KineWorld: Latent Kinetic World Modeling for Contrast-Free DCE-MRI Synthesis

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) provides clinically informative enhancement trajectories, yet contrast-agent administration is unsuitable for some patients. Synthesizing patient-specific DCE-MRI trajectories from a pre-contrast scan requires preserving anatomical consistency while faithfully modeling tissue-specific contrast kinetics. Existing methods do not explicitly represent evolving kinetic states, often resulting in structural drift and physiologically implausible kinetic trajectories. We propose KineWorld, a latent kinetic world model that anchors cumulative contrast-dependent changes to the pre-contrast latent state, thereby separating kinetic evolution from time-invariant anatomy to preserve anatomical consistency. To achieve kinetic fidelity, a pharmacokinetics-inspired rollout recursively integrates learned influx and efflux velocities, while kinetics-aware retrieval supplies relevant yet diverse long-range patient history to preserve phase-dependent evolution. Relay-guarded self-rollout training further limits accumulated anatomical and kinetic errors by selectively restoring ground-truth states upon excessive deviation. Across three breast and prostate DCE-MRI datasets, KineWorld improves SSIM by over 11% and lesion kinetic curve CCC by over 17% relative to the strongest baselines. A blinded reader study further demonstrates substantially improved agreement with report-derived kinetic categories. Source code, pretrained weights, and our curated internal dataset will be publicly released to facilitate future research.