用深度学习从基线MRI预测多年后脑部影像，实现个体化神经退行性变化预判。
Forecasting Future Anatomies: Longitudinal Brain Mri-to-Mri Prediction
- 采用五种深度模型直接预测未来全脑MRI，建模空间分布的退变模式。
- 最佳模型在双队列数据上实现高保真预测，且跨队列泛化能力强。
- 适用于阿尔茨海默病等神经退行性疾病个体化风险评估与早期干预研究。
从基线磁共振成像（MRI）预测未来脑部状态是神经影像学的核心挑战，对阿尔茨海默病（AD）等神经退行性疾病研究具有重要意义。现有方法多聚焦于预测认知评分或临床结局（如轻度认知障碍向痴呆转化），而本文首次探索纵向MRI图像到图像的预测任务，旨在预测参与者数年后的完整脑部MRI，内在建模复杂的空间分布神经退变模式。我们在两个纵向队列（ADNI 和 AIBL）上实现并评估了五种深度学习架构（UNet、U2-Net、UNETR、Time-Embedding UNet、ODE-UNet）。通过全局相似性和局部差异指标，将预测结果与真实随访扫描进行直接比较。最佳模型达到高保真度预测效果，所有模型均在独立外部数据集上表现良好，展现出稳健的跨队列泛化能力。结果表明，深度学习可可靠地在体素层面预测个体特定脑部MRI，为个性化预后提供新可能。
原文摘要 · Abstract (English)
Predicting future brain state from a baseline magnetic resonance image (MRI) is a central challenge in neuroimaging and has important implications for studying neurodegenerative diseases such as Alzheimer's disease (AD). Most existing approaches predict future cognitive scores or clinical outcomes, such as conversion from mild cognitive impairment to dementia. Instead, here we investigate longitudinal MRI image-to-image prediction that forecasts a participant's entire brain MRI several years into the future, intrinsically modeling complex, spatially distributed neurodegenerative patterns. We implement and evaluate five deep learning architectures (UNet, U2-Net, UNETR, Time-Embedding UNet, and ODE-UNet) on two longitudinal cohorts (ADNI and AIBL). Predicted follow-up MRIs are directly compared with the actual follow-up scans using metrics that capture global similarity and local differences. The best performing models achieve high-fidelity predictions, and all models generalize well to an independent external dataset, demonstrating robust cross-cohort performance. Our results indicate that deep learning can reliably predict participant-specific brain MRI at the voxel level, offering new opportunities for individualized prognosis.
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