Detection Of Brain Abnormalities Research Articles

The value of magnetic resonance imaging in congenital cytomegalovirus infection: a systematic review.

Congenital cytomegalovirus (cCMV) infection can lead to severe neurodevelopmental and hearing impairments. Imaging techniques can be used both pre- and postnatally to assess early signs of infection. The objective was to provide a systematic review of current literature regarding magnetic resonance imaging (MRI) and its value to predict clinical outcome in children with cCMV. PubMed, Embase, and Web of Science were searched for studies investigating MRI in cCMV between 2016-2024. Risk of bias was assessed using Newcastle-Ottawa quality assessment scales. Descriptive synthesis was performed. Twenty studies were included. MRI detected brain abnormalities in 5.0-53.0% of infected patients prenatally and 26.9-69.0% postnatally. The three most frequently detected abnormalities included white matter lesions, subependymal cysts, and ventricular dilatation. Symptoms at birth, first trimester seroconversion, and high viral load were associated with abnormal MRI; however, brain abnormalities were still found in 33-37% of clinically asymptomatic patients. Prenatal MRI had a negative predictive value of 94-100% and a positive predictive value of 12-60% for predicting adverse clinical outcome. Five in six studies found an association between MRI abnormalities and neurodevelopmental impairments, five in eight with (congenital) hearing loss. MRI detected additional abnormalities in 5.6-19.4% of children with normal ultrasound.In conclusion,MRI can detect a wide range of brain abnormalities, both pre- and postnatally, in symptomatic and asymptomatic patients. MRI can be a helpful tool in the prediction of clinical impairments and seems complementary to ultrasound. Therefore, both fetal and neonatal MRI should be considered in the standard work-up of all cCMV-infected children.

Selective segmentation of brain abnormalities in colour MRI images using variational model

Early detection of brain abnormalities is vital for enhancing patient outcomes and survival rates. However, accurately identifying and segmenting these abnormalities from MRI images remains a persistent challenge. This study assesses the efficacy of the Selective Local Image Fitting (SLIF) model in segmenting brain abnormalities from colour MRI images and compares its performance with converted greyscale counterparts. The rationale behind this comparison stems from standard practice in image segmentation, where colour images are often converted to greyscale before the segmentation task. Converting the image might degrade data by diminishing its dimensions, potentially affecting segmentation computations. This study intends to evaluate the influence of colour information on segmentation accuracy and efficiency by directly assessing the SLIF model on both colour and converted greyscale images. Segmentation accuracy was evaluated using metrics such as the Dice Similarity Coefficient (DSC), Matthews Correlation Coefficient (MCC), and Intersection-over-Union (IoU). Efficiency was determined by measuring the average elapsed processing time. Experimental results demonstrate that colour MRI brain images outperform their converted greyscale counterparts in segmentation accuracy, as colour providing essential supplementary information for precise abnormality delineation. Despite a slight increase in average elapsed processing time for colour images, the enhanced accuracy justifies this trade-off. These findings emphasize the importance of colour MRI in enhancing diagnostic accuracy, especially in detecting brain abnormalities. This study can be extended in future work to evaluate the segmentation accuracy and efficiency of brain abnormalities in 3D colour and greyscale MRI images.

Revealing the surprising: Computed tomography-guided exploration of paranasal sinuses incidental findings

BackgroundIncidental findings (IFs) are unintentional discoveries that are unrelated to the original imaging goal. Imaging exams of persons with suspected intracranial disorders may reveal IFs in the paranasal sinuses (PNS). The current investigation aims to determine the frequency and features of unexpected discoveries in the PNS in persons who have had brain computed tomography (CT) scans for suspected intracranial abnormalities. MethodsBetween December 2022 and February 2023, 100 patients who met the inclusion criteria for clinically suspected intracranial disorders had CT brain scans as part of this retrospective cross-sectional study. Two board-certified radiologists with at least three years of experience assessed the CT scans independently. The study investigated the incidence and proportion of paranasal sinuses incidental findings (PNS IFs) observed during brain CT scans to detect brain abnormalities. ResultsThe study discovered that 27% of patients had IFs, with the retired population (aged 61 and up) having the highest prevalence, particularly among men. Acute sinusitis was the most commonly diagnosed incidental finding (IF), accounting for 15% of total cases. In addition, we found polyps, retention cysts, chronic sinusitis, mucoceles, and fungal infections. Left-sided maxillary sinus abnormalities outnumbered right-sided ones. Bilateral involvement was unusual. ConclusionThese findings emphasize the importance of addressing IFs in the PNS when treating patients, as they may necessitate further inspection or intervention. These results have the potential to help establish strategies for treating patients with incidental paranasal sinus findings, ultimately improving patient care.

GPT-based normative models of brain sMRI correlate with dimensional psychopathology

Abstract Generative Pre-trained Transformer (GPT) models have been widely used for language tasks with surprising results. Furthermore, neuroimaging studies using deep generative normative modeling show promise in detecting brain abnormalities from brain structural MRI (sMRI). Meanwhile, psychiatric disorders are typically diagnosed through clinical assessment, which is particularly challenging in children and adolescents who present early symptoms or are in the early stages of the disease. Brain biomarkers research may contribute to the complex task of disentangling typical neurodevelopment from emergent psychiatric disorders. Here, we investigate whether a GPT-based normative architecture can detect psychiatric symptoms and disorders from brain sMRI of youths. The studied datasets contain measures of dimensional psychopathology: Brazilian High-Risk Cohort Study (BHRCS, n = 737) and Adolescent Brain Cognitive Development (ABCD, n = 11,031), and scores and diagnostic of psychiatric disorders: Attention Deficit Hyperactivity Disorder (ADHD-200, n = 922) and Autism Brain Imaging Data Exchange II (ABIDE-II, n = 580). We examined the associations of all brain regions with: the Child Behavior Checklist (CBCL) symptom groups, ADHD scores, and Autism Spectrum Disorder (ASD) diagnosis. Results showed the whole-brain typicality likelihood as correlated with social problems (ABCD test set) and ASD diagnosis (ABIDE-II dataset). Analysis by brain regions linked different areas to several CBCL scales, ADHD scores, and ASD diagnostic. This is the first successful study assessing all dimensional groups of CBCL symptoms, from all brain regions, based exclusively on sMRI. The normative models based on GPT are promising to investigate the gap between the phenotypes of psychiatric conditions and their neurobiological substrates.

A review of ADHD detection studies with machine learning methods using rsfMRI data.

Attention deficit hyperactivity disorder (ADHD) is a common mental health condition that significantly affects school-age children, causing difficulties with learning and daily functioning. Early identification is crucial, and reliable and objective diagnostic tools are necessary. However, current clinical evaluations of behavioral symptoms can be inconsistent and subjective. Functional magnetic resonance imaging (fMRI) is a non-invasive technique that has proven effective in detecting brain abnormalities in individuals with ADHD. Recent studies have shown promising outcomes in using resting state fMRI (rsfMRI)-based brain functional networks to diagnose various brain disorders, including ADHD. Several review papers have examined the detection of other diseases using fMRI data and machine learning or deep learning methods. However, no review paper has specifically addressed ADHD. Therefore, this study aims to contribute to the literature by reviewing the use of rsfMRI data and machine learning methods for detection of ADHD. The study provides general information about fMRI databases and detailed knowledge of the ADHD-200 database, which is commonly used for ADHD detection. It also emphasizes the importance of examining all stages of the process, including network and atlas selection, feature extraction, and feature selection, before the classification stage. The study compares the performance, advantages, and disadvantages of previous studies in detail. This comprehensive approach may be a useful starting point for new researchers in this area.

Design and development of a deep learning model for brain abnormality detection using MRI

ABSTRACT The research aims to develop a DL model for the detection of abnormalities in MRI images that works as an automated and accurate detection system that assists health care professionals in diagnosing the abnormalities in brain. In this research, an advanced brain abnormality prediction model associated with the deep Convolutional Neural Network (CNN) is implemented. The main advantage of this research is the proposed sonar emigration optimization that uses sonaring behaviour for predicting the position of the target with an improved convergence rate. Additionally, intensity, texture and shape-based features extract significant features for enhancing the prediction results. The sonar emigration-based deep CNN-based classifier attained the values of 95.46%, 95.72%, 94.56%, and 96.39% for dataset-1 during TP 90 for accuracy, sensitivity, specificity, and F1 score. For dataset-2 the proposed method attained the values of 94.15%,94.40%,93.25% and 95.07%, during the TP 90 while measuring the metrics, which is quite more efficient than other methods.

Predictors of Neurodevelopment in Microcephaly Associated with Congenital Zika Syndrome: A Prospective Study.

A forward-looking clinical and neurodevelopmental examination was conducted focusing on neonates diagnosed with microcephaly with CZS, birthed between September 2015 and April 2016 at the Hospital Geral Roberto Santos, in Salvador city. That infants were monitored up to their third year by a multiprofessional team. Child development was assessed using the composite Bayley III score. Undertaken by two blinded experts, cranial CT scan analysis was performed during the neonate period for the detection of brain abnormalities and to quantify ventricle enlargement, measured by Evans' index (EI). Fifty newborns were evaluated with a median head circumference of 28 cm (interquartile range 27-31 cm). EI was associated with neurodevelopmental delay at three years and remained significant after adjustment for head circumference. A 0.1-point increase in EI was associated with a delay of 3.2 months in the receptive language (p = 0.016), 3.4 months in the expressive language (p = 0.016), 3.4 months in the cognitive (p = 0.016), 2.37 months in the gross motor (p = 0.026), and 3.1 months in the fine motor (p = 0.021) domains. EI predicted neurodevelopmental delay in all Bayley domains in children with microcephaly associated with CZS.

Deep Learning in Pediatric Neuroimaging

The integration of deep learning techniques in pediatric neuroimaging has shown significant promise in advancing various aspects of the field. This paper provides a comprehensive exploration of deep learning applications in pediatric neuroimaging, focusing on image processing and reconstruction, segmentation and classification, brain abnormalities detection, and brain development and maturation analysis. It discusses key deep-learning techniques and their relevance in pediatric neuroimaging. The paper also addresses challenges and limitations such as the lack of standardization, ethical and privacy concerns, limited and heterogeneous data, and age, gender, and developmental variations. The paper highlights future directions and opportunities, including the integration of multi-modal data, ethical considerations, diagnosing and initiating treatment during early stages, and the impact of maternal emotional well-being on brain development. The insights provided in this paper aim to contribute to understanding how deep learning can positively impact pediatric neuroimaging and inspire further research and innovation in the field. Ultimately, adopting deep learning techniques in pediatric neuroimaging can improve patient outcomes, advance diagnostic accuracy, and enhance our understanding of early brain development.

Large-scale Normative Modeling of Brain Microstructure.

Normative models of brain metrics based on large populations are extremely valuable for detecting brain abnormalities in patients with dementia, psychiatric, or developmental conditions. Here we present the first large-scale normative model of the brain's white matter (WM) microstructure derived from 18 international diffusion MRI (dMRI) datasets covering almost the entire lifespan (totaling N=51,830 individuals; age: 3-80 years). We extracted regional diffusion tensor imaging (DTI) metrics using a standardized analysis and quality control protocol, and used Hierarchical Bayesian Regression (HBR) to model the statistical distribution of derived WM metrics as a function of age and sex, while modeling the site effect. HBR overcomes known weaknesses of some data harmonization methods that simply scale and shift residual distributions at each site. To illustrate the method, we applied it to detect and visualize profiles of WM microstructural deviations in cohorts of patients with Alzheimer's disease, mild cognitive impairment, Parkinson's disease and in carriers of 22q11.2 copy number variants, a rare neurogenetic condition that confers increased risk for psychosis. The resulting large-scale model offers a common reference to identify disease effects in individuals or groups, as well as to compare disorders and discover factors that influence these abnormalities.

Skull Stripping Based on the Segmentation Models

Skull image separation is one of the initial procedures used to detect brain abnormalities. In an MRI image of the brain, this process involves distinguishing the tissue that makes up the brain from the tissue that does not make up the brain. Even for experienced radiologists, separating the brain from the skull is a difficult task, and the accuracy of the results can vary quite a little from one individual to the next. Therefore, skull stripping in brain magnetic resonance volume has become increasingly popular due to the requirement for a dependable, accurate, and thorough method for processing brain datasets. Furthermore, skull stripping must be performed accurately for neuroimaging diagnostic systems since neither non-brain tissues nor the removal of brain sections can be addressed in the subsequent steps, resulting in an unfixed mistake during further analysis. Therefore, accurate skull stripping is necessary for neuroimaging diagnostic systems. This paper proposes a system based on deep learning and Image processing, an innovative method for converting a pre-trained model into another type of pre-trainer using pre-processing operations and the CLAHE filter as a critical phase. The global IBSR data set was used as a test and training set. For the system's efficacy, work was performed based on the principle of three dimensions and three sections of MR images and two-dimensional images, and the results were 99.9% accurate.

PFP-HOG: Pyramid and Fixed-Size Patch-Based HOG Technique for Automated Brain Abnormality Classification with MRI.

Detecting neurological abnormalities such as brain tumors and Alzheimer's disease (AD) using magnetic resonance imaging (MRI) images is an important research topic in the literature. Numerous machine learning models have been used to detect brain abnormalities accurately. This study addresses the problem of detecting neurological abnormalities in MRI. The motivation behind this problem lies in the need for accurate and efficient methods to assist neurologists in the diagnosis of these disorders. In addition, many deep learning techniques have been applied to MRI to develop accurate brain abnormality detection models, but these networks have high time complexity. Hence, a novel hand-modeled feature-based learning network is presented to reduce the time complexity and obtain high classification performance. The model proposed in this work uses a new feature generation architecture named pyramid and fixed-size patch (PFP). The main aim of the proposed PFP structure is to attain high classification performance using essential feature extractors with both multilevel and local features. Furthermore, the PFP feature extractor generates low- and high-level features using a handcrafted extractor. To obtain the high discriminative feature extraction ability of the PFP, we have used histogram-oriented gradients (HOG); hence, it is named PFP-HOG. Furthermore, the iterative Chi2 (IChi2) is utilized to choose the clinically significant features. Finally, the k-nearest neighbors (kNN) with tenfold cross-validation is used for automated classification. Four MRI neurological databases (AD dataset, brain tumor dataset 1, brain tumor dataset 2, and merged dataset) have been utilized to develop our model. PFP-HOG and IChi2-based models attained 100%, 94.98%, 98.19%, and 97.80% using the AD dataset, brain tumor dataset1, brain tumor dataset 2, and merged brain MRI dataset, respectively. These findings not only provide an accurate and robust classification of various neurological disorders using MRI but also hold the potential to assist neurologists in validating manual MRI brain abnormality screening.

Incidental MRI brain findings in children with sensorineural hearing loss

BackgroundSensorineural hearing loss is one of the leading causes for cognitive dysfunction. Incidental brain abnormalities are frequently seen in patient`s MRI. Our aim was to highlight the incidence of brain abnormalities in children with sensorineural hearing loss and to consider brain screening as a part of the standard cochlear implant MRI protocol.MethodsThis retrospective study included 385 prelingually deaf mute children who were referred for pre-cochlear implant imaging evaluation in the period from January 2020 to June 2022. We evaluated brain images for any structural or white matter abnormality.ResultsWe detected brain abnormalities in 62 patients (16.11%), 27 (7.01%) with white matter lesions and 35 patients (9.1%) with other structural brain abnormalities. The commonest white matter lesions were bilateral focal lesions (5.71%). The commonest structural brain abnormality was arachnoid cyst (2.86%). Four patients had two coincidental brain abnormalities. No significant correlation was found between ear abnormalities and white matter lesions or structural brain abnormalities (P > 0.05).ConclusionsThe incidence of brain abnormalities in children with sensorineural hearing loss is not uncommon. Pre-implant MRI screening of the brain helps to obtain best outcomes.

Effectiveness of the Neuroimaging Techniques in the Recognition of Psychiatric Disorders: A Systematic Review and Meta-analysis of RCTs.

Neuroimaging has helped us learn about the stages of brain development from infancy to maturity. Neuroimaging helps physicians diagnose mental illnesses and find novel treatments for them. It can distinguish depression from neurodegenerative diseases or brain tumors, and it can reveal structural defects that cause psychosis. Psychosis has been linked to lesions in the frontal or temporal lobes of the brain, as well as the thalamus and hypothalamus, which can be detected using a brain scan for mental illnesses. Neuroimaging uses quantitative and computational methods to explore the central nervous system. It can detect brain injuries and psychological illnesses. Thus, a systematic review and meta-analysis of randomized controlled trials using neuroimaging to detect psychiatric disorders assessed their efficacy and benefits. Appropriate articles were searched from PubMed, MEDLINE, and CENTRAL databases using the appropriate keywords as per the PRISMA guidelines. Randomized controlled trials and open-label studies were included as per the predefined PICOS criteria. Meta-analysis was performed using the RevMan software, and statistical parameters like odds ratio and risk difference were calculated. Twelve randomized controlled clinical trials with a total of 655 psychiatric patients were included following the criteria from the year 2000 to 2022. We included studies that use different neuroimaging techniques for the detection of organic brain lesions that would help diagnose psychiatric disorders. The primary outcome was detecting brain abnormalities in diverse psychiatric illnesses with neuroimaging versus conventional methods. We found the odds ratio value of 2.29 (95% CI 1.49-3.51). The results were heterogeneous with a Tau2 value of 0.38, chi2 value of 35.48, df value of 11, I2 value of 69%, the z value of 3.78, and p-value less than 0.05. The risk difference is 0.20 (95% CI 0.09 -0.31) with heterogeneity of Tau2 value of 0.03, chi2 value of 50, df value of 11, I2 value of 78%, the z value of 3.49, and p-value less than 0.05. The present meta-analysis strongly recommends the use of neuroimaging techniques for the detection of psychiatric disorders.

StackFBAs: Detection of fetal brain abnormalities using CNN with stacking strategy from MRI images

Predicting fetal brain abnormalities (FBAs) is an urgent global problem, as nearly three of every thousand women are pregnant with neurological abnormalities. Therefore, early detection of FBAs using deep learning (DL) can help to enhance the planning and quality of diagnosis and treatment for pregnant women. Most of the research papers focused on brain abnormalities of newborns and premature infants, but fewer studies concentrated on fetuses. This study proposed a deep learning-CNN-based framework named StackFBAs that utilized the stacking strategy to classify fetus brain abnormalities more accurately using MRI images at an early stage. We considered the Greedy-based Neural architecture search (NAS) method to identify the best CNN architectures to solve this problem utilizing brain MRI images. A total of 94 CNN architectures were generated from the NAS method, and the best 5 CNN models were selected to build the baseline models. Subsequently, the probabilistic scores of these baseline models were combined to construct the final meta-model (KNN) utilizing the stacking strategy. The experimental results demonstrated that StackFBAs outperform pre-trained CNN Models (e.g., VGG16, VGG19, ResNet50, DenseNet121, and ResNet152) with transfer learning (TL) and existing models with the 5-fold cross-validation tests. StackFBAs achieved an overall accuracy of 80%, an F1-score of 78%, 76% sensitivity, and a specificity of 78%. Moreover, we employed the federated learning technique that protects sensitive fetal MRI data, combines results, and finds common patterns from many users, making the model more robust for the privacy and security of user-sensitive data. We believe that our novel framework could be used as a helpful tool for detecting brain abnormalities at an early stage.

Hybrid coyote predator with DL network for brain disorder detection using EEG signals

ABSTRACT Brain abnormality is one of the dominant health issues in recent years, and electroencephalogram (EEG) is a mostly employed method for identifying abnormalities in the brain. Brain abnormalities can be cured only when it is detected early stage. Even though there are various techniques for the detection of brain abnormalities, the health sector concentrates more on the advanced modern method to diagnose the abnormality very precisely. Advanced DL methods have attracted physicians very much due to the fascination with developing detection methods with minimal errors. Hence, this paper deeply concentrated on developing a well-structured technique to predict brain abnormalities using the hybrid coyote predator optimisation-dependent DCNN (hybrid coyote predator-DCNN). The results of this study demonstrate the value of hybrid coyote predator optimisation, which inherits qualities like a long jump and an intelligence ability to adapt to its surroundings and aids in fine-tuning the modal parameters of the DCNN. Additionally, to improve the detection model’s precision, the frequency bands from which the features are taken are separated to identify brain changes. Utilising the CAP dataset and the performance metrics, the analysis is carried out. Regarding the TP and k-fold, the hybrid coyote predator-DCNN model attained accuracy of 98.446% and 97.384%, respectively.

Changes in Brain Activation Patterns During Working Memory Tasks in People With Post-COVID Condition and Persistent Neuropsychiatric Symptoms.

Post-COVID condition (PCC) is common and often involves neuropsychiatric symptoms. This study aimed to use blood oxygenation level-dependent fMRI (BOLD-fMRI) to assess whether participants with PCC had abnormal brain activation during working memory (WM) and whether the abnormal brain activation could predict cognitive performance, motor function, or psychiatric symptoms. The participants with PCC had documented coronavirus disease 2019 (COVID-19) at least 6 weeks before enrollment. Healthy control participants had no prior history of COVID-19 and negative tests for severe acute respiratory syndrome coronavirus 2. Participants were assessed using 3 NIH Toolbox (NIHTB) batteries for Cognition (NIHTB-CB), Emotion (NIHTB-EB), and Motor function (NIHTB-MB) and selected tests from the Patient-Reported Outcomes Measurement Information System (PROMIS). Each had BOLD-fMRI at 3T, during WM (N-back) tasks with increasing attentional/WM load. One hundred sixty-nine participants were screened; 50 fulfilled the study criteria and had complete and usable data sets for this cross-sectional cohort study. Twenty-nine participants with PCC were diagnosed with COVID-19 242 ± 156 days earlier; they had similar ages (42 ± 12 vs 41 ± 12 years), gender proportion (65% vs 57%), racial/ethnic distribution, handedness, education, and socioeconomic status, as the 21 uninfected healthy controls. Despite the high prevalence of memory (79%) and concentration (93%) complaints, the PCC group had similar performance on the NIHTB-CB as the controls. However, participants with PCC had greater brain activation than the controls across the network (false discovery rate-corrected p = 0.003, Tmax = 4.17), with greater activation in the right superior frontal gyrus (p = 0.009, Cohen d = 0.81, 95% CI 0.15-1.46) but lesser deactivation in the default mode regions (p = 0.001, d = 1.03, 95% CI 0.61-1.99). Compared with controls, participants with PCC also had poorer dexterity and endurance on the NIHTB-MB, higher T scores for negative affect and perceived stress, but lower T scores for psychological well-being on the NIHTB-EB, as well as more pain symptoms and poorer mental and physical health on measures from the PROMIS. Greater brain activation predicted poorer scores on measures that were abnormal on the NIHTB-EB. Participants with PCC and neuropsychiatric symptoms demonstrated compensatory neural processes with greater usage of alternate brain regions, and reorganized networks, to maintain normal performance during WM tasks. BOLD-fMRI was sensitive for detecting brain abnormalities that correlated with various quantitative neuropsychiatric symptoms.

Optimized EEG based mood detection with signal processing and deep neural networks for brain-computer interface

Electroencephalogram (EEG) is a very promising and widely implemented procedure to study brain signals and activities by amplifying and measuring the post-synaptical potential arising from electrical impulses produced by neurons and detected by specialized electrodes attached to specific points in the scalp. It can be studied for detecting brain abnormalities, headaches, and other conditions. However, there are limited studies performed to establish a smart decision-making model to identify EEG’s relation with the mood of the subject. In this experiment, EEG signals of 28 healthy human subjects have been observed with consent and attempts have been made to study and recognise moods. Savitzky-Golay band-pass filtering and Independent Component Analysis have been used for data filtration.Different neural network algorithms have been implemented to analyze and classify the EEG data based on the mood of the subject. The model is further optimised by the usage of Blackman window-based Fourier Transformation and extracting the most significant frequencies for each electrode. Using these techniques, up to 96.01% detection accuracy has been obtained.

Detection of brain abnormalities in Parkinson's rats by combining deep learning and motion tracking.

Parkinson's disease (PD) is a chronic neurodegenerative disease that affects the central nervous system. PD mainly affects the motor nervous system and may cause cognitive and behavioral problems. One of the best tools to investigate the pathogenesis of PD is animal models, among which the 6-OHDA-treated rat is a widely employed rodent model. In this research, three-dimensional motion capture technology was employed to obtain real-time three-dimensional coordinate information about sick and healthy rats freely moving in an open field. This research also proposes an end-to-end deep learning model of CNN-BGRU to extract spatiotemporal information from 3D coordinate information and perform classification. The experimental results show that the model proposed in this research can effectively distinguish sick rats from healthy rats with a classification accuracy of 98.73%, providing a new and effective method for the clinical detection of Parkinson's syndrome.

A hybrid approach to segment and detect brain abnormalities from MRI scan

The Detection of brain abnormality is a complex task. The images captured from the MRI scan machines have numerous information, and it is difficult to segment the appropriate information from the images. Earlier studies have shown various challenges in detecting brain abnormalities through image processing. And one among them is the segmentation of the appropriate region with abnormalities. Thus, the paper proposes a hybrid approach using GAN, K-means clustering, and MobileNet to detect brain abnormalities. Here, the GAN is used to generate fake images from the real images of MR scans. This fake image has been enhanced, and it supports segmenting the abnormal zone from the image using K-means clustering. The segmented region is identified and analyzed for the abnormalities using the MobileNet. Finally, the proposed model could detect abnormalities from the MR images of the brain. The performance metrics for the proposed model are measured and compared, which indicates the improved performance of the proposed model.

Anatomical and diffusion-weighted imaging of brain abnormalities in third-trimester fetuses with cytomegalovirus infection.

In this study of cytomegalovirus (CMV)-infected fetuses with first-trimester seroconversion, we aimed to evaluate the detection of brain abnormalities using magnetic resonance imaging (MRI) and neurosonography (NSG) in the third trimester, and compare the grading systems of the two modalities. We also evaluated the feasibility of routine use of diffusion-weighted imaging (DWI) fetal MRI and compared the regional apparent diffusion coefficient (ADC) values between CMV-infected fetuses and presumed normal, non-infected fetuses in the third trimester. This was a retrospective review of MRI and NSG scans in fetuses with confirmed first-trimester CMV infection performed between September 2015 and August 2019. Brain abnormalities were recorded and graded using fetal MRI and NSG grading systems to compare the two modalities. To investigate feasibility of DWI, a four-point rating scale (poor, suboptimal, good, excellent) was applied to assess the quality of the images. Quantitative assessment was performed by placing a freehand drawn region of interest in the white matter of the frontal, parietal, temporal and occipital lobes and the basal ganglia, pons and cerebellum to calculate ADC values. Regional ADC measurements were obtained similarly in a control group of fetuses with negative maternal CMV serology in the first trimester, normal brain findings on fetal MRI and normal genetic testing. Fifty-three MRI examinations of 46 fetuses with confirmed first-trimester CMV infection were included. NSG detected 24 of 27 temporal cysts seen on MRI scans, with a sensitivity of 78% and an accuracy of 83%. NSG did not detect abnormal gyration visible on two (4%) MRI scans. Periventricular calcifications were detected on two MRI scans compared with 10 NSG scans. While lenticulostriate vasculopathy was detected on 11 (21%) NSG scans, no fetus demonstrated this finding on MRI. MRI grading correlated significantly with NSG grading of brain abnormalities (P < 0.0001). Eight (15%) of the DWI scans in the CMV cohort were excluded from further analysis because of insufficient quality. The ADC values of CMV-infected fetuses were significantly increased in the frontal (both sides, P < 0.0001), temporal (both sides, P < 0.0001), parietal (left side, P = 0.0378 and right side, P = 0.0014) and occipital (left side, P = 0.0002 and right side, P < 0.0001) lobes and decreased in the pons (P = 0.0085) when compared with non-infected fetuses. The ADC values in the basal ganglia and the cerebellum were not significantly different in CMV-infected fetuses compared with normal controls (all P > 0.05). Temporal and frontal ADC values were higher in CMV-infected fetuses with more severe brain abnormalities compared to fetuses with mild abnormalities. Ultrasound and MRI are complementary during the third trimester in the assessment of brain abnormalities in CMV-infected fetuses, with a significant correlation between the grading systems of the two modalities. On DWI in the third trimester, the ADC values in several brain regions are abnormal in CMV-infected fetuses compared with normal controls. Furthermore, they seem to correlate in the temporal area and, to a lesser extent, frontal area with the severity of brain abnormalities associated with CMV infection. Larger prospective studies are needed for further investigation of the microscopic nature of diffusion abnormalities and correlation of different imaging findings with postnatal outcome. © 2022 International Society of Ultrasound in Obstetrics and Gynecology.