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“sCT-Feasibility” - a feasibility study for deep learning-based MRI-only brain radiotherapy
BackgroundRadiotherapy (RT) is an important treatment modality for patients with brain malignancies. Traditionally, computed tomography (CT) images...
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Human Brain MRI Segmentation Approaches and Challenges: A Review
In many clinical applications, brain MRI breakdown is a crucial step since it affects how the overall study turns out. This is so that various...
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Brain MRI correlations with disease burden and biomarkers in Fabry disease
ObjectiveTo quantitatively evaluate cerebral small vessel disease (CSVD) in brain magnetic resonance imaging (MRI) and its correlation with disease...
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Segmentation of Brain Tumours from MRI Images Using CNN
Identification of brain tumours in the early stage is key to proper treatment and diagnosis. It can be classified as malignant or benign based on the...
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An open resource combining multi-contrast MRI and microscopy in the macaque brain
Understanding brain structure and function often requires combining data across different modalities and scales to link microscale cellular...
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Cross2SynNet: cross-device–cross-modal synthesis of routine brain MRI sequences from CT with brain lesion
ObjectivesCT and MR are often needed to determine the location and extent of brain lesions collectively to improve diagnosis. However, patients with...
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Elective one-minute full brain multi-contrast MRI versus brain CT in pediatric patients: a prospective feasibility study
BackgroundBrain CT can be used to evaluate pediatric patients with suspicion of cerebral pathology when anesthetic and MRI resources are scarce. This...
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Site Effects in Multisite Fetal Brain MRI: A Morphological Study of Early Brain Development
Studies have shown that the non-biological site-related effects may induce bias in multisite neuroimaging studies among adults and adolescents. It is...
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Refining neural network algorithms for accurate brain tumor classification in MRI imagery
Brain tumor diagnosis using MRI scans poses significant challenges due to the complex nature of tumor appearances and variations. Traditional methods...
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Automated neonatal nnU-Net brain MRI extractor trained on a large multi-institutional dataset
Brain extraction, or skull-strip**, is an essential data preprocessing step for machine learning approaches to brain MRI analysis. Currently, there...
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Feasibility study on the clinical application of CT-based synthetic brain T1-weighted MRI: comparison with conventional T1-weighted MRI
ObjectivesThis study aimed to examine the equivalence of computed tomography (CT)–based synthetic T1-weighted imaging (T1WI) to conventional T1WI for...
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FetMRQC: Automated Quality Control for Fetal Brain MRI
Quality control (QC) has long been considered essential to guarantee the reliability of neuroimaging studies. It is particularly important for fetal...
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Speed of Diagnosis for Brain Diseases Using MRI and Convolutional Neural Networks
Accurately diagnosing brain diseases is crucial for effective treatment and improved patient outcomes. Magnetic Resonance Imaging is a regularly used...
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Brain Tumor Segmentation for Multi-Modal MRI with Missing Information
Deep convolutional neural networks (DCNNs) have shown promise in brain tumor segmentation from multi-modal MRI sequences, accommodating heterogeneity...
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Advanced AI-driven approach for enhanced brain tumor detection from MRI images utilizing EfficientNetB2 with equalization and homomorphic filtering
Brain tumors pose a significant medical challenge necessitating precise detection and diagnosis, especially in Magnetic resonance imaging(MRI)....
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Kernel induced semi-supervised spatial clustering: a novel brain MRI segmentation technique
Segmentation of different brain tissues such as white matter (WM), cerebrospinal fluid (CSF), and gray matter (GM) form magnetic resonance image...
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A Review of Brain Tumor MRI Classification and Feature Extraction Using Varying Methods
Early detection of brain tumors is critical for saving human lives. MRI is generally used in medical imaging for diagnosing and classifying brain...
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Automated classification of brain MRI reports using fine-tuned large language models
PurposeThis study aimed to investigate the efficacy of fine-tuned large language models (LLM) in classifying brain MRI reports into pretreatment,...
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Predicting brain age using Tri-UNet and various MRI scale features
In the process of human aging, significant age-related changes occur in brain tissue. To assist individuals in assessing the degree of brain aging,...
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Abnormal neonatal brain microstructure in gestational diabetes mellitus revealed by MRI texture analysis
To investigate the value of MRI texture analysis in evaluating the effect of gestational diabetes mellitus (GDM) on neonatal brain microstructure...
