Computational cardiovascular flow models are highly sensitive to prescribed inlet velocity profiles. While imaging-derived velocity fields provide physiologically realistic information, they can introduce increased preprocessing complexity, imaging noise, and computational burden. Simplified analytical formulations are computationally efficient but may not fully capture subject-specific flow characteristics. In this study, we present an uncertainty-aware framework that combines two-dimensional phase-contrast magnetic resonance imaging (2D PC-MRI) with mechanistic velocity-profile formulations to generate subject-specific pulmonary artery velocity representations. Imaging-derived radial velocity distributions were constructed from main pulmonary artery (MPA) PC-MRI data in canine and swine subjects using elliptical radial binning and normalization. Power-law and Womersley velocity-profile formulations were fitted within a Bayesian inference framework while accounting for uncertainty associated with imaging measurements and model representation. The two formulations were compared using regional and global weighted root mean square error (wRMSE) metrics. Both models demonstrated close agreement with the imaging-derived velocity profiles across subjects. Although the Womersley formulation provided greater flexibility near the vessel wall, it did not result in statistically significant improvements in fitting performance compared with the simpler power-law model. The proposed framework provides low-dimensional, physiologically interpretable, and uncertainty-aware velocity-profile representations that may serve as computationally efficient alternatives for subject-specific cardiovascular flow modeling.

翻译：计算心血管流动模型对预设入口速度剖面高度敏感。虽然影像学衍生的速度场能提供生理学真实信息，但会增加预处理复杂度、成像噪声和计算负担。简化解析公式虽计算高效，却可能无法充分捕捉受试者特异性流动特性。本研究提出一种不确定性感知框架，通过整合二维相位对比磁共振成像（2D PC-MRI）与力学速度剖面公式，生成受试者特异性肺动脉速度表征。采用椭圆径向分箱与归一化方法，从犬和猪受试者的主肺动脉（MPA）PC-MRI数据中构建影像学衍生的径向速度分布。在贝叶斯推断框架内，考虑成像测量与模型表征相关的不确定性，对标律模型和沃默斯利速度剖面公式进行拟合。采用区域和全局加权均方根误差（wRMSE）指标对两种公式进行比较。两种模型均与跨受试者的影像学衍生速度剖面高度吻合。虽然沃默斯利公式在血管壁附近具有更大灵活性，但与简化的标律模型相比，其拟合性能未呈现出统计学显著改善。所提出框架提供了低维、生理学可解释且具有不确定性感知的速度剖面表征，可作为受试者特异性心血管血流建模的高效计算替代方案。