Dense Temporal Contrast Synthesis via Conditioned Latent Transport

2026-07-31

AI Summary

This work addresses the limitations of conventional dynamic contrast-enhanced MRI (DCE-MRI), which relies on gadolinium-based contrast agents and suffers from contraindications, prolonged scan times, and environmental toxicity, while existing synthesis methods struggle to simultaneously preserve spatial fidelity and temporal continuity. The authors propose a conditional latent space transport framework that leverages anatomical priors to anchor latent trajectories and incorporates continuous-time embeddings to generate high-fidelity, patient-specific DCE-MRI sequences at arbitrary time points in a single forward pass. This approach represents the first non-iterative, temporally continuous method for DCE-MRI synthesis. In multi-center clinical validation, it significantly outperforms current techniques, improving tumor segmentation Dice scores by 22.4%, reducing boundary errors by over 39%, and achieving radiologist approval for clinical decision-making in 70% of cases.

Abstract

Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is essential for breast cancer management, but reliance on gadolinium-based contrast agents (GBCAs) restricts use in contraindicated populations, prolongs scan protocols, and presents environmental toxicity concerns. Contrast synthesis offers a non-invasive alternative; however, existing approaches struggle to balance spatial realism with temporal continuity, suffer from slow iterative sampling, underutilize structural priors, and lack clinical validation. We propose a novel conditioned latent transport framework that predicts contrast enhancement in a single forward pass. By anchoring the latent trajectory to the pre-contrast anatomy and applying continuous time conditioning, the model synthesizes patient-specific contrast evolution at any acquisition time. The proposed approach outperforms baseline and the state-of-the-art models across spatial, perceptual, temporal, and distributional metrics. Evaluated on an independent external cohort, the method demonstrates robustness to domain shifts induced by scanner noise as well as differing acquisition protocol. Furthermore, our synthetic contrast enhancement significantly improved downstream tumor segmentation performance, yielding a 22.4% relative increase in Dice coefficient (0.60 vs. 0.49 baseline pre-contrast, p < 0.01), reducing boundary segmentation error by over 39%, while outperforming all other generative model baselines. Finally, a reader study involving four breast radiologists evaluated the image quality, kinetic fidelity, and diagnostic viability of our synthesized sequences across 40 randomly selected cases. The results demonstrated that in 70% of cases, synthesized images provided sufficient clinical information to support the same management decisions as real DCE-MRI, suggesting a path toward safer and faster contrast-free or contrast-reduced imaging workflows.

Problem

• DCE-MRI
• contrast synthesis
• gadolinium-based contrast agents
• temporal continuity
• clinical validation

Innovation

• conditioned latent transport
• contrast synthesis
• DCE-MRI
• temporal continuity
• clinical validation