Precise delineation of brain tissues, including lesions, in MR images is crucial for data analysis and objectively assessing conditions like neurological disorders and brain tumors. Existing methods for tissue segmentation often fall short in addressing patients with lesions, particularly those with brain tumors. This study aimed to develop and evaluate a robust pipeline utilizing convolutional neural networks for rapid and automatic segmentation of whole brain tissues, including tumor lesions. The proposed pipeline was developed using BraTS'21 data (1251 patients) and tested on local hospital data (100 patients). Ground truth masks for lesions as well as brain tissues were generated. Two convolutional neural networks based on deep residual U-Net framework were trained for segmenting brain tissues and tumor lesions. The performance of the pipeline was evaluated on independent test data using dice similarity coefficient (DSC) and volume similarity (VS). The proposed pipeline achieved a mean DSC of 0.84 and a mean VS of 0.93 on the BraTS'21 test data set. On the local hospital test data set, it attained a mean DSC of 0.78 and a mean VS of 0.91. The proposed pipeline also generated satisfactory masks in cases where the SPM12 software performed inadequately. The proposed pipeline offers a reliable and automatic solution for segmenting brain tissues and tumor lesions in MR images. Its adaptability makes it a valuable tool for both research and clinical applications, potentially streamlining workflows and enhancing the precision of analyses in neurological and oncological studies.

Brain plays a crucial role in regulating body functions and cognitive processes, with brain tumors posing significant risks to human health. Precise and prompt detection is a key factor in proper treatment and better patient outcomes. Traditional methods for detecting brain tumors, that include biopsies, MRI, and CT scans often face challenges due to their high costs and the need for specialized medical expertise. Recent developments in machine learning (ML) and deep learning (DL) has exhibited strong capabilities in automating the identification and categorization of brain tumors from medical images, especially MRI scans. However, these classical ML models have limitations, such as high computational demands, the need for large datasets, and long training times, which hinder their accessibility and efficiency. Our research uses MobileNET model for efficient detection of these tumors. The novelty of this project lies in building an accurate tumor detection model which use less computing re-sources and runs in less time followed by efficient decision making through the use of image processing technique for accurate results. The suggested method attained an average accuracy of 98.5%.

Deep learning (DL) methods are increasingly outperforming classical approaches in brain imaging, yet their generalizability across diverse imaging cohorts remains inadequately assessed. As age and sex are key neurobiological markers in clinical neuroscience, influencing brain structure and disease risk, this study evaluates three of the existing three-dimensional architectures, namely Simple Fully Connected Network (SFCN), DenseNet, and Shifted Window (Swin) Transformers, for age and sex prediction using T1-weighted MRI from four independent cohorts: UK Biobank (UKB, n=47,390), Dallas Lifespan Brain Study (DLBS, n=132), Parkinson's Progression Markers Initiative (PPMI, n=108 healthy controls), and Information eXtraction from Images (IXI, n=319). We found that SFCN consistently outperformed more complex architectures with AUC of 1.00 [1.00-1.00] in UKB (internal test set) and 0.85-0.91 in external test sets for sex classification. For the age prediction task, SFCN demonstrated a mean absolute error (MAE) of 2.66 (r=0.89) in UKB and 4.98-5.81 (r=0.55-0.70) across external datasets. Pairwise DeLong and Wilcoxon signed-rank tests with Bonferroni corrections confirmed SFCN's superiority over Swin Transformer across most cohorts (p<0.017, for three comparisons). Explainability analysis further demonstrates the regional consistency of model attention across cohorts and specific to each task. Our findings reveal that simpler convolutional networks outperform the denser and more complex attention-based DL architectures in brain image analysis by demonstrating better generalizability across different datasets.

Cerebral infarction remains a leading cause of mortality and long-term disability globally, underscoring the critical importance of early diagnosis and timely intervention to enhance patient outcomes. This study introduces a novel approach to cerebral infarction segmentation using a novel dataset comprising MRI scans of 110 patients, retrospectively collected from Yozgat Bozok University Research Hospital. Two convolutional neural network architectures, the basic U-Net and the advanced U-Net3 + , are employed to segment infarction regions with high precision. Ground-truth annotations are generated under the supervision of an experienced radiologist, and data augmentation techniques are applied to address dataset limitations, resulting in 6732 balanced images for training, validation, and testing. Performance evaluation is conducted using metrics such as the dice score, Intersection over Union (IoU), pixel accuracy, and specificity. The basic U-Net achieved superior performance with a dice score of 0.8947, a mean IoU of 0.8798, a pixel accuracy of 0.9963, and a specificity of 0.9984, outperforming U-Net3 + despite its simpler architecture. U-Net3 + , with its complex structure and advanced features, delivered competitive results, highlighting the potential trade-off between model complexity and performance in medical imaging tasks. This study underscores the significance of leveraging deep learning for precise and efficient segmentation of cerebral infarction. The results demonstrate the capability of CNN-based architectures to support medical decision-making, offering a promising pathway for advancing stroke diagnosis and treatment planning.

We evaluated a dedicated dose-reduced UHR-CT for head and neck imaging, combined with a novel deep learning reconstruction algorithm to assess its impact on image quality and radiation exposure. Retrospective analysis of ninety-eight consecutive patients examined using a new body weight-adapted protocol. Images were reconstructed using adaptive iterative dose reduction and advanced intelligent Clear-IQ engine with an already established (DL-1) and a newly implemented reconstruction algorithm (DL-2). Additional thirty patients were scanned without body-weight-adapted dose reduction (DL-1-SD). Three readers evaluated subjective image quality regarding image quality and assessment of several anatomic regions. For objective image quality, signal-to-noise ratio and contrast-to-noise ratio were calculated for temporalis and masseteric muscle and the floor of the mouth. Radiation dose was evaluated by comparing the computed tomography dose index (CTDIvol) values. Deep learning-based reconstruction algorithms significantly improved subjective image quality (diagnostic acceptability: DL‑1 vs AIDR OR of 25.16 [6.30;38.85], p < 0.001 and DL‑2 vs AIDR 720.15 [410.14;> 999.99], p < 0.001). Although higher doses (DL-1-SD) resulted in significantly enhanced image quality, DL‑2 demonstrated significant superiority over all other techniques across all defined parameters (p < 0.001). Similar results were demonstrated for objective image quality, e.g. image noise (DL‑1 vs AIDR OR of 19.0 [11.56;31.24], p < 0.001 and DL‑2 vs AIDR > 999.9 [825.81;> 999.99], p < 0.001). Using weight-adapted kV reduction, very low radiation doses could be achieved (CTDIvol: 7.4 ± 4.2 mGy). AI-based reconstruction algorithms in ultra-high resolution head and neck imaging provide excellent image quality while achieving very low radiation exposure.

We introduce MRF-DiPh, a novel physics informed denoising diffusion approach for multiparametric tissue mapping from highly accelerated, transient-state quantitative MRI acquisitions like Magnetic Resonance Fingerprinting (MRF). Our method is derived from a proximal splitting formulation, incorporating a pretrained denoising diffusion model as an effective image prior to regularize the MRF inverse problem. Further, during reconstruction it simultaneously enforces two key physical constraints: (1) k-space measurement consistency and (2) adherence to the Bloch response model. Numerical experiments on in-vivo brain scans data show that MRF-DiPh outperforms deep learning and compressed sensing MRF baselines, providing more accurate parameter maps while better preserving measurement fidelity and physical model consistency-critical for solving reliably inverse problems in medical imaging.

The connection between cognition, eye, and brain remains inconclusive in Alzheimer's disease (AD) spectrum disorders. To explore the relationship between cognitive function, retinal biometrics, and brain alterations in the AD spectrum. Prospective. Healthy control (HC) (n = 16), subjective cognitive decline (SCD) (n = 35), mild cognitive impairment (MCI) (n = 18), and AD group (n = 7). 3-T, 3D T1-weighted Brain Volume (BRAVO) and resting-state functional MRI (fMRI). In all subgroups, cortical thickness was measured from BRAVO and segmented using the Desikan-Killiany-Tourville (DKT) atlas. The fractional amplitude of low-frequency fluctuations (FALFF) and regional homogeneity (ReHo) were measured in fMRI using voxel-based analysis. The eye was imaged by optical coherence tomography angiography (OCTA), with the deep learning model FARGO segmenting the foveal avascular zone (FAZ) and retinal vessels. FAZ area and perimeter, retinal blood vessels curvature (RBVC), thicknesses of the retinal nerve fiber layer (RNFL) and ganglion cell layer-inner plexiform layer (GCL-IPL) were calculated. Cognition-eye-brain associations were compared across the HC group and each AD spectrum stage using multivariable linear regression. Multivariable linear regression analysis. Statistical significance was set at p < 0.05 with FWE correction for fMRI and p < 1/62 (Bonferroni-corrected) for structural analyses. Reductions of FALFF in temporal regions, especially the left superior temporal gyrus (STG) in MCI patients, were linked to decreased RNFL thickness and increased FAZ area significantly. In AD patients, reduced ReHo values in occipital regions, especially the right middle occipital gyrus (MOG), were significantly associated with an enlarged FAZ area. The SCD group showed widespread cortical thickening significantly associated with all aforementioned retinal biometrics, with notable thickening in the right fusiform gyrus (FG) and right parahippocampal gyrus (PHG) correlating with reduced GCL-IPL thickness. Brain function and structure may be associated with cognition and retinal biometrics across the AD spectrum. Specifically, cognition-eye-brain connections may be present in SCD. 2. 3.

Cerebral perivascular spaces (PVS) are involved in cerebrospinal fluid (CSF) circulation and clearance of metabolic waste in adult humans. A high number of PVS has been reported in autism spectrum disorder (ASD) but its relationship with CSF and disease severity is unclear. To quantify PVS in children with ASD through MRI. Retrospective. Sixty six children with ASD (mean age: 4.7 ± 1.5 years; males/females: 59/7). 3T, 3D T1-weighted GRE and 3D T2-weighted turbo spin echo sequences. PVS were segmented using a weakly supervised PVS algorithm. PVS count, white matter-perivascular spaces (WM-PVS_tot) and normalized volume (WM-PVS_voln) were analyzed in the entire white matter. Six regions: frontal, parietal, limbic, occipital, temporal, and deep WM (WM-PVS_sr). WM, GM, CSF, and extra-axial CSF (eaCSF) volumes were also calculated. Autism Diagnostic Observation Schedule, Wechsler Intelligence Scale, and Griffiths Mental Developmental scales were used to assess clinical severity and developmental quotient (DQ). Kendall correlation analysis (continuous variables) and Friedman (categorical variables) tests were used to compare medians of PVS variables across different WM regions. Post hoc pairwise comparisons with Wilcoxon tests were used to evaluate distributions of PVS in WM regions. Generalized linear models were employed to assess DQ, clinical severity, age, and eaCSF volume in relation to PVS variables. A p-value < 0.05 indicated statistical significance. Severe DQ (β = 0.0089), mild form of autism (β = -0.0174), and larger eaCSF (β = 0.0082) volume was significantly associated with greater WM-PVS_tot count. WM-PVS_voln was predominantly affected by normalized eaCSF volume (eaCSF_voln) (β = 0.0242; adjusted for WM volumes). The percentage of WM-PVS_sr was higher in the frontal areas (32%) and was lowest in the temporal regions (11%). PVS count and volume in ASD are associated with eaCSF_voln. PVS count is related to clinical severity and DQ. PVS count was higher in frontal regions and lower in temporal regions. 4. Stage 3.

Segmentation of brain structures from MRI is crucial for evaluating brain morphology, yet existing CNN and transformer-based methods struggle to delineate complex structures accurately. While current diffusion models have shown promise in image segmentation, they are inadequate when applied directly to brain MRI due to neglecting anatomical information. To address this, we propose Collaborative Anatomy Diffusion (CA-Diff), a framework integrating spatial anatomical features to enhance segmentation accuracy of the diffusion model. Specifically, we introduce distance field as an auxiliary anatomical condition to provide global spatial context, alongside a collaborative diffusion process to model its joint distribution with anatomical structures, enabling effective utilization of anatomical features for segmentation. Furthermore, we introduce a consistency loss to refine relationships between the distance field and anatomical structures and design a time adapted channel attention module to enhance the U-Net feature fusion procedure. Extensive experiments show that CA-Diff outperforms state-of-the-art (SOTA) methods.

Understanding brain dynamics through functional Magnetic Resonance Imaging (fMRI) remains a fundamental challenge in neuroscience, particularly in capturing how the brain transitions between various functional states. Recently, metastability, which refers to temporarily stable brain states, has offered a promising paradigm to quantify complex brain signals into interpretable, discretized representations. In particular, compared to cluster-based machine learning approaches, tokenization approaches leveraging vector quantization have shown promise in representation learning with powerful reconstruction and predictive capabilities. However, most existing methods ignore brain transition dependencies and lack a quantification of brain dynamics into representative and stable embeddings. In this study, we propose a Hierarchical State space-based Tokenization network, termed HST, which quantizes brain states and transitions in a hierarchical structure based on a state space-based model. We introduce a refined clustered Vector-Quantization Variational AutoEncoder (VQ-VAE) that incorporates quantization error feedback and clustering to improve quantization performance while facilitating metastability with representative and stable token representations. We validate our HST on two public fMRI datasets, demonstrating its effectiveness in quantifying the hierarchical dynamics of the brain and its potential in disease diagnosis and reconstruction performance. Our method offers a promising framework for the characterization of brain dynamics, facilitating the analysis of metastability.