Microstructural Analysis of Mild Traumatic Brain Injury in Pediatrics Using Diffusion Tensor Imaging and Quantitative Susceptibility Mapping

Abstract

Each year in the United States, approximately 1.35 million people are a ected by mTBI (aka concussion) and subsequent cognitive impairment. Approximately 33% of mTBI cases results in persistent long-term cognitive de cits despite no abnormalities appearing on conventional neuroimaging scans. Therefore, an accurate and reliable imaging method is needed to determine injury location and extent of healing. The goal of this study was to characterize and quantify mTBI through DTI, an advanced MRI technique that encodes voxel-wise tissue water microstructural di usivity as a tensor, as well as QSM, which measures iron deposition within tissues. We hypothesized that personalizing the analysis of DTI and QSM will provide a better understanding of trauma-induced microstructural damage leading to improved diagnosis and prognosis accuracy. Through regression analysis, a preliminary comparison between DTI data to QSM measurements was performed to determine potential correlations between the two MRI techniques. Further, a large database of healthy pediatric brain DTI data was downloaded and each was warped into a standardized brain template to ultimately use for voxel-wise z-score analysis of individual mTBI patients (n=26). This allowed localization and quantitation of abnormal regions on a per-patient basis. Signi cant abnormalities were commonly observed in a number of regions including the longitudinal fasciculus, fronto-occipital fasciculus, and corticospinal tract, while unique abnormalities were localized in a host of other areas (due to the individuality of each childs injury). Further, through group-based Bonferroni corrected T-test analysis, the mTBI group was signi cantly di erent from controls in approximately 65% of regions analyzed. These results show that DTI is sensitive to the detection of microstructural changes caused by mTBI and has potential to be a useful tool for improving mTBI diagnosis accuracy