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：虚拟介入技术实现了在冠状动脉内进行基于物理的设备部署模拟。该框架通过在不同动脉解剖结构中部署同一设备，实现了反事实推理。然而，当前创建此类反事实动脉的方法面临着可控性与真实性之间的权衡。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、局部形态结构和全局骨架结构）为虚拟介入研究定制合成冠状动脉解剖。我们还将扩散模型引导策略扩展至形态-骨架条件控制的语境，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地更新负向引导条件。我们的框架能够以可控方式生成和编辑冠状动脉解剖结构，使设备设计者能够获得关于解剖变异和模拟设备部署的机制性见解。