FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching
This work addresses the need for diverse and balanced datasets in brain MRI analysis, particularly for Brain Age Prediction, by providing a generative data augmentation method, though it is incremental as it builds on flow matching and wavelet techniques.
The paper tackled the problem of generating age-conditioned 3D brain MRI data to improve Brain Age Prediction models, which suffer from demographic skews in existing datasets, and resulted in high-fidelity volumes that enhanced performance for underrepresented age groups.
Brain Magnetic Resonance Imaging (MRI) plays a central role in studying neurological development, aging, and diseases. One key application is Brain Age Prediction (BAP), which estimates an individual's biological brain age from MRI data. Effective BAP models require large, diverse, and age-balanced datasets, whereas existing 3D MRI datasets are demographically skewed, limiting fairness and generalizability. Acquiring new data is costly and ethically constrained, motivating generative data augmentation. Current generative methods are often based on latent diffusion models, which operate in learned low dimensional latent spaces to address the memory demands of volumetric MRI data. However, these methods are typically slow at inference, may introduce artifacts due to latent compression, and are rarely conditioned on age, thereby affecting the BAP performance. In this work, we propose FlowLet, a conditional generative framework that synthesizes age-conditioned 3D MRIs by leveraging flow matching within an invertible 3D wavelet domain, helping to avoid reconstruction artifacts and reducing computational demands. Experiments show that FlowLet generates high-fidelity volumes with few sampling steps. Training BAP models with data generated by FlowLet improves performance for underrepresented age groups, and region-based analysis confirms preservation of anatomical structures.