CardioComposer：利用可微几何实现解剖扩散模型的组合控制
CardioComposer: Leveraging Differentiable Geometry for Compositional Control of Anatomical Diffusion Models
AI总结：
CardioComposer通过可微几何实现对解剖扩散模型的组合控制，利用可解释的椭球体原语生成多类解剖标签图，通过可微测量函数实现几何属性的约束与控制。
摘要：
生成3D心血管解剖模型可以为临床研究和医疗设备评估合成信息结构，但面临几何可控性与真实性的权衡。我们提出了CardioComposer：一种可编程的推理时框架，用于从可解释的椭球体原语生成多类解剖标签图。这些原语代表几何属性，如离散子结构的大小、形状和位置。我们特别开发了基于体素几何矩的可微测量函数，使扩散模型采样期间能够通过损失进行梯度引导。我们证明这些损失可以以解耦的方式约束单个几何属性，并提供对多个子结构的组合控制。最后，我们展示了我们的方法可以兼容包含非凸子结构的广泛解剖系统，涵盖心脏、血管和骨骼器官。我们发布代码在https://github.com/kkadry/CardioComposer。
英文摘要：
Generative models of 3D cardiovascular anatomy can synthesize informative structures for clinical research and medical device evaluation, but face a trade-off between geometric controllability and realism. We propose CardioComposer: a programmable, inference-time framework for generating multi-class anatomical label maps from interpretable ellipsoidal primitives. These primitives represent geometric attributes such as the size, shape, and position of discrete substructures. We specifically develop differentiable measurement functions based on voxel-wise geometric moments, enabling loss-based gradient guidance during diffusion model sampling. We demonstrate that these losses can constrain individual geometric attributes in a disentangled manner and provide compositional control over multiple substructures. Finally, we show that our method is compatible with a broad range of anatomical systems containing non-convex substructures, spanning cardiac, vascular, and skeletal organs. We release our code at https://github.com/kkadry/CardioComposer.
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。
