Search Results (1,453)

Background/Objectives: This study aims to evaluate the additional value of [¹⁸F]F-fluorocholine ([¹⁸F]F-FCH) PET/CT over contrast-enhanced magnetic resonance imaging (CE-MRI) in detecting the recurrence of brain metastases (BMs) after stereotactic radiosurgery (SRS) in patients with lung cancer brain metastases (LCBMs). Methods: Thirty-one patients with suspected recurrence of BM in LCBM after SRS were enrolled in this retrospective study. They underwent both [¹⁸F]F-FCH PET/CT and CE-MRI within 2 weeks. The tumor imaging parameters and clinical features were analyzed. The results of histopathology or radiographic follow-up served as the reference standard for the final diagnosis. Results: In these 31 patients, there were 54 lesions, of which 27 lesions were proven to be BM recurrence, while 27 lesions were non-recurrence. [¹⁸F]F-FCH PET/CT showed high radiotracer uptake in recurrent lesions of BM and identified 24 positive lesions (88.89% of sensitivity), while CE-MRI indicated 23 positive lesions (85.19% of sensitivity). [¹⁸F]F-FCH PET/CT indicated higher specificity (81.48%) and accuracy (85.19%) in detecting recurrence of BM than CE-MRI (40.74% and 62.96%, both p < 0.05), particularly in frontal lobes and cerebella. For lesion sizes, the accuracy of [¹⁸F]F-FCH PET/CT in detecting recurrent lesions was higher than that of CE-MRI for lesions over 1.0 cm but below 2.0 cm (p = 0.016). The detective performance of [¹⁸F]F-FCH PET/CT combined with CE-MRI was higher than [¹⁸F]F-FCH PET/CT or CE-MRI alone (all p < 0.05). Interestingly, TLC (≥4.11) was significantly correlated with poor intracranial PFS (iPFS), meaning it was a significant prognostic factor for iPFS. Conclusions: This study identified that compared with CE-MRI, [¹⁸F]F-FCH PET/CT demonstrated higher specificity and accuracy in diagnosing recurrence of BM in LCBM after SRS. Combining [¹⁸F]F-FCH PET/CT with CE-MRI has the potential to improve diagnostic performance for recurrence of BM and management of patient treatment. TLC was an independent risk factor for iPFS. Full article

Accurate detection of Alzheimer’s disease (AD) is critical yet challenging for early medical intervention. Deep learning methods, especially convolutional neural networks (CNNs), have shown promising potential for improving diagnostic accuracy using magnetic resonance imaging (MRI). This study aims to identify the most informative combination of MRI slice orientation and anatomical location for AD classification. We propose an automated framework that first selects the most relevant slices using a feature entropy-based method applied to activation maps from a pretrained CNN model. For classification, we employ a lightweight CNN architecture based on depthwise separable convolutions to efficiently analyze the selected 2D MRI slices extracted from preprocessed 3D brain scans. To further interpret model behavior, an attention mechanism is integrated to analyze which feature level contributes the most to the classification process. The model is evaluated on three binary tasks: AD vs. mild cognitive impairment (MCI), AD vs. cognitively normal (CN), and MCI vs. CN. The experimental results show the highest accuracy (97.4%) in distinguishing AD from CN when utilizing the selected slices from the ninth axial segment, followed by the tenth segment of coronal and sagittal orientations. These findings demonstrate the significance of slice location and orientation in MRI-based AD diagnosis and highlight the potential of lightweight CNNs for clinical use. Full article

Background/Objectives: Schizophrenia exhibits symptoms linked to the hippocampus and parahippocampal gyrus. This includes the entorhinal cortex (ERC) and perirhinal cortex (PRC) as anterior parts, along with the posterior segment known as the parahippocampal cortex (PHC). However, recent research has detailed atlases based on cytoarchitectural characteristics and the hippocampus divided into four subregions: cornu ammonis (CA), dentate gyrus (DG), subiculum (SUB), and hippocampal–amygdaloid transition (HATA). This study aimed to explore the functional connectivity (FC) changes between these hippocampal subregions and the parahippocampal gyrus structures (ERC, PRC, and PHC) as well as between hippocampal subregions and various functional brain networks in schizophrenia. Methods: In total, 50 individuals with schizophrenia and 50 matched healthy subjects were examined using resting state functional magnetic resonance imaging (rs-fMRI). Results: The results showed alterations characterized by increases and decreases in the strength of the positive connectivity between the parahippocampal gyrus structures and the four hippocampal subregions when comparing patients with schizophrenia with healthy subjects. Alterations were observed among the hippocampal subregions and functional brain networks, as well as the formation of new connections and absence of connections. Conclusions: There is strong evidence that the different subregions of the hippocampus have unique functions and their connectivity with the parahippocampal cortices and brain networks are affected by schizophrenia. Full article

Since the first emergence of the Bluetongue virus (BTV) in 2006 in Northern Europe, there has been a reported association between BTV Serotype 8 (BTV-8) and brain malformations in calves. The first BTV-3 outbreak in Germany was registered in October 2023. Since then, numbers have increased steadily. In a suckler cow herd in the Lower Saxony region, two Angus calves with clinical signs of diffuse encephalopathy, including ataxia, abnormal gait, and central blindness, were born in autumn 2024. Both calves were submitted for Magnetic Resonance Imaging (MRI) and pathological examination, revealing hydranencephaly and internal hydrocephalus, respectively. BTV-3 was detected in blood and tissue samples of both calves using BTV-specific real-time PCR. The presented findings demonstrate that there seems to be an association between transplacental BTV-3 infections and congenital malformations in calves, as previously reported for BTV-8 and -10. Full article

Background: Hypoxic–ischemic encephalopathy (HIE) is a leading cause of severe neurological impairments in childhood. Therapeutic hypothermia (TH) is both safe and effective in neonates born at ≥36 weeks gestation with moderate to severe HIE. We aimed to evaluate short-term outcomes—including brain injury detected on magnetic resonance imaging (MRI)—in infants born at 34–35 weeks of gestation drawing on our clinical experience with neonates under 36 weeks of gestational age (GA). Methods: In this retrospective cohort study, 20 preterm infants with a GA of 34 to 35 weeks and a matched cohort of 80 infants with a GA of ≥36 weeks who were diagnosed with moderate to severe HIE and underwent TH were included. Infants were matched in a 1:4 ratio based on the worst base deficit in blood gas and sex. Maternal and neonatal characteristics, brain MRI findings and short term outcomes were compared. Results: Infants with a GA of 34–35 weeks had a lower birth weight and a higher rate of caesarean delivery (both p < 0.001). Apgar scores, sex, intubation rate in delivery room, blood gas pH, base deficit and lactate were comparable between the groups. Compared to infants born at ≥36 weeks of GA, preterm neonates were more likely to receive inotropes, had a longer time to achieve full enteral feeding, and experienced a longer hospital stay. The mortality rate was 10% in the 34–35 weeks GA group. Neuroimaging revealed injury in 66.7% of infants born at 34–35 weeks of gestation and in 58.8% of those born at ≥36 weeks (p = 0.56). Injury was observed across multiple brain regions, with white matter being the most frequently affected in the 34–35 weeks GA group. Thalamic and cerebellar abnormal signal intensity or diffusion restriction, punctate white matter lesions, and diffusion restriction in the corpus callosum and optic radiations were more frequently detected in infants born at 34–35 weeks of gestation. Conclusions: Our study contributes to the growing body of literature suggesting that TH may be feasible and tolerated in late preterm infants. Larger randomized controlled trials focused on this vulnerable population are necessary to establish clear guidelines regarding the safety and efficacy of TH in late preterm infants. Full article

Background: Congenital anosmia (CA) is a rare condition characterized by a lifelong inability to perceive odors, which significantly affects daily life and may be linked to broader neurodevelopmental alterations. This study aimed to investigate structural brain differences in patients with CA using MRI, focusing on gray matter (GM) and white matter (WM) changes and their implications for neurodevelopment. Methods: This retrospective study included 28 patients with CA and 28 age- and gender-matched healthy controls. Patients with CA were diagnosed at a single medical center between 1 January 2001 and 30 August 2024. Controls were randomly selected from an imaging database and had no history of olfactory dysfunction. Brain Magnetic Resonance Imaging (MRI)was analyzed using volumetric analysis in SPM12.GM and WM volumes were quantified across 11 anatomical brain regions based on theWFU_PickAtlas toolbox, including frontal, temporal, parietal, occipital, limbic, sub-lobar, cerebellum (anterior/posterior), midbrain, the pons, and the frontal–temporal junction. Left–right hemispheric comparisons were also conducted. Results: Patients with CA exhibited significantly smaller GM volumes compared to healthy controls (560.6 ± 114.7 cc vs. 693.7 ± 96.3 cc, p < 0.001) but larger WM volumes (554.2 ± 75.4 cc vs. 491.1 ± 79.7 cc, p = 0.015). Regionally, GM reductions were observed in the frontal (131.9 ± 33.7 cc vs. 173.7 ± 27.0 cc, p < 0.001), temporal (81.1 ± 18.4 cc vs. 96.5 ± 14.1 cc, p = 0.001), parietal (52.4 ± 15.2 cc vs. 77.2 ± 12.4 cc, p < 0.001), sub-lobar (57.8 ± 9.7 cc vs. 68.2 ± 10.2 cc, p = 0.001), occipital (39.1 ± 13.0 cc vs. 57.8 ± 8.9 cc, p < 0.001), and midbrain (2.0 ± 0.5 cc vs. 2.3 ± 0.4 cc, p = 0.006) regions. Meanwhile, WM increases were notable in the frontal(152.0 ± 19.9 cc vs. 139.2 ± 24.0 cc, p = 0.027), temporal (71.5 ± 11.5 cc vs. 60.8 ± 9.5 cc, p = 0.001), parietal (75.8 ± 12.4 cc vs. 61.9 ± 11.5 cc, p < 0.001), and occipital (58.7 ± 10.3 cc vs. 41.9 ± 7.9 cc, p < 0.001) lobes. A separate analysis of the left and right hemispheres revealed similar patterns of reduced GM and increased WM volumes in patients with CA across both sides. An exception was noted in the right cerebellum-posterior, where patients with CA showed significantly greater WM volume (5.625 ± 1.667 cc vs. 4.666 ± 1.583 cc, p = 0.026). Conclusions: This study demonstrates widespread structural brain differences in individuals with CA, including reduced GM and increased WM volumes across multiple cortical and sub-lobar regions. These findings suggest that congenital olfactory deprivation may impact brain maturation beyond primary olfactory pathways, potentially reflecting altered synaptic pruning and increased myelination during early neurodevelopment. The involvement of the cerebellum further implies potential adaptations beyond motor functions. These structural differences may serve as potential neuroimaging markers for monitoring CA-associated cognitive or emotional comorbidities. Full article

Background/Objectives: Multimodal large language models (LLMs) are increasingly used in radiology. However, their ability to recognize fundamental imaging features, including modality, anatomical region, imaging plane, contrast-enhancement status, and particularly specific magnetic resonance imaging (MRI) sequences, remains underexplored. This study aims to evaluate and compare the performance of three advanced multimodal LLMs (ChatGPT-4o, Claude 4 Opus, and Gemini 2.5 Pro) in classifying brain MRI sequences. Methods: A total of 130 brain MRI images from adult patients without pathological findings were used, representing 13 standard MRI series. Models were tested using zero-shot prompts for identifying modality, anatomical region, imaging plane, contrast-enhancement status, and MRI sequence. Accuracy was calculated, and differences among models were analyzed using Cochran’s Q test and McNemar test with Bonferroni correction. Results: ChatGPT-4o and Gemini 2.5 Pro achieved 100% accuracy in identifying the imaging plane and 98.46% in identifying contrast-enhancement status. MRI sequence classification accuracy was 97.7% for ChatGPT-4o, 93.1% for Gemini 2.5 Pro, and 73.1% for Claude 4 Opus (p < 0.001). The most frequent misclassifications involved fluid-attenuated inversion recovery (FLAIR) sequences, often misclassified as T1-weighted or diffusion-weighted sequences. Claude 4 Opus showed lower accuracy in susceptibility-weighted imaging (SWI) and apparent diffusion coefficient (ADC) sequences. Gemini 2.5 Pro exhibited occasional hallucinations, including irrelevant clinical details such as “hypoglycemia” and “Susac syndrome.” Conclusions: Multimodal LLMs demonstrate high accuracy in basic MRI recognition tasks but vary significantly in specific sequence classification tasks. Hallucinations emphasize caution in clinical use, underlining the need for validation, transparency, and expert oversight. Full article

Diffuse axonal brain injury (DAI) is a common, debilitating consequence of traumatic brain injury, yet its detection and severity grading remain challenging in clinical and experimental settings. This study evaluated the sensitivity of diffusion tensor imaging (DTI), histology, and neurological severity scoring (NSS) in assessing injury severity in a rat model of isolated DAI. A rotational injury model induced mild, moderate, or severe DAI in male and female rats. Neurological deficits were assessed 48 h after injury via NSS. Magnetic resonance imaging, including DTI metrics, such as fractional anisotropy (FA), relative anisotropy (RA), axial diffusivity (AD), mean diffusivity (MD), and radial diffusivity (RD), was performed prior to tissue collection. Histological analysis used beta amyloid precursor protein immunohistochemistry. Sensitivity and variability of each method were compared across brain regions and the whole brain. Histology was the most sensitive method, requiring very small groups to detect differences. Anisotropy-based MRI metrics, especially whole-brain FA and RA, showed strong correlations with histology and NSS and demonstrated high sensitivity with low variability. NSS identified injury but required larger group sizes. Diffusivity-based MRI metrics, particularly RD, were less sensitive and more variable. Whole-brain FA and RA were the most sensitive MRI measures of DAI severity and were comparable to histology in moderate and severe groups. These findings support combining NSS and anisotropy-based DTI for non-terminal DAI assessment in preclinical studies. Full article

Background: Autism spectrum disorder (ASD) is characterised by atypical behavioural and cognitive diversity, yet the neural underpinnings linking brain activity and individual presentations remain poorly understood. In this study, we investigated the relationship between resting-state functional magnetic resonance imaging (fMRI) signal complexity and intelligence (full-scale intelligence quotient (FIQ); verbal intelligence quotient (VIQ); and performance intelligence quotient (PIQ)) in male adults with ASD (n = 14) and matched neurotypical controls (n = 15). Methods: We used three complexity-based metrics: Hurst exponent (H), fuzzy approximate entropy (fApEn), and fuzzy sample entropy (fSampEn) to characterise resting-state fMRI signal dynamics, and correlated these measures with standardised intelligence scores. Results: Using a whole-brain measure, ASD participants showed significant negative correlations between PIQ and both fApEn and fSampEn, suggesting that increased neural irregularity may relate to reduced cognitive–perceptual performance in autistic individuals. No significant associations between entropy (fApEn and fSampEn) and PIQ were found in the control group. Group differences in brain–behaviour associations were confirmed through formal interaction testing using Fisher’s r-to-z transformation, which showed significantly stronger correlations in the ASD group. Complementary regression analyses with interaction terms further demonstrated that the entropy (fApEn and fSampEn) and PIQ relationship was significantly moderated by group, reinforcing evidence for autism-specific neural mechanisms underlying cognitive function. Conclusions: These findings provide insight into how cognitive functions in autism may not only reflect deficits but also an alternative neural strategy, suggesting that distinct temporal patterns may be associated with intelligence in ASD. These preliminary findings could inform clinical practice and influence health and social care policies, particularly in autism diagnosis and personalised support planning. Full article

Artificial intelligence (AI) has already played a significant role in the healthcare sector, particularly in image-based medical diagnosis. Deep learning models have produced satisfactory and useful results for accurate decision-making. Among the various types of medical images, magnetic resonance imaging (MRI) is frequently utilized in deep learning applications to analyze detailed structures and organs in the body, using advanced intelligent software. However, challenges related to performance and data privacy often arise when using medical data from patients and healthcare institutions. To address these issues, new approaches have emerged, such as federated learning. This technique ensures the secure exchange of sensitive patient and institutional data. It enables machine learning or deep learning algorithms to establish a client–server relationship, whereby specific parameters are securely shared between models while maintaining the integrity of the learning tasks being executed. Federated learning has been successfully applied in medical settings, including diagnostic applications involving medical images such as MRI data. This research introduces an analytical intelligence system based on an Internet of Medical Things (IoMT) framework that employs federated learning to provide a safe and effective diagnostic solution for brain tumor identification. By utilizing specific brain MRI datasets, the model enables multiple local capsule networks (CapsNet) to achieve improved classification results. The average accuracy rate of the CapsNet model exceeds 97%. The precision rate indicates that the CapsNet model performs well in accurately predicting true classes. Additionally, the recall findings suggest that this model is effective in detecting the target classes of meningiomas, pituitary tumors, and gliomas. The integration of these components into an analytical intelligence system that supports the work of healthcare personnel is the main contribution of this work. Evaluations have shown that this approach is effective for diagnosing brain tumors while ensuring data privacy and security. Moreover, it represents a valuable tool for enhancing the efficiency of the medical diagnostic process. Full article

(1) Background: Evidence from non-human animal and spaceflight analog studies have suggested that traveling to outer space could have a significant impact on the structural properties of the hippocampus, a brain region within the medial temporal lobe that is critical for learning and memory. Here, we tested this hypothesis in a group of astronauts who participated in a six-month mission in the International Space Station (ISS). (2) Methods: We collected magnetic resonance imaging (MRI) scans from a sample of 17 (9 males, 8 females) astronauts before and after the ISS mission, and calculated percent gray matter volume changes in the whole hippocampus and its (anterior, body, and posterior) subregions in both hemispheres. (3) Following the six-month mission in the ISS, we found a significantly decreased volume in the whole left hippocampus; in addition, when looking at subregions separately, we detected a significantly decreased volume in the anterior subregion of the left hippocampus and the body subregion of the right hippocampus. We also found a significantly decreased volume in the whole right hippocampus of male astronauts as compared to female astronauts. (4) Conclusions: This study, providing the very first evidence of hippocampal volumetric changes in astronauts following a six-month mission to the ISS, could have significant implications for cognitive performance during future long-duration spaceflights. Full article

Using neural networks has become the standard technique for medical diagnostics, especially in cancer detection and classification. This work evaluates the performance of Vision Transformer architectures, including Swin Transformer and MaxViT, for several datasets of magnetic resonance imaging (MRI) and computed tomography (CT) scans. We used three training sets of images with brain, lung, and kidney tumors. Each dataset included different classification labels, from brain gliomas and meningiomas to benign and malignant lung conditions and kidney anomalies such as cysts and cancers. This work aims to analyze the behavior of the neural networks in each dataset and the benefits of combining different image modalities and tumor classes. We designed several experiments by fine-tuning the models on combined and individual datasets. The results revealed that the Swin Transformer achieved the highest accuracy, with an average of 99.0% on single datasets and reaching 99.43% on the combined dataset. This research highlights the adaptability of Transformer-based models to various human organs and image modalities. The main contribution lies in evaluating multiple ViT architectures across multi-organ tumor datasets, demonstrating their generalization to multi-organ classification. Integrating these models across diverse datasets could mark a significant advance in precision medicine, paving the way for more efficient healthcare solutions. Full article

Background/Objectives: This paper presents a medical image analysis application designed to facilitate advanced edge detection and fuzzy processing techniques within an intuitive, modular graphical user interface. Methods: Key functionalities include classical edge detection, Ant Colony Optimization (ACO)-based edge extraction, and fuzzy edge generation, which offer improved boundary representation in images where uncertainty and soft transitions are prevalent. Results: One of the main novelties in contrast to the initial innovative Medical Image Analyzer, iMIA, is the fact that the system includes fuzzy C-means clustering to support tissue classification and unsupervised segmentation based on pixel intensity distribution. The application also features an interactive zooming and panning module with the option to overlay edge detection results. As another novelty, fuzzy performance metrics were added, including fuzzy false negatives, fuzzy false positives, fuzzy true positives, and the fuzzy index, offering a more comprehensive and uncertainty-aware evaluation of edge detection accuracy. Conclusions: The application executable file is provided at no cost for the purposes of evaluation and testing. Full article

Introduction: Brain tumor, marked by abnormal and rapid cell growth, poses severe health risks and requires accurate diagnosis for effective treatment. Classifying brain tumors using deep learning techniques applied to Magnetic Resonance Imaging (MRI) images has attracted the attention of many researchers, and specifically, reducing the bias of models and enhancing robustness is still a very pertinent active topic of attention. Methods: For capturing diverse information from different feature sets, we propose a Features Concatenation-based Brain Tumor Classification (FCBTC) Framework using Hybrid Deep Learning Models. For this, we have chosen three pretrained models—ResNet50; VGG16; and DensetNet121—as the baseline models. Our proposed hybrid models are built by the fusion of feature vectors. Results: The testing phase results show that, for the FCBTC Model-3, values for Precision, Recall, F1-score, and Accuracy are 98.33%, 98.26%, 98.27%, and 98.40%, respectively. This reinforces our idea that feature diversity does improve the classifier’s performance. Conclusions: Comparative performance evaluation of our work shows that, the proposed hybrid FCBTC Models have performed better than other proposed baseline models. Full article

The quality of life (QoL) perception has been studied in neurological diseases; however, there is limited information linking brain morphological characteristics, QoL, and cognition. Human behavior and perception are associated with specific brain areas that interact through diffuse electrochemical networking. We used magnetic resonance imaging (MRI) to analyze the brain region volume (BRV) correlation with the scores of Rand’s 36-item Short Form Survey (SF-36) and cognitive domains (memory and dementia status). We analyzed data from 420 adult participants in the Maracaibo Aging Study (MAS). Principal component analysis with oblimin axis rotation was used to gather redundant information from brain parcels and SF-36 domains. Canonical correlation was used to analyze the relationships between SF-36 domains and BRV (adjusted for intracranial cavity), as well as sex, age, education, obesity, and hypertension. The average age (±SD) of subjects was 56 ± 11.5 years; 71% were female; 39% were obese; 12% had diabetes, 52% hypertension, and 7% dementia. No sex-related differences were found in memory and orientation scores, but women had lower QoL scores. The 1st and 2nd canonical correlation roots support the association of SF-36 domains (except social functioning and role emotional) and total brain volume, frontal lobe volume, frontal pole, lateral orbital lobe, cerebellar, and entorhinal areas. Other variables, including age, dementia, memory score, and systolic blood pressure, had a significant influence. The results of this study demonstrate significant correlations between BRV and SF-36 components, adjusted for covariates. The frontal lobe and insula were associated with the mental health component; the lateral-orbital frontal lobe and entorhinal area were correlated with the physical component. Full article