Kio Kim

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.

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? of rotation for individual slices, and produced full 3-D reconstructions containing clinically useful additional information not visible in the original 2-D slices.

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.

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. Hum Brain Mapp, 2010.

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.

On super-resolution for fetal brain MRI

Super-resolution techniques provide a route to studying fine scale anatomical detail using multiple lower resolution acquisitions. In particular, techniques that do not depend on regular sampling can be used in medical imaging situations where imaging time and resolution are limited by subject motion. We investigate in this work the use of a super-resolution technique for anisotropic fetal brain MR data reconstruction without modifying the data acquisition protocol. The approach, which consists of iterative motion correction and high resolution image estimation, is compared with a previously used scattered data interpolation-based reconstruction method. To optimize acquisition time, an evaluation of the influence of the number of input images and image noise is also performed. Evaluation on simulated MR images and real data show significant improvements in performance provided by the super-resolution approach.

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.

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.

Intersection based registration of slice stacks to form 3D images of the human fetal brain

Clinical fetal MR imaging of the brain commonly makes use of fast 2D acquisitions of multiple sets of approximately orthogonal 2D slices. We and others have previously proposed an iterative slice-to-volume registration process to recover a geometrically consistent 3D image. However, these approaches depend on a 3D volume reconstruction step during the slice alignment. This is both computationally expensive and makes the convergence of the registration process poorly defined. In this paper our key contribution is a new approach which considers the collective alignment of all slices directly, via shared structure in their intersections, rather than to an estimated 3D volume. We derive an analytical expression for the gradient of the collective similarity of the slices along their intersections, with respect to the 3D location and orientation of each 2D slice. We include examples of the approach applied to simulated data and clinically acquired fetal images.