Abstract
Atlases of the human brain have numerous applications in neurological imaging such as the analysis of brain growth. Publicly available atlases of the developing brain have previously been constructed using the arithmetic mean of free-form deformations which were obtained by asymmetric pairwise registration of brain images. Most of these atlases represent cross-sections of the growth process only. In this work, we use the Log-Euclidean mean of inverse consistent transformations which belong to the one-parameter subgroup of diffeomorphisms, as it more naturally represents average morphology. During the registration, similarity is evaluated symmetrically for the images to be aligned. As both images are equally affected by the deformation and interpolation, asymmetric bias is reduced. We further propose to represent longitudinal change by exploiting the numerous transformations computed during the atlas construction in order to derive a deformation model of mean growth. Based on brain images of 118 neonates, we constructed an atlas which describes the dynamics of early development through mean images at weekly intervals and a continuous spatio-temporal deformation. The evolution of brain volumes calculated on preterm neonates is in agreement with recently published findings based on measures of cortical folding of fetuses at the equivalent age range.
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Schuh, A. et al. (2015). Construction of a 4D Brain Atlas and Growth Model Using Diffeomorphic Registration. In: Durrleman, S., Fletcher, T., Gerig, G., Niethammer, M., Pennec, X. (eds) Spatio-temporal Image Analysis for Longitudinal and Time-Series Image Data. STIA 2014. Lecture Notes in Computer Science(), vol 8682. Springer, Cham. https://doi.org/10.1007/978-3-319-14905-9_3
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DOI: https://doi.org/10.1007/978-3-319-14905-9_3
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