Quantitative brain volumetry in neurological disorders: from disease mechanisms to software solutions

Quantitative magnetic resonance imaging (MRI) volumetry has become a pivotal component in modern neurology, bridging the gap between detailed neuroimaging and clinical decision-making. By employing advanced imaging techniques like 3D T1-weighted, T2-weighted, and fluid-attenuated inversion recovery (FLAIR) sequences, MRI volumetry enables clinicians to objectively quantify brain volume changes associated with neurological conditions such as Alzheimer's disease, multiple sclerosis, epilepsy, and myotonic dystrophy. Automated segmentation tools, including FreeSurfer, NeuroQuant, volBrain, and AccuBrain, facilitate precise and reproducible analysis of structural brain changes, contributing significantly to early diagnosis, patient monitoring, and therapeutic planning. In Alzheimer's disease, volumetric MRI enables the detection of early hippocampal and temporal lobe atrophy, providing a crucial biomarker for diagnosis and monitoring disease progression. Similarly, in multiple sclerosis, volumetric analyses quantify grey and white matter degeneration, reflecting motor and cognitive impairment severity. Moreover, quantitative MRI techniques precisely delineate structural abnormalities like hippocampal sclerosis and focal cortical dysplasia in epilepsy, crucial for accurate surgical intervention. Ongoing advances in artificial intelligence and machine learning are set to further enhance these volumetric approaches, addressing current limitations such as inter-observer variability and expanding their clinical applicability. This review outlines the existing landscape and future trajectory of quantitative MRI volumetry, underscoring its expanding role in clinical neurology and personalised medicine.