Fetal and perinatal outcome associated with small cerebellar diameter based on second- or third-trimester ultrasonography

Analysis of MRI brain biometrics in fetuses monitored for intra uterine growth restriction and their prognostic value: Results of a prospective multicenter study

OBJECTIVE

Show a prognostic value of brain changes in fetuses with intra uterine growth restriction (IUGR) on early neonatal outcome.

STUDY DESIGN

We prospectively recruited pregnant women whose fetuses presented fetal weight < 5th centile. A brain MRI was performed between 28 and 32 weeks of gestation (WG). Several brain biometrics were measured (as fronto-occipital diameter (FOD) and transverse cerebellar diameter (TCD)). Neonatal prognosis was evaluated according to a composite criterion.

RESULTS

Of the 78 patients included, 62 had a fetal brain MRI. The mean centile value of FOD was lower in the unfavorable outcome group (n = 9) compared to the favorable outcome group (n = 53) (24.5 ± 16.8 vs. 8.6 ± 13.2, p = 0.004). The ROC curve for predicting risk of unfavorable neonatal outcome based on FOD presented an area under the curve of 0.81 (95 % CI, [0.63---0.99]) and a threshold determined at the 3rd centile was associated with sensitivity of 0.78 and a specificity of 0.89. In multivariate analysis, a FOD less than the 3rd centile was significantly associated with an unfavorable neonatal risk. There also was a reduction in TCD (25.5 ± 21.5 vs. 10.4 ± 10.4, p = 0.03) in the unfavorable neonatal outcome group.

CONCLUSION

We found an association between a reduction in FOD and TCD in fetal MRIs conducted between 28 and 32 WG in fetuses monitored for IUGR with an unfavorable neonatal outcome. Our results suggest that these biometric changes could constitute markers of poor neonatal prognosis.

A Rare Case of Unilateral Fetal Cataract and Coincidental Polydactyly in Congenital Toxoplasmosis

Congenital toxoplasmosis is caused by transplacental infection of Toxoplasma gondii during pregnancy. We present a case of a congenital toxoplasma with intracranial calcifications, microcephaly, growth restriction, a unilateral cataract that developed in the third trimester, and a coincidental post-axial-polydactyly. Antenatal imaging findings are important to guide further testing and confirmation of diagnosis, it is important to know all possible associations and prognoses for timely counseling, testing, and intervention. To our knowledge, no case has been published with findings of unilateral cataract in congenital toxoplasmosis and associated coincidental polydactyly. Therefore, we wish to add this case to the current scientific literature.

Fetal brain growth and infant autistic traits

BACKGROUND

Structural differences exist in the brains of autistic individuals. To date only a few studies have explored the relationship between fetal brain growth and later infant autistic traits, and some have used fetal head circumference (HC) as a proxy for brain development. These findings have been inconsistent. Here we investigate whether fetal subregional brain measurements correlate with autistic traits in toddlers.

METHODS

A total of 219 singleton pregnancies (104 males and 115 females) were recruited at the Rosie Hospital, Cambridge, UK. 2D ultrasound was performed at 12-, 20- and between 26 and 30 weeks of pregnancy, measuring head circumference (HC), ventricular atrium (VA) and transcerebellar diameter (TCD). A total of 179 infants were followed up at 18-20 months of age and completed the quantitative checklist for autism in toddlers (Q-CHAT) to measure autistic traits.

RESULTS

Q-CHAT scores at 18-20 months of age were positively associated with TCD size at 20 weeks and with HC at 28 weeks, in univariate analyses, and in multiple regression models which controlled for sex, maternal age and birth weight.

LIMITATIONS

Due to the nature and location of the study, ascertainment bias could also have contributed to the recruitment of volunteer mothers with a higher than typical range of autistic traits and/or with a significant interest in the neurodevelopment of their children.

CONCLUSION

Prenatal brain growth is associated with toddler autistic traits and this can be ascertained via ultrasound starting at 20 weeks gestation.

Automatic biometry of fetal brain MRIs using deep and machine learning techniques

Linear biometric measurements on magnetic resonance images are important for the assessment of fetal brain development, which is expert knowledge dependent and laborious. This study aims to construct a segmentation-based method for automatic two-dimensional biometric measurements of fetal brain on magnetic resonance images that provides a fast and accurate measurement of fetal brain. A total of 268 volumes (5360 images) magnetic resonance images of normal fetuses were included. The automatic method involves two steps. First, the fetal brain was segmented into four parts with a deep segmentation network: cerebrum, cerebellum, and left and right lateral ventricles. Second, the measurement plane was determined, and the corresponding biometric parameters were calculated according to clinical guidelines, including cerebral biparietal diameter (CBPD), transverse cerebellar diameter (TCD), left and right atrial diameter (LAD/RAD). Pearson correlation coefficient and Bland-Altman plots were used to assess the correlation and agreement between computer-predicted values and manual measurements. Mean differences were used to evaluate the errors quantitatively. Analysis of fetal cerebral growth based on the automatic measurements was also displayed. The experiment results show that correlation coefficients for CBPD, TCD, LAD and RAD were as follows: 0.977, 0.990, 0.817, 0.719, mean differences were - 2.405 mm, - 0.008 mm, - 0.33 mm, - 0.213 mm, respectively. The correlation between the errors and gestational age was not statistically significant (p values were 0.2595, 0.0510, 0.1995, and 0.0609, respectively). The proposed automatic method for linear measurements on fetal brain MRI achieves excellent performance, which is expected to be applied in clinical practice and be helpful for prenatal diagnosis and clinical work efficiency improvement.

Automatic Deep Learning-Based Pipeline for Automatic Delineation and Measurement of Fetal Brain Structures in Routine Mid-Trimester Ultrasound Images

INTRODUCTION

The aim of this study was to develop a pipeline using state-of-the-art deep learning methods to automatically delineate and measure several of the most important brain structures in fetal brain ultrasound (US) images.

METHODS

The dataset was composed of 5,331 images of the fetal brain acquired during the routine mid-trimester US scan. Our proposed pipeline automatically performs the following three steps: brain plane classification (transventricular, transthalamic, or transcerebellar plane); brain structures delineation (9 different structures); and automatic measurement (from the structure delineations). The methods were trained on a subset of 4,331 images and each step was evaluated on the remaining 1,000 images.

RESULTS

Plane classification reached 98.6% average class accuracy. Brain structure delineation obtained an average pixel accuracy higher than 96% and a Jaccard index higher than 70%. Automatic measurements get an absolute error below 3.5% for the four standard head biometries (head circumference, biparietal diameter, occipitofrontal diameter, and cephalic index), 9% for transcerebellar diameter, 12% for cavum septi pellucidi ratio, and 26% for Sylvian fissure operculization degree.

CONCLUSIONS

The proposed pipeline shows the potential of deep learning methods to delineate fetal head and brain structures and obtain automatic measures of each anatomical standard plane acquired during routine fetal US examination.

Normal human brainstem development in vivo: a quantitative fetal MRI study

OBJECTIVES

To characterize spatiotemporal growth differences of prenatal brainstem substructures and cerebellum, using linear biometry and planimetry on fetal magnetic resonance imaging (MRI).

METHODS

In this retrospective study, we included fetuses with normal brain and a precise midsagittal T2-weighted brain MRI sequence obtained between May 2003 and April 2019. The cross-sectional area, rostrocaudal diameter and anteroposterior diameter of the midbrain, pons (basis pontis and pontine tegmentum), medulla oblongata and cerebellar vermis, as well as the transverse cerebellar diameter, were quantified by a single observer. The diameters were also assessed by a second observer to test inter-rater variability.

RESULTS

We included 161 fetuses with normal brain and a precise midsagittal MRI sequence, examined at a mean ± SD gestational age of 25.7 ± 5.4 (range, 14 + 0 to 39 + 2) weeks. All substructures of the fetal brainstem and the cerebellum could be measured consistently (mean ± SD interobserver intraclass correlation coefficient, 0.933 ± 0.065). We provide reference data for diameters and areas of the brainstem and cerebellum in the second and third trimesters. There was a significant quadratic relationship between vermian area and gestational age, and all other measured parameters showed a significant linear growth pattern within the observed period (P < 0.001). A significant change in the relative proportions of the brainstem substructures occurred between the beginning of the second trimester and the end of the third trimester, with an increase in the area of the pons (P < 0.001) and a decrease in that of the midbrain (P < 0.001), relative to the total brainstem area.

CONCLUSIONS

The substructures of the fetal brainstem follow a distinct spatiotemporal growth pattern, characterized by a relative increase in the pons and decrease in the midbrain, between 15 and 40 weeks of gestation. Caution is needed when interpreting fetal brainstem appearance during the early second trimester, as the brainstem proportions differ significantly from the adult morphology. The reference data provided herein should help to increase diagnostic accuracy in detecting disorders of defective hindbrain segmentation. © 2020 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.