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arXiv · 2512.16577

CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series

Nico Albert Disch·Saikat Roy·Constantin Ulrich·Yannick Kirchhoff·Maximilian Rokuss·Robin Peretzke·David Zimmerer·Klaus Maier-Hein
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Abstract

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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Nico Albert Disch, Saikat Roy, Constantin Ulrich, Yannick Kirchhoff, Maximilian Rokuss, Robin Peretzke, David Zimmerer, Klaus Maier-Hein. 2025-12-18. CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series. https://arxiv.org/abs/2512.16577

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