提出统一生成方法，精准预测医学影像随时间的动态变化。
Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging
- 基于时序流匹配学习影像时空演化规律，可退化为最近图像预测。
- 在三个公开数据集上超越现有方法，实现4D医学影像预测新基准。
- 支持3D体积、多基线扫描和不规则采样，适用于多种临床场景。
理解医学影像中的时序动态对疾病进展建模、治疗规划和解剖发育追踪至关重要。然而，多数深度学习方法仅考虑单一时间上下文，或聚焦于分类、回归等任务，难以实现精细的空间预测。尽管已有部分研究尝试，但通常局限于单一时点、特定疾病或存在其他技术限制。为此，我们提出时序流匹配（Temporal Flow Matching, TFM），一种统一的生成轨迹方法：(i) 目标是学习潜在的时间分布；(ii) 可自然退化为最近图像预测（LCI）这一特例；(iii) 支持3D体数据、多个先验扫描及不规则采样。在三个公开纵向数据集上的广泛基准测试表明，TFM持续优于自然图像领域的时空方法，确立了4D医学图像预测的新标杆与鲁棒基线。
原文摘要 · Abstract (English)
Understanding temporal dynamics in medical imaging is crucial for applications such as disease progression modeling, treatment planning and anatomical development tracking. However, most deep learning methods either consider only single temporal contexts, or focus on tasks like classification or regression, limiting their ability for fine-grained spatial predictions. While some approaches have been explored, they are often limited to single timepoints, specific diseases or have other technical restrictions. To address this fundamental gap, we introduce Temporal Flow Matching (TFM), a unified generative trajectory method that (i) aims to learn the underlying temporal distribution, (ii) by design can fall back to a nearest image predictor, i.e. predicting the last context image (LCI), as a special case, and (iii) supports $3D$ volumes, multiple prior scans, and irregular sampling. Extensive benchmarks on three public longitudinal datasets show that TFM consistently surpasses spatio-temporal methods from natural imaging, establishing a new state-of-the-art and robust baseline for $4D$ medical image prediction.
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