用潜在空间扩散模型预测个体脑部疾病进展，兼顾精度与效率。
Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion
- 在低维潜在空间中建模，降低3D脑影像计算负担。
- 结合患者信息与疾病动态先验，提升预测个性化水平。
- 提出稳定算法，实现时空一致性并量化预测不确定性。
纵向磁共振成像（MRI）数据的增多推动了人工智能驱动的疾病进展建模，使预测个体未来影像成为可能。然而，现有方法仍面临个性化不足、时空一致性差、纵向数据利用效率低及3D图像内存消耗大等挑战。为此，本文提出脑部潜在进展模型（BrLP），通过四个关键创新克服上述问题：(i) 在小维度潜在空间运行，缓解高维影像的计算压力；(ii) 显式融合受试者元数据，增强预测个体化；(iii) 引入辅助模型整合疾病动态先验，有效利用纵向数据；(iv) 提出潜在平均稳定化（LAS）算法，实现推理时的时空一致性，并支持全局与体素级预测不确定性度量。模型在11,730例来自2,805名受试者的T1加权（T1w）脑部MRI上训练并验证，外部测试集包含962名受试者的2,257例影像。实验表明，生成影像与真实随访影像相比，达到当前最优精度。代码已公开于：https://github.com/LemuelPuglisi/BrLP。
原文摘要 · Abstract (English)
The growing availability of longitudinal Magnetic Resonance Imaging (MRI) datasets has facilitated Artificial Intelligence (AI)-driven modeling of disease progression, making it possible to predict future medical scans for individual patients. However, despite significant advancements in AI, current methods continue to face challenges including achieving patient-specific individualization, ensuring spatiotemporal consistency, efficiently utilizing longitudinal data, and managing the substantial memory demands of 3D scans. To address these challenges, we propose Brain Latent Progression (BrLP), a novel spatiotemporal model designed to predict individual-level disease progression in 3D brain MRIs. The key contributions in BrLP are fourfold: (i) it operates in a small latent space, mitigating the computational challenges posed by high-dimensional imaging data; (ii) it explicitly integrates subject metadata to enhance the individualization of predictions; (iii) it incorporates prior knowledge of disease dynamics through an auxiliary model, facilitating the integration of longitudinal data; and (iv) it introduces the Latent Average Stabilization (LAS) algorithm, which (a) enforces spatiotemporal consistency in the predicted progression at inference time and (b) allows us to derive a measure of the uncertainty for the prediction at the global and voxel level. We train and evaluate BrLP on 11,730 T1-weighted (T1w) brain MRIs from 2,805 subjects and validate its generalizability on an external test set comprising 2,257 MRIs from 962 subjects. Our experiments compare BrLP-generated MRI scans with real follow-up MRIs, demonstrating state-of-the-art accuracy compared to existing methods. The code is publicly available at: https://github.com/LemuelPuglisi/BrLP.
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