Department of Mechanical Engineering, Michigan State University, East Lansing, MI, USA.
The Joint Department of Biomedical Engineering, Marquette University and Medical College of Wisconsin, Milwaukee, WI, USA.
Comput Methods Programs Biomed. 2024 Jan;243:107908. doi: 10.1016/j.cmpb.2023.107908. Epub 2023 Oct 31.
Capillary transit time (CTT) is a fundamental determinant of gas exchange between blood and tissues in the heart and other organs. Despite advances in experimental techniques, it remains difficult to measure coronary CTT in vivo. Here, we developed a novel computational framework that couples coronary microcirculation with cardiac mechanics in a closed-loop system that enables prediction of hemodynamics in the entire coronary network, including arteries, veins, and capillaries. We also developed a novel "particle-tracking" approach for computing CTT where "virtual tracers" are individually tracked as they traverse the capillary network. Model predictions compare well with blood pressure and flow rate distributions in the arterial network reported in previous studies. Model predictions of transit times in the capillaries (1.21 ± 1.5 s) and entire coronary network (11.8 ± 1.8 s) also agree with measurements. We show that, with increasing coronary artery stenosis (as quantified by fractional flow reserve, FFR), intravascular pressure and flow rate downstream are reduced but remain non-stationary even at 100 % stenosis because some flow (∼3 %) is redistributed from the non-occluded to the occluded territories. Importantly, the model predicts that occlusion of a large artery results in higher CTT. For moderate stenosis (FFR > 0.6), the increase in CTT (from 1.21 s without stenosis to 2.23 s at FFR=0.6) is caused by a decrease in capillary flow rate. In severe stenosis (FFR = 0.1), the increase in CTT to 14.2 s is due to both a decrease in flow rate and an increase in path length taken by "virtual tracers" in the capillary network.
毛细血管渡越时间(CTT)是心脏和其他器官中血液与组织之间气体交换的基本决定因素。尽管实验技术取得了进步,但在体内测量冠状动脉 CTT 仍然很困难。在这里,我们开发了一种新的计算框架,该框架将冠状动脉微循环与心脏力学耦合在一个闭环系统中,从而能够预测整个冠状动脉网络中的血液动力学,包括动脉、静脉和毛细血管。我们还开发了一种新的“粒子追踪”方法来计算 CTT,其中“虚拟示踪剂”在穿过毛细血管网络时被单独追踪。模型预测与以前研究中报告的动脉网络中的血压和流量分布吻合良好。模型预测的毛细血管(1.21±1.5s)和整个冠状动脉网络(11.8±1.8s)的渡越时间也与测量值一致。我们表明,随着冠状动脉狭窄(以血流储备分数,FFR 来量化)的增加,下游的血管内压力和流量降低,但即使在 100%狭窄时仍保持非稳态,因为一些流量(约 3%)从非闭塞区重新分配到闭塞区。重要的是,该模型预测大血管闭塞会导致 CTT 增加。对于中度狭窄(FFR>0.6),CTT 的增加(从无狭窄时的 1.21s 增加到 FFR=0.6 时的 2.23s)是由于毛细血管流量减少所致。在严重狭窄(FFR=0.1)中,CTT 增加到 14.2s 是由于流量减少和毛细血管网络中“虚拟示踪剂”的路径长度增加所致。