Treatment of traumatic brain injury (TBI) requires proper classification of the pathophysiology. Clinical classifiers and conventional neuroimaging are limited in TBI detection, outcome prediction, and treatment guidance. Advanced magnetic resonance imaging (MRI) techniques such as susceptibility weighted imaging, diffusion tensor imaging, and magnetic resonance spectroscopic imaging are sensitive to microhemorrhages, white matter injury, and abnormal metabolic activities, respectively, in brain injury. In this article, we reviewed these 3 advanced MRI methods and their applications in TBI and report some new findings from our research. These MRI techniques have already demonstrated their potential to improve TBI detection and outcome prediction. As such, they have demonstrated the capacity of serving as a set of biomarkers to reveal the heterogeneous and complex nature of brain injury in a regional and temporal manner. Further longitudinal studies using advanced MRI in a synergistic approach are expected to provide insight in understanding TBI and imaging implications for treatment.
Departments of Biomedical Engineering and Radiology (Dr Kou), Department of Neurology, School of Medicine (Dr Benson), and MR Research Facility, Department of Radiology, School of Medicine (Drs Hu and Haacke), Wayne State University, Detroit, Michigan; School of Biomedical Engineering, McMaster University, Hamilton, Ontario, Canada (Drs Wu and Haacke); and Department of Radiology, Loma Linda University Medical Center, Loma Linda, California (Drs Tong and Holshouser).
Corresponding Author: E. Mark Haacke, PhD, MR Research Facility, Department of Radiology, School of Medicine, Wayne State University, 3990 John R St, Detroit, MI 48201 (firstname.lastname@example.org).
E. Mark Haacke has federal funding via NIH 62983 on susceptibility-weighted imaging and a contract with Siemens Medical Systems on susceptibility-weighted imaging. The authors thank Jie Yang, PhD, for assistance in editing; Ramtilak Gattu, MS, for DTI image processing; and Zahid Latif, RT, and Yang Xuan, BS, for data acquisition.