In brain mapping, structural templates derived from population-specific MRI scans are essential for normalizing individual brains into a common space. This normalization facilitates accurate group comparisons and statistical analyses. Although templa...
Refined hybrid convolutional neural networks are proposed in this work for classifying brain tumor classes based on MRI scans. A dataset of 2880 T1-weighted contrast-enhanced MRI brain scans are used. The dataset contains three main classes of brain...
Brain tumors are nonlinear and present with variations in their size, form, and textural variation; this might make it difficult to diagnose them and perform surgical excision using magnetic resonance imaging (MRI) scans. The procedures that are curr...
One of the most common challenges in brain MRI scans is to perform different MRI sequences depending on the type and properties of tissues. In this paper, we propose a generative method to translate T2-Weighted (T2W) Magnetic Resonance Imaging (MRI)...
Translational brain research using Magnetic Resonance Imaging (MRI) is becoming increasingly popular as animal models are an essential part of scientific studies and more ultra-high-field scanners are becoming available. Some disadvantages of MRI are...
In-scanner head motion often leads to degradation in MRI scans and is a major source of error in diagnosing brain abnormalities. Researchers have explored various approaches, including blind and nonblind deconvolutions, to correct the motion artifact...
This work presents the development and evaluation of Artificial Intelligence (AI) models for the automatic classification of brain tumors in Magnetic Resonance Imaging (MRI) scans. Several deep learning architectures were implemented and compared, in...
In this paper, we propose a novel squeeze M-SegNet (SM-SegNet) architecture featuring a fire module to perform accurate as well as fast segmentation of the brain on magnetic resonance imaging (MRI) scans. The proposed model utilizes uniform input pat...
Accurately identifying tumors from MRI scans is of the utmost importance for clinical diagnostics and when making plans regarding brain tumor treatment. However, manual segmentation is a challenging and time-consuming process in practice and exhibits...
With the advancements in neuroimaging techniques, understanding the relationship between brain morphology and behavioral tendencies such as criminal behavior has garnered interest. This research addresses the investigation of disparities in neuroanat...
A brain tumor is a significant health concern that directly or indirectly affects thousands of people worldwide. The early and accurate detection of brain tumors is vital to the successful treatment of brain tumors and the improved quality of life of...
Introduction: Brain tumor, marked by abnormal and rapid cell growth, poses severe health risks and requires accurate diagnosis for effective treatment. Classifying brain tumors using deep learning techniques applied to Magnetic Resonance Imaging (MRI...
As an alternative to true isotropic 3D imaging, image super-resolution (SR) has been applied to reconstruct an isotropic 3D volume from multiple anisotropic scans. However, traditional SR methods struggle with inadequate performance, prolonged proces...
The accurate segmentation of brain tumors from medical images is critical for diagnosis and treatment planning. However, traditional segmentation methods struggle with complex tumor shapes and inconsistent image quality which leads to suboptimal resu...
Background/Objectives: Brain tumors arise from abnormal, uncontrolled cell growth due to changes in the DNA. Magnetic Resonance Imaging (MRI) is vital for early diagnosis and treatment planning. Artificial intelligence (AI), especially deep learning,...
In this study, an automated medical decision support system is presented to assist physicians with accurate and immediate brain tumor detection, segmentation, and volume estimation from MRI which is very important in the success of surgical operation...
Background: BCI and AI-driven image segmentation are revolutionizing precision neurosurgery by enhancing surgical accuracy, reducing human error, and improving patient outcomes. Methods: This systematic review explores the integration of AI technique...
The pathophysiology of nausea and vomiting in gastroparesis is complicated and multifaceted involving the collaboration of both the peripheral and central nervous systems. Most treatment strategies and studies performed in gastroparesis have focused...
The design of an accurate cross-domain model for Alzheimer disease AD classification from MRI scans faces critical challenges, including domain shifts caused by acquisition protocol variations. To address this issue, we propose a novel unsupervised t...
Brain tumor detection at early stages can increase the chances of the patient’s recovery after treatment. In the last decade, we have noticed a substantial development in the medical imaging technologies, and they are now becoming an integral p...
Patients with COVID-19 can require radiological examination, with chest CT being more frequent than neuro-imaging. The objective is to identify epidemiological, clinical and radiological factors considered as predictors of neurological involvement in...
Background/Objectives: Physicians face clinical dilemmas in the diagnosis of non-optic intraparenchymal lesions on MRI brain scans of patients with neurofibromatosis type 1. As the incidence and evolution of these lesions into adulthood remain unclea...
Objective: We present the first clinical experience with the BIOGRAPH One next-generation PET/MRI system scanner, evaluating its performance for body and brain imaging in patients across multiple tracers. Methods: A total of 59 patients were scanned...
We studied how brain volume loss in old age is affected by age, the APOE gene, sex, and the level of education completed. The quantitative characterization of brain volume loss at an old age relative to a young age requires—at least in principl...
Magnetic resonance imaging (MRI) plays an important role in assessing early brain development and injury in neonates. When using an automated volumetric analysis, brain tissue segmentation is necessary, preceded by brain extraction (BE) to remove non...
An abnormal growth of cells in the brain, often known as a brain tumor, has the potential to develop into cancer. Carcinogenesis of glial cells in the brain and spinal cord is the root cause of gliomas, which are the most prevalent type of primary br...
The extraction of brain tissue from brain MRI images is an important pre-procedure for the neuroimaging analyses. The brain is bilaterally symmetric both in coronal plane and transverse plane, but is usually asymmetric in sagittal plane. To address t...
In the domain of radiological diagnostics, accurately detecting and classifying brain tumors from magnetic resonance imaging (MRI) scans presents significant challenges, primarily due to the complex and diverse manifestations of tumors in these scans...
Young children with severe traumatic brain injury (TBI) have frequently been excluded from studies due to age and/or mechanism of injury. Magnetic resonance imaging (MRI) is now frequently being utilized to detect parenchymal injuries and early cereb...
Distinguishing between tumefactive demyelinating lesions (TDLs) and brain tumors in multiple sclerosis (MS) can be challenging. A progressive course is highly common with brain tumors in MS and no single neuroimaging technique is foolproof when disti...
A brain tumor occurs in humans when a normal cell turns into an aberrant cell inside the brain. Primarily, there are two types of brain tumors in Homo sapiens: benign tumors and malignant tumors. In brain tumor diagnosis, magnetic resonance imaging (...
In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into...
Background/Objectives: Little is known about the impact of myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD) on brain atrophy. This multicenter longitudinal study compares brain MRI volumes and T2 lesion volume between MOGAD pat...
To improve the accuracy of tumor identification, it is necessary to develop a reliable automated diagnostic method. In order to precisely categorize brain tumors, researchers developed a variety of segmentation algorithms. Segmentation of brain image...
Brain tumor segmentation in magnetic resonance imaging (MRI) is considered a complex procedure because of the variability of tumor shapes and the complexity of determining the tumor location, size, and texture. Manual tumor segmentation is a time-con...
Purpose: Stereotactic radiotherapy (SRT) has become widely accepted as a treatment of choice for patients with a small number of brain metastases that are of an acceptable size, allowing for better target dose conformity, resulting in high local cont...
At present, presurgical functional mapping is the most prevalent clinical application of functional magnetic resonance imaging (fMRI). Signal dropouts and distortions caused by susceptibility effects in the current standard echo planar imaging (EPI)-...
Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, an...
22 July 2024
Background: Intracranial neoplasm, often referred to as a brain tumor, is an abnormal growth or mass of tissues in the brain. The complexity of the brain and the associated diagnostic delays cause significant stress for patients. This study aims to e...
Hypoxic-ischemic encephalopathy (HIE) is a severe neonatal complication with up to 40–60% long-term morbidity. This study evaluates the distribution and burden of MRI changes as a prognostic indicator of neurodevelopmental (ND) outcomes at 18&n...
We previously showed that infants born with long-gap esophageal atresia (LGEA) demonstrate clinically significant brain MRI findings following repair with the Foker process. The current pilot study sought to identify any pre-existing (PRE-Foker proce...
Early detection of cancer is critical for effective treatment, particularly for aggressive malignancies like skin cancer and brain tumors. This research presents an integrated deep learning approach combining augmentation, segmentation, and classific...
Background and Objectives: 68Ga-DOTA peptides positron emission tomography/computed tomography (PET/CT) is usually applied for the study of neuroendocrine tumours, but other tumours such as meningioma may also have an increased radiopharmaceutical up...
The diagnosis of brain pathologies usually involves imaging to analyze the condition of the brain. Magnetic resonance imaging (MRI) technology is widely used in brain disorder diagnosis. The image quality of MRI depends on the magnetostatic field str...
In current research processes, mathematical learning has significantly impacted the brain’s plasticity and cognitive functions. While biochemical changes in brain have been investigated by magnetic resonance spectroscopy, our study attempts to...
Differentiating between primary and secondary headaches can be challenging, especially in the emergency department (ED). Since symptoms alone are inadequate criteria for distinguishing between primary and secondary headaches, many children with heada...
Background: Volumetric analysis and segmentation of magnetic resonance imaging (MRI) data is an important tool for evaluating neurological disease progression and neurodevelopment. Fully automated segmentation pipelines offer faster and more reproduc...
Background: Resting-state functional magnetic resonance imaging (rs-fMRI) can evaluate brain functional connectivity without requiring subjects to perform a specific task. This rs-fMRI is very useful in patients with cognitive decline or unable to re...
Background: The accurate classification of brain tumors from magnetic resonance imaging (MRI) scans is pivotal for timely clinical intervention, yet remains challenged by tumor heterogeneity, morphological variability, and imaging artifacts. Methods:...
Automatic segmentation of brain tumors in intra-operative ultrasound (iUS) images could facilitate localization of tumor tissue during the resection surgery. The lack of large annotated datasets limits the current models performances. In this paper,...
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