用流匹配方法联合推断生理边界条件，提升个性化心血管建模精度。
FalconBC: Flow matching for Amortized inference of Latent-CONditioned physiologic Boundary Conditions
- 基于概率流构建通用推断框架，联合建模解剖特征与临床目标。
- 在主动脉-髂动脉分叉和冠状动脉树模型上验证，支持病变影响下的条件估计。
- 适用于血流已知但波形固定的开放环场景，适合临床个性化建模研究者。
边界条件调优是个性化心血管建模中的关键步骤。尽管数据驱动的变分推断方法通过摊销训练成本，可高效估计边界条件的联合后验分布，但在两类重要场景中仍存在不足：一是已知平均血流且假设波形形状的开环模型；二是受血管病变影响、分割结果影响压力或血流分配目标可达性的解剖结构。在这两种情况下，边界条件无法独立调优。本文提出一种基于概率流的通用摊销推断框架，将临床目标、流入特征及患者解剖的点云嵌入作为条件变量或需联合估计的量。我们在两个个性化模型上进行了验证：一个具有不同狭窄位置与严重程度的腹主动脉-髂动脉分叉模型，以及一个冠状动脉树模型。结果表明该方法能有效处理复杂解剖与多源约束下的联合估计问题。
原文摘要 · Abstract (English)
Boundary condition tuning is a fundamental step in patient-specific cardiovascular modeling. Despite an increase in offline training cost, recent methods in data-driven variational inference can efficiently estimate the joint posterior distribution of boundary conditions, with amortization of training efforts over clinical targets. However, even the most modern approaches fall short in two important scenarios: open-loop models with known mean flow and assumed waveform shapes, and anatomies affected by vascular lesions where segmentation influences the reachability of pressure or flow split targets. In both cases, boundary conditions cannot be tuned in isolation. We introduce a general amortized inference framework based on probabilistic flow that treats clinical targets, inflow features, and point cloud embeddings of patient-specific anatomies as either conditioning variables or quantities to be jointly estimated. We demonstrate the approach on two patient-specific models: an aorto-iliac bifurcation with varying stenosis locations and severity, and a coronary arterial tree.
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