The primary objective of this study was to evaluate changes in cerebral blood flow (CBF) using arterial spin labeling MRI between day 4 of life (DOL4) and day 11 of life (DOL11) in neonates with hypoxic-ischemic encephalopathy (HIE) treated with hypothermia. The secondary objectives were to compare CBF values between the different regions of interest (ROIs) and between infants with ischemic lesions on MRI and infants with normal MRI findings. We prospectively included all consecutive neonates with HIE admitted to the neonatal intensive care unit of our institution who were eligible for therapeutic hypothermia. Each neonate systematically underwent two MRI examinations as close as possible to day 4 (early MRI) and day 11 (late MRI) of life. A custom processing pipeline of morphological and perfusion imaging data adapted to neonates was developed to perform automated ROI analysis. Twenty-eight neonates were included in the study between April 2015 and December 2017. There were 16 boys and 12 girls. Statistical analysis was finally performed on 37 MRIs, 17 early MRIs and 20 late MRIs. Eleven neonates had both early and late MRIs of good quality available. Eight out of 17 neonates (47%) had an abnormal on late MRI as performed and 7/20 neonates (35%) had an abnormal late MRI. CBF values in the basal ganglia and thalami (BGT) and temporal lobes were significantly higher on DOL4 than on DOL11. There were no significant differences between DOL4 and DOL11 for the other ROIs. CBF values were significantly higher in the BGT vs. the cortical GM, on both DOL4 and DOL11. On DOL4, the CBF was significantly higher in the cortical GM, the BGT, and the frontal and parietal lobes in subjects with an abnormal MRI compared to those with a normal MRI. On DOL11, CBF values in each ROI were not significantly different between the normal MRI group and the abnormal MRI group, except for the temporal lobes. This article proposes an innovative processing pipeline for morphological and ASL data suited to neonates that enable automated segmentation to obtain CBF values over ROIs. We evaluate CBF on two successive scans within the first 15 days of life in the same subjects. ASL imaging in asphyxiated neonates seems more relevant when used relatively early, in the first days of life. The correlation of intra-subject changes in cerebral perfusion between early and late MRI with neurodevelopmental outcome warrants investigation in a larger cohort, to determine whether the CBF pattern change can provide prognostic information beyond that provided by visible structural abnormalities on conventional MRI.

We present a new method to create a diffeomorphic longitudinal (4D) atlas composed of a set of 3D atlases each representing an average model at a given age. This is achieved by generalizing atlasing methods to produce atlases unbiased with respect to the initial reference up to a rigid transformation and ensuring diffeomorphic deformations thanks to the Baker-Campbell-Hausdorff formula and the log-Euclidean framework for diffeomorphisms. Subjects are additionally weighted using an asymmetric function to closely match specified target ages. Creating a longitudinal atlas also implies dealing with subjects with large brain differences that can lead to registration errors. This is overcome by a robust rigid registration based on polar decomposition. We illustrate these techniques for the creation of a 4D pediatric atlas, showing their ability to create a temporally consistent atlas.

Online atlasing, i.e. incrementing an atlas with new images as they are acquired, is key when performing studies on databases very large or still being gathered. We propose to this end a new diffeomorphic online atlasing method without having to perform again the atlasing process from scratch. New subjects are integrated following an iterative procedure gradually shifting the centroid of the images to its final position, making it computationally cheap to update regularly an atlas as new images are acquired (only needing a number of registrations equal to the number of new subjects). We evaluate this iterative centroid approach through the analysis of the sharpness and variance of the resulting atlases, and the transformations of images, comparing their deviations from a conventional method. We demonstrate that the transformations divergence between the two approaches is small and stable and that both atlases reach equivalent levels of image quality.

ObjectivesThe severity of neurocognitive impairment increases with prematurity. However, its mechanisms remain poorly understood. Our aim was firstly to identify multiparametric magnetic resonance imaging (MRI) markers that differ according to the degree of prematurity, and secondly to evaluate the impact of clinical complications on these markers.Materials and MethodsWe prospectively enrolled preterm infants who were divided into two groups according to their degree of prematurity: extremely preterm (<28 weeks’ gestational age) and very preterm (28–32 weeks’ gestational age). They underwent a multiparametric brain MRI scan at term-equivalent age including morphological, diffusion tensor and arterial spin labeling (ASL) perfusion sequences. We quantified overall and regional volumes, diffusion parameters, and cerebral blood flow (CBF). We then compared the parameters for the two groups. We also assessed the effects of clinical data and potential MRI morphological abnormalities on those parameters.ResultsThirty-four preterm infants were included. Extremely preterm infants (n = 13) had significantly higher frontal relative volumes (p = 0.04), frontal GM relative volumes (p = 0.03), and regional CBF than very preterm infants, but they had lower brainstem and insular relative volumes (respectively p = 0.008 and 0.04). Preterm infants with WM lesions on MRI had significantly lower overall GM CBF (13.3 ± 2 ml/100 g/min versus 17.7 ± 2.5, < ml/100 g/min p = 0.03).ConclusionMagnetic resonance imaging brain scans performed at term-equivalent age in preterm infants provide quantitative imaging parameters that differ with respect to the degree of prematurity, related to brain maturation.

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Diffusion-weighted magnetic resonance imaging (DWI) allows the observation of (micro)structural brain remodelling in cortical and subcortical regions after time-limited motor learning. Post-learning sleep consolidation should lead to long-term microstructural brain changes, but concrete evidence remains limited. Here, we used both conventional diffusion tensor imaging (DTI) and Neurite Orientation Dispersion & Density Imaging (NODDI), that estimates dendritic and axonal complexity in white and grey matter, to investigate the microstructural brain mechanisms underlying time- and sleep-dependent motor memory consolidation. Sixty-one young healthy adults underwent 2 DWI sessions, with sequential motor training in between, on day 1 followed by the experimental night of total sleep deprivation or regular sleep. After 3 recovery nights at home, they underwent 2 further DWI sessions separated by a motor retraining session. Sequential motor learning resulted in immediate modifications in structural parameters in occipitoparietal and temporal regions, as well as in subcortical regions of interest. Similar changes were observed following relearning but at a smaller magnitude. Regarding delayed consolidation effects, learning-related changes only partially persisted 3 days after initial learning, and no post-learning sleep effect was detected. Our results show rapid motor learning-related remodelling, reflecting temporary processes in learning-related neuronal brain plasticity. Post-learning sleep-related cellular changes remain to be evidenced, possibly using more sophisticated brain imaging measures and spanning more extended timescales to allow the expression of structural changes.

We propose a novel method to quantify brain growth in 3 arbitrary orthogonal directions of the brain or its sub-regions through linear registration. This is achieved by introducing a 9 degrees of freedom (dof) transformation called anisotropic similarity which is an affine transformation with constrained scaling directions along arbitrarily chosen orthogonal vectors. This gives the opportunity to extract scaling factors describing brain growth along those directions by registering a database of subjects onto a common reference. This information about directional growth brings insights that are not usually available in longitudinal volumetry analysis. The interest of this method is illustrated by studying the anisotropic regional and global brain development of 308 healthy subjects betwen 0 and 19 years old. A gender comparison of those scaling factors is also performed for 4 classes of age. We demonstrate through these applications the stability of the method to the chosen reference and its ability to highlight growth differences accros regions and gender.

Echo planar imaging (EPI) is the most common approach for acquiring diffusion and functional MRI data due to its high temporal resolution. However, this comes at the cost of higher sensitivity to susceptibility-induced B0 field inhomogeneities around air/tissue interfaces. This leads to severe geometric distortions along the phase encoding direction (PED). To correct this distortion, the standard approach involves an analogous acquisition using an opposite PED leading to images with inverted distortions and then non-linear image registration, with a transformation model constrained along the PED, to estimate the voxel-wise shift that undistorts the image pair and generates a distortion-free image. With conventional image registration approaches, this type of processing is computationally intensive. Recent advances in unsupervised deep learning-based approaches to image registration have been proposed to drastically reduce the computational cost of this task. However, they rely on maximizing an intensity-based similarity measure, known to be suboptimal surrogate measures of image alignment. To address this limitation, we propose a semi-supervised deep learning algorithm that directly leverages ground truth spatial transformations during training. Simulated and real data experiments demonstrate improvement to distortion field recovery compared to the unsupervised approach, improvement image similarity compared to supervised approach and precision similar to TOPUP but with much faster processing.