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September 17, 2025

A Deep Learning Approach to Longitudinal Infant MRI Synthesis

YFYu FangNational University of Singapore HXHonglin XiongShanghaiTech University JHJiawei HuangGuangzhou University of Chinese Medicine

Key Points

Our method significantly improves image quality and segmentation tasks in synthesizing missing infant MRIs, enhancing research capabilities.
The framework employs a three-stage approach featuring global affine transformation, local deformation, and contrast synthesis for realistic age-specific MRIs.
Results demonstrate our method's superiority in constructing developmental trajectories, ultimately aiding in neurodevelopmental research.
The ability to fill missing MRI data enables accurate tracking of brain development, which is essential for clinical interventions.

Abstract

Motivation: Longitudinal MRI studies of infants often face missing time points due to dropout, motion, and other issues. We propose a method to synthesize missing infant MRIs and aid early brain development research. Goal(s): We present a deep learning framework that synthesizes T1w images for any target age (0-2 yrs) from a different source age. Approach: Our three-stage framework includes global affine transformation, local deformation, and contrast synthesis, guided by temporal attention for realistic age-specific MRIs. Results: Our method outperforms others in image quality and segmentation tasks, and aids in constructing developmental trajectories. Impact: This framework enables accurate tracking of infant brain development by filling missing MRI data, aiding in the creation of developmental atlases, and supporting early detection of disorders. It may thus advance both neurodevelopmental research and clinical interventions.

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