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Isolated mild–moderate ventriculomegaly. a Fetal US in a 19-week fetus demonstrates mild–moderate ventriculomegaly on axial image of the brain, with transverse atrial diameter of 12–13 mm (calipers). This prompted further evaluation with fetal MRI. b Axial T2-W single-shot fast spin-echo (SSFSE) MR image shows no additional findings. c Postnatal brain MRI was performed at 9 months of age in this girl for further evaluation of seizures. Axial T2-W fast spin-echo image demonstrates mild but persistent ventriculomegaly without additional structural abnormalities to explain the girl’s seizure activity
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Fetal MRI and neonatal MRI of the central nervous system (CNS) are complementary tools that can help to accurately counsel and direct the management of children with anomalies of the central nervous system. Postnatal MRI can add to fetal MRI by allowing for monitoring of changes in the severity of disease, better delineation of a suspected prenatal...
Citations
... Fetal MRI requires no special MRI equipment, is noninvasive, safe (Gowland, 2011;Zvi et al., 2020), and its value in the diagnosis of certain central nervous system or somatic disorders is being increasingly recognized Nagaraj et al., 2022). The development of ultra-fast MRI sequences such as the single shot T2-weighted sequence have also led to the increasing popularity of fetal MRI as these images have excellent soft tissue contrast and reduced motion artefact (Gholipour et al., 2014). ...
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, gray matter, white matter, ventricles, cerebellum, brainstem, deep gray matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.
... Fetal MRI requires no special MRI equipment, is noninvasive, safe (Gowland, 2011; Zvi et al., 2020), and its value in the diagnosis of certain central nervous system or somatic disorders is being increasingly recognized (Griffiths et al., 2019; Nagaraj et al., 2022). The development of ultra-fast MRI sequences such as the single shot T2-weighted sequence have also led to the increasing popularity of fetal MRI as these images have excellent soft tissue contrast and reduced motion artefact (Gholipour et al., 2014). ...
In-utero fetal MRI is emerging as an important tool in the diagnosis and analysis of the developing human brain. Automatic segmentation of the developing fetal brain is a vital step in the quantitative analysis of prenatal neurodevelopment both in the research and clinical context. However, manual segmentation of cerebral structures is time-consuming and prone to error and inter-observer variability. Therefore, we organized the Fetal Tissue Annotation (FeTA) Challenge in 2021 in order to encourage the development of automatic segmentation algorithms on an international level. The challenge utilized FeTA Dataset, an open dataset of fetal brain MRI reconstructions segmented into seven different tissues (external cerebrospinal fluid, grey matter, white matter, ventricles, cerebellum, brainstem, deep grey matter). 20 international teams participated in this challenge, submitting a total of 21 algorithms for evaluation. In this paper, we provide a detailed analysis of the results from both a technical and clinical perspective. All participants relied on deep learning methods, mainly U-Nets, with some variability present in the network architecture, optimization, and image pre- and post-processing. The majority of teams used existing medical imaging deep learning frameworks. The main differences between the submissions were the fine tuning done during training, and the specific pre- and post-processing steps performed. The challenge results showed that almost all submissions performed similarly. Four of the top five teams used ensemble learning methods. However, one team's algorithm performed significantly superior to the other submissions, and consisted of an asymmetrical U-Net network architecture. This paper provides a first of its kind benchmark for future automatic multi-tissue segmentation algorithms for the developing human brain in utero.
... Gray matter heterotopia is a cortical malformation that develops as a result of the interruption of the migration of neurons in the brain to the cortex during the fetal period. It consists of normal neuron clusters located in abnormal localization in the brain as a result of a disorder in the migration of neurons [2,3]. ...
Introduction:
To compare the apparent diffusion coefficient (ADC) values of the white matter around heterotopia in children with unilateral subependymal heterotopia with those of the symmetrical normal cerebral hemisphere and control group.
Methods:
Between January 2011 and September 2021, 15 pediatric patients with unilateral focal subependymal heterotopia among 47 patients with heterotopia detected in brain magnetic resonance imaging (MRI) in our hospital were included in the study. The control group consisted of 15 age- and sex-matched children with normal neurological examination and normal brain MRI. In brain MRIs, ADC value was measured from the white matter around the heterotopia area and from the opposite cerebral hemisphere matched to the location, and from the bilateral location-matched white matter of the control group. The area of heterotopia was measured on axial T1-weighted MRI. The data were evaluated statistically.
Results:
There were eight girls and seven boys in the heterotopia group. The median age was 5.00 (min: 3, max: 14). There was no statistically significant difference between the ADC values of the heterotopia side and contralateral white matter of the heterotopia group. In addition, no statistically significant difference was found between the heterotopia side and opposite sides of the heterotopia and control groups ADC values.
Conclusion:
According to the findings of this study, no difference was found in the ADC values of the white matter around the lesion in children with subependymal heterotopia compared to the opposite cerebral hemisphere and control groups.
... 28 For this reason, even malformations that can be detected on fetal MRI (e.g., congenital hydrocephalus, heterotopias, polymicrogyria, and lissencephaly) benefit from reimaging in the postnatal period to better delineate the anomaly, evaluate for associated pathologies, and monitor for changes in severity. 29,30 An important secondary finding is that the seizures were electrographic only in about one quarter of infants with neonatal onset epilepsy. This is in agreement with prior studies demonstrating that seizures in neonates are commonly subclinical, 31-33 although children with acute provoked seizures are more likely to have any or exclusively subclinical seizures as compared to children with neonatal onset epilepsy. ...
Background
While seizures in neonates are common and often due to acute brain injury, 10-15% are unprovoked from congenital brain malformations. A better understanding of the risk of neonatal onset epilepsy by type of brain malformation is essential for counseling and monitoring.
Methods
In this retrospective cohort study, we evaluated 132 neonates with congenital brain malformations and their risk of neonatal onset epilepsy. Malformations were classified into one of five categories based on imaging patterns on pre- or postnatal imaging. Infants were monitored with continuous video EEG (cEEG) for encephalopathy and paroxysmal events in addition to abnormal neuroimaging.
Results
Seventy-four of 132 (56%) neonates underwent EEG monitoring and 18/132 (14%) were diagnosed with neonatal onset epilepsy. The highest prevalence of epilepsy was in neonates with disorders of neuronal migration/organization (9/34, 26%; 95% CI = 13-44%), followed by disorders of early prosencephalic development (6/38, 16%; 95% CI = 6-31%), complex total brain malformations (2/16, 13%; 95% CI = 2-38%), and disorders of midbrain/hindbrain malformations (1/30, 3%; 95% CI = 0-17%). Of neonates with epilepsy, 5/18 (28%) had only electrographic seizures, 13/18 (72%) required treatment with two or more anti-seizure medicines (ASMs), and 7/18 (39%) died within the neonatal period.
Conclusion
Our results demonstrate that disorders of neuronal migration/organization represent the highest risk group for early onset epilepsy. Seizures are frequently electrographic only, require treatment with multiple ASMs, and portend a high mortality rate. These results support ACNS recommendations for EEG monitoring during the neonatal period for infants with congenital brain malformations.
Malformations of the central nervous system belong to the most common developmental disorders in humans. The clinical presentation of brain malformations is nonspecific including developmental delay, hypotonia, and/or epilepsy. The great heterogeneity concerning etiology, mechanisms of development and morphology is challenging for diagnosis and classification of brain malformations. Thereby recognizing specific malformations is essential for optimal patient management and prognostic evaluation. The aim of this article is to give an overview of several clinically relevant brain malformations occurring from different disrupted developmental processes in brain formation.
Several brain malformations are already diagnosed during routine ultrasound in pregnancy. However pre- and postnatal magnetic resonance imaging remains the gold standard in detecting the partially subtle changes and to classify the malformations.
Advances in pre- and postnatal neuroimaging techniques and increasing investigation of genetic mechanisms underlying brain formation and its abnormalities have led to a better understanding of embryologic development and pathogeneses of brain malformations.
Besides patient’s history and clinical phenotype, neuroimaging plays a key role in diagnosis. Not always a specific diagnosis can be made, but neuroimaging patterns often enable a focused genetic testing and therefore are revolutionary for etiologic and prognostic assignment. Basic knowledge of brain development facilitates understanding and classifying of structural brain abnormalities.
Introduction
Fetal neurology is a rapidly evolving field. Consultations aim to diagnose, prognosticate and coordinate pre and perinatal management along with other specialists and counsel expectant parents. Practice parameters and guidelines are limited.
Methods
A 48-question online survey was administered to child neurologists. Questions targeted current care practices and perceived priorities for the field.
Results
Representatives from 43 institutions in the United States responded. 83% had prenatal diagnosis centers and the majority performed on-site neuroimaging. The earliest gestational age for fetal MRI was variable. Annual consultations ranged <20 to >100 patients. Fewer than half (n=17, 40%) were subspecialty trained. Most respondents (n=39, 91%) were interested in participating in a collaborative registry and educational initiatives.
Conclusions
The survey highlights heterogeneity in clinical practice. Large multi-site and multidisciplinary collaborations are essential to gather data that inform outcomes for fetuses evaluated across institutions through registries as well as creation of guidelines and educational material.
Background and Objectives
An increasing number of centers are offering fetal neurology consultation services; however, there is limited information available in overall institutional experiences. Data are lacking on the fetal characteristics, pregnancy course, and the influence of fetal consultation on perinatal outcomes. The aim of this study is to provide insight on the institutional fetal neurology consult process and areas of strengths and weaknesses.
Methods
We performed a retrospective electronic chart review of fetal consults from April 2, 2009, to August 8, 2019, at Nationwide Children's Hospital. The objectives were to summarize clinical characteristics, agreement of prenatal and postnatal diagnoses based on best available imaging, and postnatal outcomes.
Results
Of the 174 maternal-fetal neurology consults placed, 130 qualified for inclusion based on data available for review. Of the 131 anticipated fetuses, 5 experienced fetal demise, 7 underwent elective termination, and 10 died in the postnatal period. The majority were admitted to the neonatal intensive care unit; 34 (31%) required supportive intervention for feeding, breathing, or hydrocephalus, and 10 (8%) experienced seizures during their neonatal intensive care unit (NICU) stay. Imaging results from 113 babies who had prenatal and postnatal imaging of the brain were analyzed based on the primary diagnosis. The most common malformations were as follows (prenatal % vs postnatal %): midline anomalies (37% vs 29%), posterior fossa abnormalities (26% vs 18%), and ventriculomegaly (14% vs 8%). Additional disorders of neuronal migration were not seen on fetal imaging but were present in 9% of the postnatal studies. Analysis of agreement between prenatal and postnatal diagnostic imaging for the 95 babies who had MRIs at both time points found moderate concordance (Cohen kappa: 0.62, 95% CI 0.5–0.73; percent agreement: 69%, 95% CI 60%–78%). Consult recommendations for neonatal blood tests affected postnatal care in 64 of 73 cases in which the infant survived and data were available.
Discussion
Establishing a multidisciplinary fetal clinic can provide timely counseling and create rapport with families to have continuity of care for birth planning and postnatal management. Prognosis based on radiographic prenatal diagnosis requires caution as some neonatal outcomes may vary considerably.
