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.

Automated body organ segmentation, volumetry and population-averaged atlas for 3D motion-corrected T2-weighted fetal body MRI.

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range.

Craniofacial phenotyping with fetal MRI: a feasibility study of 3D visualisation, segmentation, surface-rendered and physical models.

This study explores the potential of 3D Slice-to-Volume Registration (SVR) motion-corrected fetal MRI for craniofacial assessment, traditionally used only for fetal brain analysis. In addition, we present the first description of an automated pipeline based on 3D Attention UNet trained for 3D fetal MRI craniofacial segmentation, followed by surface refinement. Results of 3D printing of selected models are also presented.Qualitative analysis of multiplanar volumes, based on the SVR output and surface segmentations outputs, were assessed with computer and printed models, using standardised protocols that we developed for evaluating image quality and visibility of diagnostic craniofacial features. A test set of 25, postnatally confirmed, Trisomy 21 fetal cases (24-36 weeks gestational age), revealed that 3D reconstructed T2 SVR images provided 66-100% visibility of relevant craniofacial and head structures in the SVR output, and 20-100% and 60-90% anatomical visibility was seen for the baseline and refined 3D computer surface model outputs respectively. Furthermore, 12 of 25 cases, 48%, of refined surface models demonstrated good or excellent overall quality with a further 9 cases, 36%, demonstrating moderate quality to include facial, scalp and external ears. Additional 3D printing of 12 physical real-size models (20-36 weeks gestational age) revealed good/excellent overall quality in all cases and distinguishable features between healthy control cases and cases with confirmed anomalies, with only minor manual adjustments required before 3D printing.Despite varying image quality and data heterogeneity, 3D T2w SVR reconstructions and models provided sufficient resolution for the subjective characterisation of subtle craniofacial features. We also contributed a publicly accessible online 3D T2w MRI atlas of the fetal head, validated for accurate representation of normal fetal anatomy.Future research will focus on quantitative analysis, optimizing the pipeline, and exploring diagnostic, counselling, and educational applications in fetal craniofacial assessment.

Adrenal volumes in fetuses delivering prior to 32 weeks' gestation: An MRI pilot study.

INTRODUCTION: Spontaneous preterm birth prior to 32 weeks' gestation accounts for 1% of all deliveries and is associated with high rates of morbidity and mortality. A total of 70% are associated with chorioamnionitis which increases the incidence of morbidity, but for which there is no noninvasive antenatal test. Fetal adrenal glands produce cortisol and dehydroepiandosterone-sulphate which upregulate prior to spontaneous preterm birth. Ultrasound suggests that adrenal volumes may increase prior to preterm birth, but studies are limited. This study aimed to: (i) demonstrate reproducibility of magnetic resonance imaging (MRI) derived adrenal volumetry; (ii) derive normal ranges of total adrenal volumes, and adrenal: body volume for normal; (iii) compare with those who have spontaneous very preterm birth; and (iv) correlate with histopathological chorioamnionitis. MATERIAL AND METHODS: Patients at high risk of preterm birth prior to 32 weeks were prospectively recruited, and included if they did deliver prior to 32 weeks; a control group who delivered an uncomplicated pregnancy at term was also recruited. T2 weighted images of the entire uterus were obtained, and a deformable slice-to-volume method was used to reconstruct the fetal abdomen. Adrenal and body volumes were obtained via manual segmentation, and adrenal: body volume ratios generated. Normal ranges were created using control data. Differences between groups were investigated accounting for the effect of gestation by use of regression analysis. Placental histopathology was reviewed for pregnancies delivering preterm. RESULTS: A total of 56 controls and 26 cases were included in the analysis. Volumetry was consistent between observers. Adrenal volumes were not higher in the case group (p = 0.2); adrenal: body volume ratios were higher (p = 0.011), persisting in the presence of chorioamnionitis (p = 0.017). A cluster of three pairs of adrenal glands below the fifth centile were noted among the cases all of whom had a protracted period at risk of preterm birth prior to MRI. CONCLUSIONS: Adrenal: body volume ratios are significantly larger in fetuses who go on to deliver preterm than those delivering at term. Adrenal volumes were not significantly larger, we hypothesize that this could be due to an adrenal atrophy in fetuses with fulminating chorioamnionitis. A straightforward relationship of adrenal size being increased prior to preterm birth should not be assumed.

Functional MRI assessment of the lungs in fetuses that deliver very Preterm: An MRI pilot study.

OBJECTIVES: To compare mean pulmonary T2* values and pulmonary volumes in fetuses that subsequently spontaneously delivered before 32 weeks with a control cohort with comparable gestational ages and to assess the value of mean pulmonary T2* as a predictor of preterm birth < 32 weeks' gestation. METHODS: MRI datasets scanned at similar gestational ages were selected from fetuses who spontaneously delivered < 32 weeks of gestation and a control group who subsequently delivered at term with no complications. All women underwent a fetal MRI on a 3 T MRI imaging system. Sequences included T2-weighted single shot fast spin echo and T2* sequences, using gradient echo single shot echo planar sequencing of the fetal thorax. Motion correction was performed using slice-to-volume reconstruction and T2* maps generated using in-house pipelines. Lungs were manually segmented and volumes and mean T2* values calculated for both lungs combined and left and right lung separately. Linear regression was used to compare values between the preterm and control cohorts accounting for the effects of gestation. Receiver operating curves were generated for mean T2* values and pulmonary volume as predictors of preterm birth < 32 weeks' gestation. RESULTS: Datasets from twenty-eight preterm and 74 control fetuses were suitable for analysis. MRI images were taken at similar fetal gestational ages (preterm cohort (mean ± SD) 24.9 ± 3.3 and control cohort (mean ± SD) 26.5 ± 3.0). Mean gestational age at delivery was 26.4 ± 3.3 for the preterm group and 39.9 ± 1.3 for the control group. Mean pulmonary T2* values remained constant with increasing gestational age while pulmonary volumes increased. Both T2* and pulmonary volumes were lower in the preterm group than in the control group for all parameters (both combined, left, and right lung (p < 0.001 in all cases). Adjusted for gestational age, pulmonary volumes and mean T2* values were good predictors of premature delivery in fetuses < 32 weeks (area under the curve of 0.828 and 0.754 respectively). CONCLUSION: These findings indicate that mean pulmonary T2* values and volumes were lower in fetuses that subsequently delivered very preterm. This may suggest potentially altered oxygenation and indicate that pulmonary morbidity associated with prematurity has an antenatal antecedent. Future work should explore these results correlating antenatal findings with long term pulmonary outcomes.

Total and Regional Brain Volumes in Fetuses With Congenital Heart Disease.

BACKGROUND: Congenital heart disease (CHD) is common and is associated with impaired early brain development and neurodevelopmental outcomes, yet the exact mechanisms underlying these associations are unclear. PURPOSE: To utilize MRI data from a cohort of fetuses with CHD as well as typically developing fetuses to test the hypothesis that expected cerebral substrate delivery is associated with total and regional fetal brain volumes. STUDY TYPE: Retrospective case-control study. POPULATION: Three hundred eighty fetuses (188 male), comprising 45 healthy controls and 335 with isolated CHD, scanned between 29 and 37 weeks gestation. Fetuses with CHD were assigned into one of four groups based on expected cerebral substrate delivery. FIELD STRENGTH/SEQUENCE: T2-weighted single-shot fast-spin-echo sequences and a balanced steady-state free precession gradient echo sequence were obtained on a 1.5 T scanner. ASSESSMENT: Images were motion-corrected and reconstructed using an automated slice-to-volume registration reconstruction technique, before undergoing segmentation using an automated pipeline and convolutional neural network that had undergone semi-supervised training. Differences in total, regional brain (cortical gray matter, white matter, deep gray matter, cerebellum, and brainstem) and brain:body volumes were compared between groups. STATISTICAL TESTS: ANOVA was used to test for differences in brain volumes between groups, after accounting for sex and gestational age at scan. PFDR -values <0.05 were considered statistically significant. RESULTS: Total and regional brain volumes were smaller in fetuses where cerebral substrate delivery is reduced. No significant differences were observed in total or regional brain volumes between control fetuses and fetuses with CHD but normal cerebral substrate delivery (all PFDR > 0.12). Severely reduced cerebral substrate delivery is associated with lower brain:body volume ratios. DATA CONCLUSION: Total and regional brain volumes are smaller in fetuses with CHD where there is a reduction in cerebral substrate delivery, but not in those where cerebral substrate delivery is expected to be normal. EVIDENCE LEVEL: 3 TECHNICAL EFFICACY: Stage 3.

An automated pipeline for quantitative T2* fetal body MRI and segmentation at low field.

Fetal Magnetic Resonance Imaging at low field strengths is emerging as an exciting direction in perinatal health. Clinical low field (0.55T) scanners are beneficial for fetal imaging due to their reduced susceptibility-induced artefacts, increased T2* values, and wider bore (widening access for the increasingly obese pregnant population). However, the lack of standard automated image processing tools such as segmentation and reconstruction hampers wider clinical use. In this study, we introduce a semi-automatic pipeline using quantitative MRI for the fetal body at low field strength resulting in fast and detailed quantitative T2* relaxometry analysis of all major fetal body organs. Multi-echo dynamic sequences of the fetal body were acquired and reconstructed into a single high-resolution volume using deformable slice-to-volume reconstruction, generating both structural and quantitative T2* 3D volumes. A neural network trained using a semi-supervised approach was created to automatically segment these fetal body 3D volumes into ten different organs (resulting in dice values > 0.74 for 8 out of 10 organs). The T2* values revealed a strong relationship with GA in the lungs, liver, and kidney parenchyma (R2 >0.5). This pipeline was used successfully for a wide range of GAs (17-40 weeks), and is robust to motion artefacts. Low field fetal MRI can be used to perform advanced MRI analysis, and is a viable option for clinical scanning.

3D T2w fetal body MRI: automated organ volumetry, growth charts and population-averaged atlas.

Structural fetal body MRI provides true 3D information required for volumetry of fetal organs. However, current clinical and research practice primarily relies on manual slice-wise segmentation of raw T2-weighted stacks, which is time consuming, subject to inter- and intra-observer bias and affected by motion-corruption. Furthermore, there are no existing standard guidelines defining a universal approach to parcellation of fetal organs. This work produces the first parcellation protocol of the fetal body organs for motion-corrected 3D fetal body MRI. It includes 10 organ ROIs relevant to fetal quantitative volumetry studies. We also introduce the first population-averaged T2w MRI atlas of the fetal body. The protocol was used as a basis for training of a neural network for automated organ segmentation. It showed robust performance for different gestational ages. This solution minimises the need for manual editing and significantly reduces time. The general feasibility of the proposed pipeline was also assessed by analysis of organ growth charts created from automated parcellations of 91 normal control 3T MRI datasets that showed expected increase in volumetry during 22-38 weeks gestational age range. In addition, the results of comparison between 60 normal and 12 fetal growth restriction datasets revealed significant differences in organ volumes.

BOUNTI: Brain vOlumetry and aUtomated parcellatioN for 3D feTal MRI.

Fetal MRI is widely used for quantitative brain volumetry studies. However, currently, there is a lack of universally accepted protocols for fetal brain parcellation and segmentation. Published clinical studies tend to use different segmentation approaches that also reportedly require significant amounts of time-consuming manual refinement. In this work, we propose to address this challenge by developing a new robust deep learning-based fetal brain segmentation pipeline for 3D T2w motion corrected brain images. At first, we defined a new refined brain tissue parcellation protocol with 19 regions-of-interest using the new fetal brain MRI atlas from the Developing Human Connectome Project. This protocol design was based on evidence from histological brain atlases, clear visibility of the structures in individual subject 3D T2w images and the clinical relevance to quantitative studies. It was then used as a basis for developing an automated deep learning brain tissue parcellation pipeline trained on 360 fetal MRI datasets with different acquisition parameters using semi-supervised approach with manually refined labels propagated from the atlas. The pipeline demonstrated robust performance for different acquisition protocols and GA ranges. Analysis of tissue volumetry for 390 normal participants (21-38 weeks gestational age range), scanned with three different acquisition protocols, did not reveal significant differences for major structures in the growth charts. Only minor errors were present in < 15% of cases thus significantly reducing the need for manual refinement. In addition, quantitative comparison between 65 fetuses with ventriculomegaly and 60 normal control cases were in agreement with the findings reported in our earlier work based on manual segmentations. These preliminary results support the feasibility of the proposed atlas-based deep learning approach for large-scale volumetric analysis. The created fetal brain volumetry centiles and a docker with the proposed pipeline are publicly available online at https://hub.docker.com/r/fetalsvrtk/segmentation (tag brain_bounti_tissue).

Effect of schizophrenia common variants on infant brain volumes: cross-sectional study in 207 term neonates in developing Human Connectome Project.

Increasing lines of evidence suggest deviations from the normal early developmental trajectory could give rise to the onset of schizophrenia during adolescence and young adulthood, but few studies have investigated brain imaging changes associated with schizophrenia common variants in neonates. This study compared the brain volumes of both grey and white matter regions with schizophrenia polygenic risk scores (PRS) for 207 healthy term-born infants of European ancestry. Linear regression was used to estimate the relationship between PRS and brain volumes, with gestational age at birth, postmenstrual age at scan, ancestral principal components, sex and intracranial volumes as covariates. The schizophrenia PRS were negatively associated with the grey (ß = -0.08, p = 4.2 × 10-3) and white (ß = -0.13, p = 9.4 × 10-3) matter superior temporal gyrus volumes, white frontal lobe volume (ß = -0.09, p = 1.5 × 10-3) and the total white matter volume (ß = -0.062, p = 1.66 × 10-2). This result also remained robust when incorporating individuals of Asian ancestry. Explorative functional analysis of the schizophrenia risk variants associated with the right frontal lobe white matter volume found enrichment in neurodevelopmental pathways. This preliminary result suggests possible involvement of schizophrenia risk genes in early brain growth, and potential early life structural alterations long before the average age of onset of the disease.

Assessment of normal pulmonary development using functional magnetic resonance imaging techniques.

BACKGROUND: The mainstay of assessment of the fetal lungs in clinical practice is via evaluation of pulmonary size, primarily using 2D ultrasound and more recently with anatomical magnetic resonance imaging. The emergence of advanced magnetic resonance techniques such as T2* relaxometry in combination with the latest motion correction post-processing tools now facilitates assessment of the metabolic activity or perfusion of fetal pulmonary tissue in vivo. OBJECTIVE: This study aimed to characterize normal pulmonary development using T2* relaxometry, accounting for fetal motion across gestation. METHODS: Datasets from women with uncomplicated pregnancies that delivered at term, were analyzed. All subjects had undergone T2-weighted imaging and T2* relaxometry on a Phillips 3T magnetic resonance imaging system antenatally. T2* relaxometry of the fetal thorax was performed using a gradient echo single-shot echo planar imaging sequence. Following correction for fetal motion using slice-to-volume reconstruction, T2* maps were generated using in-house pipelines. Lungs were manually segmented and mean T2* values calculated for the right and left lungs individually, and for both lungs combined. Lung volumes were generated from the segmented images, and the right and left lungs, as well as both lungs combined were assessed. RESULTS: Eighty-seven datasets were suitable for analysis. The mean gestation at scan was 29.9±4.3 weeks (range: 20.6-38.3) and mean gestation at delivery was 40±1.2 weeks (range: 37.1-42.4). Mean T2* values of the lungs increased over gestation for right and left lungs individually and for both lungs assessed together (P=.003; P=.04; P=.003, respectively). Right, left, and total lung volumes were also strongly correlated with increasing gestational age (P<.001 in all cases). CONCLUSION: This large study assessed developing lungs using T2* imaging across a wide gestational age range. Mean T2* values increased with gestational age, which may reflect increasing perfusion and metabolic requirements and alterations in tissue composition as gestation advances. In the future, evaluation of findings in fetuses with conditions known to be associated with pulmonary morbidity may lead to enhanced prognostication antenatally, consequently improving counseling and perinatal care planning.

Fetal MRI by Robust Deep Generative Prior Reconstruction and Diffeomorphic Registration.

Magnetic resonance imaging of whole fetal body and placenta is limited by different sources of motion affecting the womb. Usual scanning techniques employ single-shot multi-slice sequences where anatomical information in different slices may be subject to different deformations, contrast variations or artifacts. Volumetric reconstruction formulations have been proposed to correct for these factors, but they must accommodate a non-homogeneous and non-isotropic sampling, so regularization becomes necessary. Thus, in this paper we propose a deep generative prior for robust volumetric reconstructions integrated with a diffeomorphic volume to slice registration method. Experiments are performed to validate our contributions and compare with ifdefined tmiformat R2.5a state of the art method methods in the literature in a cohort of 72 fetal datasets in the range of 20-36 weeks gestational age. Results suggest improved image resolution Quantitative as well as radiological assessment suggest improved image quality and more accurate prediction of gestational age at scan is obtained when comparing to a state of the art reconstruction method methods. In addition, gestational age prediction results from our volumetric reconstructions compare favourably are competitive with existing brain-based approaches, with boosted accuracy when integrating information of organs other than the brain. Namely, a mean absolute error of 0.618 weeks ( R2=0.958 ) is achieved when combining fetal brain and trunk information.

Retrospective motion correction in foetal MRI for clinical applications: existing methods, applications and integration into clinical practice.

Foetal MRI is a complementary imaging method to antenatal ultrasound. It provides advanced information for detection and characterisation of foetal brain and body anomalies. Even though modern single shot sequences allow fast acquisition of 2D slices with high in-plane image quality, foetal MRI is intrinsically corrupted by motion. Foetal motion leads to loss of structural continuity and corrupted 3D volumetric information in stacks of slices. Furthermore, the arbitrary and constantly changing position of the foetus requires dynamic readjustment of acquisition planes during scanning.

High resolution and contrast 7 tesla MR brain imaging of the neonate.

Introduction: Ultra-high field MR imaging offers marked gains in signal-to-noise ratio, spatial resolution, and contrast which translate to improved pathological and anatomical sensitivity. These benefits are particularly relevant for the neonatal brain which is rapidly developing and sensitive to injury. However, experience of imaging neonates at 7T has been limited due to regulatory, safety, and practical considerations. We aimed to establish a program for safely acquiring high resolution and contrast brain images from neonates on a 7T system. Methods: Images were acquired from 35 neonates on 44 occasions (median age 39 + 6 postmenstrual weeks, range 33 + 4 to 52 + 6; median body weight 2.93 kg, range 1.57 to 5.3 kg) over a median time of 49 mins 30 s. Peripheral body temperature and physiological measures were recorded throughout scanning. Acquired sequences included T2 weighted (TSE), Actual Flip angle Imaging (AFI), functional MRI (BOLD EPI), susceptibility weighted imaging (SWI), and MR spectroscopy (STEAM). Results: There was no significant difference between temperature before and after scanning (p = 0.76) and image quality assessment compared favorably to state-of-the-art 3T acquisitions. Anatomical imaging demonstrated excellent sensitivity to structures which are typically hard to visualize at lower field strengths including the hippocampus, cerebellum, and vasculature. Images were also acquired with contrast mechanisms which are enhanced at ultra-high field including susceptibility weighted imaging, functional MRI, and MR spectroscopy. Discussion: We demonstrate safety and feasibility of imaging vulnerable neonates at ultra-high field and highlight the untapped potential for providing important new insights into brain development and pathological processes during this critical phase of early life.

3D black blood cardiovascular magnetic resonance atlases of congenital aortic arch anomalies and the normal fetal heart: application to automated multi-label segmentation.

BACKGROUND: Image-domain motion correction of black-blood contrast T2-weighted fetal cardiovascular magnetic resonance imaging (CMR) using slice-to-volume registration (SVR) provides high-resolution three-dimensional (3D) images of the fetal heart providing excellent 3D visualisation of vascular anomalies [1]. However, 3D segmentation of these datasets, important for both clinical reporting and the application of advanced analysis techniques is currently a time-consuming process requiring manual input with potential for inter-user variability. METHODS: In this work, we present novel 3D fetal CMR population-averaged atlases of normal and abnormal fetal cardiovascular anatomy. The atlases are created using motion-corrected 3D reconstructed volumes of 86 third trimester fetuses (gestational age range 29-34 weeks) including: 28 healthy controls, 20 cases with postnatally confirmed neonatal coarctation of the aorta (CoA) and 38 vascular rings (21 right aortic arch (RAA), 17 double aortic arch (DAA)). We used only high image quality datasets with isolated anomalies and without any other deviations in the cardiovascular anatomy.In addition, we implemented and evaluated atlas-guided registration and deep learning (UNETR) methods for automated 3D multi-label segmentation of fetal cardiac vessels. We used images from CoA, RAA and DAA cohorts including: 42 cases for training (14 from each cohort), 3 for validation and 6 for testing. In addition, the potential limitations of the network were investigated on unseen datasets including 3 early gestational age (22 weeks) and 3 low SNR cases. RESULTS: We created four atlases representing the average anatomy of the normal fetal heart, postnatally confirmed neonatal CoA, RAA and DAA. Visual inspection was undertaken to verify expected anatomy per subgroup. The results of the multi-label cardiac vessel UNETR segmentation showed 100[Formula: see text] per-vessel detection rate for both normal and abnormal aortic arch anatomy. CONCLUSIONS: This work introduces the first set of 3D black-blood T2-weighted CMR atlases of normal and abnormal fetal cardiovascular anatomy including detailed segmentation of the major cardiovascular structures. Additionally, we demonstrated the general feasibility of using deep learning for multi-label vessel segmentation of 3D fetal CMR images.

Automated 3D reconstruction of the fetal thorax in the standard atlas space from motion-corrupted MRI stacks for 21-36 weeks GA range.

Slice-to-volume registration (SVR) methods allow reconstruction of high-resolution 3D images from multiple motion-corrupted stacks. SVR-based pipelines have been increasingly used for motion correction for T2-weighted structural fetal MRI since they allow more informed and detailed diagnosis of brain and body anomalies including congenital heart defects (Lloyd et al., 2019). Recently, fully automated rigid SVR reconstruction of the fetal brain in the atlas space was achieved in Salehi et al. (2019) that used convolutional neural networks (CNNs) for segmentation and pose estimation. However, these CNN-based methods have not yet been applied to the fetal trunk region. Meanwhile, the existing rigid and deformable SVR (DSVR) solutions (Uus et al., 2020) for the fetal trunk region are limited by the requirement of manual input as well the narrow capture range of the classical gradient descent based registration methods that cannot resolve severe fetal motion frequently occurring at the early gestational age (GA). Furthermore, in our experience, the conventional 2D slice-wise CNN-based brain masking solutions are reportedly prone to errors that require manual corrections when applied on a wide range of acquisition protocols or abnormal cases in clinical setting. In this work, we propose a fully automated pipeline for reconstruction of the fetal thorax region for 21-36 weeks GA range T2-weighted MRI datasets. It includes 3D CNN-based intra-uterine localisation of the fetal trunk and landmark-guided pose estimation steps that allow automated DSVR reconstruction in the standard radiological space irrespective of the fetal trunk position or the regional stack coverage. The additional step for generation of the common template space and rejection of outliers provides the means for automated exclusion of stacks affected by low image quality or extreme motion. The pipeline was quantitatively evaluated on a series of experiments including fetal MRI datasets and simulated rotation motion. Furthermore, we performed a qualitative assessment of the image reconstruction quality in terms of the definition of vascular structures on 100 early (median 23.14 weeks) and late (median 31.79 weeks) GA group MRI datasets covering 21 to 36 weeks GA range.

The Developing Human Connectome Project Neonatal Data Release.

The Developing Human Connectome Project has created a large open science resource which provides researchers with data for investigating typical and atypical brain development across the perinatal period. It has collected 1228 multimodal magnetic resonance imaging (MRI) brain datasets from 1173 fetal and/or neonatal participants, together with collateral demographic, clinical, family, neurocognitive and genomic data from 1173 participants, together with collateral demographic, clinical, family, neurocognitive and genomic data. All subjects were studied in utero and/or soon after birth on a single MRI scanner using specially developed scanning sequences which included novel motion-tolerant imaging methods. Imaging data are complemented by rich demographic, clinical, neurodevelopmental, and genomic information. The project is now releasing a large set of neonatal data; fetal data will be described and released separately. This release includes scans from 783 infants of whom: 583 were healthy infants born at term; as well as preterm infants; and infants at high risk of atypical neurocognitive development. Many infants were imaged more than once to provide longitudinal data, and the total number of datasets being released is 887. We now describe the dHCP image acquisition and processing protocols, summarize the available imaging and collateral data, and provide information on how the data can be accessed.

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.

Motion corrected fetal body magnetic resonance imaging provides reliable 3D lung volumes in normal and abnormal fetuses.

OBJECTIVES: To calculate 3D-segmented total lung volume (TLV) in fetuses with thoracic anomalies using deformable slice-to-volume registration (DSVR) with comparison to 2D-manual segmentation. To establish a normogram of TLV calculated by DSVR in healthy control fetuses. METHODS: A pilot study at a single regional fetal medicine referral centre included 16 magnetic resonance imaging (MRI) datasets of fetuses (22-32 weeks gestational age). Diagnosis was CDH (n = 6), CPAM (n = 2), and healthy controls (n = 8). Deformable slice-to-volume registration was used for reconstruction of 3D isotropic (0.85 mm) volumes of the fetal body followed by semi-automated lung segmentation. 3D TLV were compared to traditional 2D-based volumetry. Abnormal cases referenced to a normogram produced from 100 normal fetuses whose TLV was calculated by DSVR only. RESULTS: Deformable slice-to-volume registration-derived TLV values have high correlation with the 2D-based measurements but with a consistently lower volume; bias -1.44 cm3 [95% limits: -2.6 to -0.3] with improved resolution to exclude hilar structures even in cases of motion corruption or very low lung volumes. CONCLUSIONS: Deformable slice-to-volume registration for fetal lung MRI aids analysis of motion corrupted scans and does not suffer from the interpolation error inherent to 2D-segmentation. It increases information content of acquired data in terms of visualising organs in 3D space and quantification of volumes, which may improve counselling and surgical planning.

Assessment of the fetal thymus gland: Comparing MRI-acquired thymus volumes with 2D ultrasound measurements.

OBJECTIVES: The fetal thymus gland has been shown to involute in response to intrauterine infection, and therefore could be used as a non-invasive marker of fetal compartment infection. The objective of this study was to evaluate how accurately 2D ultrasound-derived measurements of the fetal thymus reflect the 3D volume of the gland derived from motion corrected MRI images. STUDY DESIGN: A retrospective study was performed using paired ultrasound and MRI datasets from the iFIND project (http://www.ifindproject.com). To obtain 3D volumetry of the thymus gland, T2-weighted single shot turbo spin echo (ssTSE) sequences of the fetal thorax were acquired. Thymus volumes were manually segmented from deformable slice-to-volume reconstructed images. To obtain 2D ultrasound measurements, previously stored fetal cine loops were used and measurements obtained at the 3-vessel-view (3VV) and 3-vessel-trachea view (3VT): anterior-posterior diameter (APD), intrathoracic diameter (ITD), transverse diameter (TD), perimeter and 3-vessel-edge (3VE). Inter-observer and intra-observer reliability (ICC) was calculated for both MRI and ultrasound measurements. Pearson correlation coefficients (PCC) were used to compare 2D-parameters with acceptable ICC to TV. RESULTS: 38 participants were identified. Adequate visualisation was possible on 37 MRI scans and 31 ultrasound scans. Of the 30 datasets where both MRI and ultrasound data were available, MRI had good interobserver reliability (ICC 0.964) and all ultrasound 3VV 2D-parameters and 3VT 3VE had acceptable ICC (>0.75). Four 2D parameters were reflective of the 3D thymus volume: 3VV TD r = 0.540 (P = 0.002); 3VV perimeter r = 0.446 (P = 0.013); 3VV APD r = 0.435 (P = 0.110) and 3VT TD r = 0.544 (P = 0.002). CONCLUSIONS: MRI appeared superior to ultrasound for visualization of the thymus gland and reproducibility of measurements. Three 2D US parameters, 3VV TD, perimeter and 3VT APD, correlated well with TV. Therefore, these represent a more accurate reflection of the true size of the gland than other 2D measurements, where MRI is not available.