Early folding patterns and asymmetries of the normal human brain detected from in utero MRI.

Early cortical folding and the emergence of structural brain asymmetries have been previously analyzed by neuropathology as well as qualitative analysis of magnetic resonance imaging (MRI) of fetuses and preterm neonates. In this study, we present a dedicated image analysis framework and its application for the detection of folding patterns during the critical period for the formation of many primary sulci (20-28 gestational weeks). Using structural information from in utero MRI, we perform morphometric analysis of cortical plate surface development and modeling of early folding in the normal fetal brain. First, we identify regions of the fetal brain surface that undergo significant folding changes during this developmental period and provide precise temporal staging of these changes for each region of interest. Then, we highlight the emergence of interhemispheric structural asymmetries that may be related to future functional specialization of cortical areas. Our findings complement previous descriptions of early sulcogenesis based on neuropathology and qualitative evaluation of 2D in utero MRI by accurate spatial and temporal mapping of the emergence of individual sulci as well as structural brain asymmetries. The study provides the missing starting point for their developmental trajectories and extends our understanding of normal cortical folding.

Local tissue growth patterns underlying normal fetal human brain gyrification quantified in utero.

Existing knowledge of growth patterns in the living fetal human brain is based upon in utero imaging studies by magnetic resonance imaging (MRI) and ultrasound, which describe overall growth and provide mainly qualitative findings. However, formation of the complex folded cortical structure of the adult brain requires, in part, differential rates of regional tissue growth. To better understand these local tissue growth patterns, we applied recent advances in fetal MRI motion correction and computational image analysis techniques to 40 normal fetal human brains covering a period of primary sulcal formation (20-28 gestational weeks). Growth patterns were mapped by quantifying tissue locations that were expanding more or less quickly than the overall cerebral growth rate, which reveal increasing structural complexity. We detected increased local relative growth rates in the formation of the precentral and postcentral gyri, right superior temporal gyrus, and opercula, which differentiated between the constant growth rate in underlying cerebral mantle and the accelerating rate in the cortical plate undergoing folding. Analysis focused on the cortical plate revealed greater volume increases in parietal and occipital regions compared to the frontal lobe. Cortical plate growth patterns constrained to narrower age ranges showed that gyrification, reflected by greater growth rates, was more pronounced after 24 gestational weeks. Local hemispheric volume asymmetry was located in the posterior peri-Sylvian area associated with structural lateralization in the mature brain. These maps of fetal brain growth patterns construct a spatially specific baseline of developmental biomarkers with which to correlate abnormal development in the human.

Mapping directionality specific volume changes using tensor based morphometry: an application to the study of gyrogenesis and lateralization of the human fetal brain.

Tensor based morphometry (TBM) is a powerful approach to analyze local structural changes in brain anatomy. However, conventional scalar TBM methods do not completely capture all direction specific volume changes required to model complex changes such as those during brain growth. In this paper, we describe novel TBM descriptors for studying direction-specific changes in a subject population which can be used in conjunction with scalar TBM to analyze local patterns in directionality of volume change during brain development. We also extend the methodology to provide a new approach to mapping directional asymmetry in deformation tensors associated with the emergence of structural asymmetry in the developing brain. We illustrate the use of these methods by studying developmental patterns in the human fetal brain, in vivo. Results show that fetal brain development exhibits a distinct spatial pattern of anisotropic growth. The most significant changes in the directionality of growth occur in the cortical plate at major sulci. Our analysis also detected directional growth asymmetry in the peri-Sylvian region and the medial frontal lobe of the fetal brain.

3D morphometric analysis of human fetal cerebellar development.

To date, growth of the human fetal cerebellum has been estimated primarily from linear measurements from ultrasound and 2D magnetic resonance imaging (MRI). In this study, we use 3D analytical methods to develop normative growth trajectories for the cerebellum in utero. We measured cerebellar volume, linear dimensions, and local surface curvature from 3D reconstructed MRI of the human fetal brain (N = 46). We found that cerebellar volume increased approximately 7-fold from 20 to 31 gestational weeks. The better fit of the exponential curve (R (2) = 0.96) compared to the linear curve (R (2) = 0.92) indicated acceleration in growth. Within-subject cerebellar and cerebral volumes were highly correlated (R (2) = 0.94), though the cerebellar percentage of total brain volume increased from approximately 2.4% to 3.7% (R (2) = 0.63). Right and left hemispheric volumes did not significantly differ. Transcerebellar diameter, vermal height, and vermal anterior to posterior diameter increased significantly at constant rates. From the local curvature analysis, we found that expansion along the inferior and superior aspects of the hemispheres resulted in decreased convexity, which is likely due to the physical constraints of the dura surrounding the cerebellum and the adjacent brainstem. The paired decrease in convexity along the inferior vermis and increased convexity of the medial hemisphere represents development of the paravermian fissure, which becomes more visible during this period. In this 3D morphometric analysis of the human fetal cerebellum, we have shown that cerebellar growth is accelerating at a greater pace than the cerebrum and described how cerebellar growth impacts the shape of the structure.

Fetal hippocampal development: analysis by magnetic resonance imaging volumetry.

The hippocampal formation plays an important role in learning and memory; however, data on its development in utero in humans are limited. This study was performed to evaluate hippocampal development in healthy fetuses using 3D reconstructed MRI. A cohort of 20 healthy pregnant women underwent prenatal MRI at a median GA of 24.9 wk (range, 21.3-31.9 wk); six of the women also had a second fetal MRI performed at a 6-wk interval. Routine 2D ultrafast T2-weighted images were used to reconstruct a 3D volume image, which was then used to manually segment the right and left hippocampi. Total hippocampal volume was calculated for each subject and compared against GA. There was a linear increase in total hippocampal volume with increasing GA (p < 0.001). For subjects scanned twice, there was an increase in hippocampal size on the second fetal MRI (p = 0.0004). This represents the first volumetric study of fetal hippocampal development in vivo. This normative volumetric data will be helpful for future comparison studies of suspected developmental abnormalities of hippocampal structure and function.

A spatiotemporal atlas of MR intensity, tissue probability and shape of the fetal brain with application to segmentation.

Modeling and analysis of MR images of the developing human brain is a challenge due to rapid changes in brain morphology and morphometry. We present an approach to the construction of a spatiotemporal atlas of the fetal brain with temporal models of MR intensity, tissue probability and shape changes. This spatiotemporal model is created from a set of reconstructed MR images of fetal subjects with different gestational ages. Groupwise registration of manual segmentations and voxelwise nonlinear modeling allow us to capture the appearance, disappearance and spatial variation of brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific MR templates and tissue probability maps and use them to initialize automatic tissue delineation in new MR images. The choice of model parameters and the final performance are evaluated using clinical MR scans of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Experimental results indicate that quadratic temporal models can correctly capture growth-related changes in the fetal brain anatomy and provide improvement in accuracy of atlas-based tissue segmentation.

Atlas-based segmentation of developing tissues in the human brain with quantitative validation in young fetuses.

Imaging of the human fetus using magnetic resonance (MR) is an essential tool for quantitative studies of normal as well as abnormal brain development in utero. However, because of fundamental differences in tissue types, tissue properties and tissue distribution between the fetal and adult brain, automated tissue segmentation techniques developed for adult brain anatomy are unsuitable for this data. In this paper, we describe methodology for automatic atlas-based segmentation of individual tissue types in motion-corrected 3D volumes reconstructed from clinical MR scans of the fetal brain. To generate anatomically correct automatic segmentations, we create a set of accurate manual delineations and build an in utero 3D statistical atlas of tissue distribution incorporating developing gray and white matter as well as transient tissue types such as the germinal matrix. The probabilistic atlas is associated with an unbiased average shape and intensity template for registration of new subject images to the space of the atlas. Quantitative whole brain 3D validation of tissue labeling performed on a set of 14 fetal MR scans (20.57-22.86 weeks gestational age) demonstrates that this atlas-based EM segmentation approach achieves consistently high DSC performance for the main tissue types in the fetal brain. This work indicates that reliable measures of brain development can be automatically derived from clinical MR imaging and opens up possibility of further 3D volumetric and morphometric studies with multiple fetal subjects.

Volumetric and surface-based 3D MRI analyses of fetal isolated mild ventriculomegaly: brain morphometry in ventriculomegaly.

Diagnosis of fetal isolated mild ventriculomegaly (IMVM) is the most common brain abnormality on prenatal ultrasound. We have set to identify potential alterations in brain development specific to IMVM in tissue volume and cortical and ventricular local surface curvature derived from in utero magnetic resonance imaging (MRI). Multislice 2D T2-weighted MRI were acquired from 32 fetuses (16 IMVM, 16 controls) between 22 and 25.5 gestational weeks. The images were motion-corrected and reconstructed into 3D volumes for volumetric and curvature analyses. The brain images were automatically segmented into cortical plate, cerebral mantle, deep gray nuclei, and ventricles. Volumes were compared between IMVM and control subjects. Surfaces were extracted from the segmentations for local mean surface curvature measurement on the inner cortical plate and the ventricles. Linear models were estimated for age-related and ventricular volume-associated changes in local curvature in both the inner cortical plate and ventricles. While ventricular volume was enlarged in IMVM, all other tissue volumes were not different from the control group. Ventricles increased in curvature with age along the atrium and anterior body. Increasing ventricular volume was associated with reduced curvature over most of the ventricular surface. The cortical plate changed in curvature with age at multiple sites of primary sulcal formation. Reduced cortical folding was detected near the parieto-occipital sulcus in IMVM subjects. While tissue volume appears to be preserved in brains with IMVM, cortical folding may be affected in regions where ventricles are dilated.

Spatiotemporal morphometry of adjacent tissue layers with application to the study of sulcal formation.

The process of brain growth involves the expansion of tissue at different rates at different points within the brain. As the layers within the developing brain evolve they can thicken or increase in area as the brain surface begins to fold. In this work we propose a new spatiotemporal formulation of tensor based volume morphometry that is derived in relation to tissue boundaries. This allows the study of the directional properties of tissue growth by separately characterizing the changes in area and thickness of the adjacent layers. The approach uses temporally weighted, local regression across a population of anatomies with different ages to model changes in components of the growth radial and tangential to the boundary between tissue layers. The formulation is applied to the study of sulcal formation from in-utero MR imaging of human fetal brain anatomy. Results show that the method detects differential growth of tissue layers adjacent to the cortical surface, particularly at sulcal locations, as early as 22 gestational weeks.

Bias field inconsistency correction of motion-scattered multislice MRI for improved 3D image reconstruction.

A common solution to clinical MR imaging in the presence of large anatomical motion is to use fast multislice 2D studies to reduce slice acquisition time and provide clinically usable slice data. Recently, techniques have been developed which retrospectively correct large scale 3D motion between individual slices allowing the formation of a geometrically correct 3D volume from the multiple slice stacks. One challenge, however, in the final reconstruction process is the possibility of varying intensity bias in the slice data, typically due to the motion of the anatomy relative to imaging coils. As a result, slices which cover the same region of anatomy at different times may exhibit different sensitivity. This bias field inconsistency can induce artifacts in the final 3D reconstruction that can impact both clinical interpretation of key tissue boundaries and the automated analysis of the data. Here we describe a framework to estimate and correct the bias field inconsistency in each slice collectively across all motion corrupted image slices. Experiments using synthetic and clinical data show that the proposed method reduces intensity variability in tissues and improves the distinction between key tissue types.

Growth trajectories of the human fetal brain tissues estimated from 3D reconstructed in utero MRI.

In the latter half of gestation (20-40 gestational weeks), human brain growth accelerates in conjunction with cortical folding and the deceleration of ventricular zone progenitor cell proliferation. These processes are reflected in changes in the volume of respective fetal tissue zones. Thus far, growth trajectories of the fetal tissue zones have been extracted primarily from 2D measurements on histological sections and magnetic resonance imaging (MRI). In this study, the volumes of major fetal zones-cortical plate (CP), subplate and intermediate zone (SP+IZ), germinal matrix (GMAT), deep gray nuclei (DG), and ventricles (VENT)--are calculated from automatic segmentation of motion-corrected, 3D reconstructed MRI. We analyzed 48 T2-weighted MRI scans from 39 normally developing fetuses in utero between 20.57 and 31.14 gestational weeks (GW). The supratentorial volume (STV) increased linearly at a rate of 15.22% per week. The SP+IZ (14.75% per week) and DG (15.56% per week) volumes increased at similar rates. The CP increased at a greater relative rate (18.00% per week), while the VENT (9.18% per week) changed more slowly. Therefore, CP increased as a fraction of STV and the VENT fraction declined. The total GMAT volume slightly increased then decreased after 25 GW. We did not detect volumetric sexual dimorphisms or total hemispheric volume asymmetries, which may emerge later in gestation. Further application of the automated fetal brain segmentation to later gestational ages will bridge the gap between volumetric studies of premature brain development and normal brain development in utero.

Parallel nonlinear analysis of weighted brain's gray and white matter images for Alzheimer's dementia diagnosis.

In this study, we are proposing a novel nonlinear classification approach to discriminate between Alzheimer's Disease (AD) and a control group using T1-weighted and T2-weighted Magnetic Resonance Images (MRI's) of brain. Since T1-weighted images and T2-weighted images have inherent physical differences, obviously each of them has its own particular medical data and hence, we extracted some specific features from each. Then the variations of the relevant eigenvalues of the extracted features were tracked to pick up the most informative ones. The final features were assigned to two parallel systems to be nonlinearly categorized. Considering the fact that AD defects the white and gray regions of brain more than its black and marginal regions, and also since T1-weighted has more medical data of white and gray regions than T2-weighted images, we put optimal weights for the two outputs. Combination of these two results made the final decision of AD diagnosis system. The dataset includes 60 T1-weighted images and 60 T2-weighted images of normal and abnormal cases. The dataset which includes different cross-sections of the brain, after an accurate registration, was split to two groups of test set (40 percent of the dataset) and training set (60 percent of the dataset). The results demonstrate more than two thirds of accuracy in detection of normal and abnormal images.

Intersection based motion correction of multislice MRI for 3-D in utero fetal brain image formation.

In recent years, postprocessing of fast multislice magnetic resonance imaging (MRI) to correct fetal motion has provided the first true 3-D MR images of the developing human brain in utero. Early approaches have used reconstruction based algorithms, employing a two-step iterative process, where slices from the acquired data are realigned to an approximate 3-D reconstruction of the fetal brain, which is then refined further using the improved slice alignment. This two step slice-to-volume process, although powerful, is computationally expensive in needing a 3-D reconstruction, and is limited in its ability to recover subvoxel alignment. Here, we describe an alternative approach which we term slice intersection motion correction (SIMC), that seeks to directly co-align multiple slice stacks by considering the matching structure along all intersecting slice pairs in all orthogonally planned slices that are acquired in clinical imaging studies. A collective update scheme for all slices is then derived, to simultaneously drive slices into a consistent match along their lines of intersection. We then describe a 3-D reconstruction algorithm that, using the final motion corrected slice locations, suppresses through-plane partial volume effects to provide a single high isotropic resolution 3-D image. The method is tested on simulated data with known motions and is applied to retrospectively reconstruct 3-D images from a range of clinically acquired imaging studies. The quantitative evaluation of the registration accuracy for the simulated data sets demonstrated a significant improvement over previous approaches. An initial application of the technique to studying clinical pathology is included, where the proposed method recovered up to 15 mm of translation and 30 degrees of rotation for individual slices, and produced full 3-D reconstructions containing clinically useful additional information not visible in the original 2-D slices.

Non-iterative relative bias correction for 3D reconstruction of in utero fetal brain MR imaging.

The slice intersection motion correction (SIMC) method is a powerful tool to compensate for motion that occurs during in utero acquisition of the multislice magnetic resonance (MR) images of the human fetal brain. The SIMC method makes use of the slice intersection intensity profiles of orthogonally planned slice pairs to simultaneously correct for the relative motion occurring between all the acquired slices. This approach is based on the assumption that the bias field is consistent between slices. However, for some clinical studies where there is a strong bias field combined with significant fetal motion relative to the coils, this assumption is broken and the resulting motion estimate and the reconstruction to a 3D volume can both contain errors. In this work, we propose a method to correct for the relative differences in bias field between all slice pairs. For this, we define the energy function as the mean square difference of the intersection profiles, that is then minimized with respect to the bias field parameters of the slices. A non iterative method which considers the relative bias between each slice simultaneously is used to efficiently remove inconsistencies. The method, when tested on synthetic simulations and actual clinical imaging studies where bias was an issue, brought a significant improvement to the final reconstructed image.

Measures for characterizing directionality specific volume changes in TBM of brain growth.

Tensor based morphology (TBM) is a powerful approach to analyze local structural changes in brain anatomy. However, conventional scalar TBM methods are unable to present direction-specific analysis of volume changes required to model complex changes such as those during brain growth. In this paper, we describe novel TBM descriptors for studying direction-specific changes in a subject population which can be used in conjunction with scalar TBM to analyze local patterns in directionality of volume change during brain development. We illustrate the use of these methods by studying brain developmental patterns in fetuses. Results show that this approach detects early changes local growth that are related to the early stages of sulcal and gyral formation.

A spatio-temporal atlas of the human fetal brain with application to tissue segmentation.

Modeling and analysis of MR images of the early developing human brain is a challenge because of the transient nature of different tissue classes during brain growth. To address this issue, a statistical model that can capture the spatial variation of structures over time is needed. Here, we present an approach to building a spatio-temporal model of tissue distribution in the developing brain which can incorporate both developed tissues as well as transient tissue classes such as the germinal matrix by using constrained higher order polynomial models. This spatiotemporal model is created from a set of manual segmentations through groupwise registration and voxelwise non-linear modeling of tissue class membership, that allows us to represent the appearance as well as disappearance of the transient brain structures over time. Applying this model to atlas-based segmentation, we generate age-specific tissue probability maps and use them to initialize an EM segmentation of the fetal brain tissues. The approach is evaluated using clinical MR images of young fetuses with gestational ages ranging from 20.57 to 24.71 weeks. Results indicate improvement in performance of atlas-based EM segmentation provided by higher order temporal models that capture the variation of tissue occurrence over time.

Atlas-based segmentation of the germinal matrix from in utero clinical MRI of the fetal brain.

Recently developed techniques for reconstruction of high-resolution 3D images from fetal MR scans allows us to study the morphometry of developing brain tissues in utero. However, existing adult brain analysis methods cannot be directly applied as the anatomy of the fetal brain is significantly different in terms of geometry and tissue morphology. We describe an approach to atlas-based segmentation of the fetal brain with particular focus on the delineation of the germinal matrix, a transient structure related to brain growth. We segment 3D images reconstructed from in utero clinical MR scans and measure volumes of different brain tissue classes for a group of fetal subjects at gestational age 20.5-22.5 weeks. We also include a partial validation of the approach using manual tracing of the germinal matrix at different gestational ages.