3-D volumetric MRI evaluation of the placenta in fetuses with complex congenital heart disease.

INTRODUCTION: Placental insufficiency remains a common cause of perinatal mortality and neurodevelopmental morbidity. Congenital heart disease (CHD) in the fetus and its relationship to placental function is unknown. This study explores placental health and its relationship to neonatal outcomes by comparing placental volumes in healthy pregnancies and pregnancies complicated by CHD using in vivo three-dimensional MRI studies. METHODS: In a prospective observational study, pregnant women greater than 18 weeks gestation with normal pregnancies or pregnancies complicated by CHD were recruited and underwent fetal MR imaging. The placenta was manually outlined and the volume was calculated in cm(3). Brain volume was also calculated and clinical data were also collected. Relationships, including interactive effects, between placental and fetal growth, including brain growth, were evaluated using longitudinal multiple linear regression analysis. RESULTS: 135 women underwent fetal MRI between 18 and 39 weeks gestation (mean 31.6 ± 4.4). Placental volume increased exponentially with gestational age (p=0.041). Placental volume was positively associated with birth weight (p <0.001) and increased more steeply with birth weight in CHD-affected fetuses (p=0.046). Total brain and cerebral volumes were smaller in the CHD group (p<0.001), but brainstem volume (p<0.001) was larger. Placental volumes were not associated with brain volumes. DISCUSSION: Impaired placental growth in CHD is associated with gestational age and birth weight at delivery. Abnormalities in placental development may contribute to the significant morbidity in this high-risk population. Assessment of placental volume by MRI allows for in vivo assessments of placental development

In vivo placental MRI shape and textural features predict fetal growth restriction and postnatal outcome

© 2017 International Society for Magnetic Resonance in Medicine. Purpose: To investigate the ability of three-dimensional (3D) MRI placental shape and textural features to predict fetal growth restriction (FGR) and birth weight (BW) for both healthy and FGR fetuses. Materials and Methods: We recruited two groups of pregnant volunteers between 18 and 39 weeks of gestation; 46 healthy subjects and 34 FGR. Both groups underwent fetal MR imaging on a 1.5 Tesla GE scanner using an eight-channel receiver coil. We acquired T2-weighted images on either the coronal or the axial plane to obtain MR volumes with a slice thickness of either 4 or 8 mm covering the full placenta. Placental shape features (volume, thickness, elongation) were combined with textural features; first order textural features (mean, variance, kurtosis, and skewness of placental gray levels), as well as, textural features computed on the gray level co-occurrence and run-length matrices characterizing placental homogeneity, symmetry, and coarseness. The features were used in two machine learning frameworks to predict FGR and BW. Results: The proposed machine-learning based method using shape and textural features identified FGR pregnancies with 86% accuracy, 77% precision and 86% recall. BW estimations were 0.3 ± 13.4% (mean percentage error ± standard error) for healthy fetuses and -2.6 ± 15.9% for FGR. Conclusion: The proposed FGR identification and BW estimation methods using in utero placental shape and textural features computed on 3D MR images demonstrated high accuracy in our healthy and high-risk cohorts. Future studies to assess the evolution of each feature with regard to placental development are currently underway. Level of Evidence: 2. Technical Efficacy: Stage 2. J. Magn. Reson. Imaging 2018;47:449–458