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Understanding human brain development in utero and detecting cortical abnormalities related to specific clinical conditions is an important area of research. In this paper, we describe and evaluate methodology for detection and mapping of delays in early cortical folding from population-based studies of fetal brain anatomies imaged in utero. We use a general linear modeling framework to describe spatiotemporal changes in curvature of the developing brain and explore the ability to detect and localize delays in cortical folding in the presence of uncertainty in estimation of the fetal age. We apply permutation testing to examine which regions of the brain surface provide the most statistical power to detect a given folding delay at a given developmental stage. The presented methodology is evaluated using MR scans of fetuses with normal brain development and gestational ages ranging from 20.57 to 27.86 weeks. This period is critical in early cortical folding and the formation of the primary and secondary sulci. Finally, we demonstrate a clinical application of the framework for detection and localization of folding delays in fetuses with isolated mild ventriculomegaly.

Recent studies reported the development of methods for rigid registration of 2D fetal brain imaging data to correct for unconstrained fetal and maternal motion, and allow the formation of a true 3D image of conventional fetal brain anatomy from conventional MRI. Diffusion tensor imaging provides additional valuable insight into the developing brain anatomy, however the correction of motion artifacts in clinical fetal diffusion imaging is still a challenging problem. This is due to the challenging problem of matching lower signal-to-noise ratio diffusion weighted EPI slice data to recover between-slice motion, compounded by the presence of possible geometric distortions in the EPI data. In addition, the problem of estimating a diffusion model (such as a tensor) on a regular grid that takes into account the inconsistent spatial and orientation sampling of the diffusion measurements needs to be solved in a robust way. Previous methods have used slice to volume registration within the diffusion dataset. In this work, we describe an alternative approach that makes use of an alignment of diffusion weighted EPI slices to a conventional structural MRI scan which provides a geometrically correct reference image. After spatial realignment of each diffusion slice, a tensor field representing the diffusion profile is estimated by weighted least squared fitting. By qualitative and quantitative evaluation of the results, we confirm the proposed algorithm successfully corrects the motion and reconstructs the diffusion tensor field.

Recent advances in MR and image analysis allow for reconstruction of high-resolution 3D images from clinical in utero scans of the human fetal brain. Automated segmentation of tissue types from MR images (MRI) is a key step in the quantitative analysis of brain development. Conventional atlas-based methods for adult brain segmentation are limited in their ability to accurately delineate complex structures of developing tissues from fetal MRI. In this paper, we formulate a novel geometric representation of the fetal brain aimed at capturing the laminar structure of developing anatomy. The proposed model uses a depth-based encoding of tissue occurrence within the fetal brain and provides an additional anatomical constraint in a form of a laminar prior that can be incorporated into conventional atlas-based EM segmentation. Validation experiments are performed using clinical in utero scans of 5 fetal subjects at gestational ages ranging from 20.5 to 22.5 weeks. Experimental results are evaluated against reference manual segmentations and quantified in terms of Dice similarity coefficient (DSC). The study demonstrates that the use of laminar depth-encoded tissue priors improves both the overall accuracy and precision of fetal brain segmentation. Particular refinement is observed in regions of the parietal and occipital lobes where the DSC index is improved from 0.81 to 0.82 for cortical grey matter, from 0.71 to 0.73 for the germinal matrix, and from 0.81 to 0.87 for white matter.

In this paper we describe the application of folding measures to tracking in vivo cortical brain development in premature neonatal brain anatomy. The outer gray matter and the gray-white matter interface surfaces were extracted from semi-interactively segmented high-resolution T1 MRI data. Nine curvature- and geometric descriptor-based folding measures were applied to six premature infants, aged 28-37 weeks, using a direct voxelwise iso-surface representation. We have shown that using such an approach it is feasible to extract meaningful surfaces of adequate quality from typical clinically acquired neonatal MRI data. We have shown that most of the folding measures, including a new proposed measure, are sensitive to changes in age and therefore applicable in developing a model that tracks development in premature infants. For the first time gyrification measures have been computed on the gray-white matter interface and on cases whose age is representative of a period of intense brain development.