56 research outputs found
High-Performance Motion Correction of Fetal MRI
Fetal Magnetic Resonance Imaging (MRI) shows promising results for pre-natal diagnostics. The detection of potentially lifethreatening abnormalities in the fetus can be difficult with ultrasound alone. MRI is one of the few safe alternative imaging modalities in pregnancy. However, to date it has been limited by unpredictable fetal and maternal motion during acquisition. Motion between the acquisitions of individual slices of a 3D volume results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms to solve this problem have evolved from very slow implementations targeting a single organ to general high-performance solutions to reconstruct the whole uterus. In this paper we give a brief overview over the current state-of-the art in fetal motion compensation methods and show currently emerging clinical applications of these technique
Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels
Segmentation of the developing fetal brain is an important step in
quantitative analyses. However, manual segmentation is a very time-consuming
task which is prone to error and must be completed by highly specialized
indi-viduals. Super-resolution reconstruction of fetal MRI has become standard
for processing such data as it improves image quality and resolution. However,
dif-ferent pipelines result in slightly different outputs, further complicating
the gen-eralization of segmentation methods aiming to segment super-resolution
data. Therefore, we propose using transfer learning with noisy multi-class
labels to automatically segment high resolution fetal brain MRIs using a single
set of seg-mentations created with one reconstruction method and tested for
generalizability across other reconstruction methods. Our results show that the
network can auto-matically segment fetal brain reconstructions into 7 different
tissue types, regard-less of reconstruction method used. Transfer learning
offers some advantages when compared to training without pre-initialized
weights, but the network trained on clean labels had more accurate
segmentations overall. No additional manual segmentations were required.
Therefore, the proposed network has the potential to eliminate the need for
manual segmentations needed in quantitative analyses of the fetal brain
independent of reconstruction method used, offering an unbiased way to quantify
normal and pathological neurodevelopment.Comment: Accepted for publication at PIPPI MICCAI 202
The Developing Human Connectome Project: a minimal processing pipeline for neonatal cortical surface reconstruction
The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WU-MINN Human Connectome Project (HCP), and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processing pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction, and cortical surface inflation, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be processed fully automatically; generating cortical surface models that are topologically correct, and correspond well with manual evaluations of tissue boundaries in 85% of cases. Results improve on state-of-the-art neonatal tissue segmentation models and significant errors were found in only 2% of cases, where these corresponded to subjects with high motion. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, supporting the modelling of emerging patterns of brain connectivity
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Mother-infant interactions and regional brain volumes in infancy: an MRI study
Background: It is generally agreed that the human brain is responsive to environmental influences, and that the male brain may be particularly sensitive to early adversity. However, this is largely based on retrospective studies of older children and adolescents exposed to extreme environments in childhood. Less is understood about how normative variations in parent-child interactions are associated with the development of the infant brain in typical settings.
Method: To address this, we used magnetic resonance imaging to investigate the relationship between observational measures of mother-infant interactions and regional brain volumes in a community sample of 3-6 month old infants (N=39). In addition, we examined whether this relationship differed in male and female infants.
Results: We found that lower maternal sensitivity was correlated with smaller subcortical grey matter volumes in the whole sample, and that this was similar in both sexes. However, male infants who showed greater levels of positive communication and engagement during early interactions had smaller cerebellar volumes.
Conclusion These preliminary findings suggest that variations in mother-infant interaction dimensions are associated with differences in infant brain development. Although the study is cross-sectional and causation cannot be inferred, the findings reveal a dynamic interaction between brain and environment that may be important when considering interventions to optimize infant outcomes
Infant Brain Atlases from Neonates to 1- and 2-Year-Olds
Background: Studies for infants are usually hindered by the insufficient image contrast, especially for neonates. Prior knowledge, in the form of atlas, can provide additional guidance for the data processing such as spatial normalization, label propagation, and tissue segmentation. Although it is highly desired, there is currently no such infant atlas which caters for all these applications. The reason may be largely due to the dramatic early brain development, image processing difficulties, and the need of a large sample size. Methodology: To this end, after several years of subject recruitment and data acquisition, we have collected a unique longitudinal dataset, involving 95 normal infants (56 males and 39 females) with MRI scanned at 3 ages, i.e., neonate, 1-yearold, and 2-year-old. State-of-the-art MR image segmentation and registration techniques were employed, to construct which include the templates (grayscale average images), tissue probability maps (TPMs), and brain parcellation maps (i.e., meaningful anatomical regions of interest) for each age group. In addition, the longitudinal correspondences between agespecific atlases were also obtained. Experiments of typical infant applications validated that the proposed atlas outperformed other atlases and is hence very useful for infant-related studies. Conclusions: We expect that the proposed infant 0–1–2 brain atlases would be significantly conducive to structural and functional studies of the infant brains. These atlases are publicly available in our website
Accurate Learning with Few Atlases (ALFA): an algorithm for MRI neonatal brain extraction and comparison with 11 publicly available methods
Accurate whole-brain segmentation, or brain extraction, of magnetic resonance imaging (MRI) is a critical first step in most neuroimage analysis pipelines. The majority of brain extraction algorithms have been developed and evaluated for adult data and their validity for neonatal brain extraction, which presents age-specific challenges for this task, has not been established. We developed a novel method for brain extraction of multi-modal neonatal brain MR images, named ALFA (Accurate Learning with Few Atlases). The method uses a new sparsity-based atlas selection strategy that requires a very limited number of atlases ‘uniformly’ distributed in the low-dimensional data space, combined with a machine learning based label fusion technique. The performance of the method for brain extraction from multi-modal data of 50 newborns is evaluated and compared with results obtained using eleven publicly available brain extraction methods. ALFA outperformed the eleven compared methods providing robust and accurate brain extraction results across different modalities. As ALFA can learn from partially labelled datasets, it can be used to segment large-scale datasets efficiently. ALFA could also be applied to other imaging modalities and other stages across the life course
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