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Modeling Longitudinal MRI Changes in Populations using a Localized, Information-Theoretic Measure of Contrast

Institution:
1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
Publisher:
ISBI 2013
Publication Date:
Dec-2013
Journal:
Proc IEEE Int Symp Biomed Imaging.
Volume Number:
2013
Pages:
1396-9
Citation:
Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:1396-9.
PubMed ID:
24443698
PMCID:
PMC3892761
Keywords:
Contrast, longitudinal MRI, regression, Kullback-Leibler
Appears in Collections:
NA-MIC
Sponsors:
R01 HD055741/HD/NICHD NIH HHS/United States
P50 MH064065/MH/NIMH NIH HHS/United States
R01 MH070890/MH/NIMH NIH HHS/United States
U54 EB005149/EB/NIBIB NIH HHS/United States
Generated Citation:
Vardhan A., Prastawa M., Sharma A., Piven J., Gerig G. Modeling Longitudinal MRI Changes in Populations using a Localized, Information-Theoretic Measure of Contrast. Proc IEEE Int Symp Biomed Imaging. 2013 Dec 31;2013:1396-9. PMID: 24443698. PMCID: PMC3892761.
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Longitudinal MR imaging during early brain development provides important information about growth patterns and the development of neurological disorders. We propose a new framework for studying brain growth patterns within and across populations based on MRI contrast changes, measured at each time point of interest and at each voxel. Our method uses regression in the LogOdds space and an information- theoretic measure of distance between distributions to capture contrast in a manner that is robust to imaging parameters and without requiring intensity normalization. We apply our method to a clinical neuroimaging study on early brain development in autism, where we obtain a 4D spatiotemporal model of contrast changes in multimodal structural MRI.

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