Assessment of MRI-Based Automated Fetal Cerebral Cortical Folding Measures in Prediction of Gestational Age in the Third Trimester

Abstract

BACKGROUND AND PURPOSE Traditional methods of dating a pregnancy based on history or sonographic assessment have a large variation in the third trimester. We aimed to assess the ability of various quantitative measures of brain cortical folding on MR imaging in determining fetal gestational age in the third trimester. MATERIALS AND METHODS We evaluated 8 different quantitative cortical folding measures to predict gestational age in 33 healthy fetuses by using T2-weighted fetal MR imaging. We compared the accuracy of the prediction of gestational age by these cortical folding measureswiththeaccuracyofpredictionbybrainvolumemeasurementandbyapreviouslyreportedsemiquantitativevisualscaleofbrain maturity. Regression models were constructed, and measurement biases and variances were determined via a cross-validation procedure. RESULTS The cortical folding measures are accurate in the estimation and prediction of gestational age (mean of the absolute error, 0.43 ± 0.45 weeks) and perform better than (P = .024) brain volume (mean of the absolute error, 0.72 ± 0.61 weeks) or sonography measures (SDs approximately 1.5 weeks, as reported in literature). Prediction accuracy is comparable with that of the semiquantitative visual assessment score (mean, 0.57 ± 0.41 weeks). CONCLUSIONS Quantitative cortical folding measures such as global average curvedness can be an accurate and reliable estimator of gestational age and brain maturity for healthy fetuses in the third trimester and have the potential to be an indicator of brain-growth delays for at-risk fetuses and preterm neonates.

Neurodevelopmental Outcomes in Children With Congenital Heart Disease—What Can We Impact?

“…Fetuses with aortic arch anomalies fared the worst, with the most stagnant brain growth. Further work from this dataset showed measures of fetal cortical complexity similarly diverged from normal starting at the same time (37). …”

Cortical folding alterations in fetuses with isolated non-severe ventriculomegaly

“…Gyrification changes related to ventricular enlargement were investigated at both hemispheric and regional levels using a curvature-based approach. This approach has been previously used to study cortical folding in premature neonates Rodriguez-Carranza et al, 2008 , Shimony et al, 2016 and healthy fetuses Wright et al, 2014 , Wu et al, 2015 . For each fetus, principal curvatures (i.e., k 1 and k 2 ) were computed at each vertex on the inner cortical surface mesh and 15 curvature-based folding measures were derived as described in Rodriguez-Carranza et al, 2008 , Shimony et al, 2016 , and Wright et al (2014) .…”

“…These descriptors offer a different perspective into intrauterine neurodevelopment than brain volumetry. Curvedness, for instance, was shown to provide different information and be more accurate in the prediction of GA than brain volume Hu et al, 2013 , Wu et al, 2015 . With several descriptors we can, furthermore, capture different shape characteristics of the cortex.…”

Sulcal pits and patterns in developing human brains

“…With the possibility to perform fast 3D fetal MRI in vivo and head motion correction using post-processing technique (Gholipour et al, 2010;Keraudren et al, 2014;Kim et al, 2010;Kuklisova-Murgasova et al, 2012), previous MRI studies have quantified cortical growth and folding development in human fetal brains. The temporal changes of GI were observed during fetal life (Lefevre et al, 2016), and cortical surface curvatures and sulcal depth at the global level have been measured to quantify the overall degree of cortical folding from 22-39 weeks of gestational age (Clouchoux et al, 2013;Clouchoux et al, 2012;Dubois et al, 2008;Hu et al, 2013;Lefevre et al, 2016;Wright et al, 2014;Wu et al, 2015). The curvature and depth changes of cortical folding and local cortical expansion at the vertex level have also been observed using volume-or surface-based registration techniques (Clouchoux et al, 2013;Habas et al, 2012;Schwartz et al, 2016;Scott et al, 2013).…”