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.

翻译：脑部磁共振成像（MRI）在神经发育、衰老及疾病研究中具有核心作用。脑龄预测是其关键应用之一，旨在通过MRI数据估计个体的生物学脑龄。有效的脑龄预测模型需要大规模、多样化且年龄均衡的数据集，而现有三维MRI数据集存在人口统计学偏差，限制了模型的公平性与泛化能力。获取新数据成本高昂且受伦理约束，这推动了生成式数据增强方法的发展。现有生成方法多基于潜在扩散模型，通过在学习的低维潜在空间中操作以应对三维MRI数据的存储需求。然而，这些方法通常推理速度较慢，可能因潜在空间压缩引入伪影，且鲜少支持年龄条件控制，从而影响脑龄预测性能。本研究提出FlowLet——一种条件生成框架，通过在可逆三维小波域中利用流匹配技术合成年龄条件化的三维MRI，有助于避免重建伪影并降低计算需求。实验表明，FlowLet能以较少采样步骤生成高保真三维体数据。使用FlowLet生成数据训练的脑龄预测模型在代表性不足的年龄组中表现出性能提升，基于脑区的分析结果也证实了其解剖结构的保持能力。