Registration of 3D fetal neurosonography and MRI.

We propose a method for registration of 3D fetal brain ultrasound with a reconstructed magnetic resonance fetal brain volume. This method, for the first time, allows the alignment of models of the fetal brain built from magnetic resonance images with 3D fetal brain ultrasound, opening possibilities to develop new, prior information based image analysis methods for 3D fetal neurosonography. The reconstructed magnetic resonance volume is first segmented using a probabilistic atlas and a pseudo ultrasound image volume is simulated from the segmentation. This pseudo ultrasound image is then affinely aligned with clinical ultrasound fetal brain volumes using a robust block-matching approach that can deal with intensity artefacts and missing features in the ultrasound images. A qualitative and quantitative evaluation demonstrates good performance of the method for our application, in comparison with other tested approaches. The intensity average of 27 ultrasound images co-aligned with the pseudo ultrasound template shows good correlation with anatomy of the fetal brain as seen in the reconstructed magnetic resonance image

Fast volume reconstruction from motion corrupted stacks of 2D slices

Capturing an enclosing volume of moving subjects and organs using fast individual image slice acquisition has shown promise in dealing with motion artefacts. Motion between slice acquisitions results in spatial inconsistencies that can be resolved by slice-to-volume reconstruction (SVR) methods to provide high quality 3D image data. Existing algorithms are, however, typically very slow, specialised to specific applications and rely on approximations, which impedes their potential clinical use. In this paper, we present a fast multi-GPU accelerated framework for slice-to-volume reconstruction. It is based on optimised 2D/3D registration, super-resolution with automatic outlier rejection and an additional (optional) intensity bias correction. We introduce a novel and fully automatic procedure for selecting the image stack with least motion to serve as an initial registration target. We evaluate the proposed method using artificial motion corrupted phantom data as well as clinical data, including tracked freehand ultrasound of the liver and fetal Magnetic Resonance Imaging. We achieve speed-up factors greater than 30 compared to a single CPU system and greater than 10 compared to currently available state-of-the-art multi-core CPU methods. We ensure high reconstruction accuracy by exact computation of the point-spread function for every input data point, which has not previously been possible due to computational limitations. Our framework and its implementation is scalable for available computational infrastructures and tests show a speed-up factor of 1.70 for each additional GPU. This paves the way for the online application of image based reconstruction methods during clinical examinations. The source code for the proposed approach is publicly available

Resting State fMRI in the moving fetus: A robust framework for motion, bias field and spin history correction

There is growing interest in exploring fetal functional brain development, particularly with Resting State fMRI. However, during a typical fMRI acquisition, the womb moves due to maternal respiration and the fetus may perform large-scale and unpredictable movements. Conventional fMRI processing pipelines, which assume that brain movements are infrequent or at least small, are not suitable. Previous published studies have tackled this problem by adopting conventional methods and discarding as much as 40% or more of the acquired data. In this work, we developed and tested a processing framework for fetal Resting State fMRI, capable of correcting gross motion. The method comprises bias field and spin history corrections in the scanner frame of reference, Combined with slice to volume registration and scattered data interpolation to place all data into a consistent anatomical space. The aim is to recover an ordered set of samples suitable for further analysis using standard tools such as Group Independent Component Analysis (Group ICA). We have tested the approach using simulations and in vivo data acquired at 1.5 T. After full motion correction, Group ICA performed on a population of 8 fetuses extracted 20 networks, 6 of which were identified as matching those previously observed in preterm babies

Familial risk of autism alters subcortical and cerebellar brain anatomy in infants and predicts the emergence of repetitive behaviors in early childhood.

Autism spectrum disorder (ASD) is a common neurodevelopmental condition, and infant siblings of children with ASD are at a higher risk of developing autistic traits or an ASD diagnosis, when compared to those with typically developing siblings. Reports of differences in brain anatomy and function in high-risk infants which predict later autistic behaviors are emerging, but although cerebellar and subcortical brain regions have been frequently implicated in ASD, no high-risk study has examined these regions. Therefore, in this study, we compared regional MRI volumes across the whole brain in 4-6-month-old infants with (high-risk, n = 24) and without (low-risk, n = 26) a sibling with ASD. Within the high-risk group, we also examined whether any regional differences observed were associated with autistic behaviors at 36 months. We found that high-risk infants had significantly larger cerebellar and subcortical volumes at 4-6-months of age, relative to low-risk infants; and that larger volumes in high-risk infants were linked to more repetitive behaviors at 36 months. Our preliminary observations require replication in longitudinal studies of larger samples. If correct, they suggest that the early subcortex and cerebellum volumes may be predictive biomarkers for childhood repetitive behaviors. Autism Res 2019, 12: 614-627. © 2019 The Authors. Autism Research published by International Society for Autism Research published byWiley Periodicals, Inc. LAY SUMMARY: Individuals with a family history of autism spectrum disorder (ASD) are at risk of ASD and related developmental difficulties. This study revealed that 4-6-month-old infants at high-risk of ASD have larger cerebellum and subcortical volumes than low-risk infants, and that larger volumes in high-risk infants are associated with more repetitive behaviors in childhood

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

Dynamic field mapping and motion correction using interleaved double spin-echo diffusion MRI

Diffusion MRI (dMRI) analysis requires combining data from many images and this generally requires corrections for image distortion and for subject motion during what may be a prolonged acquisition. Particularly in non-brain applications, changes in pose such as respiration can cause image distortion to be time varying, impeding static field map-based correction. In addition, motion and distortion correction is challenging at high b-values due to the low signal-to-noise ratio (SNR). In this work we develop a new approach that breaks the traditional “one-volume, one-weighting” paradigm by interleaving low-b and high-b slices, and combine this with a reverse phase-encoded double-spin echo sequence. Interspersing low and high b-value slices ensures that the low-b, high-SNR data is in close spatial and temporal proximity to support dynamic field map estimation from the double spin-echo acquisition and image-based motion correction. This information is propagated to high-b slices with interpolation across space and time. The method is tested in the challenging environment of fetal dMRI and it is demonstrated using data from 8 pregnant volunteers that combining dynamic distortion correction with slice-by-slice motion correction increases data consistency to facilitate advanced analyses where conventional methods fail