Magnetic Resonance Imaging (MRI) is a critical imaging technique that provides detailed
visualization of internal structures without harmful radiation. This review focuses on key MRI …

Deep learning has become a leading subset of machine learning and has been successfully
employed in diverse areas, ranging from natural language processing to medical image …

Neuroimaging biomarkers have shown high potential to map the disease processes in the
application to neurodegenerative diseases (NDD), eg, diffusion tensor imaging (DTI). For …

In medical research and clinical applications, the utilization of MRI datasets from multiple
centers has become increasingly prevalent. However, inherent variability between these …

Combining large multi-center datasets can enhance statistical power, particularly in the field
of neurology, where data can be scarce. However, applying a deep learning model trained …

Abstract Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) is pivotal in
delineating abnormal lesions and cancerous regions in the anatomy of interest. However …

Abstract ALS (Amyotrophic Lateral Sclerosis) is a neurodegenerative disorder causing
profound physical disability that severely impairs a patient's life expectancy and quality of …

Introduction: Quantitative spinal cord imaging has facilitated the objective appraisal of spinal
cord pathology in a range of neurological conditions both in the academic and clinical …

The arrival of genotype-specific therapies in amyotrophic lateral sclerosis (ALS) signals the
dawn of precision medicine in motor neuron diseases (MNDs). After decades of academic …

The use of MR images in medical image analysis from different centers in clinical
applications and medical research has grown in popularity. However, challenges arise due …