TimeFlow：用于纵向脑MRI配准和衰老分析的时间条件
TimeFlow: Temporal Conditioning for Longitudinal Brain MRI Registration and Aging Analysis
AI总结：
TimeFlow通过时间条件学习框架实现纵向脑MRI配准与衰老分析，利用两个扫描数据预测未来脑状态，无需密集采样数据，提升预测精度与连续性。
摘要：
纵向脑分析对于理解健康衰老和识别病理偏差至关重要。纵向MRI配准是此类分析的基础。然而，现有方法受限于对密集采样时间序列的依赖，准确性和时间平滑性之间存在权衡，且无法前瞻性预测未来脑状态。为克服这些挑战，我们引入TimeFlow，一种基于学习的纵向脑MRI配准框架。TimeFlow使用U-Net主干与时间条件建模神经解剖学作为年龄的连续函数。仅给定个体的两个扫描，TimeFlow估计准确且时间一致的变形场，实现非线性外推以预测未来脑状态。这通过我们提出的插值/外推一致性约束应用于变形场和变形图像实现。值得注意的是，这些约束在不需显式平滑正则化器或密集采样序列数据的情况下，保留时间一致性与连续性。广泛实验表明，TimeFlow在预测未来时间点和配准精度方面均优于现有最先进方法。此外，TimeFlow通过区分神经退行性轨迹与正常衰老，支持新的生物脑衰老分析，无需分割，从而消除劳动密集型注释需求并缓解分割不一致问题。TimeFlow提供了一个准确、数据高效且无注释的框架，用于纵向分析脑衰老和慢性疾病，能够预测观察研究期外的脑变化。
英文摘要：
Longitudinal brain analysis is essential for understanding healthy aging and identifying pathological deviations. Longitudinal registration of sequential brain MRI underpins such analyses. However, existing methods are limited by reliance on densely sampled time series, a trade-off between accuracy and temporal smoothness, and an inability to prospectively forecast future brain states. To overcome these challenges, we introduce \emph{TimeFlow}, a learning-based framework for longitudinal brain MRI registration. TimeFlow uses a U-Net backbone with temporal conditioning to model neuroanatomy as a continuous function of age. Given only two scans from an individual, TimeFlow estimates accurate and temporally coherent deformation fields, enabling non-linear extrapolation to predict future brain states. This is achieved by our proposed inter-/extra-polation consistency constraints applied to both the deformation fields and deformed images. Remarkably, these constraints preserve temporal consistency and continuity without requiring explicit smoothness regularizers or densely sampled sequential data. Extensive experiments demonstrate that TimeFlow outperforms state-of-the-art methods in terms of both future timepoint forecasting and registration accuracy. Moreover, TimeFlow supports novel biological brain aging analyses by differentiating neurodegenerative trajectories from normal aging without requiring segmentation, thereby eliminating the need for labor-intensive annotations and mitigating segmentation inconsistency. TimeFlow offers an accurate, data-efficient, and annotation-free framework for longitudinal analysis of brain aging and chronic diseases, capable of forecasting brain changes beyond the observed study period.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
