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Modeling of Neonatal Skull Development Using Computed Tomography Images

Abstract : Neonatal skull anatomy and its evolution have received less attention with respect to the brain anatomy in neuroscience and neuroanatomy studies. Meanwhile, their influence on normal brain development and their impact on the results of functional brain studies have been demonstrated by several researches. Such disesteem is due to the weak appearance of the cranial bones, fontanels and sutures in images acquired by MRI which presents actually the only available aperture for observing the neonatal head volume in details. This paper presents an unprecedented retrospective CT-based study on modeling the neonatal skull and its development during the first weeks of life in a standard space defined by the available neonatal MRI model. We create two neonatal head atlases for the age ranges of 39-40 and 41-42 week's gestational age using symmetric group-wise normalization method. The created atlases allow direct observation of ossification patterns and precise three-dimensional measurement of anatomical features from neonatal skull during development. Development of the neonatal skull has been examined here using nineteen CT scans of neonates with two-week gestational age ranges of 39 to 40 and 41 to 42. Deformation-based morphometry method is applied with the use of Jacobian determinant maps to identify growth patterns and observe ossification during specified time interval. Precise three-dimensional measurements of anterior fontanel size, scalp eliminated head circumference and the area corresponding to the fontanel-sutures were performed by extracting fontanels and sutures. (c) 2020 AGBM. Published by Elsevier Masson SAS. All rights reserved.
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https://hal-u-picardie.archives-ouvertes.fr/hal-03602674
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Soumis le : mercredi 9 mars 2022 - 11:55:37
Dernière modification le : jeudi 10 mars 2022 - 03:00:16

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M. Mohtasebi, M. Bayat, S. Ghadimi, H. Abrishami Moghaddam, Fabrice Wallois. Modeling of Neonatal Skull Development Using Computed Tomography Images. IRBM, 2021, 42 (1), ⟨10.1016/j.irbm.2020.02.002⟩. ⟨hal-03602674⟩

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