T1-weighted Brain Magnetic Resonance Imaging Research Articles

Structural covariance alterations reveal motor damage in periventricular leukomalacia

Abstract Periventricular leukomalacia is a common neuroimaging finding in patients with spastic cerebral palsy. Myelin damage disrupts neuronal connectivity. However, specific alterations in the gray matter structure and their impact on the whole brain remain unclear, particularly when differentiating between preterm and full-term periventricular leukomalacia. This study investigated the gray matter network alterations following early white matter injury in infants and young children. High-resolution T1-weighted 3T brain magnetic resonance imaging, clinical data, and motor function scores were collected from 42 children with periventricular leukomalacia and 38 age- and sex-matched healthy controls. Based on gestational age, the periventricular leukomalacia group was stratified into preterm (n = 27) and full-term (n = 15) groups. Voxel-based morphometry was used to analyze whole-brain structural metrics, and motor-related regions were selected as nodes for network construction. Structural covariance analysis was used to quantify the strength of the structural connections between gray matter regions, and graph theory metrics were used to assess network properties. Motor assessments included gross and fine motor skills, and their associations with brain regions were analyzed. Both preterm and full-term periventricular leukomalacia groups exhibited abnormal motor networks. Preterm periventricular leukomalacia showed more extensive central gray matter nuclei atrophy, whereas full-term periventricular leukomalacia was predominantly localized to the motor cortex. Children with periventricular leukomalacia displayed decreased connectivity between the central gray matter nuclei and other regions, coupled with increased connectivity between the motor cortex and cerebellar hemispheres. Thalamic volume correlated with gross motor scores in preterm infants. These findings suggest that ischemic-hypoxic injury disrupts motor gray matter networks, with preterm infants being more severely affected. This study highlights the potential of structural covariance patterns for monitoring brain development and advancing our understanding of aberrant brain development in children with periventricular leukomalacia.

Association of open skill exercise and long-chain polyunsaturated fatty acid intake with brain volume changes among older community-dwelling Japanese individuals

Considering that a multifactorial lifestyle approach may prove more effective than a single factor approach to improve or maintain brain health, we evaluated the association of exercise (open skill exercise [OSE] or closed skill exercise [CSE]) combined with long-chain polyunsaturated fatty acid (LCPUFAs) (docosahexaenoic acid [C22:6n-3, DHA], eicosapentaenoic acid [C20:5n-3, EPA], and arachidonic acid [C20:4n-6, ARA]) intake with brain atrophy among older Japanese individuals (n = 795, aged 60–88 years) without a self-reported history of dementia based on the datasets of a two-year longitudinal study. Brain volumes were measured using three-dimensional T1-weighted brain magnetic resonance imaging for follow-up periods of two years. The associations between multivariate-adjusted changes in brain volumes and OSE or CSE frequency (≥ once/month and < once/month) along with LCPUFA intake (≥ median and < median) at the baseline were assessed using a general linear model. Subgroup analysis was performed by restricting DHA and EPA intakes (n = 263; median, 323 mg/d), which represented levels similar to those in countries with low fish consumption. Higher OSE frequencies, ARA intakes, and their combination were inversely associated with decreases in total gray matter and frontal cortex volumes. In subgroup analysis, a combination of higher OSE frequencies and DHA intakes was also associated with a smaller decrease in total gray matter volume. Overall, our findings suggest that regular OSE engagement and appropriate LCPUFA intake may contribute to preventing brain volume decreases in older individuals.

Dominant hippocampus segmentation with brain atrophy analysis-based AD subtype classification using KLW-RU-Net and T1FL

A progressive neurodegenerative disorder that leads to cognitive decline and memory loss is known as Alzheimer's Disease (AD). Researchers propose an integrated framework centered on brain atrophy analysis and hippocampus segmentation to predict AD and its subtypes. This approach augments the accurate prediction of AD subtype classification, thus addressing the lack of emphasis in prevailing works. Primarily, the T1-weighted brain Magnetic Resonance Imaging (MRI) images from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database undergo preprocessing. After that, for spatiotemporal evaluation, region segmentation, tau accumulation analysis, and Feature Extraction (FE), the image is processed. By employing the Fisher-Kolmogorov (FK) model, the essential parameters are extracted in spatio-temporal evaluation. In the meantime, for tissue segmentation, the Knowledge Partitioned Clustering (KPC) is utilized, and for hippocampus segmentation, the Kullback-Leibler Within-layer Regularized UNet (KLW-RU-Net) is employed. Moreover, by binarizing the image, the tau accumulation is analyzed; in addition, Principal Component Analysis (PCA) is utilized for FE. After PCA, the Adaptive Step-sized Gauss Rabbits Optimization (ASGRO) is employed for feature selection. Subsequently, to classify AD, the Dilated Derivative Sigmoid-Weighted DenseNet (DD-SWDN) is employed. Additionally, by employing Type-1 Fuzzy Logic (T1FL), the subtype of AD is classified, thus achieving higher accuracy for AD subtype classification. Therefore, as per these outcomes, the proposed work performs better than the other conventional approaches.

A 2.5D Self-Training Strategy for Carotid Artery Segmentation in T1-Weighted Brain Magnetic Resonance Images.

Precise annotations for large medical image datasets can be time-consuming. Additionally, when dealing with volumetric regions of interest, it is typical to apply segmentation techniques on 2D slices, compromising important information for accurately segmenting 3D structures. This study presents a deep learning pipeline that simultaneously tackles both challenges. Firstly, to streamline the annotation process, we employ a semi-automatic segmentation approach using bounding boxes as masks, which is less time-consuming than pixel-level delineation. Subsequently, recursive self-training is utilized to enhance annotation quality. Finally, a 2.5D segmentation technique is adopted, wherein a slice of a volumetric image is segmented using a pseudo-RGB image. The pipeline was applied to segment the carotid artery tree in T1-weighted brain magnetic resonance images. Utilizing 42 volumetric non-contrast T1-weighted brain scans from four datasets, we delineated bounding boxes around the carotid arteries in the axial slices. Pseudo-RGB images were generated from these slices, and recursive segmentation was conducted using a Res-Unet-based neural network architecture. The model's performance was tested on a separate dataset, with ground truth annotations provided by a radiologist. After recursive training, we achieved an Intersection over Union (IoU) score of (0.68 ± 0.08) on the unseen dataset, demonstrating commendable qualitative results.

Brain artery diameters and risk of dementia and stroke.

We tested the association of brain artery diameters with dementia and stroke risk in three distinct population-based studies using conventional T2-weighted brain magnetic resonance imaging (MRI) images. We included 8420 adults> 40 years old from three longitudinal population-based studies with brain MRI scans. We estimated and meta-analyzed the hazard ratios (HRs) of the brain and carotids and basilar diameters associated with dementia and stroke. Overall and carotid artery diameters>95th percentile increased the risk for dementia by 1.74 (95% confidence interval [CI], 1.13-2.68) and 1.48 (95% CI, 1.12-1.96) fold, respectively. For stroke, meta-analyses yielded HRs of 1.59 (95% CI, 1.04-2.42) for overall arteries and 2.11 (95% CI, 1.45-3.08) for basilar artery diameters> 95th percentile. Individuals with dilated brain arteries are at higher risk for dementia and stroke, across distinct populations. Our findings underline the potential value of T2-weighted brain MRI-based brain diameter assessment in estimating the risk of dementia and stroke.

Regional decreases of cortical thickness in major depressive disorder and their correlation with illness duration: a case-control study.

Alterations in brain structure and function in major depressive disorder (MDD) have been identified in a number of studies, but findings regarding cortical thickness were various and inconsistent. Our current study aims to explore the differences in cortical thickness between individuals with MDD and healthy controls (HC) in a Chinese population. We investigated T1-weighted brain magnetic resonance imaging data from 61 participants (31 MDD and 30 HC). The cortical thickness between the two groups and analyzed correlations between cortical thickness and demographic variables in the MDD group for regions with significant between-group differences were conducted. Compared with the HC group, patients with MDD had significantly decreased cortical thickness, in left pars triangularis, left pars orbitalis, left rostral middle frontal gyrus, left supramarginal gyrus, right parahippocampal gyrus, right lingual gyrus, right fusiform and right inferior parietal gyrus. The cortical thickness of left rostral middle frontal gyrus was negatively correlated (r = -0.47, p = 0.028) with the illness duration in patients with MDD. Our study distinguished that cortical thickness decreases in numerous brain regions both in the left and right hemisphere in individuals with MDD, and the negative correlation between the cortical thickness of left rostral middle frontal gyrus illness duration. Our current findings are valuable in providing neural markers to identify MDD and understanding the potential pathophysiology of mood disorders.

Normal diameter of the optic nerve using magnetic resonance imaging: A retrospective Nigerian study.

The variations in the diameter of the optic nerve (ON) are important clinically in the diagnosis of conditions associated with the ON such as raised intracranial pressure, meningioma, optic neuritis, and Grave's orbitopathy. This study determined the normal diameters of the ON in adult Nigerians seen in a Hospital in Delta State. Axial T1-weighted brain magnetic resonance imaging images of 150 patients (75 males and 75 females) aged ≥20 years were retrieved from the hospital's radiological database and retrospectively used to evaluate the diameter of the ON on axial and coronal sections. The data were analyzed and summarized using descriptive statistics. The mean diameters were compared based on gender, side, and age groups and correlated with age using inferential statistics. The significance level was considered at 5%. The diameter of the ON measured 0.45 ± 0.07 cm on the coronal section, besides 0.50 ± 0.07 cm, and 0.46 ± 0.06 cm at 0.3 cm and 0.8 cm from the posterior pole of the globe, respectively, on the axial slices. The diameters were significantly larger in males than in females (P < 0.05) and were symmetrical. However, they lacked significant association with age (P > 0.05). The three diameters measured had a significant positive correlation with each other (P < 0.05). The study provides a normal range of ON diameter in the study center to aid in the diagnosis of raised intracranial pressure and pathologies involving the nerve and its sheath.

Adversarial Learning for MRI Reconstruction and Classification of Cognitively Impaired Individuals.

Game theory-inspired deep learning using a generative adversarial network provides an environment to competitively interact and accomplish a goal. In the context of medical imaging, most work has focused on achieving single tasks such as improving image resolution, segmenting images, and correcting motion artifacts. We developed a dual-objective adversarial learning framework that simultaneously 1) reconstructs higher quality brain magnetic resonance images (MRIs) that 2) retain disease-specific imaging features critical for predicting progression from mild cognitive impairment (MCI) to Alzheimer's disease (AD). We obtained 3-Tesla, T1-weighted brain MRIs of participants from the Alzheimer's Disease Neuroimaging Initiative (ADNI, N=342) and the National Alzheimer's Coordinating Center (NACC, N = 190) datasets. We simulated MRIs with missing data by removing 50% of sagittal slices from the original scans (i.e., diced scans). The generator was trained to reconstruct brain MRIs using the diced scans as input. We introduced a classifier into the GAN architecture to discriminate between stable (i.e., sMCI) and progressive MCI (i.e., pMCI) based on the generated images to facilitate encoding of disease-related information during reconstruction. The framework was trained using ADNI data and externally validated on NACC data. In the NACC cohort, generated images had better image quality than the diced scans (Structural similarity (SSIM) index: 0.553 ± 0.116 versus 0.348 ± 0.108). Furthermore, a classifier utilizing the generated images distinguished pMCI from sMCI more accurately than with the diced scans (F1-score: 0.634 ± 0.019 versus 0.573 ± 0.028). Competitive deep learning has potential to facilitate disease-oriented image reconstruction in those at risk of developing Alzheimer's disease.

A Case-Control Clinical Trial on a Deep Learning-Based Classification System for Diagnosis of Amyloid-Positive Alzheimer's Disease.

A deep learning-based classification system (DLCS) which uses structural brain magnetic resonance imaging (MRI) to diagnose Alzheimer's disease (AD) was developed in a previous recent study. Here, we evaluate its performance by conducting a single-center, case-control clinical trial. We retrospectively collected T1-weighted brain MRI scans of subjects who had an accompanying measure of amyloid-beta (Aβ) positivity based on a 18F-florbetaben positron emission tomography scan. The dataset included 188 Aβ-positive patients with mild cognitive impairment or dementia due to AD, and 162 Aβ-negative controls with normal cognition. We calculated the sensitivity, specificity, positive predictive value, negative predictive value, and area under the receiver operating characteristic curve (AUC) of the DLCS in the classification of Aβ-positive AD patients from Aβ-negative controls. The DLCS showed excellent performance, with sensitivity, specificity, positive predictive value, negative predictive value, and AUC of 85.6% (95% confidence interval [CI], 79.8-90.0), 90.1% (95% CI, 84.5-94.2), 91.0% (95% CI, 86.3-94.1), 84.4% (95% CI, 79.2-88.5), and 0.937 (95% CI, 0.911-0.963), respectively. The DLCS shows promise in clinical settings where it could be routinely applied to MRI scans regardless of original scan purpose to improve the early detection of AD.

Automatic motion artefact detection in brain T1-weighted magnetic resonance images from a clinical data warehouse using synthetic data

Containing the medical data of millions of patients, clinical data warehouses (CDWs) represent a great opportunity to develop computational tools. Magnetic resonance images (MRIs) are particularly sensitive to patient movements during image acquisition, which will result in artefacts (blurring, ghosting and ringing) in the reconstructed image. As a result, a significant number of MRIs in CDWs are corrupted by these artefacts and may be unusable. Since their manual detection is impossible due to the large number of scans, it is necessary to develop tools to automatically exclude (or at least identify) images with motion in order to fully exploit CDWs. In this paper, we propose a novel transfer learning method from research to clinical data for the automatic detection of motion in 3D T1-weighted brain MRI. The method consists of two steps: a pre-training on research data using synthetic motion, followed by a fine-tuning step to generalise our pre-trained model to clinical data, relying on the labelling of 4045 images. The objectives were both (1) to be able to exclude images with severe motion, (2) to detect mild motion artefacts. Our approach achieved excellent accuracy for the first objective with a balanced accuracy nearly similar to that of the annotators (balanced accuracy>80 %). However, for the second objective, the performance was weaker and substantially lower than that of human raters. Overall, our framework will be useful to take advantage of CDWs in medical imaging and highlight the importance of a clinical validation of models trained on research data.

Extreme deviations from the normative model reveal cortical heterogeneity and associations with negative symptom severity in first-episode psychosis from the OPTiMiSE and GAP studies

There is currently no quantifiable method to predict long-term clinical outcomes in patients presenting with a first episode of psychosis. A major barrier to developing useful markers for this is biological heterogeneity, where many different pathological mechanisms may underly the same set of symptoms in different individuals. Normative modelling has been used to quantify this heterogeneity in established psychotic disorders by identifying regions of the cortex which are thinner than expected based on a normative healthy population range. These brain atypicalities are measured at the individual level and therefore potentially useful in a clinical setting. However, it is still unclear whether alterations in individual brain structure can be detected at the time of the first psychotic episode, and whether they are associated with subsequent clinical outcomes. We applied normative modelling of cortical thickness to a sample of first-episode psychosis patients, with the aim of quantifying heterogeneity and to use any pattern of cortical atypicality to predict symptoms and response to antipsychotic medication at timepoints from baseline up to 95 weeks (median follow-ups = 4). T1-weighted brain magnetic resonance images from the GAP and OPTiMiSE samples were processed with Freesurfer V6.0.0 yielding 148 cortical thickness features. An existing normative model of cortical thickness (n = 37,126) was adapted to integrate data from each clinical site and account for effects of gender and site. Our test sample consisted of control participants (n = 149, mean age = 26, SD = 6.7) and patient data (n = 295, mean age = 26, SD = 6.7), this sample was used for estimating deviations from the normative model and subsequent statistical analysis. For each individual, the 148 cortical thickness features were mapped to centiles of the normative distribution and converted to z-scores reflecting the distance from the population mean. Individual cortical thickness metrics of +/– 2.6 standard deviations from the mean were considered extreme deviations from the norm. We found that no more than 6.4% of psychosis patients had extreme deviations in a single brain region (regional overlap) demonstrating a high degree of heterogeneity. Mann-Whitney U tests were run on z-scores for each region and significantly lower z-scores were observed in FEP patients in the frontal, temporal, parietal and occipital lobes. Finally, linear mixed-effects modelling showed that negative deviations in cortical thickness in parietal and temporal regions at baseline are related to more severe negative symptoms over the medium-term. This study shows that even at the early stage of symptom onset normative modelling provides a framework to identify individualised cortical markers which can be used for early personalised intervention and stratification.

A Case with Anti-ganglioside Antibodies Showing Multiple Cranial Nerve Palsies Detected on Gadolinium-enhanced Magnetic Resonance Imaging.

The anti-GQ1b IgG antibody is often accompanied by other anti-ganglioside antibodies, which induces various neurological symptoms. We herein report a patient with anti-ganglioside antibodies, including anti-GQ1b IgG and anti-GT1a IgG antibodies, showing bilateral ophthalmoplegia, facial nerve palsies, dysarthria, dysphagia, dysesthesia in both hands, and enhancement of the bilateral oculomotor, abducens, and facial nerves on gadolinium (Gd)-enhanced T1-weighted brain magnetic resonance imaging (MRI). He was first treated with intravenous immunoglobulin, which improved ophthalmoplegia, bulbar palsies, and dysesthesia of hands, but the facial nerve palsies worsened, and Gd enhancement of the brain nerves persisted. High-dose methylprednisolone therapy subsequently improved the facial nerve palsies and Gd enhancement of the cranial nerves. This is the first case with anti-ganglioside antibodies presenting with multiple cranial nerve palsies that was followed to track the changes in the Gd enhancement of cranial nerves on MRI.

A three-dimensional deep learning model for inter-site harmonization of structural MR images of the brain: Extensive validation with a multicenter dataset

In multicenter MRI studies, pooling the imaging data can introduce site-related variabilities and can therefore bias the subsequent analyses. To harmonize the intensity distributions of brain MR images in a multicenter dataset, unsupervised deep learning methods can be employed. Here, we developed a model based on cycle-consistent adversarial networks for the harmonization of T1-weighted brain MR images. In contrast to previous works, it was designed to process three-dimensional whole-brain images in a stable manner while optimizing computation resources. Using six different MRI datasets for healthy adults (n=1525 in total) with different acquisition parameters, we tested the model in (i) three pairwise harmonizations with site effects of various sizes, (ii) an overall harmonization of the six datasets with different age distributions, and (iii) a traveling-subject dataset. Our results for intensity distributions, brain volumes, image quality metrics and radiomic features indicated that the MRI characteristics at the various sites had been effectively homogenized. Next, brain age prediction experiments and the observed correlation between the gray-matter volume and age showed that thanks to an appropriate training strategy and despite biological differences between the dataset populations, the model reinforced biological patterns. Furthermore, radiologic analyses of the harmonized images attested to the conservation of the radiologic information in the original images. The robustness of the harmonization model (as judged with various datasets and metrics) demonstrates its potential for application in retrospective multicenter studies.

Improved segmentation of basal ganglia from MR images using convolutional neural network with crossover-typed skip connection.

Accurate and automatic segmentation of basal ganglia from magnetic resonance (MR) images is important for diagnosis and treatment of various brain disorders. However, the basal ganglia segmentation is a challenging task because of the class imbalance and the unclear boundaries among basal ganglia anatomical structures. Thus, we aim to present an encoder-decoder convolutional neural network (CNN)-based method for improved segmentation of basal ganglia by focusing on skip connections that determine the segmentation performance of encoder-decoder CNNs. We also aim to reveal the effect of skip connections on the segmentation of basal ganglia with unclear boundaries. We used the encoder-decoder CNNs with the following five patterns of skip connections: without skip connection, with full-resolution horizontal skip connection, with horizontal skip connections, with vertical skip connections, and with crossover-typed skip connections (the proposed method). We compared and evaluated the performance of the CNNs in the experiment of basal ganglia segmentation using T1-weighted MR brain images of 79 patients. The experimental results showed that the skip connections at each scale level help CNNs to acquire multi-scale image features, the vertical skip connections contribute on acquiring finer image features for segmentation of smaller anatomical structures with more blurred boundaries, and the crossover-typed skip connections, a combination of horizontal and vertical skip connections, provided better segmentation accuracy. This paper investigated the effect of skip connections on the basal ganglia segmentation and revealed the crossover-typed skip connections might be effective for improving the segmentation of basal ganglia with the class imbalance and the unclear boundaries.

Efficient organisation of the contralateral hemisphere connectome is associated with improvement in intelligence quotient after paediatric epilepsy surgery

ObjectiveAims of epilepsy surgery in childhood include optimising seizure control and facilitating cognitive development. Predicting which children will improve cognitively is challenging. We investigated the association of the pre-operative structural connectome of the contralateral non-operated hemisphere with improvement in intelligence quotient (IQ) post-operatively. MethodsConsecutive children who had undergone unilateral resective procedures for epilepsy at a single centre were retrospectively identified. We included those with pre-operative volume T1-weighted non-contrast brain magnetic resonance imaging (MRI), no visible contralateral MRI abnormalities, and both pre-operative and two years post-operative IQ assessment. The MRI of the hemisphere contralateral to the side of resection was anatomically parcellated into 34 cortical regions and the covariance of cortical thickness between regions was used to create binary and weighted group connectomes. ResultsEleven patients with a post-operative IQ increase of at least 10 points at two years were compared with twenty-four patients with no change in IQ score. Children who gained at least 10 IQ points post-operatively had a more efficiently structured contralateral hemisphere connectome with higher global efficiency (0.74) compared to those whose IQ did not change at two years (0.58, p = 0.014). This was consistent across thresholds and both binary and weighted networks. There were no statistically significant group differences in age, sex, age at onset of epilepsy, pre-operative IQ, mean cortical thickness, side or site of procedure, two year post-operative Engel scores or use of anti-seizure medications between the two groups. ConclusionsSurgical procedures to reduce or stop seizures may allow children with an efficiently structured contralateral hemisphere to achieve their cognitive potential.

The Effect of Posterior Fossa Decompression Surgery on Brainstem and Cervical Spinal Cord Dimensions in Adults with Chiari Malformation Type 1

Posterior fossa decompression (PFD) surgery creates more space at the skull base, reduces the resistance to the cerebrospinal fluid motion, and alters craniocervical biomechanics. In this paper, we retrospectively examined the changes in neural tissue dimensions following PFD surgery on Chiari malformation type 1 adults. Measurements were performed on T2-weighted brain magnetic resonance images acquired before and 4months after surgery. Measurements were conducted for neural tissue volume and spinal cord/brainstem width at 4 different locations; 2 width measurements were made on the brainstem and 2 on the spinal cord in the midsagittal plane. Cerebellar tonsillar position (CTP) was also measured before and after surgery. Twenty-five adult patients, with a mean age of 38.9±8.8years, were included in the study. The cervical cord volume increased by an average of 2.3 ± 3.3% (P= 0.002). The width at the pontomedullary junction increased by 2.2 ± 3.5% (P < 0.01), while the width 10mm caudal to this junction increased by 4.2 ± 3.9% (P < 0.0001). The spinal cord width at the base of second cervical vertebra and third cervical vertebra did not significantly change after surgery. The CTP decreased by 60 ± 37% (P < 0.0001) after surgery, but no correlation was found between CTP change and dimension change. The brainstem width and cervical cord volume showed a modest increase after PFD surgery, although standard deviations were large. A reduction in compression after PFD surgery may allow for an increase in neural tissue dimension. However, clinical relevance is unclear and should be assessed in future studies with high-resolution imaging.

P13.09.B FULLY AUTOMATED BRAIN METASTASES SEGMENTATION USING T1-WEIGHTED CONTRAST-ENHANCED MR IMAGES BEFORE AND AFTER STEREOTACTIC RADIOSURGERY

Abstract BACKGROUND Brain metastases (BM) represent the most common intracranial tumor in adults. An estimated 20% of all patients with cancer will develop BM. Stereotactic Radiosurgery (SRS) is a major treatment option for BM. For SRS treatment planning and outcome evaluation, magnetic resonance images (MRI) are acquired before and at multiple stages during the follow-up. Accurate segmentation of brain tumors on MRI is crucial for treatment planning and response evaluation. Detection and segmentation of BM which is a tedious and time-consuming task for many radiologists could be optimized with machine learning METHODS . The accuracy of the auto-segmentation, however, is influenced by the presence of false-positive and false-negative segmentation. There are studies which evaluated the segmentation performance of several deep learning algorithms, these were mainly focused on training and testing the models on either the pre-treatment or post-treatment images. The purpose of this study was to investigate a well-known deep learning approach (nnU-Net) for BM segmentation and to evaluate its performance on both pre-treatment and post-treatment images to assess if it could be a handy tool for the clinicians. METHODS Pre-treatment T1-weighted brain MRIs which were contrast-enhanced with triple-dose gadolinium were collected retrospectively for 266 patients with BM. Scans were made as part of clinical care at the Gamma Knife Center of the Elisabeth-TweeSteden Hospital (Tilburg, the Netherlands). All patients underwent Gamma Knife Radiosurgery, a form of SRS. This total of 266 patients were randomly split into 210 patients for model training/validation and 56 patients for testing. For these 56 patients used for testing, the post treatment follow-up T1-weighted brain MRI scans which were contrast-enhanced with single-dose gadolinium were also retrospectively collected. The nnU-Net model was trained with the pre-treatment training data, and then tested separately against the pre-treatment and follow-up data. RESULTS The model obtained a Dice score of 0.91 when tested with the pre-treatment images and a Dice score of 0.80 when tested with the follow-up after treatment T3 images. The False Negative Rate (FNR) when tested with the pre-treatment images was 0.07 and 0.24 when tested with post-treatment T3 images. CONCLUSION The model achieved a good performance score for pre-treatment images. The nnU-Net model can automatically detect and segment brain metastases with high sensitivity, and low FNR for treatment planning. This could be a beneficial tool for clinicians and assist SRS management for diagnosis and treatment planning. Though there is a decline in the Dice score and an increase in the FNR of the model for the post-treatment images, the performance still remained higher than in other similar studies.

Structural brain correlates of non-verbal cognitive ability in 5-year-old children: Findings from the FinnBrain birth cohort study.

Non-verbal cognitive ability predicts multiple important life outcomes, for example, school and job performance. It has been associated with parieto-frontal cortical anatomy in prior studies in adult and adolescent populations, while young children have received relatively little attention. We explored the associations between cortical anatomy and non-verbal cognitive ability in 165 5-year-old participants (mean scan age 5.40 years, SD 0.13; 90 males) from the FinnBrain Birth Cohort study. T1-weighted brain magnetic resonance images were processed using FreeSurfer. Non-verbal cognitive ability was measured using the Performance Intelligence Quotient (PIQ) estimated from the Block Design and Matrix Reasoning subtests from the Wechsler Preschool and Primary Scale of Intelligence (WPPSI-III). In vertex-wise general linear models, PIQ scores associated positively with volumes in the left caudal middle frontal and right pericalcarine regions, as well as surface area in left the caudal middle frontal, left inferior temporal, and right lingual regions. There were no associations between PIQ and cortical thickness. To the best of our knowledge, this is the first study to examine structural correlates of non-verbal cognitive ability in a large sample of typically developing 5-year-olds. The findings are generally in line with prior findings from older age groups, with the important addition of the positive association between volume / surface area in the right medial occipital region and non-verbal cognitive ability. This finding adds to the literature by discovering a new brain region that should be considered in future studies exploring the role of cortical structure for cognitive development in young children.

Posterior Reversible Encephalopathy Syndrome in a Patient with Septic Shock: A Case Report.

Posterior reversible encephalopathy syndrome (PRES) is a reversible condition with nonspecific neurologic and characteristic radiologic findings. Clinical presentation may include headache, nausea, vomiting, altered mental status, seizures, and vision changes. Diagnosis is confirmed through T2-weighted brain magnetic resonance imaging (MRI) showing bilateral hyperintensities in the white matter of posterior circulatory regions. We report a case of PRES in a patient suffering from complicated diverticulitis. Following medical management in the emergency department, the patient deteriorated, becoming hypotensive and altered. Bowel resection under general anesthesia was performed. Postoperative brain MRI demonstrated bilateral and symmetric T2 signal hyperintensities suggestive of PRES. Following supportive treatment, the patient was discharged from the surgical intensive care unit on postoperative day 21 with no residual deficits. It is important to recognize the nonspecific neurologic symptoms associated with PRES. Emergency physicians should suspect acute PRES when managing patients with prolonged or unexplained encephalopathy, while recognizing that hypertension need not be present.

A robust MRI-based brain tumor classification via a hybrid deep learning technique

The brain is the most vital component of the neurological system. Therefore, brain tumor classification is a very challenging task in the field of medical image analysis. There has been a qualitative leap in the field of artificial intelligence, deep learning, and their medical imaging applications in the last decade. The importance of this remarkable development has emerged in the field of biomedical engineering due to the sensitivity and seriousness of the issues related to it. The use of deep learning in the field of detecting and classifying tumors in general and brain tumors in particular using magnetic resonance imaging (MRI) is a crucial factor in the accuracy and speed of diagnosis. This is due to its great ability to deal with huge amounts of data and avoid errors resulting from human intervention. The aim of this research is to develop an efficient automated approach for classifying brain tumors to assist radiologists instead of consuming time looking at several images for a precise diagnosis. The proposed approach is based on 3064 T1-weighted contrast-enhanced brain MR images (T1W-CE MRI) from 233 patients. In this study, the proposed system is based on the results of five different models to use the combined potential of multiple models, trying to achieve promising results. The proposed system has led to a significant improvement in the results, with an overall accuracy of 99.31%.