ANALYZING IMAGING BIOMARKERS FOR TRAUMATIC BRAIN INJURY USING 4D MODELING OF LONGITUDINAL MRI.
Quantitative imaging biomarkers are important for assessment of impact, recovery and treatment efficacy in patients with traumatic brain injury (TBI). To our knowledge, the identification of such biomarkers characterizing disease progress and recovery has been insufficiently explored in TBI due to difficulties in registration of baseline and follow-up data and automatic segmentation of tissue and lesions from multimodal, longitudinal MR image data. We propose a new methodology for computing imaging biomarkers in TBI by extending a recently proposed spatiotemporal 4D modeling approach in order to compute quantitative features of tissue change. The proposed method computes surface-based and voxel-based measurements such as cortical thickness, volume changes, and geometric deformation. We analyze the potential for clinical use of these biomarkers by correlating them with TBI-specific patient scores at the level of the whole brain and of individual regions. Our preliminary results indicate that the proposed voxel-based biomarkers are correlated with clinical outcomes.
Scientific Computing and Imaging Institute, University of Utah ; School of Computing, University of Utah.
Bo
B
Wang
Scientific Computing and Imaging Institute, University of Utah ; School of Computing, University of Utah.
Marcel
M
Prastawa
Laboratory of Neuro Imaging, University of California at Los Angeles.
Andrei
A
Irimia
Laboratory of Neuro Imaging, University of California at Los Angeles.
Micah C
MC
Chambers
Scientific Computing and Imaging Institute, University of Utah.
Neda
N
Sadeghi
Brain Injury Research Center, University of California at Los Angeles.
Paul M
PM
Vespa
Laboratory of Neuro Imaging, University of California at Los Angeles.
John D
JD
van Horn
Scientific Computing and Imaging Institute, University of Utah ; School of Computing, University of Utah.
Guido
G
Gerig
Imaging biomarkers; clinical outcome; correlation analysis; longitudinal MRI