We presented an approach to modeling of early human brain growth from clinical MRI. Using a set of reconstructed 3D MR images and their manually defined tissue segmentations, we created a spatiotemporal atlas of MR intensity, tissue probability and shape changes in the human fetal brain during early in utero development. From this continuous mathematical model, we can synthesize maps of MR intensity and tissue occurrence for any gestational age covered by the atlas and apply them for improved tissue segmentation in new fetal anatomies.
As multiple time point imaging of the same fetal anatomy is not feasible, the spatiotemporal atlas was built using MR images of different fetuses with different gestational ages. To bring all anatomies into collective alignment in the average space of the group, we applied groupwise registration of tissue maps extracted from fixed manual segmentations. Although this approach did not define full correspondence between images with different gestational ages, it allowed us to consistently align the main tissue boundaries present in the developing fetal brain and resolve matching ambiguities between multiple tissue types that appear with similar intensities on T2w MR images. Extending this framework to a wider range of gestational ages, we plan to replace the single average space with temporarily parameterized average geometry. Another possible direction of future work is to combine the groupwise registration with simultaneous tissue segmentation and atlas building (Ashburner and Friston, 2005
; Pohl et al., 2006a
After registration, we performed parametric modeling of changes in MR intensity, tissue probability and shape of the developing fetal brain. Although additional features such as gender or disease group could be also considered, we used gestational age as the only independent variable for this initial work. For all analyzed properties of the fetal brain anatomy, quadratic temporal models provided a better description of patterns observed for the study population than linear models. For modeling of tissue probabilities, quadratic polynomials allowed us to represent both appearance and disappearance of the germinal matrix from different regions of the brain. As tissue occurrence or MR intensity are not expected to oscillate, the use of higher order temporal models was neither justified nor necessary.
Our morphology model included three main tissue types present in the young fetal brain (developing cortical gray matter, developing white matter, the germinal matrix) and ventricles. Other structures, such as the basal ganglia or the subplate, are difficult to see at this early stage (20–24 weeks GA) but become more visible as the fetuses mature. As a temporary solution, we combined these regions of the fetal brain with developing white matter. In the future, however, they will be fully incorporated into the modeling process as separate and important structures.
Spatiotemporal modeling of all properties was performed in a voxelwise manner, without explicit constraints on spatial smoothness of synthesized maps. Although not an issue for MR intensity and tissue probabilities, this may potentially cause problems in modeling of shape changes. In practice, however, given the spatial smoothness of all subject maps, we found that the shape model provided invertible diffeomorphic mappings for all time points. In the future, we plan to compare our voxelwise approach to alternative techniques such as kernel regression for large deformation models (Davis et al., 2007
) and explore extensions to global modeling using ridge regression (Sjostrand et al., 2008
Finally, we presented a practical application of the spatiotemporal atlas for tissue segmentation in new MR images. As the synthesis of age-matched MR templates reduces the geometric differences that have to be resolved during registration, the anatomy to be segmented can be precisely aligned with the source of tissue priors. The use of age-specific probabilistic atlases improves the overall accuracy of automatic tissue segmentation, as measured by the average DSC values, and allows for correct delineation of transient brain structures that evolve rapidly with gestational age. In clinical applications, accurate quantification of the fetal brain anatomy in relation to its developmental stage may help detect subtle abnormalities that otherwise could be missed.