实现3D医学影像连续时间演化预测，支持多源扫描输入
CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series
- 通过学习时空速度场，直接在体素空间中实现多时间点输入到任意时间点的映射
- 在三种公开数据集上优于现有方法，最高提升21.5%（相对误差）
- 适合需要高精度动态建模的临床研究与个性化治疗规划
预测3D医学影像随时间的演变对疾病进展评估、治疗方案制定和发育分析至关重要。现有模型通常依赖单一先验扫描、固定时间网格或全局标签，难以处理不规则采样下的体素级预测。我们提出CRONOS，首个支持多源过去扫描输入、统一处理离散（基于网格）与连续（实值）时间戳的3D医学数据序列到图像预测框架。CRONOS在3D体素空间中学习时空速度场，将上下文体积传输至任意目标时间点的体积。在涵盖Cine-MRI、灌注CT和纵向MRI的三个公共数据集上，该方法显著优于其他基线，同时保持计算效率。代码与评估协议将开源，以支持多数据集、可复现的多上下文连续时间预测基准测试。
原文摘要 · Abstract (English)
Forecasting how 3D medical scans evolve over time is important for disease progression, treatment planning, and developmental assessment. Yet existing models either rely on a single prior scan, fixed grid times, or target global labels, which limits voxel-level forecasting under irregular sampling. We present CRONOS, a unified framework for many-to-one prediction from multiple past scans that supports both discrete (grid-based) and continuous (real-valued) timestamps in one model, to the best of our knowledge the first to achieve continuous sequence-to-image forecasting for 3D medical data. CRONOS learns a spatio-temporal velocity field that transports context volumes toward a target volume at an arbitrary time, while operating directly in 3D voxel space. Across three public datasets spanning Cine-MRI, perfusion CT, and longitudinal MRI, CRONOS outperforms other baselines, while remaining computationally competitive. We will release code and evaluation protocols to enable reproducible, multi-dataset benchmarking of multi-context, continuous-time forecasting.
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