scholarly journals The Effect of Deep Learning-Based QSM Magnetic Resonance Imaging on the Subthalamic Nucleus

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuanqin Liu ◽  
Qinglu Zhang ◽  
Lingchong Liu ◽  
Cuiling Li ◽  
Rongwei Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Large Individual ◽  
Resonance Imaging ◽  
Abdominal Ct ◽  
Multiple Input ◽  
Image Center ◽  
Deep Gray Matter ◽  
Layer I ◽  
Case Data ◽  
The Brain

In order to study the influence of quantitative magnetic susceptibility mapping (QSM) on them. A 2.5D Attention U-Net Network based on multiple input and multiple output, a method for segmenting RN, SN, and STN regions in high-resolution QSM images is proposed, and deep learning realizes accurate segmentation of deep nuclei in brain QSM images. Experimental results show data first cuts each layer of 0 100 case data, based on the image center, from 384 × 288 to the size of 128 × 128. Image combination: each layer of the image in the layer direction combines with two adjacent images into a 2.5D image, i.e., (It − m It; It + i), where It represents the layer i image. At this time, the size of the image changes from 128 × 128 to 128 × 128 × 3, in which 3 represents three consecutive layers of images. The SNR of SWP I to STN is twice that of SWI. The small deep gray matter nuclei (RN, SN, and STN) in QSM images of the brain and the pancreas with irregular shape and large individual differences in abdominal CT images can be automatically segmented.

scholarly journals 60 Precision Medicine in Developmental Pediatrics: Image-based Classification of Children with Autism Spectrum Disorder using Deep Learning

2020 ◽  
Vol 25 (Supplement_2) ◽  
pp. e25-e25
Author(s):  
Sarah MacEachern ◽  
Deepthi Rajashekar ◽  
Pauline Mouches ◽  
Nathan Rowe ◽  
Emily Mckenna ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Deep Learning ◽  
Gray Matter ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Classification Model ◽  
Children With Asd ◽  
Adc Values ◽  
Deep Gray Matter ◽  
The Brain

Abstract Introduction/Background Autism spectrum disorder (ASD) is a neurodevelopmental disorder resulting in challenges with social communication, sensory differences, and repetitive and restricted patterns of behavior. ASD affects approximately 1 in 66 children in North America, with boys being affected four times more frequently than girls. Currently, diagnosis is made primarily based on clinical features and no robust biomarker for ASD diagnosis has been identified. Potential image-based biomarkers to aid ASD diagnosis may include structural properties of deep gray matter regions in the brain. Objectives The primary objective of this work was to investigate if children with ASD show micro- and macrostructural alterations in deep gray matter structures compared to neurotypical children, and if these biomarkers can be used for an automatic ASD classification using deep learning. Design/Methods Quantitative apparent diffusion coefficient (ADC) magnetic resonance imaging data was obtained from 23 boys with ASD ages 0.8 – 19.6 years (mean 7.6 years) and 39 neurotypical boys ages 0.3 – 17.75 years (mean 7.6 years). An atlas-based method was used for volumetric analysis and extraction of median ADC values for each subject within the cerebral cortex, hippocampus, thalamus, caudate, putamen, globus pallidus, amygdala, and nucleus accumbens. The extracted quantitative regional volumetric and median ADC values were then used for the development and evaluation of an automatic classification method using an artificial neural network. Results The classification model was evaluated using 10-fold cross validation resulting in an overall accuracy of 76%, which is considerably better than chance level (62%). Specifically, 33 neurotypical boys were correctly classified, whereas 6 neurotypical boys were incorrectly classified. For the ASD group, 14 boys were correctly classified, while 9 boys were incorrectly classified. This translates to a precision of 70% for the children with ASD and 79% for neurotypical boys. Conclusion To the best of our knowledge, this is the first method to classify children with ASD using micro- and macrostructural properties of deep gray matter structures in the brain. The first results of the proposed deep learning method to identify children with ASD using image-based biomarkers are promising and could serve as the platform to create a more accurate and robust deep learning model for clinical application.

scholarly journals Development of brain tumor segmentation of magnetic resonance imaging (MRI) using U-Net deep learning

2021 ◽  
Vol 4 (9(112)) ◽  
pp. 23-31
Author(s):  
Wasan M. Jwaid ◽  
Zainab Shaker Matar Al-Husseini ◽  
Ahmad H. Sabry
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Brain Tumor ◽  
Magnetic Resonance ◽  
Tumor Segmentation ◽  
Resonance Imaging ◽  
Brain Tumor Segmentation ◽  
Brain Images ◽  
Magnetic Resonance Imaging Mri ◽  
The Brain

Brain tumors are the growth of abnormal cells or a mass in a brain. Numerous kinds of brain tumors were discovered, which need accurate and early detection techniques. Currently, most diagnosis and detection methods rely on the decision of neuro-specialists and radiologists to evaluate brain images, which may be time-consuming and cause human errors. This paper proposes a robust U-Net deep learning Convolutional Neural Network (CNN) model that can classify if the subject has a tumor or not based on Brain Magnetic resonance imaging (MRI) with acceptable accuracy for medical-grade application. The study built and trained the 3D U-Net CNN including encoding/decoding relationship architecture to perform the brain tumor segmentation because it requires fewer training images and provides more precise segmentation. The algorithm consists of three parts; the first part, the downsampling part, the bottleneck part, and the optimum part. The resultant semantic maps are inserted into the decoder fraction to obtain the full-resolution probability maps. The developed U-Net architecture has been applied on the MRI scan brain tumor segmentation dataset in MICCAI BraTS 2017. The results using Matlab-based toolbox indicate that the proposed architecture has been successfully evaluated and experienced for MRI datasets of brain tumor segmentation including 336 images as training data and 125 images for validation. This work demonstrated comparative performance and successful feasibility of implementing U-Net CNN architecture in an automated framework of brain tumor segmentations in Fluid-attenuated inversion recovery (FLAIR) MR Slices. The developed U-Net CNN model succeeded in performing the brain tumor segmentation task to classify the input brain images into a tumor or not based on the MRI dataset.

Insights from neuroanatomical imaging into ADHD throughout the lifespan

2018 ◽  
Author(s):  
Philip Shaw ◽  
Eszter Szekely
Keyword(s):  
Cognitive Processes ◽  
Clinical Course ◽  
Developmental Trajectories ◽  
Continuous Distribution ◽  
Large Individual ◽  
Resonance Imaging ◽  
Entire Genome ◽  
Pilot Studies ◽  
Meta Analyses ◽  
The Brain

The relatively recent advent of magnetic resonance imaging has given us an invaluable ‘window’ into the brain in ADHD. Here we review the literature on the structural neuroimaging of ADHD throughout the lifespan. Meta-analyses and large individual studies converge to find anomalies in the basal ganglia in ADHD; some appear developmentally stable, while others are progressive. Compromise of the cerebral cortex and cerebellum are also commonly reported, and developmental trajectories of these structures have been linked with the highly variable clinical course of the disorder. ADHD can be considered dimensionally, lying at the extreme end of a continuous distribution of symptoms and underlying cognitive processes. Some studies find such dimensionality is also present in ADHD-related neuroanatomical change. Pilot studies have examined how variation in some candidate genes is tied to neuroanatomy in the disorder. Studies at the level of the entire genome await much larger cohorts.

scholarly journals Brain Tumor/Mass Classification Framework Using Magnetic-Resonance-Imaging-Based Isolated and Developed Transfer Deep-Learning Model

Sensors ◽  
2022 ◽  
Vol 22 (1) ◽  
pp. 372
Author(s):  
Muhannad Faleh Alanazi ◽  
Muhammad Umair Ali ◽  
Shaik Javeed Hussain ◽  
Amad Zafar ◽  
Mohammed Mohatram ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Brain Tumors ◽  
Brain Mri ◽  
Learning Model ◽  
High Accuracy ◽  
Resonance Imaging ◽  
Deep Learning Model ◽  
The Brain

With the advancement in technology, machine learning can be applied to diagnose the mass/tumor in the brain using magnetic resonance imaging (MRI). This work proposes a novel developed transfer deep-learning model for the early diagnosis of brain tumors into their subclasses, such as pituitary, meningioma, and glioma. First, various layers of isolated convolutional-neural-network (CNN) models are built from scratch to check their performances for brain MRI images. Then, the 22-layer, binary-classification (tumor or no tumor) isolated-CNN model is re-utilized to re-adjust the neurons’ weights for classifying brain MRI images into tumor subclasses using the transfer-learning concept. As a result, the developed transfer-learned model has a high accuracy of 95.75% for the MRI images of the same MRI machine. Furthermore, the developed transfer-learned model has also been tested using the brain MRI images of another machine to validate its adaptability, general capability, and reliability for real-time application in the future. The results showed that the proposed model has a high accuracy of 96.89% for an unseen brain MRI dataset. Thus, the proposed deep-learning framework can help doctors and radiologists diagnose brain tumors early.

Сauses of Dizziness in Patients with Suspected Stroke

2018 ◽  
Vol 7 (3) ◽  
pp. 217-221
Author(s):  
E. V. Shevchenko ◽  
G. R. Ramazanov ◽  
S. S. Petrikov
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Demyelinating Disease ◽  
Heart Rhythm ◽  
Diagnostic Methods ◽  
Deficiency Anemia ◽  
Resonance Imaging ◽  
Peripheral Vestibulopathy ◽  
Suspected Stroke ◽  
The Brain

Background Acute dizziness may be the only symptom of stroke. Prevalence of this disease among patients with isolated dizziness differs significantly and depends on study design, inclusion criteria and diagnostic methods. In available investigations, we did not find any prospective studies where magnetic resonance imaging, positional maneuvers, and Halmagyi-Curthoys test had been used to clarify a pattern of diseases with isolated acute dizziness and suspected stroke.Aim of study To clarify the pattern of the causes of dizziness in patients with suspected acute stroke.Material and methods We examined 160 patients admitted to N.V. Sklifosovsky Research Institute for Emergency Medicine with suspected stroke and single or underlying complaint of dizziness. All patients were examined with assessment of neurological status, Dix-Hollpike and Pagnini-McClure maneuvers, HalmagyiCurthoys test, triplex scans of brachiocephalic arteries, transthoracic echocardiography, computed tomography (CT) and magnetic resonance imaging (MRI) of the brain with magnetic field strength 1.5 T. MRI of the brain was performed in patients without evidence of stroke by CT and in patients with stroke of undetermined etiology according to the TOAST classification.Results In 16 patients (10%), the cause of dizziness was a disease of the brain: ischemic stroke (n=14 (88%)), hemorrhage (n=1 (6%)), transient ischemic attack (TIA) of posterior circulation (n=1 (6%)). In 70.6% patients (n=113), the dizziness was associated with peripheral vestibulopathy: benign paroxysmal positional vertigo (n=85 (75%)), vestibular neuritis (n=19 (17%)), Meniere’s disease (n=7 (6%)), labyrinthitis (n=2 (1,3%)). In 6.9% patients (n=11), the cause of dizziness was hypertensive encephalopathy, 1.9% of patients (n=3) had heart rhythm disturbance, 9.4% of patients (n=15) had psychogenic dizziness, 0.6% of patients (n=1) had demyelinating disease, and 0.6% of patients (n=1) had hemic hypoxia associated with iron deficiency anemia.Conclusion In 70.6% patients with acute dizziness, admitted to hospital with a suspected stroke, peripheral vestibulopathy was revealed. Only 10% of patients had a stroke as a cause of dizziness.

Neurometabolic Diseases in Children: Magnetic Resonance Imaging and Magnetic Resonance Spectroscopy Features

2019 ◽  
Vol 15 (3) ◽  
pp. 255-268
Author(s):  
Direnç Özlem Aksoy ◽  
Alpay Alkan
Keyword(s):  
Magnetic Resonance ◽  
Magnetic Resonance Spectroscopy ◽  
Metabolic Pathways ◽  
Wide Spectrum ◽  
Demyelinating Diseases ◽  
Resonance Spectroscopy ◽  
Resonance Imaging ◽  
Neurometabolic Disorders ◽  
Brain Involvement ◽  
The Brain

Background: Neurometabolic diseases are a group of diseases secondary to disorders in different metabolic pathways, which lead to white and/or gray matter of the brain involvement. </P><P> Discussion: Neurometabolic disorders are divided in two groups as dysmyelinating and demyelinating diseases. Because of wide spectrum of these disorders, there are many different classifications of neurometabolic diseases. We used the classification according to brain involvement areas. In radiological evaluation, MRI provides useful information for these disseases. Conclusion: Magnetic Resonance Spectroscopy (MRS) provides additional metabolic information for diagnosis and follow ups in childhood with neurometabolic diseases.

A new method to predict anomaly in brain network based on graph deep learning

2020 ◽  
Vol 31 (6) ◽  
pp. 681-689
Author(s):  
Jalal Mirakhorli ◽  
Hamidreza Amindavar ◽  
Mojgan Mirakhorli
Keyword(s):  
Deep Learning ◽  
Large Scale ◽  
Brain Plasticity ◽  
Brain Network ◽  
High Order ◽  
Brain Diseases ◽  
Simultaneous Occurrence ◽  
Generative Adversarial Network ◽  
The Brain ◽  
Brain Connections

AbstractFunctional magnetic resonance imaging a neuroimaging technique which is used in brain disorders and dysfunction studies, has been improved in recent years by mapping the topology of the brain connections, named connectopic mapping. Based on the fact that healthy and unhealthy brain regions and functions differ slightly, studying the complex topology of the functional and structural networks in the human brain is too complicated considering the growth of evaluation measures. One of the applications of irregular graph deep learning is to analyze the human cognitive functions related to the gene expression and related distributed spatial patterns. Since a variety of brain solutions can be dynamically held in the neuronal networks of the brain with different activity patterns and functional connectivity, both node-centric and graph-centric tasks are involved in this application. In this study, we used an individual generative model and high order graph analysis for the region of interest recognition areas of the brain with abnormal connection during performing certain tasks and resting-state or decompose irregular observations. Accordingly, a high order framework of Variational Graph Autoencoder with a Gaussian distributer was proposed in the paper to analyze the functional data in brain imaging studies in which Generative Adversarial Network is employed for optimizing the latent space in the process of learning strong non-rigid graphs among large scale data. Furthermore, the possible modes of correlations were distinguished in abnormal brain connections. Our goal was to find the degree of correlation between the affected regions and their simultaneous occurrence over time. We can take advantage of this to diagnose brain diseases or show the ability of the nervous system to modify brain topology at all angles and brain plasticity according to input stimuli. In this study, we particularly focused on Alzheimer’s disease.

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