Elsevier

NeuroImage

Volume 53, Issue 2, 1 November 2010, Pages 460-470
NeuroImage

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

https://doi.org/10.1016/j.neuroimage.2010.06.054Get rights and content

Abstract

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.

Research highlights

► Spatiotemporal atlas of the fetal brain created from a set of manual segmentations. ► Atlas includes developing GM and WM as well as transient tissue types such as GMAT. ► MR intensity, tissue probabilities and shape changes are modeled using temporal polynomials. ► Age-specific MR templates and tissue density maps are generated from spatiotemporal atlas. ► Quadratic temporal models provide improved age-specific priors for atlas-based EM tissue labeling.

Introduction

Magnetic resonance (MR) imaging is an essential tool for the study of early human brain development in utero (Girard et al., 1995, Prayer et al., 2006, Rutherford et al., 2008, Glenn, 2010). Recently developed methods of image reconstruction permit the formation of motion-corrected 3D volumes of the fetal brain from 2D MR scans acquired in utero (Rousseau et al., 2006, Jiang et al., 2007, Kim et al., 2010). Accurate and efficient automatic segmentation of individual tissues (Habas et al., in press) is then the next key step in quantification of fetal brain development. This allows us to extract patterns of normal brain tissue growth in utero and correctly identify cases that may be related to abnormal neurodevelopmental outcomes.

Fetal MRI is usually performed after 20 weeks gestational age (GA) when the main steps of organogenesis are completed. At this stage, the fetal brain consists of a mixture of developing gray matter, developing white matter and other transient structures such as the germinal matrix (Prayer et al., 2006). During embryology and early fetal life, the germinal matrix is a site of production of both neurons and glial cells which then migrate out to their final locations. The volume of the germinal matrix reaches its peak at about 23 weeks GA and decreases subsequently (Battin et al., 1998, Kinoshita et al., 2001).

The key challenge in automated analysis of fetal brain MRI data is rapid changes occurring over very short timescales as illustrated in Fig. 1. These include growth-related changes in the size and shape of the brain, maturation-related changes in MR intensities of developing gray and white matter and the appearance and complete disappearance of the germinal matrix from different brain regions. Therefore, to meaningfully label tissues present within a given MR image of the fetal brain, it is necessary to interpret the underlying anatomy in close relation to its developmental stage.

Numerous studies with older anatomies suggested that the use of age-specific atlases can significantly improve the results of automated analysis of brain MRI data. In multiple neonatal segmentation studies (Warfield et al., 2000, Prastawa et al., 2005, Xue et al., 2007, Weisenfeld & Warfield, 2009), specific templates for newborn brains were used to correctly label developing tissues, including myelinated and non-myelinated white matter. Murgasova et al. (2007) showed that the use of age-specific atlases significantly improves the accuracy of the expectation-maximization (EM) segmentation algorithm for brain structures in 1-year-old and 2-year-old children. Yoon et al. (2009) constructed an age-matched intensity template and tissue probability maps from a set of pediatric MR images and demonstrated their beneficial effect on morphometric analysis of brain MR data of 8-year-old children. Similarly, the results of a study with adult brain anatomies (Studholme et al., 2001) indicated the importance of using an age-appropriate template for the group being analyzed.

In our previous work (Habas et al., in press), we focused on an early developmental stage (20.5–22.5 weeks GA) and demonstrated the feasibility of atlas-based segmentation of developing tissues in the human brain from motion-corrected in utero MRI. As the study was based on a relatively homogeneous group of young fetuses, a single probabilistic atlas of tissue distribution was constructed and applied to drive automatic segmentations. Extending this framework to a wider range of gestational ages, however, given the dynamics of changes occurring in the young fetal brain, separate atlases should be constructed and used for different weeks of early brain development. Such an approach is not practical due to potentially insufficient availability of MR scans of subjects with normal brain development at certain gestational ages as well as time and expertise required to perform accurate manual segmentations.

Instead, in this paper, we present an approach to the construction of a complete spatiotemporal atlas of the human fetal brain with temporal parametric models of MR intensity, tissue probability and shape changes. Spatiotemporal registration and modeling have been previously applied for cardiac MRI (Meyer et al., 1996, Heller et al., 2001, Perperidis et al., 2005) and are becoming popular in the domain of brain image analysis (Davis et al., 2007, Pohl et al., 2007). However, building an atlas of brain tissue growth poses significantly different challenges than temporal modeling of soft tissue deformation. Here, we demonstrate that a spatiotemporal atlas of the fetal brain with an appropriate degree of nonlinearity is able to correctly capture the spatial and temporal variation in presence of developing brain tissues as well as their appearance in MR images. We also show that age-specific MR intensity templates and tissue probability maps generated from the spatiotemporal atlas can be applied for improved tissue segmentation in MR images of new fetal subjects.

Section snippets

Overview

In order to construct a spatiotemporal atlas of the fetal brain, multiple time point imaging of the same fetal anatomy is not a viable route due to physical and emotional burden for the mother. It is not ethically appropriate to scan pregnant women with short enough time intervals to capture subtle changes in morphology and morphometry of the developing fetal brain. As a result, we have to use a collection of clinical MR images of different fetuses with different gestational ages. This

Population

The study was performed using clinical MR scans of 20 fetal subjects with normal brain development selected for reconstruction and manual tracing from a larger pool collected at University of California San Francisco. The mothers were referred for fetal MRI due to questionable abnormalities on prenatal ultrasound, a prior abnormal pregnancy or recruited as volunteers. The gestational ages ts of the fetuses were almost uniformly distributed between 20.57 and 24.71 weeks as shown in Fig. 6.

MR image processing

For

Discussion

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

Acknowledgments

This work was primarily funded by NIH/NINDS Grant R01 NS 055064. Imaging studies were partially funded by NIH/NINDS Grant K23 NS 052506 and NIH/NCRR UCSF-CTSI Grant UL1 RR 024131. The work of F. Rousseau was supported by the European Research Council under the European Community Seventh Framework Programme (FP7/2007-2013 Grant Agreement 207667).

The authors thank Dr. Julia A. Scott for help with editing of the manuscript.

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