From Forward Prediction to Self-Corrective World Modeling for MRI Contrast Enhancement

Diffusion models offer strong visual quality for video generation and are promising for MRI enhancement world modeling. However, existing diffusion-based MRI enhancement world models remain passive forward simulators: they can predict future states, but cannot recognize and correct emerging kinetic errors. For the first time, we propose ReKinetics, a self-corrective world model that extends diffusion-based world modeling from forward prediction to explicit error correction. For self-correction action supervision, we introduce Corrective Credit Assignment Learning (CCAL), which shifts supervision from trajectory-level error fitting to action-level corrective credit assignment, turning latent kinetic failures into executable where–when–how action targets. For action execution, we introduce Factorized Corrective Transition Learning (FCTL), which replaces the shared denoising transition with a factorized corrective transition parameterized by state-dependent activation, spatial support, and action-specific residual transformation, decoupling heterogeneous corrective effects within diffusion. For action feedback, we introduce Reliability-Guided Corrective Optimization (RGCO), which shifts optimization from prediction-error-driven learning to correction-outcome-driven learning by converting reliability changes into trajectory-specific optimization weights. Together, these mechanisms form a closed self-corrective learning loop. Across Hand DCE-MRI and Duke Breast, ReKinetics achieves strong frame and kinetic performance. On the Hand benchmark, it reduces late-vs.-early ROI kinetic error by 42.1%, whereas competing methods accumulate 23.7%–44.7% error, confirming that explicit kinetic correction substantially benefits dynamic contrast-enhancement modeling.