Peristaltic Transport of a Physiological Fluid in an Asymmetric Porous Channel in the Presence of an External Magnetic Field

The paper deals with a theoretical investigation of the peristaltic transport of a physiological fluid in a porous asymmetric channel under the action of a magnetic field. The stream function, pressure gradient and axial velocity are studied by using appropriate analytical and numerical techniques. Effects of different physical parameters such as permeability, phase difference, wave amplitude and magnetic parameter on the velocity, pumping characteristics, streamline pattern and trapping are investigated with particular emphasis. The computational results are presented in graphical form. The results are found to be in perfect agreement with those of a previous study carried out for a non-porous channel in the absence of a magnetic field

Identifying chromophore fingerprints of brain tumor tissue on hyperspectral imaging using principal component analysis

Hyperspectral imaging (HSI) is an optical technique that processes the electromagnetic spectrum at a multitude of monochromatic, adjacent frequency bands. The wide-bandwidth spectral signature of a target object's reflectance allows fingerprinting its physical, biochemical, and physiological properties. HSI has been applied for various applications, such as remote sensing and biological tissue analysis. Recently, HSI was also used to differentiate between healthy and pathological tissue under operative conditions in a surgery room on patients diagnosed with brain tumors. In this article, we perform a statistical analysis of the brain tumor patients' HSI scans from the HELICoiD dataset with the aim of identifying the correlation between reflectance spectra and absorption spectra of tissue chromophores. By using the principal component analysis (PCA), we determine the most relevant spectral features for intra- and inter-tissue class differentiation. Furthermore, we demonstrate that such spectral features are correlated with the spectra of cytochrome, i.e., the chromophore highly involved in (hyper) metabolic processes. Identifying such fingerprints of chromophores in reflectance spectra is a key step for automated molecular profiling and, eventually, expert-free biomarker discovery

Assessment of wetland ecosystem health using the pressure-state-response (PSR) model: A case study of Mursidabad District of West Bengal (India)

© 2020 by the authors. Wetlands are essential for protein production, water sanctification, groundwater recharge, climate purification, nutrient cycling, decreasing floods and biodiversity preservation. The Mursidabad district in West Bengal (India) is situated in the floodplain of the Ganga-Padma and Bhagirathi rivers. The region is characterized by diverse types of wetlands; however, the wetlands are getting depredated day-by-day due to hydro-ecological changes, uncontrolled human activities and rapid urbanization. This study attempted to explore the health status of the wetland ecosystem in 2013 and 2020 at the block level in the Mursidabad district, using the pressure-state-response model. Based on wetland ecosystem health values, we categorized the health conditions and identified the blocks where the health conditions are poor. A total of seven Landsat ETM+ spaceborne satellite images in 2001, 2013 and 2020 were selected as the data sources. The statistical data included the population density and urbanization increase rate, for all administrative units, and were collected from the census data of India for 2001 and 2011. We picked nine ecosystem indicators for the incorporated assessment of wetland ecosystem health. The indicators were selected considering every block in the Mursidabad district and for the computation of the wetland ecosystem health index by using the analytical hierarchy processes method. This study determined that 26.92% of the blocks fell under the sick category in 2013, but increased to 30.77% in 2020, while the percentage of blocks in the very healthy category has decreased markedly from 11.54% to 3.85%. These blocks were affected by higher human pressure, such as population density, urbanization growth rate and road density, which resulted in the degradation of wetland health. The scientific protection and restoration techniques of these wetlands should be emphasized in these areas

Automated claustrum segmentation in human brain MRI using deep learning

In the last two decades, neuroscience has produced intriguing evidence for a central role of the claustrum in mammalian forebrain structure and function. However, relatively few in vivo studies of the claustrum exist in humans. A reason for this may be the delicate and sheet-like structure of the claustrum lying between the insular cortex and the putamen, which makes it not amenable to conventional segmentation methods. Recently, Deep Learning (DL) based approaches have been successfully introduced for automated segmentation of complex, subcortical brain structures. In the following, we present a multi-view DL-based approach to segment the claustrum in T1-weighted MRI scans. We trained and evaluated the proposed method in 181 individuals, using bilateral manual claustrum annotations by an expert neuroradiologist as reference standard. Cross-validation experiments yielded median volumetric similarity, robust Hausdorff distance, and Dice score of 93.3%, 1.41 mm, and 71.8%, respectively, representing equal or superior segmentation performance compared to human intra-rater reliability. The leave-one-scanner-out evaluation showed good transferability of the algorithm to images from unseen scanners at slightly inferior performance. Furthermore, we found that DL-based claustrum segmentation benefits from multi-view information and requires a sample size of around 75 MRI scans in the training set. We conclude that the developed algorithm allows for robust automated claustrum segmentation and thus yields considerable potential for facilitating MRI-based research of the human claustrum. The software and models of our method are made publicly available

A for-loop is all you need. For solving the inverse problem in the case of personalized tumor growth modeling

Solving the inverse problem is the key step in evaluating the capacity of a physical model to describe real phenomena. In medical image computing, it aligns with the classical theme of image-based model personalization. Traditionally, a solution to the problem is obtained by performing either sampling or variational inference based methods. Both approaches aim to identify a set of free physical model parameters that results in a simulation best matching an empirical observation. When applied to brain tumor modeling, one of the instances of image-based model personalization in medical image computing, the overarching drawback of the methods is the time complexity of finding such a set. In a clinical setting with limited time between imaging and diagnosis or even intervention, this time complexity may prove critical. As the history of quantitative science is the history of compression (Schmidhuber and Fridman, 2018), we align in this paper with the historical tendency and propose a method compressing complex traditional strategies for solving an inverse problem into a simple database query task. We evaluated different ways of performing the database query task assessing the trade-off between accuracy and execution time. On the exemplary task of brain tumor growth modeling, we prove that the proposed method achieves one order speed-up compared to existing approaches for solving the inverse problem. The resulting compute time offers critical means for relying on more complex and, hence, realistic models, for integrating image preprocessing and inverse modeling even deeper, or for implementing the current model into a clinical workflow. The code is available at https://github.com/IvanEz/for-loop-tumor

Multi-contrast MRI Super-resolution via Implicit Neural Representations

Clinical routine and retrospective cohorts commonly include multi-parametric Magnetic Resonance Imaging; however, they are mostly acquired in different anisotropic 2D views due to signal-to-noise-ratio and scan-time constraints. Thus acquired views suffer from poor out-of-plane resolution and affect downstream volumetric image analysis that typically requires isotropic 3D scans. Combining different views of multi-contrast scans into high-resolution isotropic 3D scans is challenging due to the lack of a large training cohort, which calls for a subject-specific framework.This work proposes a novel solution to this problem leveraging Implicit Neural Representations (INR). Our proposed INR jointly learns two different contrasts of complementary views in a continuous spatial function and benefits from exchanging anatomical information between them. Trained within minutes on a single commodity GPU, our model provides realistic super-resolution across different pairs of contrasts in our experiments with three datasets. Using Mutual Information (MI) as a metric, we find that our model converges to an optimum MI amongst sequences, achieving anatomically faithful reconstruction. Code is available at: https://github.com/jqmcginnis/multi_contrast_inr

AI-based detection of contrast-enhancing MRI lesions in patients with multiple sclerosis.

BACKGROUND Contrast-enhancing (CE) lesions are an important finding on brain magnetic resonance imaging (MRI) in patients with multiple sclerosis (MS) but can be missed easily. Automated solutions for reliable CE lesion detection are emerging; however, independent validation of artificial intelligence (AI) tools in the clinical routine is still rare. METHODS A three-dimensional convolutional neural network for CE lesion segmentation was trained externally on 1488 datasets of 934 MS patients from 81 scanners using concatenated information from FLAIR and T1-weighted post-contrast imaging. This externally trained model was tested on an independent dataset comprising 504 T1-weighted post-contrast and FLAIR image datasets of MS patients from clinical routine. Two neuroradiologists (R1, R2) labeled CE lesions for gold standard definition in the clinical test dataset. The algorithmic output was evaluated on both patient- and lesion-level. RESULTS On a patient-level, recall, specificity, precision, and accuracy of the AI tool to predict patients with CE lesions were 0.75, 0.99, 0.91, and 0.96. The agreement between the AI tool and both readers was within the range of inter-rater agreement (Cohen's kappa; AI vs. R1: 0.69; AI vs. R2: 0.76; R1 vs. R2: 0.76). On a lesion-level, false negative lesions were predominately found in infratentorial location, significantly smaller, and at lower contrast than true positive lesions (p < 0.05). CONCLUSIONS AI-based identification of CE lesions on brain MRI is feasible, approaching human reader performance in independent clinical data and might be of help as a second reader in the neuroradiological assessment of active inflammation in MS patients. CRITICAL RELEVANCE STATEMENT Al-based detection of contrast-enhancing multiple sclerosis lesions approaches human reader performance, but careful visual inspection is still needed, especially for infratentorial, small and low-contrast lesions

Electroosmotic flow of biorheological micropolar fluids through microfluidic channels

An analysis is presented in this work to assess the influence of micropolar nature of fluids in fully developed flow induced by electrokinetically driven peristaltic pumping through a parallel plate microchannel. The walls of the channel are assumed as sinusoidal wavy to analyze the peristaltic flow nature. We consider that the wavelength of the wall motion is much larger as compared to the channel width to validate the lubrication theory. To simplify the Poisson Boltzmann equation, we also use the Debye-Hückel linearization (i.e. wall zeta potential ≤ 25mV). We consider governing equation for micropolar fluid in absence of body force and couple effects however external electric field is employed. The solutions for axial velocity, spin velocity, flow rate, pressure rise and stream functions subjected to given physical boundary conditions are computed. The effects of pertinent parameters like Debye length and Helmholtz-Smoluchowski velocity which characterize the EDL phenomenon and external electric field, coupling number and micropolar parameter which characterize the micropolar fluid behavior, on peristaltic pumping are discussed through the illustrations. The results show that peristaltic pumping may alter by applying external electric fields. This model can be used to design and engineer the peristalsis-lab-on-chip and micro peristaltic syringe pumps for biomedical applications

Spatial variability of groundwater quality of Sabour block, Bhagalpur district (Bihar, India)

This paper examines the quality of groundwater of Sabour block, Bhagalpur district of Bihar state, which lies on the southern region of Indo-Gangetic plains in India. Fifty-nine samples from different sources of water in the block have been collected to determine its suitability for drinking and irrigational purposes. From the samples electrical conductivity (EC), pH and concentrations of Calcium (Ca2+), Magnesium (Mg2+), Sodium (Na+), Potassium (K+), carbonate ion (CO 2−3), Bicarbonate ion (HCO -3), Chloride ion (Cl−), and Fluoride (F−) were determined. Surface maps of all the groundwater quality parameters have been prepared using radial basis function (RBF) method. RBF model was used to interpolate data points in a group of multi-dimensional space. Root Mean Square Error (RMSE) is employed to scrutinize the best fit of the model to compare the obtained value. The mean value of pH, EC, Ca2+, Mg2+, Na+, K+, HCO3 −, Cl−, and F− are found to be 7.26, 0.69, 38.98, 34.20, 16.92, 1.19, 0.02, and 0.28, respectively. Distribution of calcium concentration is increasing to the eastern part and K+ concentrations raise to the downstream area in the southwestern part. Low pH concentrations (less than 6.71) occur in eastern part of the block. Spatial variations of hardness in Sabour block portraying maximum concentration in the western part and maximum SAR (more than 4.23) were recorded in the southern part. These results are not exceeding for drinking and irrigation uses recommended by World Health Organization. Therefore, the majority of groundwater samples are found to be safe for drinking and irrigation management practices