AI Symmetry Training Improves Prenatal MRI Diagnosis

A fetal brain scan taken while the baby is moving is, by design, a mess. MRI machines capture a series of thin 2D slices meant to stack into one clean 3D image, but a squirming fetus means those slices rarely line up. Radiologists are left piecing together a picture from mismatched fragments, which can lead to missed or incorrect readings at exactly the stage when early detection matters most, since the brain is among the first organs to form and keeps developing long after birth.

Researchers at MIT, Boston Children's Hospital, and Inria built a deep learning model called E(3)-Pose to tackle this specific problem: realigning those scattered slices into a coherent, high-resolution image a radiologist can actually examine. The work, presented at the 2026 European Conference on Computer Vision, comes out of the lab of Polina Golland, the Sunlin and Priscilla Chou Professor of Electrical Engineering and Computer Science at MIT and a principal investigator at the Jameel Clinic and CSAIL. First author Ramya Muthukrishnan, a PhD student in Golland's lab, points to motion as the core obstacle standard imaging struggles to overcome.