A Comparison of Fully-Coupled 3D In-Stent Restenosis Simulations to In-vivo Data

Abstract

We describe our fully-coupled 3D multiscale model of in-stent restenosis, with blood flow simulations coupled to smooth muscle cell proliferation, and report results of numerical simulations performed with this model. This novel model is based on several previously reported 2D models. We study the effects of various parameters on the process of restenosis and compare with in vivo porcine data where we observe good qualitative agreement. We study the effects of stent deployment depth (and related injury score), reendothelization speed, and simulate the effect of stent width. Also we demonstrate that we are now capable to simulate restenosis in real-sized (18 mm long, 2.8 mm wide) vessel geometries.

Uncertainty Quantification of a Multiscale Model for In-Stent Restenosis

“…This is similar to results obtained in an earlier studies for a similar 3D model. 34 There it was also demonstrated that a difference in re-endothelialization speed changes growth to a large extent, while the effects of the deployment depth are smaller, but still quite pronounced. As the re-endothelialization has such significant impact on the neointimal area, this part of the ISR model requires further study and validation.…”

“…9, and also by using computational models. 3,5,13,18,34 The computational models of ISR usually represent cells by agents, either freely moving 5,18,34 or placed on a lattice. 3,13 These agents take cues from the blood flow, concentration of drugs eluted from the stent, and from the vessel damage and mechanics, which affect the growth and proliferation of the cells.…”

“…5,29,30 We do this to provide a proofof-concept, using a model that is computationally relatively cheap, in preparation for a more in-depth study of our later, more physiological and more computationally expensive three-dimensional ISR model. 34 Our ultimate goal was to access the model result sensitivity to the uncertainties in the inputs and stochastic parameters, in order to be aware of the result uncertainty and the inputs which cause a greater effect on it. This will allow us to be aware of the result uncertainty quantities, when model results are analyzed, and of the inputs, which value should be as precise as possible.…”

Introducing VECMAtk - Verification, Validation and Uncertainty Quantification for Multiscale and HPC Simulations

“…Depending on the surrogate used, the simulation time of the semi-intrusive method was up to five times faster than of a Monte Carlo method. In future work, the semiintrusive metamodeling method will be applied to the three dimensional version of the ISR model employing the VECMAtk, since a black-box approach is not feasible for this application due to high computational demand [34].…”

Detection of arterial wall abnormalities via Bayesian model selection

“…Mathematical models for hemodynamics trace back to the work of Euler, who described a onedimensional treatment of blood flow through an arterial network with rigid tubes [11,33]; more sophisticated one-dimensional models are still used to study a variety of physio-pathological phenomena [1,2,13,21,23,29,30,37]. Computational advances have also allowed for the development of computationally intensive three-dimensional models [12,14,32,34,39,40], which have been used to accurately simulate specific human arteries (e.g., the carotid arteries [18]) and model their material properties (e.g., of cerebral arterial walls [38]). There also exist multicomponent models [10], which are amenable to applications such as modeling oxygen transport to solid tumors [6] and surgical tissue flaps [24,25].…”