Fetal brain MRI texture analysis identifies different microstructural patterns in adequate and small for gestational age fetuses at term.

ABSTRACT

OBJECTIVES: We tested the hypothesis whether a texture analysis (TA) algorithm applied to MRI brain images identified different patterns in small for gestational age (SGA) fetuses as compared with adequate for gestational age (AGA). STUDY DESIGN: MRI was performed on 83 SGA and 70 AGA at 37 weeks' GA. Texture features were quantified in the frontal lobe, basal ganglia, mesencephalon, cerebellum and cingulum. A classification algorithm based on discriminative models was used to correlate texture features with clinical diagnosis. RESULTS: Region of interest delineation in all areas was achieved in 61 SGA (12 vasodilated) and 52 AGA; this was the sample for TA feature extraction which allowed classifying SGA from AGA with accuracies ranging from 90.9 to 98.9% in SGA versus AGA comparison and from 93.6 to 100% in vasodilated SGA versus AGA comparison. CONCLUSIONS: This study demonstrates that TA can detect brain differences in SGA fetuses. This supports the existence of brain microstructural changes in SGA fetuses.