Project Details
Projekt
In-stent restenosis in coronary arteries – computational and data-driven investigations towards translational modeling
Subject Area
Mechanics
Cardiology, Angiology
Cardiology, Angiology
Term
since 2021
Project identifier
Deutsche Forschungsgemeinschaft (DFG) - Project number 465213526
This project aims to advance computational and data-driven techniques to establish translational in-silico modeling capability. Its clinical objective is to successfully perform coronary stenting with drug elution. A severe problem in this application is the so-called in-stent restenosis (ISR), which occurs due to pathological growth of biological material. The clinical intervention is most effective if the patient-specific situation with respect to geometry and condition of the diseased artery, calcified domains, as well as evolution of growth factors and cell migration is taken into account as precisely as possible. The long-term goal is to develop an in silico simulation tool which enables cardiologists to make sufficiently quick decisions about important parameters of the cardiological treatment. Among these are the stent geometry and the amount of drug eluted. To reach this goal, a team of scientists from three disciplines has been formed. Prof. Vogt from cardiology provides patient-specific data, and contributes data and the crucial medical knowledge about the bio-chemical processes accompanying ISR. This information is taken up by Prof. Linka from solid mechanics to model the evolution of the relevant agents (growth factors, smooth muscle cells, as well as extracellular matrix) in the arterial wall. The latter process, taking place at the cellular level, is coupled to the continuum level by making the strain tensor dependent on the smooth muscle cell density. The modeling of the interfaces stent-blood flow, stent-artery, as well as artery-blood flow is tackled together with Prof. Behr from fluid mechanics. His work is also devoted to the computation of the blood flow and the wall shear stresses, which are assumed to have an important influence on ISR. By means of numerical methods such as model order reduction and artificial neural networks, all three partners contribute to the development of a meta model. In the long run, this model will be transferred into a simulation tool applicable in cardiology. Based on the patient-specific input parameters, it will give a forecast about the ISR process for a specific patient and stent implantation procedure.
DFG Programme
Priority Programmes
