmentation framework for complex-valued magnetic resonance images. The proposed framework combines both magnitude and phase information from a complex-valued image representation to obtain an optimum segmentation result. Magnitude information of the complex-valued image provides a structural localization of the target object, while phase information identifies the existence of flowing matters within the object. Snake active contour model, which models the segmentation procedure as a force-balancing physical system, is being adopted as a framework for this work due to its interactive, dynamic, and customizable characteristics. Two snake-based segmentation models are developed to produce a more accurate segmentation result, namely the Model-constrained Gradient Vector Flow-snake (MC GVF-snake) and Stochastic-snake. MC GVF-snake elaborates a prior knowledge on common physical structure of the target object to restrict and guide the segmentation mechanism, while Stochastic-snake implements the simulated annealing stochastic procedure to produce improved segmentation accuracy. The developed segmentation framework has been evaluated on actual complex-valued MRI images, both in noise-free and noisy simulated conditions. Evaluation results indicate that both of the developed algorithms give an improved segmentation performance as well as increased robustness, in comparison to the conventional snake algorithm.