Next-Generation In-Vivo Fetal Neuroimaging The overall objective of this project is to dramatically improve fetal magnetic resonance imaging (MRI) to advance research in early human brain development and neurodevelopmental disorders, the burden of which is, unfortunately, high because of their life-long impact and high prevalence. Fetal MRI has been the technique of choice in studying prenatal brain development. Fetal motion, however, makes MRI slice acquisition unreliable at best, as the fetus frequently moves while the prescribed slices are imaged. Uncompensated fetal motion disrupts 3D coverage of the anatomy and reduces the spatial resolution of slice-to-volume reconstructions. Repeating the scans does not ensure full 3D coverage of the anatomy, but increases total acquisition time. This, in turn, dramatically reduces the success rate and reliability of fetal MRI in studying the development of transient fetal brain compartments that are selectively sensitive to injury over the course of fetal development. To mitigate these issues and improve fetal MRI, we propose to automatically measure fetal brain position and prospectively navigate slices to each new position in real-time. The impact of this approach will be to dramatically increase the success rate and spatial resolution of fetal MRI for the in-vivo investigation of developing brain compartments, while, in parallel, reducing scan time, effectively making fetal MRI less burdensome for the mother, more accurate, and cost effective. By eliminating the manual re- adjustment of stack-of-slice positions, the time that elapses between scans will be virtually continuous. Our proposed technique will also make fetal MRI less operator-dependent and thus, more reproducible across sites, which is essential to conducting multi-center studies and clinical trials. Prospective navigation of fetal MRI slices to compensate for motion requires the development of novel, real-time image processing algorithms to recognize the fetal brain and its position and orientation; to track fetal motion to steer slices; and to detect and re-acquire motion corrupted slices. In this project, we will develop innovative deep learning models to process fetal MRI slices in real-time; will translate those models into an integrated system to prospectively navigate fetal MRI slices; and will validate the system on fetuses scanned at various gestational ages. To assess the utility and impact of the proposed technology, we will measure subplate volume in fetuses. The four specific aims of this study are to 1) assess fetal MRI via variable density image acquisition and reconstruction; 2) achieve real-time recognition of the fetal brain in MRI slices; 3) develop a system of real-time fetal head motion tracking and steering of slices; and 4) measure the subplate volume in the developing fetal brain using MRI. These aims will collectively translate and validate new imaging and image processing techniques to advance fetal MRI, and effect...