Cross-Modal Physics Consistency Learning for Medical Dynamic Imaging and Video Synthesis
Keywords:
cross-modal learning, physics-informed neural networks, medical video synthesis, dynamic imaging, consistency learning, system governanceAbstract
The synthesis of medical dynamic imaging and video data has emerged as a transformative capability for augmenting training datasets, enhancing diagnostic visualization, and enabling predictive modeling of anatomical and physiological processes. However, purely data-driven generative approaches often produce sequences that violate fundamental physical constraints, undermining clinical trust and utility. This paper presents a system-level examination of cross-modal physics consistency learning as a guiding principle for the design, deployment, and governance of medical video synthesis systems. Moving beyond algorithm-centric perspectives, we analyze the architectural trade-offs inherent in integrating multimodal sensor streams—such as magnetic resonance imaging, computed tomography, and ultrasound—with inductive biases grounded in continuum mechanics, fluid dynamics, and tissue biomechanics. We explore how physics-coherent feature alignment and three-dimensional structural representations, as exemplified by recent advances in image-to-video generation through representation alignment, can be operationalized within large-scale clinical infrastructures. The discussion extends to the infrastructure requirements for real-time inference, federated data governance, and interoperability across heterogeneous hospital information systems. We critically assess the robustness and fairness implications of physics-informed models when deployed across diverse populations and acquisition protocols, highlighting both the regularizing benefits of physical constraints and the risks of embedding overly narrow mechanistic assumptions. The paper further addresses the sustainability of training and serving such models, scrutinizing the computational footprint, energy consumption, and model compression strategies that balance fidelity against resource efficiency. Policy and regulatory dimensions are examined through the lens of software-as-a-medical-device frameworks, synthetic data provenance, and liability allocation. By synthesizing these structural, societal, and technical considerations, we argue that cross-modal physics consistency learning is not merely an algorithmic refinement but a foundational systems paradigm that necessitates coordinated advances across machine learning, medical physics, software engineering, and health policy.
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This article is published under the Creative Commons Attribution 4.0 International License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
