Detail-preserving construction of neonatal brain atlases in space-frequency domain

Surface-Guided Image Fusion for Preserving Cortical Details in Human Brain Templates

Human brain templates are a basis for comparison of brain features across individuals. They should ideally capture an atomical details at both coarse and fine scales to facilitate comparison at varying granularity. Brain template construction typically involves spatial normalization and image fusion. While significant efforts have been dedicated to improving brain templates with sophisticated spatial normalization algorithms, image fusion is typically carried out using intensity-based averaging, causing blurring of anatomical structures. Here, we present an image fusion method that exploits cortical surfaces as guidance to help preserve details in brain templates. Our method encodes cortical boundary information given by a cortical surface mesh in a signed distance function (SDF) map. We use the SDF map to help determine localized contributions of the individual images, especially at cortical boundaries, in image fusion. Experimental results demonstrate that our method significantly improves the preservation of fine gyral and sulcal details, resulting in detailed brain templates with good surface-volume agreement.

Multi-Channel 4D Parametrized Atlas of Macro- and Microstructural Neonatal Brain Development

Structural (also known as anatomical) and diffusion MRI provide complimentary anatomical and microstructural characterization of early brain maturation. However, the existing models of the developing brain in time include only either structural or diffusion MRI channels. Furthermore, there is a lack of tools for combined analysis of structural and diffusion MRI in the same reference space. In this work, we propose a methodology to generate a multi-channel (MC) continuous spatio-temporal parametrized atlas of the brain development that combines multiple MRI-derived parameters in the same anatomical space during 37-44 weeks of postmenstrual age range. We co-align structural and diffusion MRI of 170 normal term subjects from the developing Human Connectomme Project using MC registration driven by both T2-weighted and orientation distribution functions channels and fit the Gompertz model to the signals and spatial transformations in time. The resulting atlas consists of 14 spatio-temporal microstructural indices and two parcellation maps delineating white matter tracts and neonatal transient structures. In order to demonstrate applicability of the atlas for quantitative region-specific studies, a comparison analysis of 140 term and 40 preterm subjects scanned at the term-equivalent age is performed using different MRI-derived microstructural indices in the atlas reference space for multiple white matter regions, including the transient compartments. The atlas and software will be available after publication of the article.

Surface-Volume Consistent Construction of Longitudinal Atlases for the Early Developing Brain

Infant brain atlases are essential for characterizing structural changes in the developing brain. Volumetric and cortical atlases are typically constructed independently, potentially causing discrepancies between tissue boundaries and cortical surfaces. In this paper, we present a method for surface-volume consistent construction of longitudinal brain atlases of infants from 2 weeks to 12 months of age. We first construct the 12-month atlas via groupwise surface-constrained volumetric registration. The longitudinal displacements of each subject with respect to different time points are then transported parallelly to the 12-month atlas space. The 12-month cortico-volumetric atlas is finally warped temporally to each month prior to the 12th month using the transported displacements. Experimental results indicate that the longitudinal atlases generated are consistent in terms of tissue boundaries and cortical surfaces, hence allowing joint surface-volume analysis to be performed in a common space.

A framework for multi-component analysis of diffusion MRI data over the neonatal period

We describe a framework for creating a time-resolved group average template of the developing brain using advanced multi-shell high angular resolution diffusion imaging data, for use in group voxel or fixel-wise analysis, atlas-building, and related applications. This relies on the recently proposed multi-shell multi-tissue constrained spherical deconvolution (MSMT-CSD) technique. We decompose the signal into one isotropic component and two anisotropic components, with response functions estimated from cerebrospinal fluid and white matter in the youngest and oldest participant groups, respectively. We build an orientationally-resolved template of those tissue components from data acquired from 113 babies between 33 and 44 weeks postmenstrual age, imaged as part of the Developing Human Connectome Project. These data were split into weekly groups, and registered to the corresponding group average templates using a previously-proposed non-linear diffeomorphic registration framework, designed to align orientation density functions (ODF). This framework was extended to allow the use of the multiple contrasts provided by the multi-tissue decomposition, and shown to provide superior alignment. Finally, the weekly templates were registered to the same common template to facilitate investigations into the evolution of the different components as a function of age. The resulting multi-tissue atlas provides insights into brain development and accompanying changes in microstructure, and forms the basis for future longitudinal investigations into healthy and pathological white matter maturation.

Neonate and infant brain development from birth to 2 years assessed using MRI-based quantitative susceptibility mapping

The human brain rapidly develops during the first two years following birth. Quantitative susceptibility mapping (QSM) provides information of iron and myelin variations. It is considered to be a valuable tool for studying brain development in early life. In the present work, QSM is performed on neonates, 1-year and 2-year old infants, as well as a group of adults for the purpose of reference. Age-specific templates representing common brain structures are built for each age group. The neonate and infant QSM templates have shown some unique findings compared to conventional T1w and T2w imaging techniques. The contrast between the gray and white matters on the QSM images did not change through brain development from neonate to adult. A linear correlation was found between brain myelination determined in this study and the microscopic myelin degree determined by a previous autopsy study. Also, the magnetic susceptibility values of the cerebral spinal fluid (CSF) exhibit a gradually decreasing trend from birth to 2 years old and to adulthood. The findings suggest that the macromolecular content, myelin, and iron may play the most important contributing factors for the magnetic susceptibility of neonate and infant brain. QSM can be a powerful means to study early brain development and related pathologies that involve alterations in macromolecular content, iron, or brain myelination.

Computational neuroanatomy of baby brains: A review

The first postnatal years are an exceptionally dynamic and critical period of structural, functional and connectivity development of the human brain. The increasing availability of non-invasive infant brain MR images provides unprecedented opportunities for accurate and reliable charting of dynamic early brain developmental trajectories in understanding normative and aberrant growth. However, infant brain MR images typically exhibit reduced tissue contrast (especially around 6 months of age), large within-tissue intensity variations, and regionally-heterogeneous, dynamic changes, in comparison with adult brain MR images. Consequently, the existing computational tools developed typically for adult brains are not suitable for infant brain MR image processing. To address these challenges, many infant-tailored computational methods have been proposed for computational neuroanatomy of infant brains. In this review paper, we provide a comprehensive review of the state-of-the-art computational methods for infant brain MRI processing and analysis, which have advanced our understanding of early postnatal brain development. We also summarize publically available infant-dedicated resources, including MRI datasets, computational tools, grand challenges, and brain atlases. Finally, we discuss the limitations in current research and suggest potential future research directions.

Improved medical image fusion based on cascaded PCA and shift invariant wavelet transforms

PURPOSE

In the medical field, radiologists need more informative and high-quality medical images to diagnose diseases. Image fusion plays a vital role in the field of biomedical image analysis. It aims to integrate the complementary information from multimodal images, producing a new composite image which is expected to be more informative for visual perception than any of the individual input images. The main objective of this paper is to improve the information, to preserve the edges and to enhance the quality of the fused image using cascaded principal component analysis (PCA) and shift invariant wavelet transforms.

METHODS

A novel image fusion technique based on cascaded PCA and shift invariant wavelet transforms is proposed in this paper. PCA in spatial domain extracts relevant information from the large dataset based on eigenvalue decomposition, and the wavelet transform operating in the complex domain with shift invariant properties brings out more directional and phase details of the image. The significance of maximum fusion rule applied in dual-tree complex wavelet transform domain enhances the average information and morphological details.

RESULTS

The input images of the human brain of two different modalities (MRI and CT) are collected from whole brain atlas data distributed by Harvard University. Both MRI and CT images are fused using cascaded PCA and shift invariant wavelet transform method. The proposed method is evaluated based on three main key factors, namely structure preservation, edge preservation, contrast preservation. The experimental results and comparison with other existing fusion methods show the superior performance of the proposed image fusion framework in terms of visual and quantitative evaluations.

CONCLUSION

In this paper, a complex wavelet-based image fusion has been discussed. The experimental results demonstrate that the proposed method enhances the directional features as well as fine edge details. Also, it reduces the redundant details, artifacts, distortions.

Graph-Constrained Sparse Construction of Longitudinal Diffusion-Weighted Infant Atlases

Constructing longitudinal diffusion-weighted atlases of infant brains poses additional challenges due to the small brain size and the dynamic changes in the early developing brains. In this paper, we introduce a novel framework for constructing longitudinally-consistent diffusion-weighted infant atlases with improved preservation of structural details and diffusion characteristics. In particular, instead of smoothing diffusion signals by simple averaging, our approach fuses the diffusion-weighted images in a patch-wise manner using sparse representation with a graph constraint that encourages spatiotemporal consistency. Diffusion-weighted atlases across time points are jointly constructed for patches that are correlated in time and space. Compared with existing methods, including the one using sparse representation with l_2,1 regularization, our approach generates longitudinal infant atlases with much richer and more consistent features of the developing infant brain, as shown by the experimental results.

Spatio-angular consistent construction of neonatal diffusion MRI atlases

Atlases constructed using diffusion-weighted imaging are important tools for studying human brain development. Atlas construction is in general a two-step process involving spatial registration and fusion of individual images. The focus of most studies so far has been on improving the accuracy of registration while image fusion is commonly performed using simple averaging, often resulting in fuzzy atlases. In this article, we propose a patch-based method for diffusion-weighted (DW) atlas construction. Unlike other atlases that are based on the diffusion tensor model, our atlas is model-free and generated directly from the diffusion-weighted images. Instead of independently generating an atlas for each gradient direction and hence neglecting angular image correlation, we propose to construct the atlas by jointly considering DW images of neighboring gradient directions. We employ a group regularization framework where local patches of angularly neighboring images are constrained for consistent spatio-angular atlas reconstruction. Experimental results confirm that our atlas, constructed for neonatal data, reveals more structural details with higher fractional anisotropy than the atlas generated without angular consistency as well as the average atlas. Also the normalization of test subjects to the proposed atlas results in better alignment of brain structures. Hum Brain Mapp 38:3175-3189, 2017. © 2017 Wiley Periodicals, Inc.

Consistent Spatial-Temporal Longitudinal Atlas Construction for Developing Infant Brains

Brain atlases are an essential component in understanding the dynamic cerebral development, especially for the early postnatal period. However, longitudinal atlases are rare for infants, and the existing ones are generally limited by their fuzzy appearance. Moreover, since longitudinal atlas construction is typically performed independently over time, the constructed atlases often fail to preserve temporal consistency. This problem is further aggravated for infant images since they typically have low spatial resolution and insufficient tissue contrast. In this paper, we propose a novel framework for consistent spatial-temporal construction of longitudinal atlases for developing infant brain MR images. Specifically, for preserving structural details, the atlas construction is performed in spatial-temporal wavelet domain simultaneously. This is achieved by a patch-based combination of results from each frequency subband. Compared with the existing infant longitudinal atlases, our experimental results indicate that our approach is able to produce longitudinal atlases with richer structural details and also better longitudinal consistency, thus leading to higher performance when used for spatial normalization of a group of infant brain images.