Fetal Brain Magnetic Resonance Research Articles

Magnetic resonance imaging of intracranial anomalies in pregnancies complicated by twin anemia-polycythemia sequence

PurposeTo describe fetal brain Magnetic Resonance Imaging (MRI) findings in a large series of monochorionic (MC) pregnancies complicated by Twin Anemia-Polycythemia Sequence (TAPS) prenatally diagnosed, so to characterize the potential intracranial complications associated with this condition, their frequency and potential treatment options.MethodsThis is a retrospective study of MC twin pregnancies complicated by TAPS and undergone fetal MRI in a single institution from 2006 to 2023.MRI control was performed and post-natal ultrasound (US) or MRI were available.Results1250 MC pregnancies were evaluated in our institution. 50 pregnancies (4%) were diagnosed with TAPS, 29 underwent a fetal brain MRI.13/29 pregnancies (44.8%) demonstrated brain findings at MRI in at least a twin. Neuroradiological findings were detected in 14/57 twins (24.6%).We detected four main categories of findings: hemorrhagic lesions, T2-weighted white-matter hyperintensities (WMH), brain edema-swelling and venous congestion. Nineteen findings were present in the anemic and three in the polycythemic twins, with a statistically significant ratio between the two groups (p-value = 0.01).Intrauterine MRI follow-up demonstrated the sequalae of hemorrhagic lesions. A complete regression of brain swelling, veins prominence and T2-WMHs was demonstrated after treatment. Postnatal imaging confirmed prenatal features.ConclusionsOur work demonstrates that TAPS-related MRI anomalies consisted in edematous/hemorrhagic lesions that occur mostly in anemic rather than in polycythemic twins. Fetoscopic laser surgery could have a potential decongestant role.Therefore, prenatal MRI may help in counselling and management in TAPS pregnancies, especially for the planning of therapy and the monitoring of its efficacy.

Association between White Matter T2 Hyper-Intense Signals in Fetal Brain Magnetic Resonance Imaging and Neurodevelopment of Fetuses with Cytomegalovirus Infection.

An association between subtle changes in T2 white matter hyper-intense signals (WMHSs) detected in fetal brain magnetic resonance imaging (fbMRI) and congenital cytomegalovirus (CMV) infection has been established. The research aim of this study is to compare children with congenital CMV infection with neurodevelopment outcome and hearing deficit with and without WMHSs in a historic prospective case study cohort of 58 fbMRIs. Of these, in 37 cases, fbMRI was normal (normal group) and WMHSs were detected in 21 cases (WMHS group). The median infection week of the WMHS group was earlier than the normal fbMRI group (8 and 17 weeks of gestation, respectively). The proportion of infants treated with valganciclovir in the WMHS group was distinctly higher. Hearing impairment was not significantly different between the groups. VABS scores in all four domains were within normal range in both groups. The median score of the motor skills corrected for week of infection was better in the WMHS group. A multivariate analysis using the week of infection interaction variable of WMHS and valganciclovir treatment showed better motor score outcomes in the valganciclovir treatment group despite an earlier week of infection. WMHSs were not associated with neurodevelopmental outcome and hearing deficit. In our cohort, valganciclovir treatment may have a protective effect on fetuses with WMHSs by improving neurodevelopmental outcome.

Tissue Segmentation of Thick-Slice Fetal Brain MR Scans With Guidance From High-Quality Isotropic Volumes.

Accurate tissue segmentation of thick-slice fetal brain magnetic resonance (MR) scans is crucial for both reconstruction of isotropic brain MR volumes and the quantification of fetal brain development. However, this task is challenging due to the use of thick-slice scans in clinically-acquired fetal brain data. To address this issue, we propose to leverage high-quality isotropic fetal brain MR volumes (and also their corresponding annotations) as guidance for segmentation of thick-slice scans. Due to existence of significant domain gap between high-quality isotropic volume (i.e., source data) and thick-slice scans (i.e., target data), we employ a domain adaptation technique to achieve the associated knowledge transfer (from high-quality "source" volumes to thick-slice "target" scans). Specifically, we first register the available high-quality isotropic fetal brain MR volumes across different gestational weeks to construct longitudinally-complete source data. To capture domain-invariant information, we then perform Fourier decomposition to extract image content and style codes. Finally, we propose a novel Cycle-Consistent Domain Adaptation Network (C 2DA-Net) to efficiently transfer the knowledge learned from high-quality isotropic volumes for accurate tissue segmentation of thick-slice scans. Our C 2DA-Net can fully utilize a small set of annotated isotropic volumes to guide tissue segmentation on unannotated thick-slice scans. Extensive experiments on a large-scale dataset of 372 clinically acquired thick-slice MR scans demonstrate that our C 2DA-Net achieves much better performance than cutting-edge methods quantitatively and qualitatively.

Malformations of cortical development: Fetal imaging and genetics.

Malformations of cortical development (MCD) are a group of congenital disorders characterized by structural abnormalities in the brain cortex. The clinical manifestations include refractory epilepsy, mental retardation, and cognitive impairment. Genetic factors play a key role in the etiology of MCD. Currently, there is no curative treatment for MCD. Phenotypes such as epilepsy and cerebral palsy cannot be observed in the fetus. Therefore, the diagnosis of MCD is typically based on fetal brain magnetic resonance imaging (MRI), ultrasound, or genetic testing. The recent advances in neuroimaging have enabled the in-utero diagnosis of MCD using fetal ultrasound or MRI. The present study retrospectively reviewed 32 cases of fetal MCD diagnosed by ultrasound or MRI. Then, the chromosome karyotype analysis, single nucleotide polymorphism array or copy number variation sequencing, and whole-exome sequencing (WES) findings were presented. Pathogenic copy number variants (CNVs) or single-nucleotide variants (SNVs) were detected in 22 fetuses (three pathogenic CNVs [9.4%, 3/32] and 19 SNVs [59.4%, 19/32]), corresponding to a total detection rate of 68.8% (22/32). The results suggest that genetic testing, especially WES, should be performed for fetal MCD, in order to evaluate the outcomes and prognosis, and predict the risk of recurrence in future pregnancies.

Agreement between Fetal Brain Ultrasonography and Magnetic Resonance Imaging in the Measurements of the Corpus Callosum and Transverse Cerebellar Diameter.

As the use of magnetic resonance imaging of the fetal brain has evolved, the need to understand its efficiency in the biometry of the fetal brain has broadened. This study aimed to assess the level of agreement and correlation between the two cardinal imaging methods of fetal neuroimaging, ultrasonography (US) and magnetic resonance imaging (MRI), by measuring the corpus callosum (CC) and transverse cerebellar diameter (TCD) in terms of length and percentile. Measurements of CC and TCD length and percentile were documented over a 7-year span in a tertiary referral medical center. All US and MRI examinations were performed in the customary planes and subcategorized by valid reference charts. Exclusion and inclusion criteria were set before the collection and processing of the data. A total of 156 fetuses out of 483 were included in the study. A positive, strong correlation and agreement were found (r = 0.78; ICC = 0.76) between US and MRI in TCD measurements. For CC length measurement, a moderate correlation and moderate agreement (r = 0.51; ICC = 0.49) between US and MRI was observed. TCD and CC percentiles had lower levels of correlation and agreement compared with the length variables. Our study indicates good agreement between MRI and US in the assessment of TCD measurement as a part of antenatal neuroimaging. Furthermore, while the two techniques are not always compatible, they are complementary methods.

PDFF-CNN: An attention-guided dynamic multi-orientation feature fusion method for gestational age prediction on imbalanced fetal brain MRI dataset.

Fetal brain magnetic resonance imaging (MRI)-based gestational age prediction has been widely used to characterize normal fetal brain development and diagnose congenital brainmalformations. The uncertainty of fetal position and external interference leads to variable localization and direction of the fetal brain. In addition, pregnant women typically concentrate on receiving MRI scans during the fetal anomaly scanning week, leading to an imbalanced distribution of fetal brain MRI data. The above-mentioned problems pose great challenges for deep learning-based fetal brain MRI gestational ageprediction. In this study, a pyramid squeeze attention (PSA)-guided dynamic feature fusion CNN (PDFF-CNN) is proposed to robustly predict gestational ages from fetal brain MRI images on an imbalanced dataset. PDFF-CNN contains four components: transformation module, feature extraction module, dynamic feature fusion module, and balanced mean square error (MSE) loss. The transformation and feature extraction modules are employed by using the PSA to learn multiscale and multi-orientation feature representations in a parallel weight-sharing Siamese network. The dynamic feature fusion module automatically learns the weights of feature vectors generated in the feature extraction module to dynamically fuse multiscale and multi-orientation brain sulci and gyri features. Considering the fact of the imbalanced dataset, the balanced MSE loss is used to mitigate the negative impact of imbalanced data distribution on gestational age predictionperformance. Evaluated on an imbalanced fetal brain MRI dataset of 1327 routine clinical T2-weighted MRI images from 157 subjects, PDFF-CNN achieved promising gestational age prediction performance with an overall mean absolute error of 0.848 weeks and an of 0.904. Furthermore, the attention activation maps of PDFF-CNN were derived, which revealed regional features that contributed to gestational age prediction at each gestationalstage. These results suggest that the proposed PDFF-CNN might have broad clinical applicability in guiding treatment interventions and delivery planning, which has the potential to be helpful with prenatal diagnosis.

Estimate of fetal brain temperature using proton resonance frequency thermometry during 3 Tesla fetal magnetic resonance imaging.

T2-weighted Single Shot Fast Spin Echo (SSFSE) scans at 3 Tesla (3T) are increasingly used to image fetal pathology due to their excellent tissue contrast resolution and signal-to-noise ratio (SNR). Temperature changes that may occur in response to radio frequency (RF) pulses used for these sequences at 3T have not been studied in human fetal brains. To evaluate the safety of T2-weighted SSFSE for fetal brains at 3T, magnetic resonance (MR) thermometry was used to measure relative temperature changes in a typical clinical fetal brain MR exam. Relative temperature was estimated using sets of gradient recalled echo (GRE) images acquired before and after T2-weighted SSFSE images which lasted 27.47±8.19 minutes. Thirty-one fetuses with cardiac abnormalities, and 20 healthy controls were included in this study. Fetal brain temperature was estimated by proton resonance frequency (PRF) thermometry and compared to the estimated temperature in the gluteal muscle of the mother. Seven scans with excessive motion were excluded. Local outlier factor (LOF) was performed to remove 12 additional scans with spurious phase measurements due to motion degradation and potential field drift. Linear regression was performed to determine if temperature changes are dependent on the rate of energy deposition during the scan. For the 32 participants used in the analysis, 17 with cardiac abnormalities and 15 healthy controls, the average relative fetal temperate change was 0.19±0.73 ℃ higher than the mother, with no correlation between relative temperature change and the rate of images acquired during the scans (regression coefficient =-0.05, R-squared =0.05, P=0.22, F-statistic =1.60). The difference in the relative temperature changes between the fetal brain and mother's gluteal tissue in the healthy controls was on average 0.08 ℃ lower and found not to be statistically different (P=0.76) to the group with cardiac abnormalities. Our results indicate that the estimated relative temperature changes of the fetal brain compared to the mother's gluteal tissue from RF pulses during the course of the T2-weighted SSFSE fetal MR exam are minimal. The differences in acquired phase between these regions through the exam were found not to be statistically different. These findings support that fetal brain imaging at 3T is within FDA limits and safe.

Biometric magnetic resonance imaging analysis of fetal brain development in Down syndrome.

To assess brain development in living fetuses with Down syndrome (DS) by biometric measurements on fetal brain magnetic resonance images (MRI). We scanned 10 MRIs of fetuses with confirmed trisomy 21 at birth and 12 control fetal MRIs without any detected anomalies. Fetal brain MRIs were analyzed using 14 fetal brain and skull biometric parameters. We compared measures between DS and controls in both raw MRIs and motion-corrected and anterior-posterior commissure-aligned images. In the reconstructed images, the measured values of the height of the cerebellar vermis (HV) and anteroposterior diameter of the cerebellar vermis (APDV) were significantly smaller, and the anteroposterior diameter of the fourth ventricle (APDF) was significantly larger in fetuses with DS than controls. In the raw MRIs, the measured values of the right lateral ventricle were significantly larger in fetuses with DS than in controls. Logistic regression analyses revealed that a new parameter, the cerebellar-to-fourth-ventricle ratio (i.e., (APDV * Height of the vermis)/APDF), was significantly smaller in fetuses with DS than controls and was the most predictive to distinguish between fetuses with DS and controls. The study revealed that fetuses with DS have smaller cerebellums and larger fourth ventricles compared to the controls.

Review of deep learning and artificial intelligence models in fetal brain magnetic resonance imaging.

Central nervous system abnormalities in fetuses are fairly common, happening in 0.1% to 0.2% of live births and in 3% to 6% of stillbirths. So initial detection and categorization of fetal Brain abnormalities are critical. Manually detecting and segmenting fetal brain magnetic resonance imaging (MRI) could be time-consuming, and susceptible to interpreter experience. Artificial intelligence (AI) algorithms and machine learning approaches have a high potential for assisting in the early detection of these problems, improving the diagnosis process and follow-up procedures. The use of AI and machine learning techniques in fetal brain MRI was the subject of this narrative review paper. Using AI, anatomic fetal brain MRI processing has investigated models to predict specific landmarks and segmentation automatically. All gestation age weeks (17-38 wk) and different AI models (mainly Convolutional Neural Network and U-Net) have been used. Some models' accuracy achieved 95% and more. AI could help preprocess and post-process fetal images and reconstruct images. Also, AI can be used for gestational age prediction (with one-week accuracy), fetal brain extraction, fetal brain segmentation, and placenta detection. Some fetal brain linear measurements, such as Cerebral and Bone Biparietal Diameter, have been suggested. Classification of brain pathology was studied using diagonal quadratic discriminates analysis, K-nearest neighbor, random forest, naive Bayes, and radial basis function neural network classifiers. Deep learning methods will become more powerful as more large-scale, labeled datasets become available. Having shared fetal brain MRI datasets is crucial because there aren not many fetal brain pictures available. Also, physicians should be aware of AI's function in fetal brain MRI, particularly neuroradiologists, general radiologists, and perinatologists.

Relevance of Fetal Brain Magnetic Resonance Imaging Compared to Ultrasound for Detecting Cerebral Anomalies in Fetuses with Cleft Lip and/or Palate: A Cohort Study

Introduction: Relevance of fetal brain magnetic resonance imaging (MRI) in cases of cleft lip and/or palate (CL/P) is still discussed to date. The aim of our study was to review the contribution of fetal brain MRI for detecting cerebral anomalies in cases of CL/P comparing antenatal data with neonatal outcomes. Methods: A retrospective multicenter study was conducted from January 2010 to October 2020 in two multidisciplinary prenatal diagnosis centers among women with a fetal ultrasound (US) diagnosis of CL/P. Prenatal imaging and genetic analysis data were collected, as well as postnatal data, including outcomes of children who had an abnormal prenatal MRI. Results: Among the 202 fetuses with a US diagnosis of CL/P, 96 underwent US and fetal brain MRI. 19 brain MRIs were found to be abnormal: 14 (73.7%) involved CL/P associated with other US abnormalities and five (26.3%) involved isolated clefts, of which four were cleft lip and alveolus and secondary palate (CLP). MRI identified severe abnormalities that changed the prognoses of 3 cases of clefts associated with other US abnormalities. In contrast, MRI found only minor abnormalities for the five isolated clefts, with no postnatal disorders found in these children. Conclusion: Fetal brain MRI should be proposed in cases of clefts associated with other anomalies or if US examination is limited by local conditions. MRI could also be discussed in cases of isolated CLP but should not be performed in cases of isolated cleft lip.

The Role of Fetal Brain Magnetic Resonance Imaging in Current Fetal Medicine.

In open spina bifida we studied the use of MRI for the assessment of the posterior fossa and prevalence of supratentorial anomalies before and after in utero repair. New postprocessing techniques were applied to evaluate fetal brain development in this population compared to controls. In fetuses with congenital diaphragmatic hernia, we evaluated the brain development in comparison to controls. Diffusion weighted imaging was applied to study difference between fetuses with proven first trimester cytomegalovirus infection and controls. Finally, we investigated the value of third trimester fetal brain MRI after treatment for complicated monochorionic diamniotic pregnancies.

A Fetal Brain magnetic resonance Acquisition Numerical phantom (FaBiAN)

Accurate characterization of in utero human brain maturation is critical as it involves complex and interconnected structural and functional processes that may influence health later in life. Magnetic resonance imaging is a powerful tool to investigate equivocal neurological patterns during fetal development. However, the number of acquisitions of satisfactory quality available in this cohort of sensitive subjects remains scarce, thus hindering the validation of advanced image processing techniques. Numerical phantoms can mitigate these limitations by providing a controlled environment with a known ground truth. In this work, we present FaBiAN, an open-source Fetal Brain magnetic resonance Acquisition Numerical phantom that simulates clinical T2-weighted fast spin echo sequences of the fetal brain. This unique tool is based on a general, flexible and realistic setup that includes stochastic fetal movements, thus providing images of the fetal brain throughout maturation comparable to clinical acquisitions. We demonstrate its value to evaluate the robustness and optimize the accuracy of an algorithm for super-resolution fetal brain magnetic resonance imaging from simulated motion-corrupted 2D low-resolution series compared to a synthetic high-resolution reference volume. We also show that the images generated can complement clinical datasets to support data-intensive deep learning methods for fetal brain tissue segmentation.

Regional brain development in fetuses with Dandy-Walker malformation: A volumetric fetal brain magnetic resonance imaging study.

Dandy-Walker malformation (DWM) is a common prenatally diagnosed cerebellar malformation, characterized by cystic dilatation of the fourth ventricle, upward rotation of the hypoplastic vermis, and posterior fossa enlargement with torcular elevation. DWM is associated with a broad spectrum of neurodevelopmental abnormalities such as cognitive, motor, and behavioral impairments, which cannot be explained solely by cerebellar malformations. Notably, the pathogenesis of these symptoms remains poorly understood. This study investigated whether fetal structural developmental abnormalities in DWM extended beyond the posterior fossa to the cerebrum even in fetuses without apparent cerebral anomalies. Post-acquisition volumetric fetal magnetic resonance imaging (MRI) analysis was performed in 12 fetuses with DWM and 14 control fetuses. Growth trajectories of the volumes of the cortical plate, subcortical parenchyma, cerebellar hemispheres, and vermis between 18 and 33 weeks of gestation were compared. The median (interquartile range) gestational ages at the time of MRI were 22.4 (19.4–24.0) and 23.9 (20.6–29.2) weeks in the DWM and control groups, respectively (p = 0.269). Eight of the 12 fetuses with DWM presented with associated cerebral anomalies, including hydrocephalus (n = 3), cerebral ventriculomegaly (n = 3), and complete (n = 2) and partial (n = 2) agenesis of the corpus callosum (ACC); 7 presented with extracerebral abnormalities. Chromosomal abnormalities were detected by microarray analysis in 4 of 11 fetuses with DWM, using amniocentesis. Volumetric analysis revealed that the cortical plate was significantly larger in fetuses with DWM than in controls (p = 0.040). Even without ACC, the subcortical parenchyma, whole cerebrum, cerebellar hemispheres, and whole brain were significantly larger in fetuses with DWM (n = 8) than in controls (p = 0.004, 0.025, 0.033, and 0.026, respectively). In conclusion, volumetric fetal MRI analysis demonstrated that the development of DWM extends throughout the brain during the fetal period, even without apparent cerebral anomalies.

Fetal Brain Volume Predicts Neurodevelopment in Congenital Heart Disease.

Neurodevelopmental impairment is common in children with congenital heart disease (CHD), but postnatal variables explain only 30% of the variance in outcomes. To explore whether the antecedents for neurodevelopmental disabilities might begin in utero, we analyzed whether fetal brain volume predicted subsequent neurodevelopmental outcome in children with CHD. Fetuses with isolated CHD and sociodemographically comparable healthy control fetuses underwent fetal brain magnetic resonance imaging and 2-year neurodevelopmental evaluation with the Bayley Scales of Infant and Toddler Development, Third Edition (Bayley-III) and the Adaptive Behavior Assessment System, Third Edition (ABAS-3). Hierarchical regression evaluated potential predictors of Bayley-III and ABAS-3 outcomes in the CHD group, including fetal total brain volume adjusted for gestational age and sex, sociodemographic characteristics, birth measures, and medical history. The CHD group (n=52) had lower Bayley-III cognitive, language, and motor scores than the control group (n=26), but fetal brain volumes were similar. Within the CHD group, larger fetal total brain volume correlated with higher Bayley-III cognitive, language, and motor scores and ABAS-3 adaptive functioning scores (r=0.32-0.47; all P<0.05), but this was not noted in the control group. Fetal brain volume predicted 10% to 21% of the variance in neurodevelopmental outcome measures in univariate analyses. Multivariable models that also included social class and postnatal factors explained 18% to 45% of the variance in outcome, depending on developmental domain. Moreover, in final multivariable models, fetal brain volume was the most consistent predictor of neurodevelopmental outcome across domains. Small fetal brain volume is a strong independent predictor of 2-year neurodevelopmental outcomes and may be an important imaging biomarker of future neurodevelopmental risk in CHD. Future studies are needed to support this hypothesis. Our findings support inclusion of fetal brain volume in risk stratification models and as a possible outcome in fetal neuroprotective intervention studies.

Computational pipeline for the generation and validation of patient-specific mechanical models of brain development

The human brain develops from a smooth cortical surface in early stages of fetal life to a convoluted one postnatally, creating an organized ensemble of folds. Abnormal folding patterns are linked to neurodevelopmental disorders. However, the complex multi-scale interactions involved in cortical folding are not fully known yet. Computational models of brain development have contributed to better understand the process of cortical folding, but still leave several questions unanswered. A major limitation of the existing models is that they have basically been applied to synthetic examples or simplified brain anatomies. However, the integration of patient-specific longitudinal imaging data is key for improving the realism of simulations. In this work we present a complete computational pipeline to build and validate patient-specific mechanical models of brain development. Starting from the processing of fetal brain magnetic resonance images (MRI), personalised finite-element 3D meshes were generated, in which biomechanical models were run to simulate brain development. Several metrics were then employed to compare simulation results with neonatal images from the same subjects, on a common reference space. We applied the computational pipeline to a cohort of 29 subjects where fetal and neonatal MRI were available, including controls and ventriculomegaly cases. The neonatal brain simulations had several sulcal patterns similar to the ones observed in neonatal MRI data. However, the pipeline also revealed some limitations of the evaluated mechanical model and the importance of including patient-specific cortical thickness as well as regional and anisotropic growth to obtain more realistic and personalised brain development models.Statement of Significance: Computational modelling has emerged as a powerful tool to study the complex process of brain development during gestation. However, most of the studies performed so far have been carried out in synthetic or two-dimensional geometries due to the difficulties involved in processing real fetal data. Moreover, as there is no correspondence between meshes, comparing them or assessing whether they are realistic or not is not a trivial task. In this work we present a complete computational pipeline to build and validate patient-specific mechanical models of brain development, mainly based on open-source tools.

Characterization of Lesions in the Central Nervous System Based on Neurosonography and Magnetic Resonance in Fetus with Isolated Congenital Heart Defects. A Systematic Review

Introduction: Congenitalheartdefectsrepresentoneofthemainmalformationsdiagnosed prenatally. These are a major cause of neuro developmental disorders and it has even been proposed that structural brain alteration is found from early stages of fetal development. Objective: The objective of this study was to review the existing evidence of prenatal diagnosis of structural braina lterations found in fetal brain magnetic resonance imaging and neurosonography in fetus with isolated congenital heart defect. Materials and Methods: A search of Pubmed, Science Direct, Clinical Key and EBSCO databases was conducted in order to review study cases of fetuses with congenital heart defect between 18 and 37 week with Doppler ultrasonography, neurosonography and /or fetal brain magnetic resonance imaging. Results: A total of 16 articles were selected and it was found that the fetuses with congenital heart defect present structural neurological alterations from intrauterine life showed by neurosonography and fetal brain magnetic resonance imaging. Conclusion: The evidence emphasizes on the importance of studying in detail the central nervous system of fetuses with congenital heart defect and the relationship between structural brain alterations and neurodevelopmental disorders impairment since prenatal stages.

Automated detection and reacquisition of motion-degraded images in fetal HASTE imaging at 3 T.

Fetal brain Magnetic Resonance Imaging suffers from unpredictable and unconstrained fetal motion that causes severe image artifacts even with half-Fourier single-shot fast spin echo (HASTE) readouts. This work presents the implementation of a closed-loop pipeline that automatically detects and reacquires HASTE images that were degraded by fetal motion without any human interaction. A convolutional neural network that performs automatic image quality assessment (IQA) was run on an external GPU-equipped computer that was connected to the internal network of the MRI scanner. The modified HASTE pulse sequence sent each image to the external computer, where the IQA convolutional neural network evaluated it, and then the IQA score was sent back to the sequence. At the end of the HASTE stack, the IQA scores from all the slices were sorted, and only slices with the lowest scores (corresponding to the slices with worst image quality) were reacquired. The closed-loop HASTE acquisition framework was tested on 10 pregnant mothers, for a total of 73 acquisitions of our modified HASTE sequence. The IQA convolutional neural network, which was successfully employed by our modified sequence in real time, achieved an accuracy of 85.2% and area under the receiver operator characteristic of 0.899. The proposed acquisition/reconstruction pipeline was shown to successfully identify and automatically reacquire only the motion degraded fetal brain HASTE slices in the prescribed stack. This minimizes the overall time spent on HASTE acquisitions by avoiding the need to repeat the entire stack if only few slices in the stack are motion-degraded.

Fetal and Perinatal Outcome Following First and Second Trimester COVID-19 Infection: Evidence from a Prospective Cohort Study.

A novel coronavirus termed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a new strain of coronavirus causing coronavirus disease 2019 (COVID-19) disease, which emerged as a global pandemic. Data regarding the implications of COVID-19 disease at early gestation on fetal and obstetric outcomes is scarce. Thus, our aim was to investigate the effect of first and second trimester maternal COVID-19 disease on fetal and perinatal outcomes. This was a prospective cohort study of pregnant women with a laboratory-proven SARS-COV-2 infection contracted prior to 26 weeks gestation. Women were followed at a single tertiary medical center by serial sonographic examinations every 4–6 weeks to assess fetal well-being, growth, placental function, anatomic evaluation and signs of fetal infection. Amniocentesis was offered to assess amniotic fluid SARS-COV-2-PCR (polymerase chain reaction) and fetal brain magnetic resonance imaging (MRI) was offered at 30–32 weeks gestation. Demographic, obstetric and neonatal data were collected from history intake, medical charts or by telephone survey. Perinatal outcomes were compared between women infected at first vs. second trimester. 55 women with documented COVID-19 disease at early gestation were included and followed at our center. The mean maternal age was 29.6 ± 6.2 years and the mean gestational age at viral infection was 14.2 ± 6.7 weeks with 28 (51%) women infected at the first trimester and 27 (49%) at the second trimester. All patients but one experienced asymptomatic to mild symptoms. Of 22 patients who underwent amniocentesis, none had evidence of vertical transmission. None of the fetuses exhibited signs of central nervous system (CNS) disease, growth restriction and placental dysfunction on serial ultrasound examinations and fetal MRI. Pregnancies resulted in perinatal survival of 100% to date with mean gestational age at delivery of 38.6 ± 3.0 weeks and preterm birth <37 weeks rate of 3.4%. The mean birthweight was 3260 ± 411 g with no cases of small for gestational age infants. The obstetric and neonatal outcomes were similar among first vs. second trimester infection groups. We conclude SARS-CoV-2 infection at early gestation was not associated with vertical transmission and resulted in favorable obstetric and neonatal outcomes.

Association Between Socioeconomic Status and In Utero Fetal Brain Development

Children raised in settings with lower parental socioeconomic status are at increased risk for neuropsychological disorders. However, to date, the association between socioeconomic status and fetal brain development remains poorly understood. To determine the association between parental socioeconomic status and in vivo fetal brain growth and cerebral cortical development using advanced, 3-dimensional fetal magnetic resonance imaging. This cohort study of fetal brain development enrolled 144 healthy pregnant women from 2 low-risk community obstetrical hospitals from 2012 through 2019 in the District of Columbia. Included women had a prenatal history without complications that included recommended screening laboratory and ultrasound studies. Exclusion criteria were multiple gestation pregnancy, known or suspected congenital infection, dysmorphic features of the fetus, and documented chromosomal abnormalities. T2-weighted fetal brain magnetic resonance images were acquired. Each pregnant woman was scanned at up to 2 points in the fetal period. Data were analyzed from June through November 2020. Parental education level and occupation status were documented. Regional fetal brain tissue volume (for cortical gray matter, white matter, cerebellum, deep gray matter, and brainstem) and cerebral cortical features (ie, lobe volume, local gyrification index, and sulcal depth) in the frontal, parietal, temporal, and occipital lobes were calculated. Fetal brain magnetic resonance imaging studies were performed among 144 pregnant women (median [interquartile range] age, 32.5 [27.0-36.1] years) with gestational age from 24.0 to 39.4 weeks; 75 fetuses (52.1%) were male, and 69 fetuses (47.9%) were female. Higher parental education level was associated with significantly increased volume in the fetal white matter (mothers: β, 2.86; 95% CI, 1.26 to 4.45; P = .001; fathers: β, 2.39; 95% CI, 0.97 to 3.81; P = .001), deep gray matter (mothers: β, 0.16; 95% CI, 0.002 to 0.32; P = .048; fathers: β, 0.16; 95% CI, 0.02 to 0.31; P = .02), and brainstem (mothers: β, 0.06; 95% CI, 0.02 to 0.10; P = .01; fathers: β, 0.04; 95% CI, 0.004 to 0.08; P = .03). Higher maternal occupation status was associated with significantly increased volume in the fetal white matter (β, 2.07; 95% CI, 0.88 to 3.26; P = .001), cerebellum (β, 0.17; 95% CI, 0.04 to 0.29; P = .01), and brainstem (β, 0.03; 95% CI, 0.001 to 0.07; P = .04), and higher paternal occupation status was associated with significantly increased white matter volume (β, 1.98; 95% CI, 0.71 to 3.25; P < .01). However, higher socioeconomic status was associated with significantly decreased fetal cortical gray matter volume (mothers: β, -0.11; 95% CI, -0.18 to -0.03; P = .01; fathers: β, -0.10; 95% CI, -0.18 to -0.03; P = .01). Higher parental socioeconomic status was associated with increased volumes of 3 brain lobes of white matter: frontal lobe (mothers: β, 0.07; 95% CI, 0.02 to 0.13; P = .01; fathers: β, 0.06; 95% CI, 0.01 to 0.11; P = .03), parietal lobe (mothers: β, 0.07; 95% CI, 0.03 to 0.11; P < .001; fathers: β, 0.06; 95% CI, 0.03 to 0.10; P = .001), and temporal lobe (mothers: β, 0.04; 95% CI, 0.02 to 0.07; P < .001; fathers: β, 0.04; 95% CI, 0.02 to 0.07; P < .001), and maternal SES score was associated with significantly decreased volume in the occipital lobe (β, 0.02; 95% CI, 0.002 to 0.04; P = .03). Higher parental socioeconomic status was associated with decreased cortical local gyrification index (for example, for the frontal lobe, mothers: β, -1.1; 95% CI, -1.9 to -0.3; P = .01; fathers: β, -0.8; 95% CI, -1.6 to -0.1; P = .03) and sulcal depth, except for the frontal lobe (for example, for the parietal lobe, mothers: β, -9.5; 95% CI, -13.8 to -5.3; P < .001; fathers: β, -8.7; 95% CI, -13.0 to -4.4; P < .001). This cohort study found an association between parental socioeconomic status and altered in vivo fetal neurodevelopment. While being born and raised in a lower socioeconomic status setting is associated with poorer neuropsychological, educational, and socioeconomic outcomes in children, these findings suggest that altered prenatal programming may be associated with these outcomes and that future targeted prenatal interventions may be needed.

Fetal Brain Magnetic Resonance Imaging Findings Predict Neurodevelopment in Children with Tuberous Sclerosis Complex

To correlate fetal brain magnetic resonance imaging (MRI) findings with epilepsy characteristics and neurodevelopment at 2years of age in children with tuberous sclerosis complex (TSC) to improve prenatal counseling. This retrospective cohort study was performed in a collaboration between centers of the EPISTOP consortium. We included children with definite TSC, fetal MRIs, and available follow-up data at 2years of age. A pediatric neuroradiologist masked to the patient's clinical characteristics evaluated all fetal MRIs. MRIs were categorized for each of the 10 brain lobes as score 0: no (sub)cortical lesions or doubt; score 1: a single small lesion; score 2: more than one small lesion or at least one large lesion (>5mm). Neurologic manifestations were correlated to lesion sum scores. Forty-one children were included. Median gestational age at MRI was 33.3weeks; (sub)cortical lesions were detected in 97.6%. Mean lesion sum score was 4.5. At 2years, 58.5% of patients had epilepsy and 22% had drug-resistant epilepsy. Cognitive, language, and motor development were delayed in 38%, 81%, and 50% of patients, respectively. Autism spectrum disorder (ASD) was diagnosed in 20.5%. Fetal MRI lesion sum scores were significantly associated with cognitive and motor development, and with ASD diagnosis, but not with epilepsy characteristics. Fetal cerebral lesion scores correlate with neurodevelopment and ASD at 2years in children with TSC.