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Characterizing Growth Patterns in Longitudinal MRI using Image Contrast

1Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA.
2Department of Psychiatry, University of North Carolina, Chapel Hill, NC, USA.
Publication Date:
Volume Number:
Proc SPIE. 2014 Mar 21;9034:90340D.
PubMed ID:
Contrast, Contrast Change Trajectories, Early brain development, Reliability, Structural MRI, Time-based biomarkers
Appears in Collections:
P50 MH064065/MH/NIMH NIH HHS/United States
R01 HD055741/HD/NICHD 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., Vachet C., Piven J., Gerig G. Characterizing Growth Patterns in Longitudinal MRI using Image Contrast. Proc SPIE. 2014 Mar 21;9034:90340D. PMID: 25309699. PMCID: PMC4193386.
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Understanding the growth patterns of the early brain is crucial to the study of neuro-development. In the early stages of brain growth, a rapid sequence of biophysical and chemical processes take place. A crucial component of these processes, known as myelination, consists of the formation of a myelin sheath around a nerve fiber, enabling the effective transmission of neural impulses. As the brain undergoes myelination, there is a subsequent change in the contrast between gray matter and white matter as observed in MR scans. In this work, gray-white matter contrast is proposed as an effective measure of appearance which is relatively invariant to location, scanner type, and scanning conditions. To validate this, contrast is computed over various cortical regions for an adult human phantom. MR (Magnetic Resonance) images of the phantom were repeatedly generated using different scanners, and at different locations. Contrast displays less variability over changing conditions of scan compared to intensity-based measures, demonstrating that it is less dependent than intensity on external factors. Additionally, contrast is used to analyze longitudinal MR scans of the early brain, belonging to healthy controls and Down's Syndrome (DS) patients. Kernel regression is used to model subject-specific trajectories of contrast changing with time. Trajectories of contrast changing with time, as well as time-based biomarkers extracted from contrast modeling, show large differences between groups. The preliminary applications of contrast based analysis indicate its future potential to reveal new information not covered by conventional volumetric or deformation-based analysis, particularly for distinguishing between normal and abnormal growth patterns.