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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 [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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 % 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. [ABSTRACT FROM AUTHOR]

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. Highlights: What's already known about this topic?Congenital diaphragmatic hernia (CDH) and congenital lung lesions (CLL) are prognosticated with ultrasound‐based measurements of the fetal lung in a single dimension; however true volumes may provide greater sensitivity for high risk cases. Current use of magnetic resonance imaging (MRI) to calculate fetal lung volumes is limited as two‐dimensional segmentation is labour intensive and risks interpolation and motion‐corruption errors. What does this study add? Three‐dimensional lung volumes can be computed from deformable slice‐to‐volume registration (DSVR) 3D reconstructions and highly correlate with traditional 2D‐derived volumes. DSVR‐derived volumes, however, should be more reliable owing to higher resolution and semi‐automated calculations that do not rely on interpolation between slices on motion‐corrupted stacks. [ABSTRACT FROM AUTHOR]

Structural (also known as anatomical) and diffusion MRI provide complimentary anatomical and microstructural characterization of early brain maturation. However, the existing models of the developing brain in time include only either structural or diffusion MRI channels. Furthermore, there is a lack of tools for combined analysis of structural and diffusion MRI in the same reference space. In this work, we propose a methodology to generate a multi-channel (MC) continuous spatio-temporal parametrized atlas of the brain development that combines multiple MRI-derived parameters in the same anatomical space during 37–44 weeks of postmenstrual age range. We co-align structural and diffusion MRI of 170 normal term subjects from the developing Human Connectomme Project using MC registration driven by both T2-weighted and orientation distribution functions channels and fit the Gompertz model to the signals and spatial transformations in time. The resulting atlas consists of 14 spatio-temporal microstructural indices and two parcellation maps delineating white matter tracts and neonatal transient structures. In order to demonstrate applicability of the atlas for quantitative region-specific studies, a comparison analysis of 140 term and 40 preterm subjects scanned at the term-equivalent age is performed using different MRI-derived microstructural indices in the atlas reference space for multiple white matter regions, including the transient compartments. The atlas and software will be available after publication of the article1. [ABSTRACT FROM AUTHOR]

Introduction: Infection and inflammation have been implicated in the etiology and subsequent morbidity associated with preterm birth. At present, there are no tests to assess for fetal compartment infection. The thymus, a gland integral in the fetal immune system, has been shown to involute in animal models of antenatal infection, but its response in human fetuses has not been studied. This study aims: (a) to generate magnetic resonance imaging (MRI) -derived fetal thymus volumes standardized for fetal weight; (b) to compare standardized thymus volumes from fetuses that delivered before 32 weeks of gestation with fetuses that subsequently deliver at term; (c) to assess thymus size as a predictor of preterm birth; and (d) to correlate the presence of chorioamnionitis and funisitis at delivery with thymic volumes in utero in fetuses that subsequently deliver preterm.Material and Methods: Women at high-risk of preterm birth at 20-32 weeks of gestation were recruited. A control group was obtained from existing data sets acquired as part of three research studies. A fetal MRI was performed on a 1.5T or 3T MRI scanner: T2 weighted images were obtained of the entire uterine content and specifically the fetal thorax. A slice-to-volume registration method was used for reconstruction of three-dimensional images of the thorax. Thymus segmentations were performed manually. Body volumes were calculated by manual segmentation and thymus:body volume ratios were generated. Comparison of groups was performed using multiple regression analysis. Normal ranges were created for thymus volume and thymus:body volume ratios using the control data. Receiver operating curves (ROC) curves were generated for thymus:body volume ratio and gestation-adjusted thymus volume centiles as predictors of preterm birth. Placental histology was analyzed where available from pregnancies that delivered very preterm and the presence of chorioamnionitis/funisitis was noted.Results: Normative ranges were created for thymus volume, and thymus volume was standardized for fetal size from fetuses that subsequently delivered at term, but were imaged at 20-32 weeks of gestation. Image data sets from 16 women that delivered <32 weeks of gestation (ten with ruptured membranes and six with intact membranes) and 80 control women that delivered >37 weeks were included. Mean gestation at MRI of the study group was 28+4  weeks (SD 3.2) and for the control group was 25+5  weeks (SD 2.4). Both absolute fetal thymus volumes and thymus:body volume ratios were smaller in fetuses that delivered preterm (P < .001). Of the 16 fetuses that delivered preterm, 13 had placental histology, 11 had chorioamnionitis, and 9 had funisitis. The strongest predictors of prematurity were the thymus volume Z-score and thymus:body volume ratio Z-score (ROC areas 0.915 and 0.870, respectively).Conclusions: We have produced MRI-derived normal ranges for fetal thymus and thymus:body volume ratios between 20 and 32 weeks of gestation. Fetuses that deliver very preterm had reduced thymus volumes when standardized for fetal size. A reduced thymus volume was also a predictor of spontaneous preterm delivery. Thymus volume may be a suitable marker of the fetal inflammatory response, although further work is needed to assess this, increasing the sample size to correlate the extent of chorioamnionitis with thymus size. [ABSTRACT FROM AUTHOR]

Deep learning based medical image segmentation has shown great potential in becoming a key part of the clinical analysis pipeline. However, many of these models rely on the assumption that the train and test data come from the same distribution. This means that such methods cannot guarantee high quality predictions when the source and target domains are dissimilar due to different acquisition protocols, or biases in patient cohorts. Recently, unsupervised domain adaptation techniques have shown great potential in alleviating this problem by minimizing the shift between the source and target distributions, without requiring the use of labeled data in the target domain. In this work, we aim to predict tissue segmentation maps on T 2-weighted magnetic resonance imaging data of an unseen preterm-born neonatal population, which has both different acquisition parameters and population bias when compared to our training data. We achieve this by investigating two unsupervised domain adaptation techniques with the objective of finding the best solution for our problem. We compare the two methods with a baseline fully-supervised segmentation network and report our results in terms of Dice scores obtained on our source test dataset. Moreover, we analyse tissue volumes and cortical thickness measures of the harmonized data on a subset of the population matched for gestational age at birth and postmenstrual age at scan. Finally, we demonstrate the applicability of the harmonized cortical gray matter maps with an analysis comparing term and preterm-born neonates and a proof-of-principle investigation of the association between cortical thickness and a language outcome measure. [ABSTRACT FROM AUTHOR]

In in-utero MRI, motion correction for fetal body and placenta poses a particular challenge due to the presence of local non-rigid transformations of organs caused by bending and stretching. The existing slice-to-volume registration (SVR) reconstruction methods are widely employed for motion correction of fetal brain that undergoes only rigid transformation. However, for reconstruction of fetal body and placenta, rigid registration cannot resolve the issue of misregistrations due to deformable motion, resulting in degradation of features in the reconstructed volume. We propose a Deformable SVR (DSVR), a novel approach for non-rigid motion correction of fetal MRI based on a hierarchical deformable SVR scheme to allow high resolution reconstruction of the fetal body and placenta. Additionally, a robust scheme for structure-based rejection of outliers minimises the impact of registration errors. The improved performance of DSVR in comparison to SVR and patch-to-volume registration (PVR) methods is quantitatively demonstrated in simulated experiments and 20 fetal MRI datasets from 28–31 weeks gestational age (GA) range with varying degree of motion corruption. In addition, we present qualitative evaluation of 100 fetal body cases from 20–34 weeks GA range. [ABSTRACT FROM AUTHOR]

Purpose: To develop an MRI acquisition and reconstruction framework for volumetric cine visualization of the fetal heart and great vessels in the presence of maternal and fetal motion. Methods: Four‐dimensional (4D) depiction was achieved using a highly‐accelerated multi‐planar real‐time balanced steady‐state free precession acquisition combined with retrospective image‐domain techniques for motion correction, cardiac synchronization and outlier rejection. The framework was validated using a numerical phantom and evaluated in a study of 20 mid‐ to late‐gestational age human fetal subjects (23‐33 weeks gestational age). Reconstructed MR data were compared with matched ultrasound. A preliminary assessment of flow‐sensitive reconstruction using the velocity information encoded in the phase of real‐time images is included. Results: Reconstructed 4D data could be visualized in any two‐dimensional plane without the need for highly specific scan plane prescription prior to acquisition or for maternal breath hold to minimize motion. Reconstruction was fully automated aside from user‐specified masks of the fetal heart and chest. The framework proved robust when applied to fetal data and simulations confirmed that spatial and temporal features could be reliably recovered. Evaluation suggested the reconstructed framework has the potential to be used for comprehensive assessment of the fetal heart, either as an adjunct to ultrasound or in combination with other MRI techniques. Conclusions: The proposed methods show promise as a framework for motion‐compensated 4D assessment of the fetal heart and great vessels. [ABSTRACT FROM AUTHOR]

Objective: Fetal brain diffusion tensor imaging (DTI) offers quantitative analysis of the developing brain. The objective was to 1) quantify DTI measures across gestation in a cohort of fetuses without brain abnormalities using full retrospective correction for fetal head motion 2) compare results obtained in utero to those in preterm infants. Materials and methods: Motion-corrected DTI analysis was performed on data sets obtained at 1.5T from 32 fetuses scanned between 21.29 and 37.57 (median 31.86) weeks. Results were compared to 32 preterm infants scanned at 3T between 27.43 and 37.14 (median 33.07) weeks. Apparent diffusion coefficient (ADC) and fractional anisotropy (FA) were quantified by region of interest measurements and tractography was performed. Results: Fetal DTI was successful in 84% of fetuses for whom there was sufficient data for DTI estimation, and at least one tract could be obtained in 25 cases. Fetal FA values increased and ADC values decreased with age at scan (PLIC FA: p = 0.001; R2 = 0.469; slope = 0.011; splenium FA: p < 0.001; R2 = 0.597; slope = 0.019; thalamus ADC: p = 0.001; R2 = 0.420; slope = − 0.023); similar trends were found in preterm infants. Conclusion: This study demonstrates that stable DTI is feasible on fetuses and provides evidence for normative values of diffusion properties that are consistent with aged matched preterm infants. [ABSTRACT FROM AUTHOR]

Diffusion-weighted MRI measures the direction and scale of the local diffusion process in every voxel through its spectrum in ${q}$ -space, typically acquired in one or more shells. Recent developments in microstructure imaging and multi-tissue decomposition have sparked renewed attention in the radial ${b}$ -value dependence of the signal. Applications in motion correction and outlier rejection, therefore, require a compact linear signal representation that extends over the radial as well as angular domain. Here, we introduce SHARD, a data-driven representation of the ${q}$ -space signal based on spherical harmonics and a radial decomposition into orthonormal components. This representation provides a complete, orthogonal signal basis, tailored to the spherical geometry of ${q}$ -space, and calibrated to the data at hand. We demonstrate that the rank-reduced decomposition outperforms model-based alternatives in human brain data, while faithfully capturing the micro- and meso-structural information in the signal. Furthermore, we validate the potential of joint radial-spherical as compared with single-shell representations. As such, SHARD is optimally suited for applications that require low-rank signal predictions, such as motion correction and outlier rejection. Finally, we illustrate its application for the latter using outlier robust regression. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1038/s41467-020-20353-3 [ABSTRACT FROM AUTHOR]

Prenatal detection of congenital heart disease facilitates the opportunity for potentially life-saving care immediately after the baby is born. Echocardiography is routinely used for screening of morphological malformations, but functional measurements of blood flow are scarcely used in fetal echocardiography due to technical assumptions and issues of reliability. Magnetic resonance imaging (MRI) is readily used for quantification of abnormal blood flow in adult hearts, however, existing in utero approaches are compromised by spontaneous fetal motion. Here, we present and validate a novel method of MRI velocity-encoding combined with a motion-robust reconstruction framework for four-dimensional visualization and quantification of blood flow in the human fetal heart and major vessels. We demonstrate simultaneous 4D visualization of the anatomy and circulation, which we use to quantify flow rates through various major vessels. The framework introduced here could enable new clinical opportunities for assessment of the fetal cardiovascular system in both health and disease. Three-dimensional imaging of the fetal heart and quantification of blood flow in the surrounding vessels is very challenging because the heart is small and the fetus is free to move in the womb. Here, the authors demonstrate motion-corrected 4D flow MRI of the whole fetal heart and major vessels. [ABSTRACT FROM AUTHOR]

Down syndrome (DS) occurs with triplication of human chromosome 21 and is associated with deviations in cortical development evidenced by simplified gyral appearance and reduced cortical surface area. Radial glia are neuronal and glial progenitors that also create a scaffolding structure essential for migrating neurons to reach cortical targets and therefore play a critical role in cortical development. The aim of this study was to characterise radial glial expression pattern and morphology in the frontal lobe of the developing human fetal brain with DS and age-matched controls. Secondly, we investigated whether microstructural information from in vivo magnetic resonance imaging (MRI) could reflect histological findings from human brain tissue samples. Immunohistochemistry was performed on paraffin-embedded human post-mortem brain tissue from nine fetuses and neonates with DS (15–39 gestational weeks (GW)) and nine euploid age-matched brains (18–39 GW). Radial glia markers CRYAB, HOPX, SOX2, GFAP and Vimentin were assessed in the Ventricular Zone, Subventricular Zone and Intermediate Zone. In vivo diffusion MRI was used to assess microstructure in these regions in one DS (21 GW) and one control (22 GW) fetal brain. We found a significant reduction in radial glial progenitor SOX2 and subtle deviations in radial glia expression (GFAP and Vimentin) prior to 24 GW in DS. In vivo, fetal MRI demonstrates underlying radial projections consistent with immunohistopathology. Radial glial alterations may contribute to the subsequent simplified gyral patterns and decreased cortical volumes observed in the DS brain. Recent advances in fetal MRI acquisition and analysis could provide non-invasive imaging-based biomarkers of early developmental deviations. [ABSTRACT FROM AUTHOR]

Myelination is considered to be an important developmental process during human brain maturation and closely correlated with gestational age. Quantitative assessment of the myelination status requires dedicated imaging, but the conventional T2-weighted scans routinely acquired during clinical imaging of neonates carry signatures that are thought to be associated with myelination. In this work, we develop a quatitative marker of progressing myelination for assessment preterm neonatal brain maturation based on novel automatic segmentation method for myelin-like signals on T2-weighted magnetic resonance images. Firstly we define a segmentation protocol for myelin-like signals. We then develop an expectation-maximization framework to obtain the automatic segmentations of myelin-like signals with explicit class for partial volume voxels whose locations are configured in relation to the composing pure tissues via second-order Markov random fields. The proposed segmentation achieves high Dice overlaps of 0.83 with manual annotations. The automatic segmentations are then used to track volumes of myelinated tissues in the regions of the central brain structures and brainstem. Finally, we construct a spatio-temporal growth models for myelin-like signals, which allows us to predict gestational age at scan in preterm infants with root mean squared error 1.41 weeks. [ABSTRACT FROM AUTHOR]