Sequence-agnostic motion-correction leveraging efficiently calibrated Pilot Tone signals.

PURPOSE: This study leverages externally generated Pilot Tone (PT) signals to perform motion-corrected brain MRI for sequences with arbitrary k-space sampling and image contrast. THEORY AND METHODS: PT signals are promising external motion sensors due to their cost-effectiveness, easy workflow, and consistent performance across contrasts and sampling patterns. However, they lack robust calibration pipelines. This work calibrates PT signal to rigid motion parameters acquired during short blocks (˜4 s) of motion calibration (MC) acquisitions, which are short enough to unobstructively fit between acquisitions. MC acquisitions leverage self-navigated trajectories that enable state-of-the-art motion estimation methods for efficient calibration. To capture the range of patient motion occurring throughout the examination, distributed motion calibration (DMC) uses data acquired from MC scans distributed across the entire examination. After calibration, PT is used to retrospectively motion-correct sequences with arbitrary k-space sampling and image contrast. Additionally, a data-driven calibration refinement is proposed to tailor calibration models to individual acquisitions. In vivo experiments involving 12 healthy volunteers tested the DMC protocol's ability to robustly correct subject motion. RESULTS: The proposed calibration pipeline produces pose parameters consistent with reference values, even when distributing only six of these approximately 4-s MC blocks, resulting in a total acquisition time of 22 s. In vivo motion experiments reveal significant ( p < 0.05 $$ p<0.05 $$ ) improved motion correction with increased signal to residual ratio for both MPRAGE and SPACE sequences with standard k-space acquisition, especially when motion is large. Additionally, results highlight the benefits of using a distributed calibration approach. CONCLUSIONS: This study presents a framework for performing motion-corrected brain MRI in sequences with arbitrary k-space encoding and contrast, using externally generated PT signals. The DMC protocol is introduced, promoting observation of patient motion occurring throughout the examination and providing a calibration pipeline suitable for clinical deployment. The method's application is demonstrated in standard volumetric MPRAGE and SPACE sequences.

Rapid and accurate navigators for motion and B0 tracking using QUEEN: Quantitatively enhanced parameter estimation from navigators.

PURPOSE: To develop a framework that jointly estimates rigid motion and polarizing magnetic field (B0 ) perturbations ( δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ ) for brain MRI using a single navigator of a few milliseconds in duration, and to additionally allow for navigator acquisition at arbitrary timings within any type of sequence to obtain high-temporal resolution estimates. THEORY AND METHODS: Methods exist that match navigator data to a low-resolution single-contrast image (scout) to estimate either motion or δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . In this work, called QUEEN (QUantitatively Enhanced parameter Estimation from Navigators), we propose combined motion and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimation from a fast, tailored trajectory with arbitrary-contrast navigator data. To this end, the concept of a quantitative scout (Q-Scout) acquisition is proposed from which contrast-matched scout data is predicted for each navigator. Finally, navigator trajectories, contrast-matched scout, and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ are integrated into a motion-informed parallel-imaging framework. RESULTS: Simulations and in vivo experiments show the need to model δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ to obtain accurate motion parameters estimated in the presence of strong δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Simulations confirm that tailored navigator trajectories are needed to robustly estimate both motion and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ . Furthermore, experiments show that a contrast-matched scout is needed for parameter estimation from multicontrast navigator data. A retrospective, in vivo reconstruction experiment shows improved image quality when using the proposed Q-Scout and QUEEN estimation. CONCLUSIONS: We developed a framework to jointly estimate rigid motion parameters and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ from navigators. Combing a contrast-matched scout with the proposed trajectory allows for navigator deployment in almost any sequence and/or timing, which allows for higher temporal-resolution motion and δ B 0 $$ \delta {\mathbf{B}}_{\mathbf{0}} $$ estimates.

In vivo T2 measurements of the fetal brain using single-shot fast spin echo sequences.

PURPOSE: We propose a quantitative framework for motion-corrected T2 fetal brain measurements in vivo and validate the single-shot fast spin echo (SS-FSE) sequence to perform these measurements. METHODS: Stacks of two-dimensional SS-FSE slices are acquired with different echo times (TE) and motion-corrected with slice-to-volume reconstruction (SVR). The quantitative T2 maps are obtained by a fit to a dictionary of simulated signals. The sequence is selected using simulated experiments on a numerical phantom and validated on a physical phantom scanned on a 1.5T system. In vivo quantitative T2 maps are obtained for five fetuses with gestational ages (GA) 21-35 weeks on the same 1.5T system. RESULTS: The simulated experiments suggested that a TE of 400 ms combined with the clinically utilized TEs of 80 and 180 ms were most suitable for T2 measurements in the fetal brain. The validation on the physical phantom confirmed that the SS-FSE T2 measurements match the gold standard multi-echo spin echo measurements. We measured average T2s of around 200 and 280 ms in the fetal brain grey and white matter, respectively. This was slightly higher than fetal T2* and the neonatal T2 obtained from previous studies. CONCLUSION: The motion-corrected SS-FSE acquisitions with varying TEs offer a promising practical framework for quantitative T2 measurements of the moving fetus.

Development of neonatal brain functional centrality and alterations associated with preterm birth.

Formation of the functional connectome in early life underpins future learning and behavior. However, our understanding of how the functional organization of brain regions into interconnected hubs (centrality) matures in the early postnatal period is limited, especially in response to factors associated with adverse neurodevelopmental outcomes such as preterm birth. We characterized voxel-wise functional centrality (weighted degree) in 366 neonates from the Developing Human Connectome Project. We tested the hypothesis that functional centrality matures with age at scan in term-born babies and is disrupted by preterm birth. Finally, we asked whether neonatal functional centrality predicts general neurodevelopmental outcomes at 18 months. We report an age-related increase in functional centrality predominantly within visual regions and a decrease within the motor and auditory regions in term-born infants. Preterm-born infants scanned at term equivalent age had higher functional centrality predominantly within visual regions and lower measures in motor regions. Functional centrality was not related to outcome at 18 months old. Thus, preterm birth appears to affect functional centrality in regions undergoing substantial development during the perinatal period. Our work raises the question of whether these alterations are adaptive or disruptive and whether they predict neurodevelopmental characteristics that are more subtle or emerge later in life.

Development of human white matter pathways in utero over the second and third trimester.

During the second and third trimesters of human gestation, rapid neurodevelopment is underpinned by fundamental processes including neuronal migration, cellular organization, cortical layering, and myelination. In this time, white matter growth and maturation lay the foundation for an efficient network of structural connections. Detailed knowledge about this developmental trajectory in the healthy human fetal brain is limited, in part, due to the inherent challenges of acquiring high-quality MRI data from this population. Here, we use state-of-the-art high-resolution multishell motion-corrected diffusion-weighted MRI (dMRI), collected as part of the developing Human Connectome Project (dHCP), to characterize the in utero maturation of white matter microstructure in 113 fetuses aged 22 to 37 wk gestation. We define five major white matter bundles and characterize their microstructural features using both traditional diffusion tensor and multishell multitissue models. We found unique maturational trends in thalamocortical fibers compared with association tracts and identified different maturational trends within specific sections of the corpus callosum. While linear maturational increases in fractional anisotropy were seen in the splenium of the corpus callosum, complex nonlinear trends were seen in the majority of other white matter tracts, with an initial decrease in fractional anisotropy in early gestation followed by a later increase. The latter is of particular interest as it differs markedly from the trends previously described in ex utero preterm infants, suggesting that this normative fetal data can provide significant insights into the abnormalities in connectivity which underlie the neurodevelopmental impairments associated with preterm birth.

Cortical morphology at birth reflects spatiotemporal patterns of gene expression in the fetal human brain.

Interruption to gestation through preterm birth can significantly impact cortical development and have long-lasting adverse effects on neurodevelopmental outcome. We compared cortical morphology captured by high-resolution, multimodal magnetic resonance imaging (MRI) in n = 292 healthy newborn infants (mean age at birth = 39.9 weeks) with regional patterns of gene expression in the fetal cortex across gestation (n = 156 samples from 16 brains, aged 12 to 37 postconceptional weeks [pcw]). We tested the hypothesis that noninvasive measures of cortical structure at birth mirror areal differences in cortical gene expression across gestation, and in a cohort of n = 64 preterm infants (mean age at birth = 32.0 weeks), we tested whether cortical alterations observed after preterm birth were associated with altered gene expression in specific developmental cell populations. Neonatal cortical structure was aligned to differential patterns of cell-specific gene expression in the fetal cortex. Principal component analysis (PCA) of 6 measures of cortical morphology and microstructure showed that cortical regions were ordered along a principal axis, with primary cortex clearly separated from heteromodal cortex. This axis was correlated with estimated tissue maturity, indexed by differential expression of genes expressed by progenitor cells and neurons, and engaged in stem cell differentiation, neuron migration, and forebrain development. Preterm birth was associated with altered regional MRI metrics and patterns of differential gene expression in glial cell populations. The spatial patterning of gene expression in the developing cortex was thus mirrored by regional variation in cortical morphology and microstructure at term, and this was disrupted by preterm birth. This work provides a framework to link molecular mechanisms to noninvasive measures of cortical development in early life and highlights novel pathways to injury in neonatal populations at increased risk of neurodevelopmental disorder.

Predicting age and clinical risk from the neonatal connectome.

The development of perinatal brain connectivity underpins motor, cognitive and behavioural abilities in later life. Diffusion MRI allows the characterisation of subtle inter-individual differences in structural brain connectivity. Individual brain connectivity maps (connectomes) are by nature high in dimensionality and complex to interpret. Machine learning methods are a powerful tool to uncover properties of the connectome which are not readily visible and can give us clues as to how and why individual developmental trajectories differ. In this manuscript we used Deep Neural Networks and Random Forests to predict demographic and neurodevelopmental characteristics from neonatal structural connectomes in a large sample of babies (n = 524) from the developing Human Connectome Project. We achieved an accurate prediction of post menstrual age (PMA) at scan in term-born infants (mean absolute error (MAE) = 0.72 weeks, r = 0.83 and p < 0.001). We also achieved good accuracy when predicting gestational age at birth in a cohort of term and preterm babies scanned at term equivalent age (MAE = 2.21 weeks, r = 0.82, p < 0.001). We subsequently used sensitivity analysis to obtain feature relevance from our prediction models, with the most important connections for prediction of PMA and GA found to predominantly involve frontal and temporal regions, thalami, and basal ganglia. From our models of PMA at scan for infants born at term, we computed a brain maturation index (predicted age minus actual age) of individual preterm neonates and found a significant correlation between this index and motor outcome at 18 months corrected age. Our results demonstrate the applicability of machine learning techniques in analyses of the neonatal connectome and suggest that a neural substrate of brain maturation with implications for future neurodevelopment is detectable at term equivalent age from the neonatal connectome.

Effects of gestational age at birth on perinatal structural brain development in healthy term-born babies.

Infants born in early term (37-38 weeks gestation) experience slower neurodevelopment than those born at full term (40-41 weeks gestation). While this could be due to higher perinatal morbidity, gestational age at birth may also have a direct effect on the brain. Here we characterise brain volume and white matter correlates of gestational age at birth in healthy term-born neonates and their relationship to later neurodevelopmental outcome using T2 and diffusion weighted MRI acquired in the neonatal period from a cohort (n = 454) of healthy babies born at term age (>37 weeks gestation) and scanned between 1 and 41 days after birth. Images were analysed using tensor-based morphometry and tract-based spatial statistics. Neurodevelopment was assessed at age 18 months using the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III). Infants born earlier had higher relative ventricular volume and lower relative brain volume in the deep grey matter, cerebellum and brainstem. Earlier birth was also associated with lower fractional anisotropy, higher mean, axial, and radial diffusivity in major white matter tracts. Gestational age at birth was positively associated with all Bayley-III subscales at age 18 months. Regression models predicting outcome from gestational age at birth were significantly improved after adding neuroimaging features associated with gestational age at birth. This work adds to the body of evidence of the impact of early term birth and highlights the importance of considering the effect of gestational age at birth in future neuroimaging studies including term-born babies.

Data-driven motion-corrected brain MRI incorporating pose-dependent B0 fields.

PURPOSE: To develop a fully data-driven retrospective intrascan motion-correction framework for volumetric brain MRI at ultrahigh field (7 Tesla) that includes modeling of pose-dependent changes in polarizing magnetic (B0 ) fields. THEORY AND METHODS: Tissue susceptibility induces spatially varying B0 distributions in the head, which change with pose. A physics-inspired B0 model has been deployed to model the B0 variations in the head and was validated in vivo. This model is integrated into a forward parallel imaging model for imaging in the presence of motion. Our proposal minimizes the number of added parameters, enabling the developed framework to estimate dynamic B0 variations from appropriately acquired data without requiring navigators. The effect on data-driven motion correction is validated in simulations and in vivo. RESULTS: The applicability of the physics-inspired B0 model was confirmed in vivo. Simulations show the need to include the pose-dependent B0 fields in the reconstruction to improve motion-correction performance and the feasibility of estimating B0 evolution from the acquired data. The proposed motion and B0 correction showed improved image quality for strongly corrupted data at 7 Tesla in simulations and in vivo. CONCLUSION: We have developed a motion-correction framework that accounts for and estimates pose-dependent B0 fields. The method improves current state-of-the-art data-driven motion-correction techniques when B0 dependencies cannot be neglected. The use of a compact physics-inspired B0 model together with leveraging the parallel imaging encoding redundancy and previously proposed optimized sampling patterns enables a purely data-driven approach.

The Developing Human Connectome Project: typical and disrupted perinatal functional connectivity.

The Developing Human Connectome Project is an Open Science project that provides the first large sample of neonatal functional MRI data with high temporal and spatial resolution. These data enable mapping of intrinsic functional connectivity between spatially distributed brain regions under normal and adverse perinatal circumstances, offering a framework to study the ontogeny of large-scale brain organization in humans. Here, we characterize in unprecedented detail the maturation and integrity of resting state networks (RSNs) at term-equivalent age in 337 infants (including 65 born preterm). First, we applied group independent component analysis to define 11 RSNs in term-born infants scanned at 43.5-44.5 weeks postmenstrual age (PMA). Adult-like topography was observed in RSNs encompassing primary sensorimotor, visual and auditory cortices. Among six higher-order, association RSNs, analogues of the adult networks for language and ocular control were identified, but a complete default mode network precursor was not. Next, we regressed the subject-level datasets from an independent cohort of infants scanned at 37-43.5 weeks PMA against the group-level RSNs to test for the effects of age, sex and preterm birth. Brain mapping in term-born infants revealed areas of positive association with age across four of six association RSNs, indicating active maturation in functional connectivity from 37 to 43.5 weeks PMA. Female infants showed increased connectivity in inferotemporal regions of the visual association network. Preterm birth was associated with striking impairments of functional connectivity across all RSNs in a dose-dependent manner; conversely, connectivity of the superior parietal lobules within the lateral motor network was abnormally increased in preterm infants, suggesting a possible mechanism for specific difficulties such as developmental coordination disorder, which occur frequently in preterm children. Overall, we found a robust, modular, symmetrical functional brain organization at normal term age. A complete set of adult-equivalent primary RSNs is already instated, alongside emerging connectivity in immature association RSNs, consistent with a primary-to-higher order ontogenetic sequence of brain development. The early developmental disruption imposed by preterm birth is associated with extensive alterations in functional connectivity.

Phenotyping the Preterm Brain: Characterizing Individual Deviations From Normative Volumetric Development in Two Large Infant Cohorts.

The diverse cerebral consequences of preterm birth create significant challenges for understanding pathogenesis or predicting later outcome. Instead of focusing on describing effects common to the group, comparing individual infants against robust normative data offers a powerful alternative to study brain maturation. Here we used Gaussian process regression to create normative curves characterizing brain volumetric development in 274 term-born infants, modeling for age at scan and sex. We then compared 89 preterm infants scanned at term-equivalent age with these normative charts, relating individual deviations from typical volumetric development to perinatal risk factors and later neurocognitive scores. To test generalizability, we used a second independent dataset comprising of 253 preterm infants scanned using different acquisition parameters and scanner. We describe rapid, nonuniform brain growth during the neonatal period. In both preterm cohorts, cerebral atypicalities were widespread, often multiple, and varied highly between individuals. Deviations from normative development were associated with respiratory support, nutrition, birth weight, and later neurocognition, demonstrating their clinical relevance. Group-level understanding of the preterm brain disguises a large degree of individual differences. We provide a method and normative dataset that offer a more precise characterization of the cerebral consequences of preterm birth by profiling the individual neonatal brain.

Scattered slice SHARD reconstruction for motion correction in multi-shell diffusion MRI.

Diffusion MRI offers a unique probe into neural microstructure and connectivity in the developing brain. However, analysis of neonatal brain imaging data is complicated by inevitable subject motion, leading to a series of scattered slices that need to be aligned within and across diffusion-weighted contrasts. Here, we develop a reconstruction method for scattered slice multi-shell high angular resolution diffusion imaging (HARDI) data, jointly estimating an uncorrupted data representation and motion parameters at the slice or multiband excitation level. The reconstruction relies on data-driven representation of multi-shell HARDI data using a bespoke spherical harmonics and radial decomposition (SHARD), which avoids imposing model assumptions, thus facilitating to compare various microstructure imaging methods in the reconstructed output. Furthermore, the proposed framework integrates slice-level outlier rejection, distortion correction, and slice profile correction. We evaluate the method in the neonatal cohort of the developing Human Connectome Project (650 scans). Validation experiments demonstrate accurate slice-level motion correction across the age range and across the range of motion in the population. Results in the neonatal data show successful reconstruction even in severely motion-corrupted subjects. In addition, we illustrate how local tissue modelling can extract advanced microstructure features such as orientation distribution functions from the motion-corrected reconstructions.

Preterm birth alters the development of cortical microstructure and morphology at term-equivalent age.

INTRODUCTION: The dynamic nature and complexity of the cellular events that take place during the last trimester of pregnancy make the developing cortex particularly vulnerable to perturbations. Abrupt interruption to normal gestation can lead to significant deviations to many of these processes, resulting in atypical trajectory of cortical maturation in preterm birth survivors. METHODS: We sought to first map typical cortical micro- and macrostructure development using invivo MRI in a large sample of healthy term-born infants scanned after birth (n = 259). Then we offer a comprehensive characterization of the cortical consequences of preterm birth in 76 preterm infants scanned at term-equivalent age (37-44 weeks postmenstrual age). We describe the group-average atypicality, the heterogeneity across individual preterm infants, and relate individual deviations from normative development to age at birth and neurodevelopment at 18 months. RESULTS: In the term-born neonatal brain, we observed heterogeneous and regionally specific associations between age at scan and measures of cortical morphology and microstructure, including rapid surface expansion, greater cortical thickness, lower cortical anisotropy and higher neurite orientation dispersion. By term-equivalent age, preterm infants had on average increased cortical tissue water content and reduced neurite density index in the posterior parts of the cortex, and greater cortical thickness anteriorly compared to term-born infants. While individual preterm infants were more likely to show extreme deviations (over 3.1 standard deviations) from normative cortical maturation compared to term-born infants, these extreme deviations were highly variable and showed very little spatial overlap between individuals. Measures of regional cortical development were associated with age at birth, but not with neurodevelopment at 18 months. CONCLUSION: We showed that preterm birth alters cortical micro- and macrostructural maturation near the time of full-term birth. Deviations from normative development were highly variable between individual preterm infants.

The effect of maternal position on venous return for pregnant women during MRI.

Magnetic resonance imaging (MRI) in pregnancy is commonly undertaken in the left lateral tilt (LLT) position to prevent inferior vena cava (IVC) compression and supine hypotensive events, although this may be suboptimal for image quality. The supine position may also have an adverse effect on fetal well-being. The spinal venous plexus may provide an alternative pathway for venous return in the presence of IVC compression. This study assesses morphology and blood flow of the IVC and spinal venous plexus for pregnant women in LLT and supine positions to ascertain the effect of maternal position on venous return during MRI. Eighty-two pregnant women underwent phase contrast MRI (PC-MRI) of the IVC and spinal venous plexus in the supine position; 25 were also imaged in the LLT position. Differences in life monitoring, IVC, spinal venous plexus and total venous return between the two positions were assessed. A linear regression assessed the relationship between flow in the IVC and the spinal venous plexus in the supine position. Increasing gestational age and the right-sided position of the uterus on IVC and spinal venous plexus venous return were also evaluated. Hypotension symptoms were similar in supine (10%) and LLT (8%) positioning. Supine positioning decreased IVC height (p < 0.004) and flow (p = 0.045) but flow in the spinal venous plexus increased (p < 0.001) compared with the LLT position. Total venous return showed no difference (p = 0.989) between the two positions. Additional measurements of flow in the aorta also showed no significant difference between the two groups (p = 0.866). Reduced IVC flow in the supine position was associated with increasing gestational age (p = 0.004) and degree of right-sided uterine position (p = 0.004). Women in the left lateral decubitus position who then rotated supine had greater flow in the IVC (p = 0.008) and spinal venous plexus (p = 0.029) than those who started supine. For the majority of women, the spinal venous plexus acts as a complementary venous return system for pregnant women in the supine position, maintaining vascular homeostasis. Further study is needed to assess the effects on the health of the fetus.

Heterogeneity in Brain Microstructural Development Following Preterm Birth.

Preterm-born children are at increased risk of lifelong neurodevelopmental difficulties. Group-wise analyses of magnetic resonance imaging show many differences between preterm- and term-born infants but do not reliably predict neurocognitive prognosis for individual infants. This might be due to the unrecognized heterogeneity of cerebral injury within the preterm group. This study aimed to determine whether atypical brain microstructural development following preterm birth is significantly variable between infants. Using Gaussian process regression, a technique that allows a single-individual inference, we characterized typical variation of brain microstructure using maps of fractional anisotropy and mean diffusivity in a sample of 270 term-born neonates. Then, we compared 82 preterm infants to these normative values to identify brain regions with atypical microstructure and relate observed deviations to degree of prematurity and neurocognition at 18 months. Preterm infants showed strikingly heterogeneous deviations from typical development, with little spatial overlap between infants. Greater and more extensive deviations, captured by a whole brain atypicality index, were associated with more extreme prematurity and predicted poorer cognitive and language abilities at 18 months. Brain microstructural development after preterm birth is highly variable between individual infants. This poorly understood heterogeneity likely relates to both the etiology and prognosis of brain injury.

Development of Microstructural and Morphological Cortical Profiles in the Neonatal Brain.

Interruptions to neurodevelopment during the perinatal period may have long-lasting consequences. However, to be able to investigate deviations in the foundation of proper connectivity and functional circuits, we need a measure of how this architecture evolves in the typically developing brain. To this end, in a cohort of 241 term-born infants, we used magnetic resonance imaging to estimate cortical profiles based on morphometry and microstructure over the perinatal period (37-44 weeks postmenstrual age, PMA). Using the covariance of these profiles as a measure of inter-areal network similarity (morphometric similarity networks; MSN), we clustered these networks into distinct modules. The resulting modules were consistent and symmetric, and corresponded to known functional distinctions, including sensory-motor, limbic, and association regions, and were spatially mapped onto known cytoarchitectonic tissue classes. Posterior regions became more morphometrically similar with increasing age, while peri-cingulate and medial temporal regions became more dissimilar. Network strength was associated with age: Within-network similarity increased over age suggesting emerging network distinction. These changes in cortical network architecture over an 8-week period are consistent with, and likely underpin, the highly dynamic processes occurring during this critical period. The resulting cortical profiles might provide normative reference to investigate atypical early brain development.

On the need for bundle-specific microstructure kernels in diffusion MRI.

Probing microstructure with diffusion magnetic resonance imaging (dMRI) on a scale orders of magnitude below the imaging resolution relies on biophysical modelling of the signal response in the tissue. The vast majority of these biophysical models of diffusion in white matter assume that the measured dMRI signal is the sum of the signals emanating from each of the constituent compartments, each of which exhibits a distinct behaviour in the b-value and/or orientation domain. Many of these models further assume that the dMRI behaviour of the oriented compartments (e.g. the intra-axonal space) is identical between distinct fibre populations, at least at the level of a single voxel. This implicitly assumes that any potential biological differences between fibre populations are negligible, at least as far as is measurable using dMRI. Here, we validate this assumption by means of a voxel-wise, model-free signal decomposition that, under the assumption above and in the absence of noise, is shown to be rank-1. We evaluate the effect size of signal components beyond this rank-1 representation and use permutation testing to assess their significance. We conclude that in the healthy adult brain, the dMRI signal is adequately represented by a rank-1 model, implying that biologically more realistic, but mathematically more complex fascicle-specific microstructure models do not capture statistically significant or anatomically meaningful structure, even in extended high-b diffusion MRI scans.

The developing Human Connectome Project (dHCP) automated resting-state functional processing framework for newborn infants.

The developing Human Connectome Project (dHCP) aims to create a detailed 4-dimensional connectome of early life spanning 20-45 weeks post-menstrual age. This is being achieved through the acquisition of multi-modal MRI data from over 1000 in- and ex-utero subjects combined with the development of optimised pre-processing pipelines. In this paper we present an automated and robust pipeline to minimally pre-process highly confounded neonatal resting-state fMRI data, robustly, with low failure rates and high quality-assurance. The pipeline has been designed to specifically address the challenges that neonatal data presents including low and variable contrast and high levels of head motion. We provide a detailed description and evaluation of the pipeline which includes integrated slice-to-volume motion correction and dynamic susceptibility distortion correction, a robust multimodal registration approach, bespoke ICA-based denoising, and an automated QC framework. We assess these components on a large cohort of dHCP subjects and demonstrate that processing refinements integrated into the pipeline provide substantial reduction in movement related distortions, resulting in significant improvements in SNR, and detection of high quality RSNs from neonates.

Simultaneous multislice imaging of the heart using multiband balanced SSFP with blipped-CAIPI.

PURPOSE: In this work, we explore the use of multiband (MB) balanced steady-state free precession (bSSFP) with blipped-controlled aliasing in parallel imaging (CAIPI), which avoids the issues of altered frequency response associated with RF phase cycling, and show its application to accelerating cardiac cine imaging. METHODS: Blipped and RF-cycled CAIPI were implemented into a retrospective-gated segmented cine multiband bSSFP sequence. The 2 methods were compared at 3T using MB2 to demonstrate the effect on frequency response. Further data (4 subjects) were acquired at both 1.5T and 3T collecting 12-slice short axis stacks using blipped-CAIPI with MB acceleration factors of 1-4. The impact on SNR and contrast was evaluated along with g-factors at different accelerations. RESULTS: Data acquired with blipped-CAIPI multiband bSSFP up to factor 4 yielded functional cine data with good SNR and contrast, while reliably keeping dark-band artefacts clear of the heart at 1.5T. SAR limits the maximum MB acceleration, particularly at 3T, where minimum TR increase is problematic and leakage artefacts are more prevalent. Mean g-factors across the heart were measured at 1.00, 1.06, and 1.12 for MB2-MB4, whereas blood-pool SNR measures (end-diastole) decreased by 11.8, 21.5, and 36.9%; ultimately LV-myocardium CNR remained sufficient at 1.5T with values ranging: 15.6, 13.4, 11.9, and 9.6 (MB1-MB4). CONCLUSION: Blipped-CAIPI multiband bSSFP can be used in cardiovascular applications without affecting the frequency response because of controlled aliasing and can be readily incorporated into segmented cine acquisitions without adding any additional constraints because of phase cycling requirements. The method was used to collect full ventricular coverage within a single breath-hold.

Autocalibrated cardiac tissue phase mapping with multiband imaging and k-t acceleration.

PURPOSE: To develop an autocalibrated multiband (MB) CAIPIRINHA acquisition scheme with in-plane k-t acceleration enabling multislice three-directional tissue phase mapping in one breath-hold. METHODS: A k-t undersampling scheme was integrated into a time-resolved electrocardiographic-triggered autocalibrated MB gradient-echo sequence. The sequence was used to acquire data on 4 healthy volunteers with MB factors of two (MB2) and three (MB3), which were reconstructed using a joint reconstruction algorithm that tackles both k-t and MB acceleration. Forward simulations of the imaging process were used to tune the reconstruction model hyperparameters. Direct comparisons between MB and single-band tissue phase-mapping measurements were performed. RESULTS: Simulations showed that the velocities could be accurately reproduced with MB2 k-t (average ± twice the SD of the RMS error of 0.08 ± 0.22 cm/s and velocity peak reduction of 1.03% ± 6.47% compared with fully sampled velocities), whereas acceptable results were obtained with MB3 k-t (RMS error of 0.13 ± 0.58 cm/s and peak reduction of 2.21% ± 13.45%). When applied to tissue phase-mapping data, the proposed technique allowed three-directional velocity encoding to be simultaneously acquired at two/three slices in a single breath-hold of 18 heartbeats. No statistically significant differences were detected between MB2/MB3 k-t and single-band k-t motion traces averaged over the myocardium. Regional differences were found, however, when using the American Heart Association model for segmentation. CONCLUSION: An autocalibrated MB k-t acquisition/reconstruction framework is presented that allows three-directional velocity encoding of the myocardial velocities at multiple slices in one breath-hold.