探究扩散模型在数字孪生解剖编辑中的极限与能力
Probing the Limits and Capabilities of Diffusion Models for the Anatomic Editing of Digital Twins
AI总结：
本研究探究潜在扩散模型编辑心脏数字孪生以生成解剖变体（数字兄弟姐妹）的能力，通过形态与拓扑分析揭示其对常见解剖特征的偏倚，并利用该偏倚进行选择性编辑以增强虚拟队列，从而界定其在体外试验中的极限与能力。
摘要：
数值模拟可以模拟控制心血管设备部署的物理过程。当此类模拟纳入数字孪生（即患者特异性解剖结构的计算模型）时，可以加速设备设计过程并降低风险。然而，仅使用患者特异性数据限制了能够精确或充分探索的解剖变异性。在本研究中，我们探究了潜在扩散模型（LDMs）编辑数字孪生以创建解剖变体（我们称之为数字兄弟姐妹）的能力。数字孪生及其对应的兄弟姐妹可以作为比较模拟的基础，从而能够研究细微的解剖变异如何影响心血管设备的模拟部署，以及增强用于设备评估的虚拟队列。然而，尽管扩散模型在编辑自然图像方面的能力已被表征，但其对数字孪生进行解剖编辑的能力尚未被研究。以心脏解剖结构的3D数字孪生为中心案例，我们实现了多种生成数字兄弟姐妹的方法，并通过形态学和拓扑学分析对其进行表征。我们特别编辑数字孪生以在不同空间尺度和局部区域内引入解剖变异，证明了对常见解剖特征存在偏倚。我们进一步表明，这种解剖偏倚可以通过选择性编辑用于虚拟队列增强，部分缓解与数据集不平衡和缺乏多样性相关的问题。我们的实验框架因此描绘了在体外试验中使用潜在扩散模型合成解剖变异的极限与能力。
英文摘要：
Numerical simulations can model the physical processes that govern cardiovascular device deployment. When such simulations incorporate digital twins; computational models of patient-specific anatomy, they can expedite and de-risk the device design process. Nonetheless, the exclusive use of patient-specific data constrains the anatomic variability which can be precisely or fully explored. In this study, we investigate the capacity of Latent Diffusion Models (LDMs) to edit digital twins to create anatomic variants, which we term digital siblings. Digital twins and their corresponding siblings can serve as the basis for comparative simulations, enabling the study of how subtle anatomic variations impact the simulated deployment of cardiovascular devices, as well as the augmentation of virtual cohorts for device assessment. However, while diffusion models have been characterized in their ability to edit natural images, their capacity to anatomically edit digital twins has yet to be studied. Using a case example centered on 3D digital twins of cardiac anatomy, we implement various methods for generating digital siblings and characterize them through morphological and topological analyses. We specifically edit digital twins to introduce anatomic variation at different spatial scales and within localized regions, demonstrating the existence of bias towards common anatomic features. We further show that such anatomic bias can be leveraged for virtual cohort augmentation through selective editing, partially alleviating issues related to dataset imbalance and lack of diversity. Our experimental framework thus delineates the limits and capabilities of using latent diffusion models in synthesizing anatomic variation for in silico trials.
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。
