SynthBrainGrow: Synthetic Diffusion Brain Aging for Longitudinal MRI Data Generation in Young People
Anna Zapaishchykova, Benjamin H. Kann, Divyanshu Tak, Zezhong Ye,, Daphne A. Haas-Kogan, Hugo J.W.L. Aerts

TL;DR
SynthBrainGrow is a diffusion-based method that generates realistic synthetic longitudinal brain MRI data to simulate aging, aiding neurodevelopmental and neurodegenerative research, data augmentation, and benchmarking with limited real longitudinal scans.
Contribution
This paper introduces SynthBrainGrow, a novel diffusion-based approach for synthetic brain aging that accurately models structural changes over two years from cross-sectional data.
Findings
Accurately captures substructure volumetrics
Simulates structural brain changes like ventricle enlargement and cortical thinning
Enables data augmentation and benchmarking for lifespan studies
Abstract
Synthetic longitudinal brain MRI simulates brain aging and would enable more efficient research on neurodevelopmental and neurodegenerative conditions. Synthetically generated, age-adjusted brain images could serve as valuable alternatives to costly longitudinal imaging acquisitions, serve as internal controls for studies looking at the effects of environmental or therapeutic modifiers on brain development, and allow data augmentation for diverse populations. In this paper, we present a diffusion-based approach called SynthBrainGrow for synthetic brain aging with a two-year step. To validate the feasibility of using synthetically-generated data on downstream tasks, we compared structural volumetrics of two-year-aged brains against synthetically-aged brain MRI. Results show that SynthBrainGrow can accurately capture substructure volumetrics and simulate structural changes such as…
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Taxonomy
TopicsRadiomics and Machine Learning in Medical Imaging · Advanced Neuroimaging Techniques and Applications · MRI in cancer diagnosis
