Biomedical Engineering, University of Wisconsin, Madison, WI, USA.
Pediatrics, Division of Cardiology, University of Wisconsin, Madison, WI, USA.
Ann Biomed Eng. 2021 Sep;49(9):2365-2376. doi: 10.1007/s10439-021-02780-5. Epub 2021 May 4.
Branch pulmonary artery stenosis (PAS) commonly occurs in congenital heart disease and the pressure gradient over a stenotic PA lesion is an important marker for re-intervention. Image based computational fluid dynamics (CFD) has shown promise for non-invasively estimating pressure gradients but one limitation of CFD is long simulation times. The goal of this study was to compare accelerated predictions of PAS pressure gradients from 3D CFD with instantaneous adaptive mesh refinement (AMR) versus a recently developed 0D distributed lumped parameter CFD model. Predictions were then experimentally validated using a swine PAS model (n = 13). 3D CFD simulations with AMR improved efficiency by 5 times compared to fixed grid CFD simulations. 0D simulations further improved efficiency by 6 times compared to the 3D simulations with AMR. Both 0D and 3D simulations underestimated the pressure gradients measured by catheterization (- 1.87 ± 4.20 and - 1.78 ± 3.70 mmHg respectively). This was partially due to simulations neglecting the effects of a catheter in the stenosis. There was good agreement between 0D and 3D simulations (ICC 0.88 [0.66-0.96]) but only moderate agreement between simulations and experimental measurements (0D ICC 0.60 [0.11-0.86] and 3D ICC 0.66 [0.21-0.88]). Uncertainty assessment indicates that this was likely due to limited medical imaging resolution causing uncertainty in the segmented stenosis diameter in addition to uncertainty in the outlet resistances. This study showed that 0D lumped parameter models and 3D CFD with instantaneous AMR both improve the efficiency of hemodynamic modeling, but uncertainty from medical imaging resolution will limit the accuracy of pressure gradient estimations.
肺动脉分支狭窄（PAS）在先天性心脏病中较为常见，狭窄肺动脉病变处的压力梯度是再次介入的重要指标。基于影像的计算流体动力学（CFD）已显示出无创性估计压力梯度的潜力，但 CFD 的一个局限性是模拟时间较长。本研究的目的是比较 3D CFD 与即时自适应网格细化（AMR）加速预测 PAS 压力梯度与最近开发的 0D 分布式集中参数 CFD 模型的结果。然后使用猪 PAS 模型（n=13）对预测结果进行了实验验证。与固定网格 CFD 模拟相比，使用 AMR 的 3D CFD 模拟效率提高了 5 倍。与 3D 模拟使用 AMR 相比，0D 模拟进一步提高了 6 倍的效率。0D 和 3D 模拟均低估了导管测量的压力梯度（分别为-1.87±4.20mmHg 和-1.78±3.70mmHg）。这部分是由于模拟忽略了狭窄处导管的影响。0D 和 3D 模拟之间具有良好的一致性（ICC 0.88 [0.66-0.96]），但模拟与实验测量之间的一致性仅为中等（0D ICC 0.60 [0.11-0.86]和 3D ICC 0.66 [0.21-0.88]）。不确定性评估表明，这可能是由于医学成像分辨率有限，除了出口阻力的不确定性外，还导致分段狭窄直径存在不确定性。本研究表明，0D 集中参数模型和 3D CFD 与即时 AMR 都可提高血流动力学建模的效率，但医学成像分辨率的不确定性将限制压力梯度估计的准确性。
