Neonatal probabilistic models for brain, CSF and skull using T1-MRI data: preliminary results

Age-specific gray and white matter DTI atlas for human brain at 33, 36 and 39 postmenstrual weeks

During the 3rd trimester, dramatic structural changes take place in the human brain, underlying the neural circuit formation. The survival rate of premature infants has increased significantly in recent years. The large morphological differences of the preterm brain at 33 or 36 postmenstrual weeks (PMW) from the brain at 40PMW (full term) make it necessary to establish age-specific atlases for preterm brains. In this study, with high quality (1.5 × 1.5 × 1.6 mm3 imaging resolution) diffusion tensor imaging (DTI) data obtained from 84 healthy preterm and term-born neonates, we established age-specific preterm and term-born brain templates and atlases at 33, 36 and 39PMW. Age-specific DTI templates include a single-subject template, a population-averaged template with linear transformation and a population-averaged template with nonlinear transformation. Each of the age-specific DTI atlases includes comprehensive labeling of 126 major gray matter (GM) and white matter (WM) structures, specifically 52 cerebral cortical structures, 40 cerebral WM structures, 22 brainstem and cerebellar structures and 12 subcortical GM structures. From 33 to 39 PMW, dramatic morphological changes of delineated individual neural structures such as ganglionic eminence and uncinate fasciculus were revealed. The evaluation based on measurements of Dice ratio and L1 error suggested reliable and reproducible automated labels from the age-matched atlases compared to labels from manual delineation. Applying these atlases to automatically and effectively delineate microstructural changes of major WM tracts during the 3rd trimester was demonstrated. The established age-specific DTI templates and atlases of 33, 36 and 39 PMW brains may be used for not only understanding normal functional and structural maturational processes but also detecting biomarkers of neural disorders in the preterm brains.

A Neonatal Bimodal MR-CT Head Template

Neonatal MR templates are appropriate for brain structural analysis and spatial normalization. However, they do not provide the essential accurate details of cranial bones and fontanels-sutures. Distinctly, CT images provide the best contrast for bone definition and fontanels-sutures. In this paper, we present, for the first time, an approach to create a fully registered bimodal MR-CT head template for neonates with a gestational age of 39 to 42 weeks. Such a template is essential for structural and functional brain studies, which require precise geometry of the head including cranial bones and fontanels-sutures. Due to the special characteristics of the problem (which requires inter-subject inter-modality registration), a two-step intensity-based registration method is proposed to globally and locally align CT images with an available MR template. By applying groupwise registration, the new neonatal CT template is then created in full alignment with the MR template to build a bimodal MR-CT template. The mutual information value between the CT and the MR template is 1.17 which shows their perfect correspondence in the bimodal template. Moreover, the average mutual information value between normalized images and the CT template proposed in this study is 1.24±0.07. Comparing this value with the one reported in a previously published approach (0.63±0.07) demonstrates the better generalization properties of the new created template and the superiority of the proposed method for the creation of CT template in the standard space provided by MR neonatal head template. The neonatal bimodal MR-CT head template is freely downloadable from https://www.u-picardie.fr/labo/GRAMFC.

Whole Brain Magnetic Resonance Image Atlases: A Systematic Review of Existing Atlases and Caveats for Use in Population Imaging

Brain MRI atlases may be used to characterize brain structural changes across the life course. Atlases have important applications in research, e.g., as registration and segmentation targets to underpin image analysis in population imaging studies, and potentially in future in clinical practice, e.g., as templates for identifying brain structural changes out with normal limits, and increasingly for use in surgical planning. However, there are several caveats and limitations which must be considered before successfully applying brain MRI atlases to research and clinical problems. For example, the influential Talairach and Tournoux atlas was derived from a single fixed cadaveric brain from an elderly female with limited clinical information, yet is the basis of many modern atlases and is often used to report locations of functional activation. We systematically review currently available whole brain structural MRI atlases with particular reference to the implications for population imaging through to emerging clinical practice. We found 66 whole brain structural MRI atlases world-wide. The vast majority were based on T1, T2, and/or proton density (PD) structural sequences, had been derived using parametric statistics (inappropriate for brain volume distributions), had limited supporting clinical or cognitive data, and included few younger (>5 and <18 years) or older (>60 years) subjects. To successfully characterize brain structural features and their changes across different stages of life, we conclude that whole brain structural MRI atlases should include: more subjects at the upper and lower extremes of age; additional structural sequences, including fluid attenuation inversion recovery (FLAIR) and T2* sequences; a range of appropriate statistics, e.g., rank-based or non-parametric; and detailed cognitive and clinical profiles of the included subjects in order to increase the relevance and utility of these atlases.

Advanced neonatal NeuroMRI

This article describes the potentials and challenges of quantitative analyses of human neonatal brain images using structural magnetic resonance (MR) imaging and diffusion tensor imaging. To maximize the potential of MR imaging for neonatal brain studies, the combination of both contrasts is highly beneficial. Based on the multicontrast data, a neonate brain atlas was created, which allows automated segmentation of neonate brain MR images. The accuracy, advantages, and potential pitfalls of this atlas-based segmentation approach are discussed. The accurate and reproducible MR imaging quantification achieved by this approach could be an initial step toward the successful clinical evaluation of the neonatal brain.

Multi-contrast human neonatal brain atlas: application to normal neonate development analysis

MRI is a sensitive method for detecting subtle anatomic abnormalities in the neonatal brain. To optimize the usefulness for neonatal and pediatric care, systematic research, based on quantitative image analysis and functional correlation, is required. Normalization-based image analysis is one of the most effective methods for image quantification and statistical comparison. However, the application of this methodology to neonatal brain MRI scans is rare. Some of the difficulties are the rapid changes in T1 and T2 contrasts and the lack of contrast between brain structures, which prohibits accurate cross-subject image registration. Diffusion tensor imaging (DTI), which provides rich and quantitative anatomical contrast in neonate brains, is an ideal technology for normalization-based neonatal brain analysis. In this paper, we report the development of neonatal brain atlases with detailed anatomic information derived from DTI and co-registered anatomical MRI. Combined with a diffeomorphic transformation, we were able to normalize neonatal brain images to the atlas space and three-dimensionally parcellate images into 122 regions. The accuracy of the normalization was comparable to the reliability of human raters. This method was then applied to babies of 37-53 post-conceptional weeks to characterize developmental changes of the white matter, which indicated a posterior-to-anterior and a central-to-peripheral direction of maturation. We expect that future applications of this atlas will include investigations of the effect of prenatal events and the effects of preterm birth or low birth weights, as well as clinical applications, such as determining imaging biomarkers for various neurological disorders.

Shifting from region of interest (ROI) to voxel-based analysis in human brain mapping

Current clinical studies involve multidimensional high-resolution images containing an overwhelming amount of structural and functional information. The analysis of such a wealth of information is becoming increasingly difficult yet necessary in order to improve diagnosis, treatment and healthcare. Voxel-wise analysis is a class of modern methods of image processing in the medical field with increased popularity. It has replaced manual region of interest (ROI) analysis and has provided tools to make statistical inferences at voxel level. The introduction of voxel-based analysis software in all modern commercial scanners allows clinical use of these techniques. This review will explain the main principles, advantages and disadvantages behind these methods of image analysis.

Segmentation of scalp and skull in neonatal MR images using probabilistic atlas and level set method

In this paper, we present a novel automatic algorithm for scalp and skull segmentation in T1-weighted neonatal head MR images. First, the probabilistic scalp and skull atlases are constructed. Second, the scalp outer surface is extracted based on an active mesh method. Third, maximum number of boundary points corresponding to the scalp inner surface is extracted using the constructed scalp probabilistic atlas and a set of knowledge based rules. In the next step, the skull inner surface and maximum number of boundary points of the outer surface are extracted using a priori information of the head anatomy and the constructed skull probabilistic atlas. Finally, the fast sweeping, tagging and level set methods are applied to reconstruct surfaces from the detected points in three-dimensional space. The results of the new segmentation algorithm on MRI data acquired from nine newborns (including three atlas and six test subjects) were compared with manual segmented data provided by an expert radiologist. The average similarity indices for the scalp and skull segmented regions were equal to 89% and 71% for the atlas and 84% and 63% for the test data, respectively.