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Proceedings Paper

Characterizing growth patterns in longitudinal MRI using image contrast
Author(s): Avantika Vardhan; Marcel Prastawa; Clement Vachet; Joseph Piven; Guido Gerig
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Paper Abstract

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.

Paper Details

Date Published: 21 March 2014
PDF: 8 pages
Proc. SPIE 9034, Medical Imaging 2014: Image Processing, 90340D (21 March 2014); doi: 10.1117/12.2043995
Show Author Affiliations
Avantika Vardhan, The Univ. of Utah (United States)
Marcel Prastawa, The Univ. of Utah (United States)
Clement Vachet, The Univ. of Utah (United States)
Joseph Piven, The Univ. of North Carolina at Chapel Hill (United States)
Guido Gerig, The Univ. of Utah (United States)


Published in SPIE Proceedings Vol. 9034:
Medical Imaging 2014: Image Processing
Sebastien Ourselin; Martin A. Styner, Editor(s)

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