Diffusion tensor imaging (DTI) could provide convenient and crucial insights into the underlying age-related biological
maturation of human brains, including myelination, axonal density changes, fiber tract reorganization, and synaptic
pruning processes. Fractional anisotropy (FA) derived from DTI has been commonly used to characterize cellular
morphological changes associated with the development of human brain, due to its sensitivity to microstructural
changes. In this paper, we aim to discern the longitudinal neurodevelopmental patterns in typically maturing human
brains using 200 healthy subjects from 5 to 22 years of age, based on the FA in cortical gray matter (GM). Specifically,
FA image is first aligned with the corresponding T1 image, which has been parcellated into different cortical ROIs, and
then the average FA in each ROI is computed. Linear mixed model is used to analyze the FA developmental pattern in
each cortical ROI. The developmental trajectory of FA in each ROI across ages is delineated, and the best-fitting models
of age-related changes in FA were linear for all ROIs. FA generally increases with the age from 5 to 22 years of age. In
addition, males and females follow the similar pattern, with the FA of females being generally lower than that of males
in most ROIs. This provides us some insights into the microstructural changes in the longitudinal cerebral cortex development.
Accurate and consistent skull stripping of serial brain MR images is of great importance in longitudinal studies that aim
to detect subtle brain morphological changes. To avoid inconsistency and the potential bias introduced by independently
performing skull-stripping for each time-point image, we propose an effective method that is capable of skull-stripping
serial brain MR images simultaneously. Specifically, all serial images of the same subject are first affine aligned in a
groupwise manner to a common space to avoid any potential bias introduced by asymmetric transforms. A brain
probability map, which encapsulates prior information gathered from a population of real brain MR images, is then
warped to the aligned serial images for guiding skull-stripping via a deformable surface method. In particular, the same
initial surface meshes representing the initial brain surfaces are first placed on all aligned serial images, and then all
these surface meshes are simultaneously evolved to the respective target brain boundaries, driven by the intensity-based
force, the force from the probability map, as well as the force from the spatial and temporal smoothness. Especially,
imposing the temporal smoothness helps achieve longitudinally consistent results. Evaluations on 20 subjects, each with
4 time points, from the ADNI database indicate that our method gives more accurate and consistent result compared with
3D skull-stripping method. To better show the advantages of our 4D brain extraction method over the 3D method, we
compute the Dice ratio in a ring area (±5mm) surrounding the ground-truth brain boundary, and our 4D method achieves
around 3% improvement over the 3D method. In addition, our 4D method also gives smaller mean and maximal surface-to-
surface distance measurements, with reduced variances.
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