Magnetic Resonance Imaging Data Research Articles

Design of a novel deep network model for spinal cord injury prediction

Degenerative cervical myelopathy must be diagnosed with magnetic resonance imaging (MRI) which predicts spinal cord injury (SCI). The growing volume of medical imaging data can be managed by deep learning models, which provide a preliminary interpretation of images taken in basic care settings. Our main goal was to create a deep-learning approach that could identify SCI using MRI data. This work concentrates on modeling a novel 2D-convolutional neural networks (2D-CNN) approach for predicting SCI. For holdouts, training, and validation, various datasets of patients were created. Two experts assigned labels to the images. The holdout dataset was used to evaluate the performance of our deep convolutional neural network (DCNN) over the image data from the available dataset. The dataset is acquired from the online resource for training and validation purpose. With the available dataset, the anticipated model attains 94% AUC, 0.1 p-value, and 92.2% accuracy. The anticipated model might make cervical spine MRI scan interpretation more accurate and reliable.

Autism spectrum disorder identification with multi-site functional magnetic resonance imaging

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by enduring difficulties in social interaction and communication. People analyzed with ASD may display repetitive behaviors and limited interests. Autism is classified as a spectrum disorder, implying that the symptom intensity might range from mild to severe depending on the individual. To detect ASD in this paper an attribute feature graph approach is designed by using the stastical dependencies features that necessarily accomplish the diagnosis of ASD. In the first phase the features extracted are designed based on the functional magnetic resonance imaging (fMRI) data, in the next-step the attribute feature graph layer learns the features of the node information of various nodes by ASD classification. Further, in the third step, it is employed to independently extract distinguishing features from the functional connectivity matrices of the brain that are derived from fMRI. The custom convolutional neural network (CNN) used in this study is trained on a comprehensive dataset comprising individuals diagnosed with ASD and typically developing individuals. In the fourth stage, a prototype learning is developed to augment the classification performance of the custom-CNN. The experimental analysis further carried out states that the proposed model works efficiently in comparison with the existing system.

Performance of LI-RADS category 5 vs combined categories 4 and 5: a systemic review and meta-analysis.

Computed tomography (CT)/magnetic resonance imaging (MRI) Liver Imaging Reporting and Data System (LI-RADS, LR) category 5 has high specificity and modest sensitivity for diagnosis of hepatocellular carcinoma (HCC). The purpose of this study was to compare the diagnostic performance of LR-5 vs combined LR-4 and LR-5 (LR-4/5) for HCC diagnosis. MEDLINE and EMBASE databases through January 03, 2023 were searched for studies reporting the performance of LR-5 and combined LR-4/5 for HCC diagnosis, using CT/MRI LI-RADS version 2014, 2017, or 2018. A bivariate random-effects model was used to calculate the pooled per-observation diagnostic performance. Subgroup analysis was performed based on imaging modalities and type of MRI contrast material. Sixty-nine studies (15,108 observations, 9928 (65.7%) HCCs) were included. Compared to LR-5, combined LR-4/5 showed significantly higher pooled sensitivity (83.0% (95% CI [80.3-85.8%]) vs 65.7% (95% CI [62.4-69.1%]); p < 0.001), lower pooled specificity (75.0% (95% CI [70.5-79.6%]) vs 91.7% (95% CI [90.2-93.1%]); p < 0.001), lower pooled positive likelihood ratio (3.60 (95% CI [3.06-4.23]) vs 6.18 (95% CI [5.35-7.14]); p < 0.001), and lower pooled negative likelihood ratio (0.22 (95% CI [0.19-0.25]) vs 0.38 (95% CI [0.35-0.41]) vs; p < 0.001). Similar results were seen in all subgroups. Our meta-analysis showed that combining LR-4 and LR-5 would increase sensitivity but decrease specificity, positive likelihood ratio, and negative likelihood ratio. These findings may inform management guidelines and individualized management. This meta-analysis estimated the magnitude of changes in the sensitivity and specificity of imaging criteria when LI-RADS categories 4 and 5 were combined; these findings can inform management guidelines and individualized management. There is no single worldwide reporting system for liver imaging, partly due to regional needs. Combining LI-RADS categories 4 and 5 increased sensitivity and decreased specificity and positive and negative likelihood ratios. Changes in the sensitivity and specificity of imaging criteria can inform management guidelines and individualized management.

BRAIN TUMOR PERCEPTION USING DEEP LEARNING

The human brain serves as the central component of the human body, orchestrating its functionality. Brain tumors emerge from abnormal cell growth and division within the brain, potentially leading to the development of brain cancer. Computer vision plays a pivotal role in healthcare by alleviating the burden of making precise decisions. Among imaging modalities like magnetic resonance imaging (MRI), computed tomography (CT) scans, and X-rays, MRI scans stand out as one of the most prevalent and safest methods for obtaining detailed images, capable of detecting minute abnormalities. Our research endeavors to explore the diverse applications of brain MRI in detecting brain cancer. In this study, we employed the bilateral filter (BF) to eliminate noise from MR images before processing. Subsequently, the tumor region was delineated using binary thresholding and Convolutional Neural Network (CNN) segmentation techniques. The datasets were divided into training, testing, and validation subsets, enabling our machine to discern the presence of brain tumors in subjects. Various performance metrics including accuracy, sensitivity, and specificity were utilized to evaluate the outcomes. Dense layers within the CNN architecture efficiently extract features from brain MRI images, enhancing diagnostic accuracy. The experiment was conducted primarily on MRI data due to its ability to provide comprehensive insights into cellular

Regional structural abnormalities in thalamus in idiopathic cervical dystonia

BackgroundThe thalamus has a central role in the pathophysiology of idiopathic cervical dystonia (iCD); however, the nature of alterations occurring within this structure remain largely elusive. Using a structural magnetic resonance imaging (MRI) approach, we examined whether abnormalities differ across thalamic subregions/nuclei in patients with iCD.MethodsStructural MRI data were collected from 37 patients with iCD and 37 healthy controls (HCs). Automatic parcellation of 25 thalamic nuclei in each hemisphere was performed based on the FreeSurfer program. Differences in thalamic nuclei volumes between groups and their relationships with clinical information were analysed in patients with iCD.ResultsCompared to HCs, a significant reduction in thalamic nuclei volume primarily in central medial, centromedian, lateral geniculate, medial geniculate, medial ventral, paracentral, parafascicular, paratenial, and ventromedial nuclei was found in patients with iCD (P < 0.05, false discovery rate corrected). However, no statistically significant correlations were observed between altered thalamic nuclei volumes and clinical characteristics in iCD group.ConclusionThis study highlights the neurobiological mechanisms of iCD related to thalamic volume changes.

Performance Enhancement of Alzheimer's Disease Diagnosis Using Generative Adversarial Network

Insufficient medical images have always been a challenge for deep learning-based Alzheimer's disease classification and detection tasks. The availability of Magnetic Resonance Imaging (MRI) data is limited due to patients' privacy issues. Access to individual medical records is strongly protected by the law, and appropriate consent is needed to utilize them for research purposes. Besides that, publicly available databases often experience imbalanced classes, further contributing to the issue of insufficient data. Thus, the performance of deep learning models used to diagnose Alzheimer's disease is often hindered by this issue. Basic data augmentation methods such as geometrical augmentation techniques also had limited applications to medical data. Hence, this study proposes a deep learning technique of Generative Adversarial Network (GAN) to expand the MRI dataset and improve the classification model performance. MRI images from the Open Access Series of Imaging Studies (OASIS) database are used in this study to perform experiments and validate hypotheses. After applying GAN to expand the dataset, a pre-trained Convolutional Neural Network (CNN) model is used to classify the data into multiple classes of Alzheimer's and the model's performance is measured. As a result, an improvement in accuracy for the classification task can be observed, indicating that the GAN is a solution for overcoming the challenge of insufficient data for Alzheimer's diagnosis.

The role of cortical structural variance in deep learning-based prediction of fetal brain age

BackgroundDeep-learning-based brain age estimation using magnetic resonance imaging data has been proposed to identify abnormalities in brain development and the risk of adverse developmental outcomes in the fetal brain. Although saliency and attention activation maps have been used to understand the contribution of different brain regions in determining brain age, there has been no attempt to explain the influence of shape-related cortical structural features on the variance of predicted fetal brain age.MethodsWe examined the association between the predicted brain age difference (PAD: predicted brain age–chronological age) from our convolution neural networks-based model and global and regional cortical structural measures, such as cortical volume, surface area, curvature, gyrification index, and folding depth, using regression analysis.ResultsOur results showed that global brain volume and surface area were positively correlated with PAD. Additionally, higher cortical surface curvature and folding depth led to a significant increase in PAD in specific regions, including the perisylvian areas, where dramatic agerelated changes in folding structures were observed in the late second trimester. Furthermore, PAD decreased with disorganized sulcal area patterns, suggesting that the interrelated arrangement and areal patterning of the sulcal folds also significantly affected the prediction of fetal brain age.ConclusionThese results allow us to better understand the variance in deep learning-based fetal brain age and provide insight into the mechanism of the fetal brain age prediction model.

Age and BMI have different effects on subcutaneous, visceral, liver, bone marrow, and muscle adiposity, as measured by CT and MRI.

We analyzed quantitative computed tomography (CT) and chemical shift-encoded magnetic resonance imaging (MRI) data from a Chinese cohort to investigate the effects of BMI and aging on different adipose tissue (AT) depots. In 400 healthy, community-dwelling individuals aged 22 to 83 years, we used MRI to quantify proton density fat fraction (PDFF) of the lumbar spine (L2-L4) bone marrow AT (BMAT), the psoas major and erector spinae (ES) muscles, and the liver. Abdominal total AT, visceral AT (VAT), and subcutaneous AT (SAT) areas were measured at the L2-L3 level using quantitative CT. Partial correlation analysis was used to evaluate the relationship of each AT variable with age and BMI. Multiple linear regression analysis was performed in which each AT variable was evaluated in turn as a function of age and the other five independent AT measurements. Of the 168 men, 29% had normal BMI (<24.0 kg/m2), 47% had overweight (24.0-27.9 kg/m2), and 24% had obesity (≥ 28.0 kg/m2). In the 232 women, the percentages were 46%, 32%, and 22%, respectively. Strong or very strong correlations with BMI were found for total AT, VAT, and SAT in both sexes. BMAT and ES PDFF was strongly correlated with age in women and moderately correlated in men. In both sexes, BMAT PDFF correlated only with age and not with any of the other AT depots. Psoas PDFF correlated only with ES PDFF and not with age or the other AT depots. Liver PDFF correlated with BMI and VAT and weakly with SAT in men. VAT and SAT correlated with age and each other in both sexes. Age and BMI are both associated with adiposity, but their effects differ depending on the type of AT.

#908 The effect of obesity and fat distribution on estimated glomerular filtration rate (eGFR): A Mendelian randomization and image data analysis study

Abstract Background and Aims Obesity is intimately linked to the initiation and progression of chronic kidney disease, but questions on causality still remain. The diagnosis of chronic kidney disease is often based solely on creatinine- and/or cystatin C-based estimated glomerular filtration rate (eGFR), but both these endogenous biomarkers are also known to be related to body composition. This study aims to further investigate how obesity causally affects eGFR, derived from both biomarkers. Method Methods used were: Traditional cross-sectional analyses and magnetic resonance imaging data analyses performed in the POEM study (502 subjects, all aged 50 years), and two-sample Mendelian randomization using already published summary data. Outcome variables for all analyses were eGFR indexed to body surface area, based on creatinine and cystatin C, respectively. In both the traditional cross-sectional and the Mendelian randomization analyses body mass index and waist circumference were used as exposure variables. In the image data analyses, eGFR was regressed nonparametically on fat content and tissue volume for each 3D voxel. Results were visualized as a correlation “imiomics” map. Results Results from the POEM study showed in an adjusted model a negative correlation between cystatin C calculated eGFR and both body mass index [beta = −0.190 (95% CI: −0.280 to −0.100)] and waist circumference [beta = −0.160 (95% CI: −0.250 to −0.060)]. In contrast, a positive association was found for eGFR when using creatinine levels [beta = 0.120 (95% CI: 0.030 to 0.210) for body mass index; beta = 0.160 (95% CI: 0.070 to 0.260) for waist circumference]. This pattern was repeated in the image data analysis, which was visualized in the imiomics map: both visceral and subcutaneous adipose tissue were negatively correlated to eGFR based on cystatin C but positively correlated to eGFR based on creatinine (Fig. 1). Mendelian randomization implied a negative causal effect of obesity-related measures on cystatin C-based eGFR, with statistically significant estimates for both body mass index [beta = −0.031 (95% CI: −0.037 to −0.026)] and waist circumference [beta = −0.038 (95% CI: −0.045 to −0.031)]. No statistically significant effects was found on creatinine-based eGFR (Fig. 2). Conclusion This study indicates a causal negative effect of obesity on cystatin C-based eGFR, but not on eGFR calculated from creatinine. This discrepancy is further highlighted by analyses of adipose tissue from magnetic resonance imaging. These findings thus warrant further research on the impact of using the endogenous biomarkers creatinine and cystatin C to estimate renal function when assessing the link between obesity and chronic kidney disease.

Grey Matter Atrophy and its Relationship with White Matter Lesions in Patients with Myelin Oligodendrocyte Glycoprotein Antibody-associated Disease, Aquaporin-4 Antibody-Positive Neuromyelitis Optica Spectrum Disorder, and Multiple Sclerosis.

To evaluate: (1) the distribution of gray matter (GM) atrophy in myelin oligodendrocyte glycoprotein antibody-associated disease (MOGAD), aquaporin-4 antibody-positive neuromyelitis optica spectrum disorder (AQP4+NMOSD), and relapsing-remitting multiple sclerosis (RRMS); and (2) the relationship between GM volumes and white matter lesions in various brain regions within each disease. A retrospective, multicenter analysis of magnetic resonance imaging data included patients with MOGAD/AQP4+NMOSD/RRMS in non-acute disease stage. Voxel-wise analyses and general linear models were used to evaluate the relevance of regional GM atrophy. For significant results (p < 0.05), volumes of atrophic areas are reported. We studied 135 MOGAD patients, 135 AQP4+NMOSD, 175 RRMS, and 144 healthy controls (HC). Compared with HC, MOGAD showed lower GM volumes in the temporal lobes, deep GM, insula, and cingulate cortex (75.79 cm3); AQP4+NMOSD in the occipital cortex (32.83 cm3); and RRMS diffusely in the GM (260.61 cm3). MOGAD showed more pronounced temporal cortex atrophy than RRMS (6.71 cm3), whereas AQP4+NMOSD displayed greater occipital cortex atrophy than RRMS (19.82 cm3). RRMS demonstrated more pronounced deep GM atrophy in comparison with MOGAD (27.90 cm3) and AQP4+NMOSD (47.04 cm3). In MOGAD, higher periventricular and cortical/juxtacortical lesions were linked to reduced temporal cortex, deep GM, and insula volumes. In RRMS, the diffuse GM atrophy was associated with lesions in all locations. AQP4+NMOSD showed no lesion/GM volume correlation. GM atrophy is more widespread in RRMS compared with the other two conditions. MOGAD primarily affects the temporal cortex, whereas AQP4+NMOSD mainly involves the occipital cortex. In MOGAD and RRMS, lesion-related tract degeneration is associated with atrophy, but this link is absent in AQP4+NMOSD. ANN NEUROL 2024.

Use of Artificial Intelligence in the Prediction of Chiari Malformation Type 1 Recurrence After Posterior Fossa Decompressive Surgery.

Purpose The purpose of this study was to train a deep learning-based method for the prediction of postoperative recurrence of symptoms in Chiari malformation type 1 (CM1) patients undergoing surgery. Studies suggest that certain radiological and clinical features do exist in patients with treatment failure, though these are inconsistent and poorly defined. Methodology This study was a retrospective cohort study of patients who underwent primary surgical intervention for CM1 from January 2010 to May 2020. Only patients who completed pre- and postoperative 12-item short form(SF-12) surveys were included and these were used to classify the recurrence or persistence of symptoms. Forty patients had an improvement in overall symptoms while 17 had recurrence or persistence. After magnetic resonance imaging (MRI) data augmentation, a ResNet50, pre-trained on the ImageNet dataset, was used for feature extraction, and then clustering-constrained attention multiple instance learning (CLAM), a weakly supervised multi-instance learning framework, was trained for prediction of recurrence. Five-fold cross-validation was used for the development of MRI only, clinical features only, and a combined machine learning model. Results This study included 57 patients who underwent CM1 decompression. The recurrence rate was 30%. The combined model incorporating MRI, pre-operative SF-12 physical component scale (PCS), and extent of cerebellar ectopia performed best with an area under the curve (AUC) of 0.71 and an F1 score of 0.74. Conclusion This is the first study to our knowledge to explore the prediction of postoperative recurrence of symptoms in CM1 patients using machine learning methods and represents the first steptowarddeveloping a clinically useful prognostication machine learning model. Further studies utilizing a similar deep learning approach with a larger sample size are needed to improve the performance.

Specialization of anterior and posterior hippocampal functional connectivity differs in autism.

Structural and functional differences in the hippocampus have been related to the episodic memory and social impairments observed in autism spectrum disorder (ASD). In neurotypical individuals, hippocampal-cortical functional connectivity systematically varies between anterior and posterior hippocampus, with changes observed during typical development. It remains unknown whether this specialization of anterior-posterior hippocampal connectivity is disrupted in ASD, and whether age-related differences in this specialization exist in ASD. We examined connectivity of the anterior and posterior hippocampus in an ASD (N = 139) and non-autistic comparison group (N = 133) aged 5-21 using resting-state functional magnetic resonance imaging (MRI) data from the Healthy Brain Network (HBN). Consistent with previous results, we observed lower connectivity between the whole hippocampus and medial prefrontal cortex in ASD. Moreover, preferential connectivity of the posterior relative to the anterior hippocampus for memory-sensitive regions in posterior parietal cortex was reduced in ASD, demonstrating a weaker anterior-posterior specialization of hippocampal-cortical connectivity. Finally, connectivity between the posterior hippocampus and precuneus negatively correlated with age in the ASD group but remained stable in the comparison group, suggesting an altered developmental specialization. Together, these differences in hippocampal-cortical connectivity may help us understand the neurobiological basis of the memory and social impairments found in ASD.

A novel CNN-based image segmentation pipeline for individualized feline spinal cord stimulation modeling.

Spinal cord stimulation (SCS) is a well-established treatment for the management of certain chronic pain conditions. More recently, it has also garnered attention as a means of modulating neural activity with the goal of restoring lost autonomic or sensory-motor function. Personalized modeling and treatment planning are critical aspects of safe and effective SCS [46, 60]. However, the generation of spine models at the required level of detail and accuracy requires time and labor intensive manual image segmentation by human experts. Hence, there is a need for maximally automated segmentation routines capable of producing high-quality anatomical models that can be used even in cases where available data is limited. To this end, we developed an automated image segmentation and model generation pipeline based on a novel Convolutional Neural Network (CNN) architecture trained on feline spinal cord magnetic resonance imaging (MRI) data. The pipeline includes steps for image preprocessing, data augmentation, transfer learning and cleanup. To assess the relative importance of each step in the pipeline and of our choice of CNN architecture, we systematically dropped steps or substituted architectures, quantifying the downstream effects in terms of tissue segmentation quality (Jaccard index and Hausdorff distance) and predicted nerve recruitment (estimated axonal depolarization). This leaveone-out analysis demonstrated that each pipeline step contributed a small but measurable increment to mean segmentation quality. Surprisingly, minor differences in segmentation accuracy translated to significant deviations (ranging between 4% and 13% for each pipeline step) in predicted nerve recruitment, highlighting the importance of careful workflow design. To our knowledge, this is the first analysis to also assess the downstream impact of segmentation quality differences on neurostimulation predictions. Furthermore, transfer learning techniques enhanced segmentation metric consistency and allowed generalization to a completely different spine region with minimal additional training data. This work helps pave the way towards fully automated, personalized SCS treatment planning in clinical settings.

Changes in the visual areas of the cerebral cortex in children with left-sided anisometropic amblyopia according to structural MRI and resting-state fMRI

Thanks to the development of structural and functional magnetic resonance imaging (MRI) methods, in recent decades there has been a lot of research aimed at elucidating brain abnormalities caused by amblyopia. In the cases of this prevalent visual disorder, the anomalies causing decreased visual acuity and other visual disabilities cannot be determined by standard ophthalmologic examination. Since there are several types of this disorder that are fundamentally different in etiology, it is natural to suggest the presence of different types of corresponding brain abnormalities. In this regard, before obtaining a general picture of the pathogenesis of amblyopia, studies conducted on groups of specially selected similar patients are very important. This paper presents the results of a study of school-age children with left-sided anisometropic amblyopia. In the patients investigated, MRI data revealed interhemispheric differences in the thickness of the lateral occipital cortex, and resting-state fMRI revealed interhemispheric differences in the local coherence of the hemodynamic signal within 17 Brodmann area and in the functional connectivity between 17 and 18+19 Brodmann areas. The data obtained contribute to the creation of a general MRI database on the pathophysiology of amblyopia, help clarify some controversial issues and indicate the advisability of using resting-state fMRI in ophthalmology.

Deep learning of left atrial structure and function provides link to atrial fibrillation risk

Increased left atrial volume and decreased left atrial function have long been associated with atrial fibrillation. The availability of large-scale cardiac magnetic resonance imaging data paired with genetic data provides a unique opportunity to assess the genetic contributions to left atrial structure and function, and understand their relationship with risk for atrial fibrillation. Here, we use deep learning and surface reconstruction models to measure left atrial minimum volume, maximum volume, stroke volume, and emptying fraction in 40,558 UK Biobank participants. In a genome-wide association study of 35,049 participants without pre-existing cardiovascular disease, we identify 20 common genetic loci associated with left atrial structure and function. We find that polygenic contributions to increased left atrial volume are associated with atrial fibrillation and its downstream consequences, including stroke. Through Mendelian randomization, we find evidence supporting a causal role for left atrial enlargement and dysfunction on atrial fibrillation risk.

The value of the malignant subregion-based texture analysis in predicting the Ki-67 status in breast cancer

ObjectiveTo evaluate the value of the malignant subregion-based texture analysis in predicting Ki-67 status in breast cancer.Materials and methodsThe dynamic contrast-enhanced magnetic resonance imaging data of 119 histopathologically confirmed breast cancer patients (81 patients with high Ki-67 expression status) from January 2018 to February 2023 in our hospital were retrospectively collected. According to the enhancement curve of each voxel within the tumor, three subregions were divided: washout subregion, plateau subregion, and persistent subregion. The washout subregion and the plateau subregion were merged as the malignant subregion. The texture features of the malignant subregion were extracted using Pyradiomics software for texture analysis. The differences in texture features were compared between the low and high Ki-67 expression cohorts and then the receiver operating characteristic (ROC) curve analysis to evaluate the predictive performance of texture features on Ki-67 expression. Finally, a support vector machine (SVM) classifier was constructed based on differential features to predict the expression level of Ki-67, the performance of the classifier was evaluated using ROC analysis and confirmed using 10-fold cross-validation.ResultsThrough comparative analysis, 51 features exhibited significant differences between the low and high Ki-67 expression cohorts. Following feature reduction, 5 features were selected to build the SVM classifier, which achieved an area under the ROC curve (AUC) of 0.77 (0.68–0.87) for predicting the Ki-67 expression status. The accuracy, sensitivity, and specificity were 0.76, 0.80, and 0.68, respectively. The average AUC from the 10-fold cross-validation was 0.72 ± 0.14.ConclusionThe texture features of the malignant subregion in breast cancer were potential biomarkers for predicting Ki-67 expression level in breast cancer, which might be used to precisely diagnose and guide the treatment of breast cancer.

TACA-RNet: Tri-Axis Based Context-Aware Reverse Network for Multimodal Brain Tumor Segmentation

Brain tumor segmentation using Magnetic Resonance Imaging (MRI) is vital for clinical decision making. Traditional deep learning-based studies using convolutional neural networks have predominantly processed MRI data as two-dimensional slices, leading to the loss of contextual information. While three-dimensional (3D) convolutional layers represent an advancement, they have not fully exploited pathological information according to the three-axis nature of 3D MRI data—axial, coronal, and sagittal. Recognizing these limitations, we introduce a Tri-Axis based Context-Aware Reverse Network (TACA-RNet). This innovative approach leverages the unique 3D spatial orientations of MRI, learning crucial information on brain anatomy and pathology. We incorporated three specialized modules: a Tri-Axis Channel Reduction module for optimizing feature dimensions, a MultiScale Contextual Fusion module for aggregating multi-scale features and enhancing spatial discernment, and a 3D Axis Reverse Attention module for the precise delineation of tumor boundaries. The TACA-RNet leverages three specialized modules to enhance the understanding of tumor characteristics and spatial relationships within MRI data by fully utilizing its tri-axial structure. Validated on the Brain Tumor Segmentation Challenge 2018 and 2020 datasets, the TACA-RNet demonstrated superior performances over contemporary methodologies. This underscores the critical role of leveraging the three-axis structure of MRI to enhance segmentation accuracy.

In situ temperature determination using magnetic resonance spectroscopy thermometry for noninvasive postmortem examinations.

Magnetic resonance spectroscopy (MRS) thermometry offers a noninvasive, localized method for estimating temperature by leveraging the temperature-dependent chemical shift of water relative to a temperature-stable reference metabolite under suitable calibration. Consequentially, this technique has significant potential as a tool for postmortem MR examinations in forensic medicine and pathology. In these examinations, the deceased are examined at a wide range of body temperatures, and MRS thermometry may be used for the temperature adjustment of magnetic resonance imaging (MRI) protocols or for corrections in the analysis of MRI or MRS data. However, it is not yet clear to what extent postmortem changes may influence temperature estimation with MRS thermometry. In addition, N-acetylaspartate, which is commonly used as an in vivo reference metabolite, is known to decrease with increasing postmortem interval (PMI). This study shows that lactate, which is not only present in significant amounts postmortem but also has a temperature-stable chemical shift, can serve as a suitable reference metabolite for postmortem MRS thermometry. Using lactate, temperature estimation in postmortem brain tissue of severed sheep heads was accurate up to 60 h after death, with a mean absolute error of less than 0.5°C. For this purpose, published calibrations intended for in vivo measurements were used. Although postmortem decomposition resulted in severe metabolic changes, no consistent deviations were observed between measurements with an MR-compatible temperature probe and MRS thermometry with lactate as a reference metabolite. In addition, MRS thermometry was applied to 84 deceased who underwent a MR examination as part of the legal examination. MRS thermometry provided plausible results of brain temperature in comparison with rectal temperature. Even for deceased with a PMI well above 60 h, MRS thermometry still provided reliable readings. The results show a good suitability of MRS thermometry for postmortem examinations in forensic medicine.

From Connectivity to Controllability: Unraveling the Brain Biomarkers of Major Depressive Disorder

Major Depressive Disorder (MDD) is a significant neurological condition associated with aberrations in brain functional networks. Traditional studies have predominantly analyzed these from a network topology perspective. However, given the brain’s dynamic and complex nature, exploring its mechanisms from a network control standpoint provides a fresh and insightful framework. This research investigates the integration of network controllability and machine learning to pinpoint essential biomarkers for MDD using functional magnetic resonance imaging (fMRI) data. By employing network controllability methods, we identify crucial brain regions that are instrumental in facilitating transitions between brain states. These regions demonstrate the brain’s ability to navigate various functional states, emphasizing the utility of network controllability metrics as potential biomarkers. Furthermore, these metrics elucidate the complex dynamics of MDD and support the development of precision medicine strategies that incorporate machine learning to improve the precision of diagnostics and the efficacy of treatments. This study underscores the value of merging machine learning with network neuroscience to craft personalized interventions that align with the unique pathological profiles of individuals, ultimately enhancing the management and treatment of MDD.

Enhanced Segmentation of Ischemic Stroke Lesion in MRI Images Using a Geometrically Customised Deep Convolution Model (GCDCM)

Ischemic stroke lesion often known as a stroke, is a significant health issue that requires accurate analysis and classification of brain magnetic resonance imaging (MRI) data. In this study, we propose a novel deep transfer learning approach, called geometrically customized deep convolution model, for the purpose of MRI analysis and classification of brain stroke. Neurostroke segmentation is a serious medical image processing challenge. Segmented regions aid disease identification and treatment. Anywhere can form thrombi. Segmentation facilitates automatic detection because they can be any size or shape. Popular image analysis tool MRI diagnoses well. This diagnostic method shows brain stroke architecture. MRI must replace manual detection. Online datasets recommended cerebral stroke detection and segmentation. Deep learning model MRI scans and detectron 2 with masked CNN Nets segment thrombus. This net architecture recognises dataset stroke boundaries. Classifying strokes with vgg16, resnet50, inceptionv3, and resnet5 transfer learning is possible. Mask the image, then binary predict by eliminating the skull, extracting features, and iterating to find stroke. The model and thrombus mask are predicted if the binary prediction matches the human forecast. Otherwise, data processing resumes. Binary prediction uses the segmentation region and pixels overlap between the ground truth and predicted segmentation to calculate parameters. Compared to reality, the categorization of medical images with weak signals seems tough, especially with a short "train" dataset. Mixing deep learning architectures avoids these drawbacks and extracts signals to accurately classify classes. Deep neural networks best recognise, find, and divide computer vision objects for clinical image analysis. Preprocessing MRI scans, skull stripping with deep CNN architecture combinational net, and brain stroke segmentation are our main tasks. Modern medical image processing is hard. Flexible and uneven borders make brain strokes hard to identify and segment. The transfer learning-based super pixel approach segments brainstrokes. Because we predict every visual pixel, dense prediction occurs. Early discovery of thrombus improves treatment and survival. These procedures have considerably improved our quality indexes.