Diffusion Tensor Imaging for Brain Injury Assessment: Methodological Foundations and Clinical Insights

Diffusion tensor imaging (DTI) has emerged as a powerful neuroimaging modality for investigating white matter microstructure and its alterations following brain injury. This review presents a comprehensive overview of DTI, encompassing its physical principles, mathematical modeling of diffusion tensors, and known limitations of the technique. We explore key methodological considerations, including acquisition protocols, preprocessing pipelines, vendor-related variability, atlas registration, and the role of diffusion phantoms in calibration. With the rise of big data in medical imaging, we highlight the influence of large-scale, multisite datasets and open-source neuroimaging repositories in advancing DTI research. A central focus is placed on the application of DTI in mild traumatic brain injury, a condition that often eludes detection in conventional imaging settings. We evaluate emerging computational strategies, including Z-score analysis, principal component analysis, random forests, and generative adversarial networks-that improve the sensitivity, specificity, and interpretability of DTI metrics in both clinical and research settings. By bridging methodological rigor with translational insight, this review underscores the evolving potential of DTI as a neuroimaging biomarker for brain injury assessment.