scholarly journals Current Clinical Brain Tumor Imaging

Neurosurgery ◽  
2017 ◽  
Vol 81 (3) ◽  
pp. 397-415 ◽  
Author(s):  
Javier E. Villanueva-Meyer ◽  
Marc C. Mabray ◽  
Soonmee Cha
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Tumor Imaging ◽  
Tumor Patient ◽  
Therapy Assessment ◽  
Noninvasive Characterization ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Post Therapy

Abstract Neuroimaging plays an ever evolving role in the diagnosis, treatment planning, and post-therapy assessment of brain tumors. This review provides an overview of current magnetic resonance imaging (MRI) methods routinely employed in the care of the brain tumor patient. Specifically, we focus on advanced techniques including diffusion, perfusion, spectroscopy, tractography, and functional MRI as they pertain to noninvasive characterization of brain tumors and pretreatment evaluation. The utility of both structural and physiological MRI in the post-therapeutic brain evaluation is also reviewed with special attention to the challenges presented by pseudoprogression and pseudoresponse.

scholarly journals Performance Analysis of Deep Belief Neural Network for Brain Tumor Classification

2020 ◽  
Vol 1 (3) ◽  
pp. 29-34
Author(s):  
Sreenivas Eeshwaroju ◽  
◽  
Praveena Jakula ◽  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Imaging Techniques ◽  
Structural Heterogeneity ◽  
Low Complexity ◽  
Automated Classification ◽  
Tumor Identification ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

The brain tumors are by far the most severe and violent disease, contributing to the highest degree of a very low life expectancy. Therefore, recovery preparation is a crucial step in improving patient quality of life. In general , different imaging techniques such as computed tomography ( CT), magnetic resonance imaging ( MRI) and ultrasound imaging have been used to examine the tumor in the brain, lung , liver, breast , prostate ... etc. MRI images are especially used in this research to diagnose tumor within the brain with classification results. The massive amount of data produced by the MRI scan, therefore, destroys the manual classification of tumor vs. non-tumor in a given period. However for a limited number of images, it is presented with some constraint that is precise quantitative measurements. Consequently, a trustworthy and automated classification scheme is important for preventing human death rates. The automatic classification of brain tumors is a very challenging task in broad spatial and structural heterogeneity of the surrounding brain tumor area. Automatic brain tumor identification is suggested in this research by the use of the classification with Deep Belief Network (DBN). Experimental results show that the DBN archive rate with low complexity seems to be 97 % accurate compared to all other state of the art methods.

Brain-tumor imaging using radiopaque perfluorocarbon

1983 ◽  
Vol 58 (5) ◽  
pp. 650-653 ◽  
Author(s):  
Nicholas J. Patronas ◽  
Javad Hekmatpanah ◽  
Kunio Doi
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Contrast Agent ◽  
Contrast Medium ◽  
Computerized Tomography ◽  
Tumor Imaging ◽  
Content Type ◽  
X Ray ◽  
The Brain ◽  
Fine Print

✓ Perfluorocarbon, a new tumor-seeking x-ray contrast agent, was injected into three rats with experimental brain tumors. After 1 to 3 days the rats were sacrificed, and the brains were removed and subjected to x-ray study. All showed dense radiopaque areas which correlated with the size and shape of the corresponding brain tumors. Conversely, none of the radiograms taken of the brain tumor in five rats receiving no perfluorocarbon (control animals) showed similar increased density. These findings suggest that perfluorocarbon may serve a useful role as a contrast medium for computerized tomography studies of brain tumors in man.

scholarly journals Focused Ultrasound and Microbubbles-Mediated Drug Delivery to Brain Tumor

Pharmaceutics ◽  
2020 ◽  
Vol 13 (1) ◽  
pp. 15
Author(s):  
Sheng-Kai Wu ◽  
Chia-Lin Tsai ◽  
Yuexi Huang ◽  
Kullervo Hynynen
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Focused Ultrasound ◽  
Therapeutic Agents ◽  
Non Invasive ◽  
Long Period ◽  
Blood Brain ◽  
Invasive Method ◽  
The Brain

The presence of blood–brain barrier (BBB) and/or blood–brain–tumor barriers (BBTB) is one of the main obstacles to effectively deliver therapeutics to our central nervous system (CNS); hence, the outcomes following treatment of malignant brain tumors remain unsatisfactory. Although some approaches regarding BBB disruption or drug modifications have been explored, none of them reach the criteria of success. Convention-enhanced delivery (CED) directly infuses drugs to the brain tumor and surrounding tumor infiltrating area over a long period of time using special catheters. Focused ultrasound (FUS) now provides a non-invasive method to achieve this goal via combining with systemically circulating microbubbles to locally enhance the vascular permeability. In this review, different approaches of delivering therapeutic agents to the brain tumors will be discussed as well as the characterization of BBB and BBTB. We also highlight the mechanism of FUS-induced BBB modulation and the current progress of this technology in both pre-clinical and clinical studies.

scholarly journals Detection and Classification of Brain Tumor in MRI Images Using Wavelet Transform and Convolutional Neural Network

2020 ◽  
pp. 15-26
Author(s):  
Ahmad M. Sarhan
Keyword(s):  
Brain Tumor ◽  
Wavelet Transform ◽  
Brain Tumors ◽  
Brain Mri ◽  
Discrete Wavelet ◽  
Abnormal Cells ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain ◽  
Mri Image

A brain tumor is a mass of abnormal cells in the brain. Brain tumors can be benign or malignant. Conventional diagnosis of a brain tumor by the radiologist, is done by examining a set of images produced by magnetic resonance imaging (MRI). Many computer-aided detection (CAD) systems have been developed in order to help the radiologist reach his goal of correctly classifying the MRI image. Convolutional neural networks (CNNs) have been widely used in the classification of medical images. This paper presents a novel CAD technique for the classification of brain tumors in MRI images The proposed system extracts features from the brain MRI images by utilizing the strong energy compactness property exhibited by the Discrete Wavelet transform (DWT). The Wavelet features are then applied to a CNN to classify the input MRI image. Experimental results indicate that the proposed approach outperforms other commonly used methods and gives an overall accuracy of 98.5%.

scholarly journals Segmentation of Brain Tumor using Glcm and Discrete Wavelet Transform

2020 ◽  
Vol 9 (6) ◽  
pp. 344-348
Keyword(s):  
Feature Extraction ◽  
Brain Tumor ◽  
Brain Tumors ◽  
Tissue Sample ◽  
Early Stage ◽  
Discrete Wavelet ◽  
Effective Technique ◽  
Magnetic Resonance Imaging Mri ◽  
Occurrence Matrix ◽  
The Brain

To identify brain tumors at an early stage is a challenging task. The brain tumor is usually diagnosed with Magnetic Resonance Imaging (MRI). When MRI spectacles a tumor in the brain, the most common way of determining the type of brain tumor after a biopsy or surgery is to look at the results of a tissue sample. In this research to detect brain tumors faster and accurately the feature extraction techniques are used to segment the tumor affected area. One of such very effective technique of feature extraction measure is the Grayscale Co-occurrence Matrix (GLCM). This research focuses on the GLCM and Discrete Wavelet Transformation (DWT) technique to detect and label the tumor from an image based on the textures and categorizing it according to a tumor or non-tumor category. The convolutional neural network (CNN) uses these features to improve the accuracy to 91%.

Brain Tumor Detection via Asymmetry Quantification across Mid Sagittal Plane

2020 ◽  
Vol 13 ◽  
Author(s):  
Shoaib Amin Banday ◽  
Mohammad Khalid Pandit
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Sagittal Plane ◽  
Early Stage ◽  
Imaging Techniques ◽  
Complex Structure ◽  
Magnetic Resonance Images ◽  
Absolute Difference ◽  
Brain Hemispheres ◽  
The Brain

Introduction: Brain tumor is among the major causes of morbidity and mortality rates worldwide. According to National Brain Tumor Foundation (NBTS), the death rate has nearly increased by as much as 300% over last couple of decades. Tumors can be categorized as benign (non-cancerous) and malignant (cancerous). The type of the brain tumor significantly depends on various factors like the site of its occurrence, its shape, the age of the subject etc. On the other hand, Computer Aided Detection (CAD) has been improving significantly in recent times. The concept, design and implementation of these systems ascend from fairly simple ones to computationally intense ones. For efficient and effective diagnosis and treatment plans in brain tumor studies, it is imperative that an abnormality is detected at an early stage as it provides a little more time for medical professionals to respond. The early detection of diseases has predominantly been possible because of medical imaging techniques developed from past many decades like CT, MRI, PET, SPECT, FMRI etc. The detection of brain tumors however, has always been a challenging task because of the complex structure of the brain, diverse tumor sizes and locations in the brain. Method: This paper proposes an algorithm that can detect the brain tumors in the presence of the Radio-Frequency (RF) inhomoginiety. The algorithm utilizes the Mid Sagittal Plane as a landmark point across which the asymmetry between the two brain hemispheres is estimated using various intensity and texture based parameters. Result: The results show the efficacy of the proposed method for the detection of the brain tumors with an acceptable detection rate. Conclusion: In this paper, we have calculated three textural features from the two hemispheres of the brain viz: Contrast (CON), Entropy (ENT) and Homogeneity (HOM) and three parameters viz: Root Mean Square Error (RMSE), Correlation Co-efficient (CC), and Integral of Absolute Difference (IAD) from the intensity distribution profiles of the two brain hemispheres to predict any presence of the pathology. First a Mid Sagittal Plane (MSP) is obtained on the Magnetic Resonance Images that virtually divides brain into two bilaterally symmetric hemispheres. The block wise texture asymmetry is estimated for these hemispheres using the above 6 parameters.

scholarly journals Beyond Oncological Hyperthermia: Physically Drivable Magnetic Nanobubbles as Novel Multipurpose Theranostic Carriers in the Central Nervous System

Molecules ◽  
2020 ◽  
Vol 25 (9) ◽  
pp. 2104 ◽  
Author(s):  
Eleonora Ficiarà ◽  
Shoeb Anwar Ansari ◽  
Monica Argenziano ◽  
Luigi Cangemi ◽  
Chiara Monge ◽  
...  
Keyword(s):  
Central Nervous System ◽  
Nervous System ◽  
Brain Tumors ◽  
Ad Hoc ◽  
Superparamagnetic Iron Oxide ◽  
Conventional Radiotherapy ◽  
The Central Nervous System ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Magnetic Oxygen-Loaded Nanobubbles (MOLNBs), manufactured by adding Superparamagnetic Iron Oxide Nanoparticles (SPIONs) on the surface of polymeric nanobubbles, are investigated as theranostic carriers for delivering oxygen and chemotherapy to brain tumors. Physicochemical and cyto-toxicological properties and in vitro internalization by human brain microvascular endothelial cells as well as the motion of MOLNBs in a static magnetic field were investigated. MOLNBs are safe oxygen-loaded vectors able to overcome the brain membranes and drivable through the Central Nervous System (CNS) to deliver their cargoes to specific sites of interest. In addition, MOLNBs are monitorable either via Magnetic Resonance Imaging (MRI) or Ultrasound (US) sonography. MOLNBs can find application in targeting brain tumors since they can enhance conventional radiotherapy and deliver chemotherapy being driven by ad hoc tailored magnetic fields under MRI and/or US monitoring.

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.

Intracranial Tumors- Overview, Histological Types, Symptoms and Treatment Plans

2021 ◽  
Vol 11 (10) ◽  
pp. 133-144
Author(s):  
Dipak Chaulagain ◽  
Volodymyr Smolanka ◽  
Andriy Smolanka
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Marital Status ◽  
Size Variation ◽  
Intracranial Tumors ◽  
Regional Center ◽  
East And Southeast Asia ◽  
Abnormal Cells ◽  
Treatment Plans ◽  
The Brain

People, in general, are affected by a brain or intracranial tumor when abnormal cells started functioning within their brain. This paper explores mainly tumors that affect the brain. Almost every type of brain tumor might create symptoms which are based on the parts of the brain affected. In order to better understand reasons of occurrence intracranial tumors in various sections of the population, the study examined the relationship between sociodemographic variables, i.e., age, gender and marital status and the relative frequency of intracranial tumors as part of a study. Samples are collected based on the information from Uzhhorod Regional Center of Neurosurgery and Neurology in Ukraine. And factors such as age, gender and marital status has been considered to examine tumor size variation. The ratios of organ cancers in Ukrainians are evidently increasing. Besides, there has been growing trend in affected rates in both the genders of CNS cancers have been noticed in all the records. The ratio of most harmful brain tumors is comparatively in minimal ratio in East and Southeast Asia, and India. On the other hand, the highest ratio has been noted in European countries and as well United States, and Ukraine is one of those countries. The major burdens of cancer frequency in Ukraine have been noticed in the lung, breast, and prostate and brain. Of these, brain tumor rate in Ukraine had been hardly studied. The major difference in frequency in Asian and European populations implies the potential influence of genetic or environmental factors in malignant brain tumors. Continuing monitoring of tumor ratio in Ukraine is essential to comprehend how best to reduce cancer burden globally and to explain the causes of provincial variations, for example access to diagnosis methods and ecological exposures. Key words: Intracranial tumors, symptoms, tumor incidence in Ukraine, treatment plans, survival rate of cancer in Ukraine.

scholarly journals Pharmacodynamic Analysis of Magnetic Resonance Imaging-Monitored Focused Ultrasound-Induced Blood-Brain Barrier Opening for Drug Delivery to Brain Tumors

2013 ◽  
Vol 2013 ◽  
pp. 1-13 ◽  
Author(s):  
Po-Chun Chu ◽  
Wen-Yen Chai ◽  
Han-Yi Hsieh ◽  
Jiun-Jie Wang ◽  
Shiaw-Pyng Wey ◽  
...  
Keyword(s):  
Brain Tumor ◽  
Brain Tumors ◽  
Small Molecule ◽  
Focused Ultrasound ◽  
Therapeutic Agents ◽  
Tumor Treatment ◽  
Target Region ◽  
Pharmacodynamic Analysis ◽  
The Brain ◽  
Bbb Opening

Microbubble-enhanced focused ultrasound (FUS) can enhance the delivery of therapeutic agents into the brain for brain tumor treatment. The purpose of this study was to investigate the influence of brain tumor conditions on the distribution and dynamics of small molecule leakage into targeted regions of the brain after FUS-BBB opening. A total of 34 animals were used, and the process was monitored by 7T-MRI. Evans blue (EB) dye as well as Gd-DTPA served as small molecule substitutes for evaluation of drug behavior. EB was quantified spectrophotometrically. Spin-spin (R1) relaxometry and area under curve (AUC) were measured by MRI to quantify Gd-DTPA. We found that FUS-BBB opening provided a more significant increase in permeability with small tumors. In contrast, accumulation was much higher in large tumors, independent of FUS. The AUC values of Gd-DTPA were well correlated with EB delivery, suggesting that Gd-DTPA was a good indicator of total small-molecule accumulation in the target region. The peripheral regions of large tumors exhibited similar dynamics of small-molecule leakage after FUS-BBB opening as small tumors, suggesting that FUS-BBB opening may have the most significant permeability-enhancing effect on tumor peripheral. This study provides useful information toward designing an optimized FUS-BBB opening strategy to deliver small-molecule therapeutic agents into brain tumors.

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