FlowLet: 基於小波流匹配的條件性三維腦部MRI合成

摘要

腦部磁振造影在神經發育、老化與疾病研究中扮演核心角色。其中一個關鍵應用是腦齡預測，透過MRI數據估算個體的生物腦齡。有效的BAP模型需要大型、多樣化且年齡平衡的數據集，然而現有的3D MRI數據集存在人口統計學偏斜，限制了公平性與泛化能力。取得新數據成本高昂且受倫理限制，因此促使生成式數據擴增技術的發展。當前生成方法多基於潛在擴散模型，該模型在學習所得的降維潛在空間中運作，以因應體積MRI數據的記憶體需求。然而，這類方法推論速度通常較慢，可能因潛在壓縮而引入偽影，且鮮少以年齡為條件，進而影響BAP效能。在本研究中，我們提出FlowLet，這是一個條件式生成框架，透過在可逆三維小波域中利用流匹配技術，合成以年齡為條件的3D MRI，有助於避免重構偽影並降低計算需求。實驗顯示，FlowLet能以極少的取樣步驟生成高保真體積影像。使用FlowLet生成的數據訓練BAP模型，可改善低代表性年齡組別的效能，而區域基礎分析則確認了解剖結構的保存。

English

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.