Yongxia Zhou1,
Yao Wang2, Damon Kenul3, Yuanyi Xue2, Yulin
Ge3, Joseph Reaume3, Robert I. Grossman4,
Yvonne W. Lui3
1Radiology/Center
for Biomedical Imaging, New York University Langone Medical Center, New York,
NY, United States; 2Electrical & Computer Engineering,
Polytechnic Institute of New York University, Brooklyn, NY, United States; 3Radiology/Center
for Biomedical Engineering, New York University Langone Medical Center, New
York, NY, United States; 4Radiology/Center for Biomedical
Engineering, New York University, New York, NY, United States
The purpose of this study is to design and develop computational techniques to identify mild traumatic brain injury (MTBI) patients that can be used to help predict patient long-term outcome ultimately using multi-dimensional feature space based on several advanced quantitative MR measures. Fourteen imaging features (e.g. kurtosis, magnetic field correlations, thalamic network connectivity and regional volumetry), and nineteen clinical features were tested with different feature selection and classifier algorithms. Our study demonstrates that an automatic classification based on objective physical and imaging measures can achieve a high accuracy of nearly 100% and a robust prediction for the long-term outcome (P0.01).
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