Brain Magnetic Resonance Imaging Scans Research Articles

Lifestyle management and brain MRI metrics in female Australian adults living with multiple sclerosis: a feasibility and acceptability study

BackgroundLimited studies of multiple sclerosis (MS) exist whereby magnetic resonance imaging (MRI) of the brain with consistent imaging protocols occurs at the same time points as collection of healthy lifestyle measures. The aim of this study was to test the feasibility, acceptability and preliminary efficacy of acquiring MRI data as an objective, diagnostic and prognostic marker of MS, at the same time point as brain-healthy lifestyle measures including diet.MethodsParticipants living with relapsing remitting MS partook in one structural MRI scanning session of the brain, completed two online 24-hour dietary recalls and demographic and self-reported lifestyle questionnaires (e.g. self-reported disability, comorbidities, physical activity, smoking status, body mass index (BMI), stress). Measures of central tenancy and level of dispersion were calculated for feasibility and acceptability of the research protocols. Lesion count was determined by one radiologist and volumetric analyses by a data analysis pipeline based on FreeSurfer software suite. Correlations between white matter lesion count, whole brain volume analyses and lifestyle measures were assessed using Spearman’s rank-order correlation coefficient.ResultsThirteen female participants were included in the study: eligibility rate 90.6% (29/32), recruitment rate 46.9% (15/32) and compliance rate 87% (13/15). The mean time to complete all required tasks, including MRI acquisition was 115.86 minutes (\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$($$\\end{document}± 23.04), over 4 days. Conversion to usual dietary intake was limited by the small sample. There was one strong, negative correlation between BMI and brain volume (rs = −0.643, p = 0.018) and one strong, positive correlation between physical activity and brain volume (rs = 0.670, p = 0.012) that were both statistically significant.ConclusionsAcquiring MRI brain scans at the same time point as lifestyle profiles in adults with MS is both feasible and accepted among adult females living with MS. Quantification of volumetric MRI data support further investigations using semi-automated pipelines among people living with MS, with pre-processing steps identified to increase automated feasibility. This protocol may be used to determine relationships between elements of a brain-healthy lifestyle, including dietary intake, and measures of disease burden and brain health, as assessed by T1-weighted and T2-weighted lesion count and whole brain volume, in an adequately powered sample.Trial registrationThe study protocol was retrospectively registered in the Australia New Zealand Clinical Trials Registry (ACTRN12624000296538).

Association of past and current sleep duration with structural brain differences: A large population-based study from the UK Biobank

ObjectiveThis study aimed to examine the association between past/current sleep duration and macro-/micro-structural brain outcomes and explore whether hypertension or social activity plays a role in such association. MethodsWithin the UK Biobank, 40 436 dementia-free participants (age 40–70 years) underwent a baseline assessment followed by a brain magnetic resonance imaging (MRI) scan 9 years later. Past (baseline) and current (MRI scans) sleep duration (hours/day) were recorded and classified as short (≤5), intermediate (6–8), and long (≥9). Brain structural volumes and diffusion markers were assessed by MRI scans. ResultsCompared with past intermediate sleep, past short sleep was related to smaller cortex volumes (standardized β [95 % CI]: −0.04 [-0.07, −0.02]) and lower regional fractional anisotropy (FA) (−0.08 [-0.13, −0.03]), while past long sleep was related to smaller regional subcortical volumes (standardized β: −0.04 to −0.07 for thalamus, accumbens, and hippocampus). Compared to current intermediate sleep, current short sleep was associated with smaller cortex volumes (−0.03 [-0.05, −0.01]), greater white matter hyperintensities (WMH) volumes (0.04 [0.01, 0.08]), and lower regional FA (−0.07 [-0.11, −0.02]). However, current long sleep was related to smaller total brain (−0.03 [-0.05, −0.02]), grey matter (−0.05 [-0.07, −0.03]), cortex (−0.05 [-0.07, −0.03]), regional subcortical volumes [standardized β: −0.05 to −0.09 for putamen, thalamus, hippocampus, and accumbens]), greater WMH volumes (0.06 [0.03, 0.09]), as well as lower regional FA (−0.05 [-0.09, −0.02]). The association between current long sleep duration and poor brain health was stronger among people with hypertension or low frequency of social activity (all Pinteraction <0.05). ConclusionsBoth past and current short/long sleep are associated with smaller brain volume and poorer white matter health in the brain, especially in individuals with hypertension and low frequency of social activity. Our findings highlight the need to maintain 6–8 h’ sleep duration for healthy brain aging.

Anatomy of the brain of capybara (Hydrochoerus hydrochaeris) using magnetic resonance imaging.

Capybara is considered the largest living rodent and is widespread distributed in the South America and in the Brazilian territory. The purpose of this study was to provide the anatomical description of the brain in the capybara (Hydrochoerus hydrochaeris) using magnetic resonance imaging (MRI). Brains of ten normal capybaras were imaged and sectioned in the anatomical studies. MRI was acquired on 0.25 Tesla equipment, promoting good-quality images capable to identify and classify the main anatomical structures of clinical interest. MRI reference images were validated by comparing them with gross anatomical sections. The capybara sulci and gyri were named for its similar location and orientation to those described in the previous descriptions in the capybara and in the domestic dog. Capybaras presented prominent cerebral sulcus and gyrus in relation to other caviomorph rodents, but in reduced number when compared to domestic animals and other wild mammals such as elephants and giraffes. The findings of this study indicate that the shape of the capybara brain is remarkably similar to that of the caviomorph rodents with a higher neocortilization. The capybara rhinencephalon was well-developed implying a good sense of smell. Due to this development of the rhinencephalon, we can suggest that capybara brain is a macrosmatic brain. The MRI and gross anatomical sections of capybara brain may help veterinary researchers and clinicians increase the accuracy of brain MRI scans interpretation in these animals.

Quantitative analysis of apparent diffusion coefficients to predict neurological prognosis in cardiac arrest survivors: an observational derivation and internal–external validation study

BackgroundThis study aimed to validate apparent diffusion coefficient (ADC) values and thresholds to predict poor neurological outcomes in out-of-hospital cardiac arrest (OHCA) survivors by quantitatively analysing the ADC values via brain magnetic resonance imaging (MRI).MethodsThis observational study used prospectively collected data from two tertiary academic hospitals. The derivation cohort comprised 70% of the patients randomly selected from one hospital, whereas the internal validation cohort comprised the remaining 30%. The external validation cohort used the data from another hospital, and the MRI data were restricted to scans conducted at 3 T within 72–96 h after an OHCA experience. We analysed the percentage of brain volume below a specific ADC value at 50-step intervals ranging from 200 to 1200 × 10–6 mm2/s, identifying thresholds that differentiate between good and poor outcomes. Poor neurological outcomes were defined as cerebral performance categories 3–5, 6 months after experiencing an OHCA.ResultsA total of 448 brain MRI scans were evaluated, including a derivation cohort (n = 224) and internal/external validation cohorts (n = 96/128, respectively). The proportion of brain volume with ADC values below 450, 500, 550, 600, and 650 × 10–6 mm2/s demonstrated good to excellent performance in predicting poor neurological outcomes in the derivation group (area under the curve [AUC] 0.89–0.91), and there were no statistically significant differences in performances among the derivation, internal validation, and external validation groups (all P > 0.5). Among these, the proportion of brain volume with an ADC below 600 × 10–6 mm2/s predicted a poor outcome with a 0% false-positive rate (FPR) and 76% (95% confidence interval [CI] 68–83) sensitivity at a threshold of > 13.2% in the derivation cohort. In both the internal and external validation cohorts, when using the same threshold, a specificity of 100% corresponded to sensitivities of 71% (95% CI 58–81) and 78% (95% CI 66–87), respectively.ConclusionsIn this validation study, by consistently restricting the MRI types and timing during quantitative analysis of ADC values in brain MRI, we observed high reproducibility and sensitivity at a 0% FPR. Prospective multicentre studies are necessary to validate these findings.

Brain MRI sequence and view plane identification using deep learning.

Brain magnetic resonance imaging (MRI) scans are available in a wide variety of sequences, view planes, and magnet strengths. A necessary preprocessing step for any automated diagnosis is to identify the MRI sequence, view plane, and magnet strength of the acquired image. Automatic identification of the MRI sequence can be useful in labeling massive online datasets used by data scientists in the design and development of computer aided diagnosis (CAD) tools. This paper presents a deep learning (DL) approach for brain MRI sequence and view plane identification using scans of different data types as input. A 12-class classification system is presented for commonly used MRI scans, including T1, T2-weighted, proton density (PD), fluid attenuated inversion recovery (FLAIR) sequences in axial, coronal and sagittal view planes. Multiple online publicly available datasets have been used to train the system, with multiple infrastructures. MobileNet-v2 offers an adequate performance accuracy of 99.76% with unprocessed MRI scans and a comparable accuracy with skull-stripped scans and has been deployed in a tool for public use. The tool has been tested on unseen data from online and hospital sources with a satisfactory performance accuracy of 99.84 and 86.49%, respectively.

Optimised ResNet50 for Multi-Class Classification of Brain Tumors

Categorizing brain cancers, including glioma, meningioma, and pituitary tumors, based on magnetic resonance imaging (MRI) images presents a significant challenge. Deep learning and machine learning techniques have shown promise in enhancing image categorization. To address this challenge, we leverage the power of the optimized ResNet50 model. Our approach involves classifying medical images using Convolutional Neural Network (CNN) features, which are then compared with the ResNet50 model. The primary goal is to detect brain tumors at an early stage using an advanced deep-learning model. We utilize an accessible dataset from Figshare, containing MRI images of the three distinct categories of brain tumors. Existing brain tumor models face limitations in handling multi-class problems and early-stage diagnosis. Therefore, we propose a fully automated approach employing Convolutional Neural Networks (CNN) to extract diverse properties from brain MRI scans. This method aims to provide accurate tumor diagnosis, even with a high number of classes and limited information in MRI data. Our proposed model involves the creation of identification blocks within a four-layered primary architecture, followed by testing and assessment of the interconnected layers. The results demonstrate that our model outperforms existing methods, achieving an impressive overall classification accuracy of 99.03%,

Multi-contrast echo-planar imaging sequence (Echo-planar imaging mix) in clinical situations demanding faster MRI-brain scans

Objectives: The excellent resolution offered by magnetic resonance imaging (MRI) has a trade-off in the form of scan duration. The purpose of the present study was to assess the clinical utility of echo-planar imaging mix (EPIMix), an echo-planar imaging-based MRI sequence for the brain with a short acquisition time. Materials and Methods: This was a retrospective observational study of 50 patients, who could benefit from faster MRI brain scans. The T1, T2, fluid attenuated inversion recovery, diffusion-weighted imaging (DWI), and T2*/susceptibility-weighted imaging sequences were acquired, conventionally and with EPIMix. Conventional and EPIMix images were assessed by two radiologists for overall quality, motion, and susceptibility artifacts and scored on a Likert scale. The scores given for conventional and EPIMix images were compared. The diagnostic performance of EPIMix was also assessed by the ability to detect clinically relevant findings. Results: The acquisition time for conventional MRI was 11 min and 45 s and for EPIMix 1 min and 15 s. All EPIMix images were sufficient for diagnostic use. On assessment of the diagnostic performance, it was excellent for ischemic and hemorrhagic strokes. Smaller lesions, lesions adjacent to bone, and post-operative tumors were difficult to identify. Moderate to perfect agreement (Kappa values 0.41–1) was seen between radiologists for all categories except skull base, calvarial, and orbital lesions. Image quality, artifact assessment showed excellent interobserver agreement (&gt;90%) for the scores. All EPIMix images showed reduced motion artifacts. The EPIMix-DWI was comparable to conventional-DWI in terms of quality and artifacts. The remaining sequences showed reduced quality and increased susceptibility. Conclusion: The EPIMix has a significantly reduced acquisition time than conventional MRI and could be used instead of conventional MRI in situations demanding faster scans such as suspected acute ischemic or hemorrhagic stroke. In other clinical scenarios, it could help tailor the MRI examination for each patient.

A survey on brain MRI segmentation

Neurological disorders pose a significant challenge in medical diagnostics, requiring accurate and efficient analysis of brain Magnetic Resonance Imaging (MRI) scans. This study introduces a novel approach for enhanced neurological diagnosis through the application of deep learning techniques to automate the segmentation of brain structures in MRI images. The proposed method leverages the power of convolutional neural networks (CNNs) to extract intricate patterns and features from complex neuro imaging data. The research involves the development and training of a deep learning model capable of accurately delineating key anatomical regions, such as the cortex, hippocampus, and ventricles. The model is trained on a large data set of annotated MRI scans, optimizing its performance through rigorous validation processes. The utilization of deep learning enables the algorithm to learn and generalize from diverse imaging data, improving its adaptability to variations in patient demographics and scanner characteristics. To evaluate the effectiveness of the proposed approach, comprehensive experiments are conducted on a diverse set of MRI datasets, encompassing various neurological conditions. Quantitative metrics, including Dice coefficient and Hausdorff distance, are employed to assess the segmentation accuracy compared to ground truth annotations. Additionally, the clinical relevance of the automated segmentation is validated through collaboration with neurologists and radiologists. The results demonstrate that the deep learning- enabled segmentation method consistently outperforms traditional image processing techniques, providing more accurate and reliable segmentation results. The proposed approach not only streamlines the diagnostic process but also has the potential to uncover subtle abnormalities that may be overlooked by manual inspection. In conclusion, the integration of deep learning into the segmentation of brain MRI scans presents a promising avenue for enhancing neurological diagnosis. The automated and precise delineation of brain structures contributes to the efficiency and accuracy of diagnostic workflows, ultimately improving patient outcomes and facilitating timely interventions in the realm of neurological disorders.

Brain Volumes in Opsoclonus-Myoclonus Ataxia Syndrome: A Longitudinal Study.

Little is known about the longitudinal trajectory of brain growth in children with opsoclonus-myoclonus ataxia syndrome. We performed a longitudinal evaluation of brain volumes in pediatric opsoclonus-myoclonus ataxia syndrome patients compared with age- and sex-matched healthy children. This longitudinal case-control study included brain magnetic resonance imaging (MRI) scans from consecutive pediatric opsoclonus-myoclonus ataxia syndrome patients (2009-2020) and age- and sex-matched healthy control children. FreeSurfer analysis provided automatic volumetry of the brain. Paired t tests were performed on the curvature of growth trajectories, with Bonferroni correction. A total of 14 opsoclonus-myoclonus ataxia syndrome patients (12 female) and 474 healthy control children (406 female) were included. Curvature of the growth trajectories of the cerebral white and gray matter, cerebellar white and gray matter, and brainstem differed significantly between opsoclonus-myoclonus ataxia syndrome patients and healthy control children (cerebral white matter, P = .01; cerebral gray matter, P = .01; cerebellar white matter, P < .001; cerebellar gray matter, P = .049; brainstem, P < .01). We found abnormal brain maturation in the supratentorial brain, brainstem, and cerebellum in children with opsoclonus-myoclonus ataxia syndrome.

Post-Operative Brain MRI Resection Cavity Segmentation Model and Follow-Up Treatment Assistance

Post-operative brain magnetic resonance imaging (MRI) segmentation is inherently challenging due to the diverse patterns in brain tissue, which makes it difficult to accurately identify resected areas. Therefore, there is a crucial need for a precise segmentation model. Due to the scarcity of post-operative brain MRI scans, it is not feasible to use complex models that require a large amount of training data. This paper introduces an innovative approach for accurately segmenting and quantifying post-operative brain resection cavities in MRI scans. The proposed model, named Attention-Enhanced VGG-U-Net, integrates VGG16 initial weights in the encoder section and incorporates a self-attention module in the decoder, offering improved accuracy for postoperative brain MRI segmentation. The attention mechanism enhances its accuracy by concentrating on a specific area of interest. The VGG16 model is comparatively lightweight, has pre-trained weights, and allows the model to extract incredibly detailed information from the input. The model is trained on publicly available post-operative brain MRI data and achieved a Dice coefficient value of 0.893. The model is then assessed using a clinical dataset of postoperative brain MRIs. The model facilitates the quantification of the resected regions and enables comparisons with each brain region based on pre-operative images. The capabilities of the model assist radiologists in evaluating surgical success and directing follow-up procedures.

Creation of a Simulated Sequence of Dynamic Susceptibility Contrast—Magnetic Resonance Imaging Brain Scans as a Tool to Verify the Quality of Methods for Diagnosing Diseases Affecting Brain Tissue Perfusion

DSC-MRI examination is one of the best methods of diagnosis for brain diseases. For this purpose, the so-called perfusion parameters are defined, of which the most used are CBF, CBV, and MTT. There are many approaches to determining these parameters, but regardless of the approach, there is a problem with the quality assessment of methods. To solve this problem, this article proposes virtual DSC-MRI brain examination, which consists of two steps. The first step is to create curves that are typical for DSC-MRI studies and characteristic of different brain regions, i.e., the gray and white matter, and blood vessels. Using perfusion descriptors, the curves are classified into three sets, which give us the model curves for each of the three regions. The curves corresponding to the perfusion of different regions of the brain in a suitable arrangement (consistent with human anatomy) form a model of the DSC-MRI examination. In the created model, one knows in advance the values of the complex perfusion parameters, as well as basic perfusion descriptors. The shown model study can be disturbed in a controlled manner—not only by adding noise, but also by determining the location of disturbances that are characteristic of specific brain diseases.

Isolated Traumatic Subarachnoid Hemorrhage on Head Computed Tomography Scan May Not Be Isolated: A TRACK-TBI Study.

Isolated traumatic subarachnoid hemorrhage (tSAH) after traumatic brain injury (TBI) on head computed tomography (CT) scan is often regarded as a "mild" injury, with reduced need for additional workup. However, tSAH is also a predictor of incomplete recovery and unfavorable outcome. This study aimed to evaluate the characteristics of CT-occult intracranial injuries on brain magnetic resonance imaging (MRI) scan in TBI patients with emergency department (ED) arrival Glasgow Coma Scale (GCS) score 13-15 and isolated tSAH on CT. The prospective, 18-center Transforming Research and Clinical Knowledge in Traumatic Brain Injury Study (TRACK-TBI; enrollment years 2014-2019) enrolled participants who presented to the ED and received a clinically-indicated head CT within 24 hours (h) of TBI. A subset of TRACK-TBI participants underwent venipuncture within 24h for plasma glial fibrillary acidic protein (GFAP) analysis, and research MRI at 2-weeks post-injury. In the current study, TRACK-TBI participants aged ≥17 years with ED arrival GCS 13-15, isolated tSAH on initial head CT, plasma GFAP level, and 2-week MRI data were analyzed. In 57 participants, median age was 46.0 years [quartile 1 to 3 (Q1-Q3): 34-57] and 52.6% were male. At ED disposition, 12.3% were discharged home, 61.4% were admitted to hospital ward, and 26.3% to intensive care unit. MRI identified CT-occult traumatic intracranial lesions in 45.6% (26 of 57 participants; 1 additional lesion type: 31.6%; 2 additional lesion types: 14.0%); of these 26 participants with CT-occult intracranial lesions, 65.4% had axonal injury, 42.3% had subdural hematoma, and 23.1% had intracerebral contusion. GFAP levels were higher in participants with CT-occult MRI lesions compared to without (median: 630.6 pg/ml, Q1-Q3: [172.4-941.2] vs. 226.4 [105.8-436.1], p=0.049), and were associated with axonal injury (no: median 226.7 pg/ml [109.6-435.1], yes: 828.6 pg/ml [204.0-1194.3], p=0.009). Our results indicate that isolated tSAH on head CT is often not the sole intracranial traumatic injury in GCS 13-15 TBI. Forty-six percent of patients in our cohort (26 of 57 participants) had additional CT-occult traumatic lesions on MRI. Plasma GFAP may be an important biomarker for the identification of additional CT-occult injuries, including axonal injury. These findings should be interpreted cautiously given our modest sample size and await validation from larger studies.

Quantitative T2 mapping-based longitudinal assessment of brain injury and therapeutic rescue in the rat following acute organophosphate intoxication

Acute intoxication with organophosphate (OP) cholinesterase inhibitors poses a significant public health risk. While currently approved medical countermeasures can improve survival rates, they often fail to prevent chronic neurological damage. Therefore, there is need to develop effective therapies and quantitative metrics for assessing OP-induced brain injury and its rescue by these therapies. In this study we used a rat model of acute intoxication with the OP, diisopropylfluorophosphate (DFP), to test the hypothesis that T2 measures obtained from brain magnetic resonance imaging (MRI) scans provide quantitative metrics of brain injury and therapeutic efficacy. Adult male Sprague Dawley rats were imaged on a 7T MRI scanner at 3, 7 and 28 days post-exposure to DFP or vehicle (VEH) with or without treatment with the standard of care antiseizure drug, midazolam (MDZ); a novel antiseizure medication, allopregnanolone (ALLO); or combination therapy with MDZ and ALLO (DUO). Our results show that mean T2 values in DFP-exposed animals were: (1) higher than VEH in all volumes of interest (VOIs) at day 3; (2) decreased with time; and (3) decreased in the thalamus at day 28. Treatment with ALLO or DUO, but not MDZ alone, significantly decreased mean T2 values relative to untreated DFP animals in the piriform cortex at day 3. On day 28, the DUO group showed the most favorable T2 characteristics. This study supports the utility of T2 mapping for longitudinally monitoring brain injury and highlights the therapeutic potential of ALLO as an adjunct therapy to mitigate chronic morbidity associated with acute OP intoxication.

Unraveling the gut-brain connection: The association of microbiota-linked structural brain biomarkers with behavior and mental health.

The gut microbiota can influence human behavior. However, due to the massive multiple-testing problem, research into the relationship between microbiome ecosystems and the human brain faces drawbacks. This problem arises when attempting to correlate thousands of gut bacteria with thousands of brain voxels. We performed brain magnetic resonance imaging (MRI) scans on 133 participants and applied machine-learning algorithms (Ridge regressions) combined with permutation tests. Using this approach, we were able to correlate specific gut bacterial families with brain MRI signals, circumventing the difficulties of massive multiple testing while considering sex, age, and body mass index as confounding factors. The relative abundance (RA) of the Selenomonadaceae, Clostridiaceae, and Veillonellaceae families in the gut was associated with altered cerebellar, visual, and frontal T2-mapping and diffusion tensor imaging measures. Conversely, decreased relative abundance of the Eubacteriaceae family was also linked to T2-mapping values in the cerebellum. Significantly, the brain regions associated with the gut microbiome were also correlated with depressive symptoms and attentional deficits. Our analytical strategy offers a promising approach for identifying potential brain biomarkers influenced by gut microbiota. By gathering a deeper understanding of the microbiota-brain connection, we can gain insights into the underlying mechanisms and potentially develop targeted interventions to mitigate the detrimental effects of dysbiosis on brain function and mental health.

Recurrence of cerebrospinal fluid-venous fistulas at different spinal levels following transvenous embolisation or blood/fibrin glue patching.

This study presents cases of recurrent cerebrospinal fluid-venous fistulas (CVFs) de novo at a different spinal level following successful treatment of initial CVFs. The aim was to highlight this rarely described phenomenon and report the clinical and imaging features after initial treatment, providing insights into the dynamics of recurrent CVFs. Understanding the course of CVFs post-treatment is crucial for optimising patient management, especially when symptoms persist or recur. We performed a retrospective chart review of all patients with recurrent CVFs at a different level after treatment of their initial CVF at our institution. Clinical and imaging records were reviewed and summarised, including Bern score features on brain magnetic resonance imaging (MRI) before and after treatment. Four patients with five recurrent CVFs were identified. Recurrent or persistent symptoms encouraged subsequent brain MRI scans, which revealed different outcomes: i.e. persistence, or improvement, or complete resolution of abnormal findings. Initial positive responses included improvement of the pachymeningeal enhancement and venous sinus distension. These improvements were reversed when recurrent symptoms arose, which was also correlated with changes in the Bern score. Recognising the factors of CVF recurrence is crucial for comprehensive management. This study underlines the significance of repeated evaluation of persistent or recurring symptoms of CSF leak after treatment for CVFs.

Synthetic Brain Images: Bridging the Gap in Brain Mapping With Generative Adversarial Model

Magnetic Resonance Imaging (MRI) is a vital modality for gaining precise anatomical information, and it plays a significant role in medical imaging for diagnosis and therapy planning. Image synthesis problems have seen a revolution in recent years due to the introduction of deep learning techniques, specifically Generative Adversarial Networks (GANs). This work investigates the use of Deep Convolutional Generative Adversarial Networks (DCGAN) for producing high-fidelity and realistic MRI image slices. The suggested approach uses a dataset with a variety of brain MRI scans to train a DCGAN architecture. While the discriminator network discerns between created and real slices, the generator network learns to synthesise realistic MRI image slices. The generator refines its capacity to generate slices that closely mimic real MRI data through an adversarial training approach. The outcomes demonstrate that the DCGAN promise for a range of uses in medical imaging research, since they show that it can effectively produce MRI image slices if we train them for a consequent number of epochs. This work adds to the expanding corpus of research on the application of deep learning techniques for medical image synthesis. The slices that are could be produced possess the capability to enhance datasets, provide data augmentation in the training of deep learning models, as well as a number of functions are made available to make MRI data cleaning easier, and a three ready to use and clean dataset on the major anatomical plans.

Based on neural network cascade abnormal texture information dissemination of classification of patients with schizophrenia and depression

This study used MRI brain image segmentation to identify novel magnetic resonance imaging (MRI) biomarkers to distinguish patients with schizophrenia (SCZ), major depressive disorder (MD), and healthy control (HC). Brain texture measurements, including entropy and contrast, were calculated to capture variability in adjacent MRI voxel intensity. These measures are then applied to group classification in deep learning techniques and combined with hierarchical correlations to locate results. Texture feature maps were extracted from segmented brain MRI scans of 141 patients with schizophrenia (SCZ), 103 patients with major depressive disorder (MD) and 238 healthy controls (HC). Gray scale coassociation matrix (GLCM) is a monomer matrix calculated in a voxel cube. Deep learning methods were evaluated to determine the application capability of texture feature mapping in binary classification (SCZ vs. HC, MD vs. HC, SCZ vs. MD). The method is implemented by repeated nesting and cross-validation for feature selection. Regions that show the highest correlation (positive or negative). In this study, the authors successfully classified SCZ, MD and HC. This suggests that texture analysis can be used as an effective feature extraction method to distinguish different disease states. Compared with other methods, texture analysis can capture richer image information and improve classification accuracy in some cases. The classification accuracy of SCZ and HC, MD and HC, SCZ and MD reached 84.6%, 86.4% and 76.21%, respectively. Among them, SCZ and HC are the most significant features with high sensitivity and specificity.

Stereotactic anatomy of the third ventricle

PurposeEndoscopic third ventriculostomy (ETV) is a surgical procedure that can lead to complications and requires detailed preoperative planning. This study aimed to provide a more accurate understanding of the anatomy of the third ventricle and the location of important structures to improve the safety and success of ETV.MethodsWe measured the stereotactic coordinates of six points of interest relative to a predefined stereotactic reference point in 23 cadaver brain hemi-sections, 200 normal brain magnetic resonance imaging (MRI) scans, and 24 hydrocephalic brain MRI scans. The measurements were statistically analyzed, and comparisons were made.ResultsWe found some statistically significant differences between genders in MRIs from healthy subjects. We also found statistically significant differences between MRIs from healthy subjects and both cadaver brains and MRIs with hydrocephalus, though their magnitude is very small and not clinically relevant. Some stereotactic points were more posteriorly and inferiorly located in cadaver brains, particularly the infundibular recess and the basilar artery. It was found that all stereotactic points studied were more posteriorly located in brains with hydrocephalus.ConclusionThe study describes periventricular structures in cadaver brains and MRI scans from healthy and hydrocephalic subjects, which can guide neurosurgeons in planning surgical approaches to the third ventricle. Overall, the study contributes to understanding ETV and provides insights for improving its safety and efficacy. The findings also support that practicing on cadaveric brains can still provide valuable information and is valid for study and training of neurosurgeons unfamiliar with the ETV technique.

White and Gray Matter Changes are Associated With Neurocognitive Decline in HIV Infection.

To investigate the relationship between neurocognitive deficits and structural changes on brain magnetic resonance imaging in people living with HIV (PLWH) with good virological control on combination antiretroviral therapy, compared with socioeconomically matched control participants recruited from the same communities. Brain magnetic resonance imaging scans, and clinical and neuropsychological data were obtained from virologically controlled PLWH (viral load of <50 c/mL and at least 1 year of combination antiretroviral therapy) and socioeconomically matched control participants. Magnetic resonance imaging was carried out on 3 T scanner with 8-channel head coils and segmented using Classification using Derivative-based Features. Multiple regression analysis was performed to examine the association between brain volume and various clinical and neuropsychiatric parameters adjusting for age, race, and sex. To evaluate longitudinal changes in brain volumes, a random coefficient model was used to evaluate the changes over time (age) adjusting for sex and race. The cross-sectional study included 164 PLWH and 51 controls, and the longitudinal study included 68 PLWH and 20 controls with 2 or more visits (mean 2.2 years, range 0.8-5.1 years). Gray matter (GM) atrophy rate was significantly higher in PLWH compared with control participants, and importantly, the GM and global atrophy was associated with the various neuropsychological domain scores. Higher volume of white matter hyperintensities were associated with increased atherosclerotic cardiovascular disease risk score, and decreased executive functioning and memory domain scores in PLWH. These findings suggest ongoing neurological damage even in virologically controlled participants, with significant implications for clinical management of PLWH. ANN NEUROL 2024.

Artificial Intelligence Assistive Software Tool for Automated Detection and Quantification of Amyloid-Related Imaging Abnormalities

Amyloid-related imaging abnormalities (ARIA) are brain magnetic resonance imaging (MRI) findings associated with the use of amyloid-β-directed monoclonal antibody therapies in Alzheimer disease (AD). ARIA monitoring is important to inform treatment dosing decisions and might be improved through assistive software. To assess the clinical performance of an artificial intelligence (AI)-based software tool for assisting radiological interpretation of brain MRI scans in patients monitored for ARIA. This diagnostic study used a multiple-reader multiple-case design to evaluate the diagnostic performance of radiologists assisted by the software vs unassisted. The study enrolled 16 US Board of Radiology-certified radiologists to perform radiological reading with (assisted) and without the software (unassisted). The study encompassed 199 retrospective cases, where each case consisted of a predosing baseline and a postdosing follow-up MRI of patients from aducanumab clinical trials PRIME, EMERGE, and ENGAGE. Statistical analysis was performed from April to July 2023. Use of icobrain aria, an AI-based assistive software for ARIA detection and quantification. Coprimary end points were the difference in diagnostic accuracy between assisted and unassisted detection of ARIA-E (edema and/or sulcal effusion) and ARIA-H (microhemorrhage and/or superficial siderosis) independently, assessed with the area under the receiver operating characteristic curve (AUC). Among the 199 participants included in this study of radiological reading performance, mean (SD) age was 70.4 (7.2) years; 105 (52.8%) were female; 23 (11.6%) were Asian, 1 (0.5%) was Black, 157 (78.9%) were White, and 18 (9.0%) were other or unreported race and ethnicity. Among the 16 radiological readers included, 2 were specialized neuroradiologists (12.5%), 11 were male individuals (68.8%), 7 were individuals working in academic hospitals (43.8%), and they had a mean (SD) of 9.5 (5.1) years of experience. Radiologists assisted by the software were significantly superior in detecting ARIA than unassisted radiologists, with a mean assisted AUC of 0.87 (95% CI, 0.84-0.91) for ARIA-E detection (AUC improvement of 0.05 [95% CI, 0.02-0.08]; P = .001]) and 0.83 (95% CI, 0.78-0.87) for ARIA-H detection (AUC improvement of 0.04 [95% CI, 0.02-0.07]; P = .001). Sensitivity was significantly higher in assisted reading compared with unassisted reading (87% vs 71% for ARIA-E detection; 79% vs 69% for ARIA-H detection), while specificity remained above 80% for the detection of both ARIA types. This diagnostic study found that radiological reading performance for ARIA detection and diagnosis was significantly better when using the AI-based assistive software. Hence, the software has the potential to be a clinically important tool to improve safety monitoring and management of patients with AD treated with amyloid-β-directed monoclonal antibody therapies.