CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series

• URL: http://arxiv.org/abs/2512.16577v1
• Date: Thu, 18 Dec 2025 14:16:46 GMT
• Title: CRONOS: Continuous Time Reconstruction for 4D Medical Longitudinal Series
• Authors: Nico Albert Disch, Saikat Roy, Constantin Ulrich, Yannick Kirchhoff, Maximilian Rokuss, Robin Peretzke, David Zimmerer, Klaus Maier-Hein,
• Abstract summary: We present CRONOS, a unified framework for many-to-one prediction from multiple past scans.<n>We will release code and evaluation protocols to enable reproducible, multi-dataset benchmarking of multi-context, continuous-time forecasting.
• Score: 4.916511768554555
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

Related papers

• $Δ$t-Mamba3D: A Time-Aware Spatio-Temporal State-Space Model for Breast Cancer Risk Prediction [3.112167541428413]
  We develop a novel state-space architecture adapted for longitudinal medical imaging analysis.<n>Our model simultaneously encodes irregular inter-visit intervals and rich-temporal context.<n>Thanks to its linear complexity, the model can efficiently process long and complex patient screening histories of mammograms.
  arXiv  Detail & Related papers  (2025-10-21T18:29:17Z)
• Multi-Task Diffusion Approach For Prediction of Glioma Tumor Progression [0.6978367196609415]
  Glioma is an aggressive brain malignancy that poses significant challenges for accurate evolution prediction.<n>In this paper, we present a multitask diffusion framework for time-agnostic, pixel-wise prediction of glioma progression.
  arXiv  Detail & Related papers  (2025-09-13T14:42:46Z)
• Temporally-Aware Diffusion Model for Brain Progression Modelling with Bidirectional Temporal Regularisation [7.097850157718258]
  Current approaches fail to explicitly capture the relationship between structural changes and time intervals.<n>Most approaches rely on 2D slice-based architectures, thereby disregarding full 3D anatomical context.<n>We propose a 3D Temporally-Aware Diffusion Model (TADM-3D), which accurately predicts brain progression on MRI volumes.
  arXiv  Detail & Related papers  (2025-09-03T08:51:38Z)
• Temporal Flow Matching for Learning Spatio-Temporal Trajectories in 4D Longitudinal Medical Imaging [4.916511768554556]
  We introduce Temporal Flow Matching (TFM), a unified generative trajectory method that aims to learn the underlying temporal distribution.<n>TFM consistently surpasses disease-temporal modeling methods from natural imaging.<n>It supports $3D$ volumes, multiple prior scans, and irregular sampling.
  arXiv  Detail & Related papers  (2025-08-29T12:34:28Z)
• MIRA: Medical Time Series Foundation Model for Real-World Health Data [39.55921588296102]
  A unified foundation model for medical time series offers the potential to reduce annotation burdens, minimize model customization, and enable robust transfer.<n>We introduce MIRA, a unified foundation model specifically designed for medical time series forecasting.<n> MIRA achieves reductions in forecasting errors by an average of 10% and 7% in out-of-distribution and in-distribution scenarios, respectively, when compared to other zero-shot and fine-tuned baselines.
  arXiv  Detail & Related papers  (2025-06-09T09:27:17Z)
• Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain [54.67888148566323]
  We introduce three large-scale time series forecasting datasets from the cloud operations domain. We show it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size. Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method.
  arXiv  Detail & Related papers  (2023-10-08T08:09:51Z)
• Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement [51.55157852647306]
  Time series forecasting has been a widely explored task of great importance in many applications. It is common that real-world time series data are recorded in a short time period, which results in a big gap between the deep model and the limited and noisy time series. We propose to address the time series forecasting problem with generative modeling and propose a bidirectional variational auto-encoder equipped with diffusion, denoise, and disentanglement.
  arXiv  Detail & Related papers  (2023-01-08T12:20:46Z)
• A generic diffusion-based approach for 3D human pose prediction in the wild [68.00961210467479]
  3D human pose forecasting, i.e., predicting a sequence of future human 3D poses given a sequence of past observed ones, is a challenging-temporal task. We provide a unified formulation in which incomplete elements (no matter in the prediction or observation) are treated as noise and propose a conditional diffusion model that denoises them and forecasts plausible poses. We investigate our findings on four standard datasets and obtain significant improvements over the state-of-the-art.
  arXiv  Detail & Related papers  (2022-10-11T17:59:54Z)
• Multivariate Time Series Forecasting with Dynamic Graph Neural ODEs [65.18780403244178]
  We propose a continuous model to forecast Multivariate Time series with dynamic Graph neural Ordinary Differential Equations (MTGODE) Specifically, we first abstract multivariate time series into dynamic graphs with time-evolving node features and unknown graph structures. Then, we design and solve a neural ODE to complement missing graph topologies and unify both spatial and temporal message passing.
  arXiv  Detail & Related papers  (2022-02-17T02:17:31Z)
• Probabilistic 3D surface reconstruction from sparse MRI information [58.14653650521129]
  We present a novel probabilistic deep learning approach for concurrent 3D surface reconstruction from sparse 2D MR image data and aleatoric uncertainty prediction. Our method is capable of reconstructing large surface meshes from three quasi-orthogonal MR imaging slices from limited training sets.
  arXiv  Detail & Related papers  (2020-10-05T14:18:52Z)
• M2Net: Multi-modal Multi-channel Network for Overall Survival Time Prediction of Brain Tumor Patients [151.4352001822956]
  Early and accurate prediction of overall survival (OS) time can help to obtain better treatment planning for brain tumor patients. Existing prediction methods rely on radiomic features at the local lesion area of a magnetic resonance (MR) volume. We propose an end-to-end OS time prediction model; namely, Multi-modal Multi-channel Network (M2Net)
  arXiv  Detail & Related papers  (2020-06-01T05:21:37Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.