scholarly journals Numerical Simulations of MREIT Conductivity Imaging for Brain Tumor Detection

2013 ◽  
Vol 2013 ◽  
pp. 1-10 ◽  
Author(s):  
Zi Jun Meng ◽  
Saurav Z. K. Sajib ◽  
Munish Chauhan ◽  
Rosalind J. Sadleir ◽  
Hyung Joong Kim ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Human Subjects ◽  
Three Dimensional ◽  
Tumor Detection ◽  
Head Model ◽  
Impedance Tomography ◽  
The Brain ◽  
T System ◽  
Brain Tissues

Magnetic resonance electrical impedance tomography (MREIT) is a new modality capable of imaging the electrical properties of human body using MRI phase information in conjunction with external current injection. Recentin vivoanimal and human MREIT studies have revealed unique conductivity contrasts related to different physiological and pathological conditions of tissues or organs. When performingin vivobrain imaging, small imaging currents must be injected so as not to stimulate peripheral nerves in the skin, while delivery of imaging currents to the brain is relatively small due to the skull’s low conductivity. As a result, injected imaging currents may induce small phase signals and the overall low phase SNR in brain tissues. In this study, we present numerical simulation results of the use of head MREIT for brain tumor detection. We used a realistic three-dimensional head model to compute signal levels produced as a consequence of a predicted doubling of conductivity occurring within simulated tumorous brain tissues. We determined the feasibility of measuring these changes in a time acceptable to human subjects by adding realistic noise levels measured from a candidate 3 T system. We also reconstructed conductivity contrast images, showing that such conductivity differences can be both detected and imaged.

A Survey on Detection and Classification of Brain Tumor Using Image Processing Techniques

2020 ◽  
pp. 967-973
Author(s):  
V. Deepika ◽  
T. Rajasenbagam
Keyword(s):  
Image Processing ◽  
Brain Tumor ◽  
Soft Tissues ◽  
Tumor Detection ◽  
Tumor Classification ◽  
Normal Brain ◽  
Image Processing Techniques ◽  
Normal Brain Function ◽  
Processing Techniques ◽  
The Brain

A brain tumor is an uncontrolled growth of abnormal brain tissue that can interfere with normal brain function. Although various methods have been developed for brain tumor classification, tumor detection and multiclass classification remain challenging due to the complex characteristics of the brain tumor. Brain tumor detection and classification are one of the most challenging and time-consuming tasks in the processing of medical images. MRI (Magnetic Resonance Imaging) is a visual imaging technique, which provides a information about the soft tissues of the human body, which helps identify the brain tumor. Proper diagnosis can prevent a patient's health to some extent. This paper presents a review of various detection and classification methods for brain tumor classification using image processing techniques.

Novel Phospholipid-Based Labrasol Nanomicelles Loaded Flavonoids for Oral Delivery with Enhanced Penetration and Anti-Brain Tumor Efficiency

2020 ◽  
Vol 17 (3) ◽  
pp. 229-245
Author(s):  
Gang Wang ◽  
Junjie Wang ◽  
Rui Guan
Keyword(s):  
Drug Delivery ◽  
Brain Tumor ◽  
Oral Delivery ◽  
Safe Delivery ◽  
Drug Delivery Vehicles ◽  
Therapeutic Benefits ◽  
Delivery Vehicles ◽  
The Brain

Background: Owing to the rich anticancer properties of flavonoids, there is a need for their incorporation into drug delivery vehicles like nanomicelles for safe delivery of the drug into the brain tumor microenvironment. Objective: This study, therefore, aimed to prepare the phospholipid-based Labrasol/Pluronic F68 modified nano micelles loaded with flavonoids (Nano-flavonoids) for the delivery of the drug to the target brain tumor. Methods: Myricetin, quercetin and fisetin were selected as the initial drugs to evaluate the biodistribution and acute toxicity of the drug delivery vehicles in rats with implanted C6 glioma tumors after oral administration, while the uptake, retention, release in human intestinal Caco-2 cells and the effect on the brain endothelial barrier were investigated in Human Brain Microvascular Endothelial Cells (HBMECs). Results: The results demonstrated that nano-flavonoids loaded with myricetin showed more evenly distributed targeting tissues and enhanced anti-tumor efficiency in vivo without significant cytotoxicity to Caco-2 cells and alteration in the Trans Epithelial Electric Resistance (TEER). There was no pathological evidence of renal, hepatic or other organs dysfunction after the administration of nanoflavonoids, which showed no significant influence on cytotoxicity to Caco-2 cells. Conclusion: In conclusion, Labrasol/F68-NMs loaded with MYR and quercetin could enhance antiglioma effect in vitro and in vivo, which may be better tools for medical therapy, while the pharmacokinetics and pharmacodynamics of nano-flavonoids may ensure optimal therapeutic benefits.

A Review of Various Machine Learning Techniques for Brain Tumor Detection from MRI Images

2020 ◽  
Vol 16 (8) ◽  
pp. 937-945
Author(s):  
Aaishwarya Sanjay Bajaj ◽  
Usha Chouhan
Keyword(s):  
Machine Learning ◽  
Brain Tumor ◽  
Ct Scan ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
X Rays ◽  
Detection Techniques ◽  
The Brain

Background: This paper endeavors to identify an expedient approach for the detection of the brain tumor in MRI images. The detection of tumor is based on i) review of the machine learning approach for the identification of brain tumor and ii) review of a suitable approach for brain tumor detection. Discussion: This review focuses on different imaging techniques such as X-rays, PET, CT- Scan, and MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and time-consuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Conclusion: The problem faced by the researchers during brain tumor detection techniques and machine learning applications for clinical settings have also been discussed.

scholarly journals An improved framework for brain tumor analysis using MRI based on YOLOv2 and convolutional neural network

2021 ◽  
Author(s):  
Muhammad Irfan Sharif ◽  
Jian Ping Li ◽  
Javeria Amin ◽  
Abida Sharif
Keyword(s):  
Brain Tumor ◽  
Tumor Detection ◽  
Entropy Method ◽  
Feature Extraction And Selection ◽  
Tumor Region ◽  
Selection For ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Complicated Task

AbstractBrain tumor is a group of anomalous cells. The brain is enclosed in a more rigid skull. The abnormal cell grows and initiates a tumor. Detection of tumor is a complicated task due to irregular tumor shape. The proposed technique contains four phases, which are lesion enhancement, feature extraction and selection for classification, localization, and segmentation. The magnetic resonance imaging (MRI) images are noisy due to certain factors, such as image acquisition, and fluctuation in magnetic field coil. Therefore, a homomorphic wavelet filer is used for noise reduction. Later, extracted features from inceptionv3 pre-trained model and informative features are selected using a non-dominated sorted genetic algorithm (NSGA). The optimized features are forwarded for classification after which tumor slices are passed to YOLOv2-inceptionv3 model designed for the localization of tumor region such that features are extracted from depth-concatenation (mixed-4) layer of inceptionv3 model and supplied to YOLOv2. The localized images are passed toMcCulloch'sKapur entropy method to segment actual tumor region. Finally, the proposed technique is validated on three benchmark databases BRATS 2018, BRATS 2019, and BRATS 2020 for tumor detection. The proposed method achieved greater than 0.90 prediction scores in localization, segmentation and classification of brain lesions. Moreover, classification and segmentation outcomes are superior as compared to existing methods.

scholarly journals Targeted Brain Tumor Therapy by Inhibiting the MDM2 Oncogene: In Vitro and In Vivo Antitumor Activity and Mechanism of Action

Cells ◽  
2020 ◽  
Vol 9 (7) ◽  
pp. 1592
Author(s):  
Surendra R. Punganuru ◽  
Viswanath Arutla ◽  
Wei Zhao ◽  
Mehrdad Rajaei ◽  
Hemantkumar Deokar ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Antitumor Activity ◽  
Cell Lines ◽  
Tumor Therapy ◽  
Single Agent ◽  
M Cell ◽  
In Vivo Antitumor Activity ◽  
The Brain

There is a desperate need for novel and efficacious chemotherapeutic strategies for human brain cancers. There are abundant molecular alterations along the p53 and MDM2 pathways in human glioma, which play critical roles in drug resistance. The present study was designed to evaluate the in vitro and in vivo antitumor activity of a novel brain-penetrating small molecule MDM2 degrader, termed SP-141. In a panel of nine human glioblastoma and medulloblastoma cell lines, SP-141, as a single agent, potently killed the brain tumor-derived cell lines with IC50 values ranging from 35.8 to 688.8 nM. Treatment with SP-141 resulted in diminished MDM2 and increased p53 and p21cip1 levels, G2/M cell cycle arrest, and marked apoptosis. In intracranial xenograft models of U87MG glioblastoma (wt p53) and DAOY medulloblastoma (mutant p53) expressing luciferase, treatment with SP-141 caused a significant 4- to 9-fold decrease in tumor growth in the absence of discernible toxicity. Further, combination treatment with a low dose of SP-141 (IC20) and temozolomide, a standard anti-glioma drug, led to synergistic cell killing (1.3- to 31-fold) in glioma cell lines, suggesting a novel means for overcoming temozolomide resistance. Considering that SP-141 can be taken up by the brain without the need for any special delivery, our results suggest that SP-141 should be further explored for the treatment of tumors of the central nervous system, regardless of the p53 status of the tumor.

A Study on Brain Tumor Detection and Segmentation Using Deep Learning Techniques

2020 ◽  
Vol 17 (4) ◽  
pp. 1925-1930
Author(s):  
Ambeshwar Kumar ◽  
R. Manikandan ◽  
Robbi Rahim
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Brain Tumor ◽  
Tumor Detection ◽  
Machine Learning Techniques ◽  
Support Vector ◽  
Learning Techniques ◽  
Convolution Algorithm ◽  
The Brain

It’s a new era technology in the field of medical engineering giving awareness about the various healthcare features. Deep learning is a part of machine learning, it is capable of handling high dimensional data and is efficient in concentrating on the right features. Tumor is an unbelievably complex disease: a multifaceted cell has more than hundred billion cells; each cell acquires mutation exclusively. Detection of tumor particles in experiment is easily done by MRI or CT. Brain tumors can also be detected by MRI, however, deep learning techniques give a better approach to segment the brain tumor images. Deep Learning models are imprecisely encouraged by information handling and communication designs in biological nervous system. Classification plays an significant role in brain tumor detection. Neural network is creating a well-organized rule for classification. To accomplish medical image data, neural network is trained to use the Convolution algorithm. Multilayer perceptron is intended for identification of a image. In this study article, the brain images are categorized into two types: normal and abnormal. This article emphasize the importance of classification and feature selection approach for predicting the brain tumor. This classification is done by machine learning techniques like Artificial Neural Networks, Support Vector Machine and Deep Neural Network. It could be noted that more than one technique can be applied for the segmentation of tumor. The several samples of brain tumor images are classified using deep learning algorithms, convolution neural network and multi-layer perceptron.

scholarly journals The vascular basement membrane in the healthy and pathological brain

2017 ◽  
Vol 37 (10) ◽  
pp. 3300-3317 ◽  
Author(s):  
Maj S Thomsen ◽  
Lisa J Routhe ◽  
Torben Moos
Keyword(s):  
Drug Delivery ◽  
Basement Membrane ◽  
Proteolytic Enzymes ◽  
Three Dimensional ◽  
Matrix Proteins ◽  
Vascular Basement Membrane ◽  
Capillary Endothelial Cells ◽  
The Brain ◽  
Astrocyte Endfeet

The vascular basement membrane contributes to the integrity of the blood-brain barrier (BBB), which is formed by brain capillary endothelial cells (BCECs). The BCECs receive support from pericytes embedded in the vascular basement membrane and from astrocyte endfeet. The vascular basement membrane forms a three-dimensional protein network predominantly composed of laminin, collagen IV, nidogen, and heparan sulfate proteoglycans that mutually support interactions between BCECs, pericytes, and astrocytes. Major changes in the molecular composition of the vascular basement membrane are observed in acute and chronic neuropathological settings. In the present review, we cover the significance of the vascular basement membrane in the healthy and pathological brain. In stroke, loss of BBB integrity is accompanied by upregulation of proteolytic enzymes and degradation of vascular basement membrane proteins. There is yet no causal relationship between expression or activity of matrix proteases and the degradation of vascular matrix proteins in vivo. In Alzheimer’s disease, changes in the vascular basement membrane include accumulation of Aβ, composite changes, and thickening. The physical properties of the vascular basement membrane carry the potential of obstructing drug delivery to the brain, e.g. thickening of the basement membrane can affect drug delivery to the brain, especially the delivery of nanoparticles.

Effects of the Coreopsis tinctoria extracts on anti-agingin the aging model mice

2016 ◽  
Author(s):  
Weixin Zhang ◽  
Haiyan Hao ◽  
Ailong Sha
Keyword(s):  
Hydrogen Peroxide ◽  
Aging Effects ◽  
Once Daily ◽  
Aging Model ◽  
Coreopsis Tinctoria ◽  
Kg Medium ◽  
The Brain ◽  
Liver Tissues ◽  
Brain Tissues

The effects of the Coreopsis tinctoria extracts on anti-aging were observed by investigating the cerebral index and viscera indexes, the contents of hydrogen peroxide (H2O2) and malondialdehyde (MDA) in the serums, the activities of glutathione peroxidase (GSH-Px) in the brain tissues and the ones of catalase (CAT) and superoxide dismutase (SOD) in the liver tissues of the aging model mice. The aging model mice were injected subcutaneously with D-galactose in vivo and intragastric administrated with the Coreopsis tinctoria extracts at doses of low (0.5g/kg), medium (1g/ kg) and high (2g/ kg) once daily for 6 weeks. The results showed that all the cerebral index, spleen index, thymus index, liver index and kidney index of the three groups dosed of the Coreopsis tinctoria extracts increased, the activities of GSH-Px in the brain tissues and the ones of CAT and SOD in the liver tissues increased to different degree while the contents of H2O2 and MDA in the serums decreased extremely and significantly (P<0.01) compared with the aging model mice. All of these results suggested that the Coreopsis tinctoria extracts might possess anti-aging effects by improving antioxidant capacity of the mice.

scholarly journals A Review Article on Brain Tumor Detection and Optimization using Hybrid Classification Algorithm

2021 ◽  
Vol 9 (11) ◽  
pp. 763-768
Author(s):  
Nitesh Yadav
Keyword(s):  
Brain Tumor ◽  
Imaging Techniques ◽  
Critical Evaluation ◽  
Tumor Detection ◽  
Review Article ◽  
Processing Method ◽  
Computational Results ◽  
X Ray ◽  
Hybrid Classification ◽  
The Brain

Abstract: This review focuses on different imaging techniques such as MRI. This survey identifies a different approach with better accuracy for tumor detection. This further includes the image processing method. In most applications, machine learning shows better performance than manual segmentation of the brain tumors from MRI images as it is a difficult and timeconsuming task. For fast and better computational results, radiology used a different approach with MRI, CT-scan, X-ray, and PET. Furthermore, summarizing the literature, this paper also provides a critical evaluation of the surveyed literature which reveals new facets of research. Keywords: Brain tumor, data mining techniques, filtering techniques, MRI, classifiers, feature selection.

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