In Vivo Placental MRI Shape and Textural Features Predict Fetal Growth Restriction and Postnatal Outcome

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.