29 March 2016 Surface displacement based shape analysis of central brain structures in preterm-born children
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Abstract
Many studies using T1 magnetic resonance imaging (MRI) data have found associations between changes in global metrics (e.g. volume) of brain structures and preterm birth. In this work, we use the surface displacement feature extracted from the deformations of the surface models of the third ventricle, fourth ventricle and brainstem to capture the variation in shape in these structures at 8 years of age that may be due to differences in the trajectory of brain development as a result of very preterm birth (24-32 weeks gestation). Understanding the spatial patterns of shape alterations in these structures in children who were born very preterm as compared to those who were born at full term may lead to better insights into mechanisms of differing brain development between these two groups. The T1 MRI data for the brain was acquired from children born full term (FT, n=14, 8 males) and preterm (PT, n=51, 22 males) at age 8-years. Accurate segmentation labels for these structures were obtained via a multi-template fusion based segmentation method. A high dimensional non-rigid registration algorithm was utilized to register the target segmentation labels to a set of segmentation labels defined on an average-template. The surface displacement data for the brainstem and the third ventricle were found to be significantly different (p < 0.05) between the PT and FT groups. Further, spatially localized clusters with inward and outward deformation were found to be associated with lower gestational age. The results from this study present a shape analysis method for pediatric MRI data and reveal shape changes that may be due to preterm birth.
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Amanmeet Garg, Ruth E. Grunau, Karteek Popuri, Steven Miller, Bruce Bjornson, Kenneth J. Poskitt, Mirza Faisal Beg, "Surface displacement based shape analysis of central brain structures in preterm-born children", Proc. SPIE 9788, Medical Imaging 2016: Biomedical Applications in Molecular, Structural, and Functional Imaging, 97880H (29 March 2016); doi: 10.1117/12.2216914; http://dx.doi.org/10.1117/12.2216914
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KEYWORDS
Magnetic resonance imaging

Shape analysis

Brain

Image segmentation

Neuroimaging

Binary data

Statistical analysis

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