# Next-generation in-vivo fetal neuroimaging

> **NIH NIH R01** · BOSTON CHILDREN'S HOSPITAL · 2022 · $560,431

## Abstract

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...

## Key facts

- **NIH application ID:** 10428634
- **Project number:** 5R01EB031849-02
- **Recipient organization:** BOSTON CHILDREN'S HOSPITAL
- **Principal Investigator:** ALI GHOLIPOUR-BABOLI
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $560,431
- **Award type:** 5
- **Project period:** 2021-06-15 → 2025-02-28

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10428634

## Citation

> US National Institutes of Health, RePORTER application 10428634, Next-generation in-vivo fetal neuroimaging (5R01EB031849-02). Retrieved via AI Analytics 2026-05-22 from https://api.ai-analytics.org/grant/nih/10428634. Licensed CC0.

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