Clinical Applications of Fetal MRI in the Brain

Nagaraj, Usha D.; Kline‐Fath, Beth M.

doi:10.3390/diagnostics12030764

Cited by 10 publications

(3 citation statements)

References 60 publications

(61 reference statements)

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“…For both eCSF and ventricle volumes, we report high variance during the fetal phase followed by a notable drop in variability in the postnatal phase, reflecting many investigations (Andescavage et al, 2016b;Kyriakopoulou et al, 2017;Story et al, 2021). We also showed that relative eCSF volumes largely decrease after birth, which is a widely published radiological observation (Gholipour et al, 2017;Li et al, 2019;Dubois et al, 2021;Nagaraj and Kline-Fath, 2022)(Figures 1 and 7). This is corroborated in Lefèvre et al (2016) where the ratios of eCSF and ventricle volumes are higher in fetuses compared to premature postnatal subjects of the same age.…”

Section: Discontinuity Of Gyrification Ecsf and Ventricles Volumessupporting

confidence: 85%

Normative models combining fetal and postnatal MRI data to characterize neurodevelopmental trajectories during the transition from in- to ex-utero

Mihailov,

Pron,

Lefèvre

et al. 2024

Preprint

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The perinatal period involves transitioning from an intra- to an extrauterine environment, which requires a complex adaptation of the brain. This period is marked with dynamic and multifaceted cortical changes in both structure and function. Most studies to date have focused either on the fetal or postnatal period, independently. To the best of our knowledge, this is the first neurodevelopmental study targeting the cortical trajectory of typically developing perinatal subjects, combining MRIs from both fetal and postnatal participants. Prior to analysis, preprocessing and segmentation parameters were harmonized across all subjects in order to overcome methodological limitations that arise when studying such different populations. We conducted a normative modeling analysis on a sample of 607 subjects, age ranged 24 to 45 weeks post-conception, to observe changes that arise as participants traverse the birth barrier. We observed that the trajectories of global surface area and several volumetric features, including total gray matter, white matter, brainstem, cerebellum and hippocampi, follow distinct but continuous patterns during this transition. We further report three features presenting a discontinuity in their neurodevelopmental trajectories as participants traverse from a fetal to a postnatal environment: the extra-cerebrospinal fluid volume, the ventricular volume and global gyrification. The current study demonstrates the presence of unique neurodevelopmental patterns for several structural features during the perinatal period, and confirms that not all features are affected in the same way as they cross the birth barrier.

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Section: Discontinuity Of Gyrification Ecsf and Ventricles Volumessupporting

confidence: 85%

Normative models combining fetal and postnatal MRI data to characterize neurodevelopmental trajectories during the transition from in- to ex-utero

Mihailov,

Pron,

Lefèvre

et al. 2024

Preprint

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“…As a result, fetal MRI is emerging as an essential imaging modality in evaluating complex fetal abnormalities and is even outperforming US in detecting many fetal pathologies [17][18][19][20]. The more accurate diagnoses from MRI, in turn, allow for better planning during the pregnancy for the postnatal and neonatal periods and even allow for possible fetal surgery where necessary [14,21]. MRI can also be used shortly before delivery for pelvimetric measurements to assess whether an attempt at vaginal delivery is feasible.…”

Section: Introductionmentioning

confidence: 99%

Fet-Net Algorithm for Automatic Detection of Fetal Orientation in Fetal MRI

et al. 2023

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Identifying fetal orientation is essential for determining the mode of delivery and for sequence planning in fetal magnetic resonance imaging (MRI). This manuscript describes a deep learning algorithm named Fet-Net, composed of convolutional neural networks (CNNs), which allows for the automatic detection of fetal orientation from a two-dimensional (2D) MRI slice. The architecture consists of four convolutional layers, which feed into a simple artificial neural network. Compared with eleven other prominent CNNs (different versions of ResNet, VGG, Xception, and Inception), Fet-Net has fewer architectural layers and parameters. From 144 3D MRI datasets indicative of vertex, breech, oblique and transverse fetal orientations, 6120 2D MRI slices were extracted to train, validate and test Fet-Net. Despite its simpler architecture, Fet-Net demonstrated an average accuracy and F1 score of 97.68% and a loss of 0.06828 on the 6120 2D MRI slices during a 5-fold cross-validation experiment. This architecture outperformed all eleven prominent architectures (p < 0.05). An ablation study proved each component’s statistical significance and contribution to Fet-Net’s performance. Fet-Net demonstrated robustness in classification accuracy even when noise was introduced to the images, outperforming eight of the 11 prominent architectures. Fet-Net’s ability to automatically detect fetal orientation can profoundly decrease the time required for fetal MRI acquisition.

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“…Because the cerebellum is located in the posterior cranial fossa and is surrounded by dense petrous ridges and the occipital bone, it can withstand extrinsic pressure deformation better than the parietal bones [5] . Because ultrasound can easily visualise the foetal cerebellum, imaging the posterior fossa is becoming an integral part of many routine foetal sonograms [9] Several authors working on transverse cerebellar diameter (TCD) have found it to be a better marker for GA estimation than other clinical and biometric parameters [10,11,12] . The current study attempted to evaluate foetal cerebellar development ultrasonographically, which aids in determining foetal gestational age.…”

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confidence: 99%

The accuracy of cerebellar diameter in detection of the expected date of delivery in comparison with femur length in third trimester

Sanad

Youssef

Elnaga

et al. 2022

Minia Journal of Medical Research

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Background:The cerebellum, the largest part of hind brain, lies in the posterior cranial fossa. In the embryo cerebellum appears at the end of the fifth week as a swelling overriding the fourth ventricle. Assessment of gestational age (G.A.) is important in the management of pregnancy. The aim is to assess the reliability of trans-cerebellum diameter in compare with the femur length in predicting gestational age. Methodology: This is descriptive, cross-sectional study carried out on seventy-one pregnant women with history of reliable menstrual cycles (sure of dates, regular cycles, no history of hormonal contraception at last 6 months, no history of abortion or delivery at last 3 months) and did not suffer from any complications for the mother or fetus, referred for routine ultrasound examination in ultrasound department of El Minia University maternity hospital, FL was used as standard method to estimate gestational age to be compared with the other gestational age that obtained with TCD. Results: among pregnant women in the GA 28-32 and 32-36 weeks, there was non-statistically significant difference between GA as estimated by TCD and as estimated by other assessed fetal biometry (SFH and FL). we found that TCD can correctly predict GA in early 3 rd trimester (28-36 weeks) at cut-off point >34.9 mm with a sensitivity of 94%. Conclusions: The result of the study revealed that is there is no significant difference between gestational age obtained with FL and TCD. The study recommended that the TCD can be used as routine measurement in estimation of the gestational age in the third trimester.

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Clinical Applications of Fetal MRI in the Brain

Cited by 10 publications

References 60 publications

Normative models combining fetal and postnatal MRI data to characterize neurodevelopmental trajectories during the transition from in- to ex-utero

Normative models combining fetal and postnatal MRI data to characterize neurodevelopmental trajectories during the transition from in- to ex-utero

Fet-Net Algorithm for Automatic Detection of Fetal Orientation in Fetal MRI

The accuracy of cerebellar diameter in detection of the expected date of delivery in comparison with femur length in third trimester

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