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Analyzing Imaging Biomarkers for Traumatic Brain Injury using 4D Modeling of Longitudinal MRI

Institution:
1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2Laboratory of Neuro Imaging, University of California at Los Angeles, CA, USA.
3Brain Injury Research Center, University of California at Los Angeles, CA, USA.
Publisher:
ISBI 2013
Publication Date:
Dec-2013
Journal:
Proc IEEE Int Symp Biomed Imaging.
Volume Number:
2013
Pages:
1392-5
Citation:
Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:1392-1395.
PubMed ID:
24443697
PMCID:
PMC3892715
Keywords:
Imaging biomarker, longitudinal MRI, correlation analysis, clinical outcome
Appears in Collections:
NA-MIC
Sponsors:
U54 EB005149/EB/NIBIB NIH HHS/United States
Utah Science Technology and Research (USTAR) initiative at the University of Utah
Generated Citation:
Wang B., Prastawa M., Irimia A., Chambers M.C., Sadeghi N., Vespa P.M., Van Horn J.D., Gerig G. Analyzing Imaging Biomarkers for Traumatic Brain Injury using 4D Modeling of Longitudinal MRI. Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:1392-1395. PMID: 24443697. PMCID: PMC3892715.
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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 followup 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.

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