CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series
This addresses disease progression and treatment planning in medical imaging by enabling voxel-level forecasting under irregular sampling, though it is incremental as it builds on existing methods for continuous-time modeling.
The authors tackled the problem of forecasting 3D medical scan evolution over time by introducing CRONOS, a framework that supports continuous-time prediction from multiple past scans, outperforming baselines across three public datasets.
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.