一种具有形态-骨骼控制的可用于模拟的冠状动脉解剖扩散模型
A Diffusion Model for Simulation Ready Coronary Anatomy with Morpho-skeletal Control
AI总结：
本文提出了一种基于潜在扩散模型的冠状动脉解剖生成方法，通过形态-骨骼控制实现可控的解剖合成，用于虚拟干预研究。
摘要：
虚拟干预使能够在冠状动脉内进行基于物理的设备部署模拟。该框架允许通过在不同的动脉解剖结构中部署相同设备来进行反事实推理。然而，当前创建此类反事实动脉的方法在可控性和真实性之间存在权衡。在本研究中，我们探讨了潜在扩散模型（LDMs）如何基于中等层次的解剖约束（如拓扑有效性、局部形态形状和全局骨骼结构）来定制合成冠状动脉解剖，用于虚拟干预研究。我们还扩展了扩散模型指导策略到形态-骨骼条件的背景下，并提出了一种新的指导方法，用于连续属性，该方法在采样过程中自适应地更新负引导条件。我们的框架使能够在可控的方式下生成和编辑冠状动脉解剖，使设备设计师能够获得关于解剖变异和模拟设备部署的机理见解。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
