TimeFlow: Temporal Conditioning for Longitudinal Brain MRI Registration and Aging Analysis
This addresses the problem of limited data and temporal smoothness in brain aging analysis for medical researchers, offering a data-efficient and annotation-free tool, though it appears incremental as it builds on existing U-Net and conditioning methods.
The paper tackled longitudinal brain MRI registration by introducing TimeFlow, a learning-based framework that uses temporal conditioning to model neuroanatomy as a continuous function of age, enabling accurate deformation field estimation and future brain state prediction from only two scans, with experiments showing it outperforms state-of-the-art methods in forecasting and accuracy.
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.