Assessing the impact of fetal growth restriction using high-resolution MRI: a comprehensive cortical analysis.

Background: Fetal Growth Restriction (FGR) is a complex neurodevelopmental condition marked by the failure of a fetus to reach its genetically determined growth potential, leading to significant neurodevelopmental risks. Emerging MR scans exhibit superiority to traditional US-based diagnostic approaches, while quantitative morphological analysis of FGR is still lacking.

Methods: This study explores the effects of FGR on fetal brain development using advanced Magnetic Resonance Imaging (MRI) techniques. A comprehensive analysis of 35 brain regions was conducted, focusing on detailed cortical properties such as curvature, sulcal depth, surface area, cortical gray matter volume, and cortical thickness, while distinguishing between the left and right hemispheres. Our comprehensive analysis leverages super-resolution reconstruction, segmentation, and surface reconstruction algorithms to obtain statistical data.

Results: Our analysis revealed that (1) FGR equally affected both brain hemispheres; (2) In the absence of differentiation between brain regions, different cortical metrics had no significant effect on FGR; and (3) the most significant cortical metrics were primarily observed in cortical thickness and sulcal depth; (4) a regression model based on only seven key brain regions could effectively predict FGR. The findings demonstrate the value of high-resolution MRI in detecting early biomarkers for FGR, paving the way for improved prenatal care and targeted therapeutic strategies.

Conclusions: The findings highlight the importance of high-resolution MRI in identifying early biomarkers for FGR. The study emphasizes the value of advanced cortical analysis in understanding the pathophysiological mechanisms underlying FGR and offers a robust framework for early diagnosis and intervention. These insights could contribute to improved prenatal care and targeted therapeutic strategies, ultimately enhancing outcomes for affected populations.