Title: FlowLet: Conditional 3D Brain MRI Synthesis using Wavelet Flow Matching
Primary area: generative models
Keywords: Medical Image Generation, Flow Matching, Wavelet Transform, Brain Age Prediction

Abstract: 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.

TL;DR: FlowLet generates realistic, age-specific 3D brain MRIs using flow matching in the wavelet domain, enabling accurate brain-age prediction and validated through both distributional metrics and region-level anatomical plausibility.