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ABSTRACT ObjectiveTortuous microvessels are characteristic of microvascular remodeling associated with numerous physiological and pathological scenarios. Three‐dimensional (3D) hemodynamics in tortuous microvessels influenced by red blood cells (RBCs), however, are largely unknown, and important questions remain. Is blood viscosity influenced by vessel tortuosity? How do RBC dynamics affect wall shear stress (WSS) patterns and the near‐wall cell‐free layer (CFL) over a range of conditions? The objective of this work was to parameterize hemodynamic characteristics unique to a tortuous microvessel. MethodsRBC‐resolved simulations were performed using an immersed boundary method‐based 3D fluid dynamics solver. A representative tortuous microvessel was selected from a stimulated angiogenic network obtained from imaging of the rat mesentery and digitally reconstructed for the simulations. The representative microvessel was a venule with a diameter of approximately 20 μm. The model assumes a constant diameter along the vessel length and does not consider variations due to endothelial cell shapes or the endothelial surface layer. ResultsMicrovessel tortuosity was observed to increase blood apparent viscosity compared to a straight tube by up to 26%. WSS spatial variations in high curvature regions reached 23.6 dyne/cm2over the vessel cross‐section. The magnitudes of WSS and CFL thickness variations due to tortuosity were strongly influenced by shear rate and negligibly influenced by tube hematocrit levels. ConclusionsNew findings from this work reveal unique tortuosity‐dependent hemodynamic characteristics over a range of conditions. The results provide new thought‐provoking information to better understand the contribution of tortuous vessels in physiological and pathological processes and help improve reduced‐order models.
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Predicting capillary vessel network hemodynamics in silico by machine learning
Abstract Blood velocity and red blood cell (RBC) distribution profiles in a capillary vessel cross-section in the microcirculation are generally complex and do not follow Poiseuille’s parabolic or uniform pattern. Existing imaging techniques used to map large microvascular networks in vivo do not allow a direct measurement of full 3D velocity and RBC concentration profiles, although such information is needed for accurate evaluation of the physiological variables, such as the wall shear stress (WSS) and near-wall cell-free layer (CFL), that play critical roles in blood flow regulation, disease progression, angiogenesis, and hemostasis. Theoretical network flow models, often used for hemodynamic predictions in experimentally acquired images of the microvascular network, cannot provide the full 3D profiles either. In contrast, such information can be readily obtained from high-fidelity computational models that treat blood as a suspension of deformable RBCs. These models, however, are computationally expensive and not feasible for extension to the microvascular network at large spatial scales up to an organ level. To overcome such limitations, here we present machine learning (ML) models that bypass such expensive computations but provide highly accurate and full 3D profiles of the blood velocity, RBC concentration, WSS, and CFL in every vessel in the microvascular network. The ML models, which are based on artificial neural network and convolution-based U-net models, predict hemodynamic quantities that compare very well against the true data but reduce the prediction time by several orders. This study therefore paves the way for ML to make detailed and accurate hemodynamic predictions in spatially large microvascular networks at an organ-scale.
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- Award ID(s):
- 2302212
- PAR ID:
- 10502908
- Publisher / Repository:
- Oxford University Press
- Date Published:
- Journal Name:
- PNAS Nexus
- ISSN:
- 2752-6542
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract The wall shear stress (WSS) exerted by blood flowing through microvascular capillaries is an established driver of new blood vessel growth, or angiogenesis. Such adaptations are central to many physiological processes in both health and disease, yet three-dimensional (3D) WSS characteristics in real angiogenic microvascular networks are largely unknown. This marks a major knowledge gap because angiogenesis, naturally, is a 3D process. To advance current understanding, we model 3D red blood cells (RBCs) flowing through rat angiogenic microvascular networks using state-of-the-art simulation. The high-resolution fluid dynamics reveal 3D WSS patterns occurring at sub-endothelial cell (EC) scales that derive from distinct angiogenic morphologies, including microvascular loops and vessel tortuosity. We identify the existence of WSS hot and cold spots caused by angiogenic surface shapes and RBCs, and notably enhancement of low WSS regions by RBCs. Spatiotemporal characteristics further reveal how fluctuations follow timescales of RBC “footprints.” Altogether, this work provides a new conceptual framework for understanding how shear stress might regulate EC dynamics in vivo.more » « less
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Beard, Daniel A (Ed.)The geometry of the blood vessel wall plays a regulatory role on the motion of red blood cells (RBCs). The overall topography of the vessel wall depends on many features, among which the endothelial lining of the endothelial surface layer (ESL) is an important one. The endothelial lining of vessel walls presents a large surface area for exchanging materials between blood and tissues. The ESL plays a critical role in regulating vascular permeability, hindering leukocyte adhesion as well as inhibiting coagulation during inflammation. Changes in the ESL structure are believed to cause vascular hyperpermeability and entrap immune cells during sepsis, which could significantly alter the vessel wall geometry and disturb interactions between RBCs and the vessel wall, including the wall-induced migration of RBCs and the thickening of a cell-free layer. To investigate the influence of the vessel wall geometry particularly changed by the ESL under various pathological conditions, such as sepsis, on the motion of RBCs, we developed two models to represent the ESL using the immersed boundary method in two dimensions. In particular, we used simulations to study how the lift force and drag force on a RBC near the vessel wall vary with different wall thickness, spatial variation, and permeability associated with changes in the vessel wall geometry. We find that the spatial variation of the wall has a significant effect on the wall-induced migration of the RBC for a high permeability, and that the wall-induced migration is significantly inhibited as the vessel diameter is increased.more » « less
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Background and objective: Wall shear stress (WSS) has been known to play a critical role in the development of several complications following coronary artery stenting, including in-stent restenosis and thrombosis. Computational fluid dynamics is often used to quantify the post-stenting WSS, which may potentially be used as a predictive metric. However, large-scale studies for WSS-based risk stratification often neglect the footprint of the stent due to reconstruction challenges. The primary objective of this study is to statistically evaluate the impact of the stent footprints (Xience and Resolute stents) on the computed endothelial WSS and quantitatively identify the relationship between these local hemodynamic alterations and the global properties of the vessel, such as curvature, on WSS. The ultimate goal is to evaluate whether and when it is worth including the footprint of the stent in an in-silico study to compute the WSS reliably. Methods: A previously developed semi-automated reconstruction approach for patient-specific coronaries was employed as a part of the SHEAR-STENT trial. A subset of patients was analyzed (N=30), and CFD simulations were performed with and without the stent to evaluate the impact of the stent footprint on WSS. Due to the computationally expensive nature of transient analyses, a sub-cohort of ten patients were used to assess the reliability of WSS obtained from steady computations as a surrogate for the time-averaged results. Global and local vessel curvature data were extracted for all cases and evaluated against stent-induced alterations in the WSS. The differences between the Xience and Resolute stent platforms were also examined to quantify each stent's unique WSS footprint. Results: Results from the surrogate analysis indicate that steady WSS serves as an excellent approximation of the time-averaged computations. The presence of either stent footprint causes a statistically significant decrease in the space-averaged WSS, and a significant increase in the endothelial regions exposed to very low WSS as well (<0.5 Pa). Negative correlations were observed between vessel curvature and WSS differences, indicating that macroscopic vessel characteristics play a more prominent role in determining endothelial WSS at higher curvature values. In our pool of cases, comparison of Xience and Resolute stents revealed that the Resolute platform seems to lead to lower space-averaged WSS and an increase in areas of very low WSS. Conclusion: These results outline (1) the necessity of including the stent footprint for accurate in-silico WSS analysis; (2) the global features of stented arteries serving as the dominant determinant of WSS past a certain curvature threshold; and (3) the Xience stent resulting in a milder presence of hemodynamically unfavorable WSS regions compared to the Resolute stent. Keywords: Computational fluid dynamics; Drug-eluting stents; In-silico clinical trials; Percutaneous coronary intervention; Wall shear stress.more » « less
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1093/pnasnexus/pgae043
