Virtual interventions enable the physics-based simulation of device deployment within coronary arteries. This framework allows for counterfactual reasoning by deploying the same device in different arterial anatomies. However, current methods to create such counterfactual arteries face a trade-off between controllability and realism. In this study, we investigate how Latent Diffusion Models (LDMs) can custom synthesize coronary anatomy for virtual intervention studies based on mid-level anatomic constraints such as topological validity, local morphological shape, and global skeletal structure. We also extend diffusion model guidance strategies to the context of morpho-skeletal conditioning and propose a novel guidance method for continuous attributes that adaptively updates the negative guiding condition throughout sampling. Our framework enables the generation and editing of coronary anatomy in a controllable manner, allowing device designers to derive mechanistic insights regarding anatomic variation and simulated device deployment.

翻译：虚拟介入技术能够实现冠状动脉内器械部署的物理仿真。该框架通过在多种动脉解剖结构中部署相同器械，支持反事实推理。然而，当前生成此类反事实动脉的方法面临可控性与真实感之间的权衡。本研究探讨了潜在扩散模型如何基于中层解剖约束（如拓扑有效性、局部形态特征和全局骨架结构）定制合成适用于虚拟介入研究的冠状动脉解剖结构。我们还将扩散模型引导策略扩展至形态-骨架条件控制场景，并提出一种针对连续属性的新型引导方法，该方法能在整个采样过程中自适应更新负向引导条件。本框架实现了冠状动脉解剖结构的可控生成与编辑，使器械设计者能够从解剖变异与仿真器械部署中获得机制性见解。