Automated template-based brain localization and extraction for fetal brain MRI reconstruction

Efficient Total Variation Algorithm for Fetal Brain MRI Reconstruction

Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014 - Lecture Notes in Computer Science ◽

10.1007/978-3-319-10470-6_32 ◽

2014 ◽

pp. 252-259 ◽

Cited By ~ 3

Author(s):

Sébastien Tourbier ◽

Xavier Bresson ◽

Patric Hagmann ◽

Jean-Philippe Thiran ◽

Reto Meuli ◽

...

Keyword(s):

Total Variation ◽

Brain Mri ◽

Fetal Brain ◽

Mri Reconstruction

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In utero MRI identifies consequences of early-gestation alcohol drinking on fetal brain development in rhesus macaques

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.1919048117 ◽

2020 ◽

Vol 117 (18) ◽

pp. 10035-10044

Author(s):

Xiaojie Wang ◽

Verginia C. Cuzon Carlson ◽

Colin Studholme ◽

Natali Newman ◽

Matthew M. Ford ◽

...

Keyword(s):

White Matter ◽

Brain Development ◽

Ex Vivo ◽

Early Recognition ◽

Brain Mri ◽

Fetal Brain ◽

Fetal Mri ◽

Diffusion Anisotropy ◽

Self Administration ◽

White Matter Tracts

One factor that contributes to the high prevalence of fetal alcohol spectrum disorder (FASD) is binge-like consumption of alcohol before pregnancy awareness. It is known that treatments are more effective with early recognition of FASD. Recent advances in retrospective motion correction for the reconstruction of three-dimensional (3D) fetal brain MRI have led to significant improvements in the quality and resolution of anatomical and diffusion MRI of the fetal brain. Here, a rhesus macaque model of FASD, involving oral self-administration of 1.5 g/kg ethanol per day beginning prior to pregnancy and extending through the first 60 d of a 168-d gestational term, was utilized to determine whether fetal MRI could detect alcohol-induced abnormalities in brain development. This approach revealed differences between ethanol-exposed and control fetuses at gestation day 135 (G135), but not G110 or G85. At G135, ethanol-exposed fetuses had reduced brainstem and cerebellum volume and water diffusion anisotropy in several white matter tracts, compared to controls. Ex vivo electrophysiological recordings performed on fetal brain tissue obtained immediately following MRI demonstrated that the structural abnormalities observed at G135 are of functional significance. Specifically, spontaneous excitatory postsynaptic current amplitudes measured from individual neurons in the primary somatosensory cortex and putamen strongly correlated with diffusion anisotropy in the white matter tracts that connect these structures. These findings demonstrate that exposure to ethanol early in gestation perturbs development of brain regions associated with motor control in a manner that is detectable with fetal MRI.

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Early Findings in Neonatal Cases of RYR1–Related Congenital Myopathies

Frontiers in Neurology ◽

10.3389/fneur.2021.664618 ◽

2021 ◽

Vol 12 ◽

Author(s):

Eleonora Mauri ◽

Daniela Piga ◽

Alessandra Govoni ◽

Roberta Brusa ◽

Serena Pagliarani ◽

...

Keyword(s):

Ryanodine Receptor ◽

Brain Mri ◽

Fetal Brain ◽

Congenital Myopathy ◽

Receptor Type ◽

Congenital Myopathies ◽

Muscle Mri ◽

Next Generation Sequencing Ngs ◽

Generation Sequencing

Ryanodine receptor type 1-related congenital myopathies are the most represented subgroup among congenital myopathies (CMs), typically presenting a central core or multiminicore muscle histopathology and high clinical heterogeneity. We evaluated a cohort of patients affected with Ryanodine receptor type 1-related congenital myopathy (RYR1-RCM), focusing on four patients who showed a severe congenital phenotype and underwent a comprehensive characterization at few months of life. To date there are few reports on precocious instrumental assessment. In two out of the four patients, a muscle biopsy was performed in the first days of life (day 5 and 37, respectively) and electron microscopy was carried out in two patients detecting typical features of congenital myopathy. Two patients underwent brain MRI in the first months of life (15 days and 2 months, respectively), one also a fetal brain MRI. In three children electromyography was performed in the first week of life and neurogenic signs were excluded. Muscle MRI obtained within the first years of life showed a typical pattern of RYR1-CM. The diagnosis was confirmed through genetic analysis in three out of four cases using Next Generation Sequencing (NGS) panels. The development of a correct and rapid diagnosis is a priority and may lead to prompt medical management and helps optimize inclusion in future clinical trials.

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Automatic linear measurements of the fetal brain on MRI with deep neural networks

10.21203/rs.3.rs-161025/v1 ◽

2021 ◽

Author(s):

Netanell Avisdris ◽

Bossmat Yehuda ◽

Ori Ben-Zvi ◽

Daphna Link-Sourani ◽

Liat Ben-Sira ◽

...

Keyword(s):

Human Error ◽

Brain Mri ◽

Fetal Brain ◽

Region Of Interest ◽

Fetal Mri ◽

Brain Structures ◽

Automatic Method ◽

Linear Measurements ◽

Biparietal Diameter ◽

Reliable Assessment

Abstract Purpose: Timely, accurate and reliable assessment of fetal brain development is essential to reduce short and long-term risks to fetus and mother. Fetal MRI is increasingly used for fetal brain assessment. Three key biometric linear measurements important for fetal brain evaluation are Cerebral Biparietal Diameter (CBD), Bone Biparietal Diameter (BBD), and Trans-Cerebellum Diameter (TCD), obtained manually by expert radiologists on reference slices, which is time consuming and prone to human error. The aim of this study was to develop a fully automatic method computing the CBD, BBD and TCD measurements from fetal brain MRI.Methods: The input is fetal brain MRI volumes which may include the fetal body and the mother's abdomen. The outputs are the measurement values and reference slices on which the measurements were computed. The method, which follows the manual measurements principle, consists of five stages: 1) computation of a Region Of Interest that includes the fetal brain with an anisotropic 3D U-Net classifier; 2) reference slice selection with a Convolutional Neural Network; 3) slice-wise fetal brain structures segmentation with a multiclass U-Net classifier; 4) computation of the fetal brain midsagittal line and fetal brain orientation, and; 5) computation of the measurements. Results: Experimental results on 214 volumes for CBD, BBD and TCD measurements yielded a mean difference of 1.55mm, 1.45mm and 1.23mm respectively, and a Bland-Altman 95% confidence interval (I of 3.92mm, 3.98mm and 2.25mm respectively. These results are similar to the manual inter-observer variability, and are consistent across gestational ages and brain conditions.Conclusions: The proposed automatic method for computing biometric linear measurements of the fetal brain from MR imaging achieves human level performance. It has the potential of being a useful method for the assessment of fetal brain biometry in normal and pathological cases, and of improving routine clinical practice.

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Automatic quantification of normal cortical folding patterns from fetal brain MRI

NeuroImage ◽

10.1016/j.neuroimage.2014.01.034 ◽

2014 ◽

Vol 91 ◽

pp. 21-32 ◽

Cited By ~ 66

Author(s):

R. Wright ◽

V. Kyriakopoulou ◽

C. Ledig ◽

M.A. Rutherford ◽

J.V. Hajnal ◽

...

Keyword(s):

Brain Mri ◽

Fetal Brain ◽

Cortical Folding ◽

Automatic Quantification

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Fetal and Postnatal Brain Imaging for the Detection of ZIKV Encephalopathy in the Fetus/Newborn

Open Forum Infectious Diseases ◽

10.1093/ofid/ofx162.055 ◽

2017 ◽

Vol 4 (suppl_1) ◽

pp. S22-S22

Author(s):

Sarah Mulkey ◽

Gilbert Vezina ◽

Yamil Fourzali ◽

Dorothy Bulas ◽

Margarita Arroyave-Wessel ◽

...

Keyword(s):

Pregnant Women ◽

Imaging Modality ◽

Brain Mri ◽

Cerebellar Atrophy ◽

Fetal Brain ◽

Fetal Mri ◽

Zikv Infection ◽

Chiari Ii ◽

Brain Changes ◽

Prospective Longitudinal

Abstract Background Up to 15% of pregnancies complicated by maternal ZIKV infection result in Zika-virus associated brain abnormalities in the fetus/newborn. Fetal ultrasound (feUS) is the standard imaging modality for the evaluation of fetal anatomy and for brain changes from congenital infection. Fetal MRI (feMRI) may be a useful adjunct. Methods We performed a prospective longitudinal neuroimaging study of fetuses/newborns of pregnant women with clinical and/or lab confirmed (RT-PCR and/or IgM/PRNT) diagnosis of Zika infection in Barranquilla, Colombia (endemic) and in Washington, DC, USA (travel-related). Gestational age (GA) at exposure and timing between ZIKV exposure/symptoms and imaging was documented. Subjects had one to two feMRIs and feUS, depending upon GA at enrollment. The feMRI and feUS protocols were standardized between sites and studies were centrally interpreted at Children’s National. Postnatally, infants received an unsedated brain MRI and head US. Results Forty-eight, ZIKV exposed/infected in first or second trimester pregnant women were enrolled (46 Colombia, 2 USA). Subjects had symptoms of ZIKV infection at mean of 8.4±5.7 week GA. The first feMRI and feUS were performed at 25.1±6.3 week GA. Thirty-six infants had a second feMRI and feUS at 31.1±4.2 week GA. Three of 48 (6%) cases had an abnormal feMRI: (1) heterotopias and abnormal cortical indent; (2) parietal encephalocele and Chiari II; (3) thin corpus callosum, dysplastic brainstem, temporal cysts, subependymal heterotopias, and generalized cerebral/cerebellar atrophy. FeUS in these three cases found (1) normal study; (2) parietal encephalocele and Chiari II; (3) significant ventriculomegaly with decreasing percentiles of head circumference from 32 to 36 week GA (38% to 3.6%). Postnatal head US revealed findings not seen on feUS: choroid plexus or germinal matrix cysts in nine infants and lenticulostriate vasculopathy in one infant. Conclusion FeMRI and feUS provide complimentary information in the assessment of fetal brain changes in ZIKV. In cases of abnormal brain structure, feMRI reveals more extensive areas of brain damage than is seen by US. Further studies are needed to determine whether cystic changes on postnatal head US are related to ZIKV infection, or are incidental findings. Disclosures All authors: No reported disclosures.

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Fetal brain MRI findings and neonatal outcome of common diagnosis at a tertiary care center

Journal of Perinatology ◽

10.1038/s41372-019-0407-9 ◽

2019 ◽

Vol 39 (8) ◽

pp. 1072-1077 ◽

Cited By ~ 2

Author(s):

Monica S. Arroyo ◽

Robert J. Hopkin ◽

Usha D. Nagaraj ◽

Beth Kline-Fath ◽

Charu Venkatesan

Keyword(s):

Neonatal Outcome ◽

Care Center ◽

Brain Mri ◽

Tertiary Care ◽

Fetal Brain ◽

Tertiary Care Center ◽

Mri Findings ◽

Common Diagnosis

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4252 Automated Fetal Brain Volumetry on Clinical Fetal MRI Using Convolutional Neural Network

Journal of Clinical and Translational Science ◽

10.1017/cts.2020.169 ◽

2020 ◽

Vol 4 (s1) ◽

pp. 45-46

Author(s):

Carol Tran ◽

Orit Glenn ◽

Christopher Hess ◽

Andreas Rauschecker

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Architecture ◽

Brain Volume ◽

Brain Mri ◽

Fetal Brain ◽

Fetal Mri ◽

Total Brain Volume ◽

Brain Volumes

OBJECTIVES/GOALS: We seek to develop an automated deep learning-based method for segmentation and volumetric quantification of the fetal brain on T2-weighted fetal MRIs. We will evaluate the performance of the algorithm by comparing it to gold standard manual segmentations. The method will be used to create a normative sample of brain volumes across gestational ages. METHODS/STUDY POPULATION: We will adapt a U-Net convolutional neural network architecture for fetal brain MRIs using 3D volumes. After re-sampling 2D fetal brain acquisitions to 3mm3 3D volumes using linear interpolation, the network will be trained to perform automated brain segmentation on 40 randomly-sampled, normal fetal brain MRI scans of singleton pregnancies. Training will be performed in 3 acquisition planes (axial, coronal, sagittal). Performance will be evaluated on 10 test MRIs (in 3 acquisition planes, 30 total test samples) using Dice scores, compared to radiologists’ manual segmentations. The algorithm’s performance on measuring total brain volume will also be evaluated. RESULTS/ANTICIPATED RESULTS: Based on the success of prior U-net architectures for volumetric segmentation tasks in medical imaging (e.g. Duong et al., 2019), we anticipate that the convolutional neural network will accurately provide segmentations and associated volumetry of fetal brains in fractions of a second. We anticipate median Dice scores greater than 0.8 across our test sample. Once validated, the method will retrospectively generate a normative database of over 1500 fetal brain volumes across gestational ages (18 weeks to 30 weeks) collected at our institution. DISCUSSION/SIGNIFICANCE OF IMPACT: Quantitative estimates of brain volume, and deviations from normative data, would be a major advancement in objective clinical assessments of fetal MRI. Such data can currently only be obtained through laborious manual segmentations; automated deep learning methods have the potential to reduce the time and cost of this process.

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