Enhancing Prenatal Diagnosis: Automated Fetal Brain MRI Morphometry

Fetal brain MRI is a pivotal tool for the prenatal diagnosis and prognosis of neurodegenerative diseases, relying on linear morphologic quantification traditionally performed manually. These manual measurements, derived from the visual assessment of 2D MRI slices, are adversely affected by high inter- and intra-rater variability due to variations in slice selection. The distortion of slices caused by fetal motion and the acquisition of images in oblique views due to uncontrolled fetal movements further exacerbates the variability in morphologic measurements. A promising method to standardize these measurements involves a sophisticated sequence of fetal MRI processing steps, including super-resolution 3D MRI reconstruction (SRR), automated detection of landmarks for performing standard morphologic metrics, and subsequent diagnosis/prognosis models. In this study, we developed and validated a fully automated pipeline that incorporates these computational steps, utilizing publicly available computational and deep learning tools and frameworks. A dataset of 81 fetuses from 22 to 37 weeks of gestation was acquired using fast multiview 2D HASTE MRI, where 35 were healthy fetuses and 46 diagnosed with ventriculomegaly. Our computational pipeline achieved overall mean localization accuracy of 3.72 +/- 2.55 mm across 22 landmarks, which was in good agreement and with lower uncertainty versus two manual raters across the 11 standard morphologic fetal brain measurements. These measurements obtained on 35 healthy fetuses were also in good agreement with the established manual normative values. Utilizing these measurements with the ventriculomegaly diagnostic criteria attained a 98.7% accuracy, underscoring the practical value of proposed pipeline in enhancing prenatal neurodegenerative disease diagnosis and prognosis. Code is publicly available at https:// github.com/zigaso/fetal-mri- markers.