Limits...
Computational simulation methodologies for mechanobiological modelling: a cell-centred approach to neointima development in stents.

Boyle CJ, Lennon AB, Early M, Kelly DJ, Lally C, Prendergast PJ - Philos Trans A Math Phys Eng Sci (2010)

Bottom Line: Tissue growth and differentiation requires simulating many of these cells together.The method is capable of capturing some of the most important aspects of restenosis, including nonlinear lesion growth with time.The approach taken in this paper provides a framework for simulating restenosis; the next step will be to couple it with more patient-specific geometries and quantitative parameter data.

View Article: PubMed Central - PubMed

Affiliation: Trinity Centre for Bioengineering, School of Engineering, Trinity College Dublin, Dublin, Republic of Ireland.

ABSTRACT
The design of medical devices could be very much improved if robust tools were available for computational simulation of tissue response to the presence of the implant. Such tools require algorithms to simulate the response of tissues to mechanical and chemical stimuli. Available methodologies include those based on the principle of mechanical homeostasis, those which use continuum models to simulate biological constituents, and the cell-centred approach, which models cells as autonomous agents. In the latter approach, cell behaviour is governed by rules based on the state of the local environment around the cell; and informed by experiment. Tissue growth and differentiation requires simulating many of these cells together. In this paper, the methodology and applications of cell-centred techniques--with particular application to mechanobiology--are reviewed, and a cell-centred model of tissue formation in the lumen of an artery in response to the deployment of a stent is presented. The method is capable of capturing some of the most important aspects of restenosis, including nonlinear lesion growth with time. The approach taken in this paper provides a framework for simulating restenosis; the next step will be to couple it with more patient-specific geometries and quantitative parameter data.

Show MeSH

Related in: MedlinePlus

Finite element mesh of the unexpanded stent (a) and after expansion in a cylindrical artery (b). For computation, the stent and artery were reduced down to a 1/8 model in the circumferential direction, the modelled portion of the geometry can be seen in red.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC2944394&req=5

RSTA20100071F3: Finite element mesh of the unexpanded stent (a) and after expansion in a cylindrical artery (b). For computation, the stent and artery were reduced down to a 1/8 model in the circumferential direction, the modelled portion of the geometry can be seen in red.

Mentions: Finite element analysis was performed and coupled with an injury threshold to quantify injury in the artery wall due to stenting. The artery was modelled as a hollow cylinder with a length of 14 mm and inner and outer diameters of 2.5 and 3.75 mm, respectively. Following Gervaso et al. (2008), the material properties of the artery were represented as a third-order Mooney–Rivlin hyperelastic model based on circumferential, uniaxial tests carried out on non-diseased media from human coronary arteries with intimal thickening by Holzapfel et al. (2005). A four-crowned, corrugated-ring, stainless-steel stent was modelled with similar designs to those used by Garasic et al. (2000); the finite element (FE) mesh of the expanded and unexpanded stent is shown in figure 3. The stent was modelled as a bi-linear elasto-plastic material with the properties of stainless steel (Early et al. 2009). Physiological conditions in the artery were simulated by applying an axial pre-stretch of 1.2 and a blood pressure of 13.3 kPa prior to stent expansion. A radial displacement was applied to all nodes of a cylinder within the stent, expanding it from an initial diameter of 1.15 to 3.3 mm. This achieved an expansion ratio of 1.2. The cylinder was retracted allowing recoil of the stents. To reduce the simulation times, symmetry was used to reduce to a 1/8th model circumferentially (figure 3). Appropriate boundary conditions were applied along the planes of symmetry. The simulations were run using ABAQUS (SIMULIA) to determine the stresses in the artery following stent expansion.


Computational simulation methodologies for mechanobiological modelling: a cell-centred approach to neointima development in stents.

Boyle CJ, Lennon AB, Early M, Kelly DJ, Lally C, Prendergast PJ - Philos Trans A Math Phys Eng Sci (2010)

Finite element mesh of the unexpanded stent (a) and after expansion in a cylindrical artery (b). For computation, the stent and artery were reduced down to a 1/8 model in the circumferential direction, the modelled portion of the geometry can be seen in red.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC2944394&req=5

RSTA20100071F3: Finite element mesh of the unexpanded stent (a) and after expansion in a cylindrical artery (b). For computation, the stent and artery were reduced down to a 1/8 model in the circumferential direction, the modelled portion of the geometry can be seen in red.
Mentions: Finite element analysis was performed and coupled with an injury threshold to quantify injury in the artery wall due to stenting. The artery was modelled as a hollow cylinder with a length of 14 mm and inner and outer diameters of 2.5 and 3.75 mm, respectively. Following Gervaso et al. (2008), the material properties of the artery were represented as a third-order Mooney–Rivlin hyperelastic model based on circumferential, uniaxial tests carried out on non-diseased media from human coronary arteries with intimal thickening by Holzapfel et al. (2005). A four-crowned, corrugated-ring, stainless-steel stent was modelled with similar designs to those used by Garasic et al. (2000); the finite element (FE) mesh of the expanded and unexpanded stent is shown in figure 3. The stent was modelled as a bi-linear elasto-plastic material with the properties of stainless steel (Early et al. 2009). Physiological conditions in the artery were simulated by applying an axial pre-stretch of 1.2 and a blood pressure of 13.3 kPa prior to stent expansion. A radial displacement was applied to all nodes of a cylinder within the stent, expanding it from an initial diameter of 1.15 to 3.3 mm. This achieved an expansion ratio of 1.2. The cylinder was retracted allowing recoil of the stents. To reduce the simulation times, symmetry was used to reduce to a 1/8th model circumferentially (figure 3). Appropriate boundary conditions were applied along the planes of symmetry. The simulations were run using ABAQUS (SIMULIA) to determine the stresses in the artery following stent expansion.

Bottom Line: Tissue growth and differentiation requires simulating many of these cells together.The method is capable of capturing some of the most important aspects of restenosis, including nonlinear lesion growth with time.The approach taken in this paper provides a framework for simulating restenosis; the next step will be to couple it with more patient-specific geometries and quantitative parameter data.

View Article: PubMed Central - PubMed

Affiliation: Trinity Centre for Bioengineering, School of Engineering, Trinity College Dublin, Dublin, Republic of Ireland.

ABSTRACT
The design of medical devices could be very much improved if robust tools were available for computational simulation of tissue response to the presence of the implant. Such tools require algorithms to simulate the response of tissues to mechanical and chemical stimuli. Available methodologies include those based on the principle of mechanical homeostasis, those which use continuum models to simulate biological constituents, and the cell-centred approach, which models cells as autonomous agents. In the latter approach, cell behaviour is governed by rules based on the state of the local environment around the cell; and informed by experiment. Tissue growth and differentiation requires simulating many of these cells together. In this paper, the methodology and applications of cell-centred techniques--with particular application to mechanobiology--are reviewed, and a cell-centred model of tissue formation in the lumen of an artery in response to the deployment of a stent is presented. The method is capable of capturing some of the most important aspects of restenosis, including nonlinear lesion growth with time. The approach taken in this paper provides a framework for simulating restenosis; the next step will be to couple it with more patient-specific geometries and quantitative parameter data.

Show MeSH
Related in: MedlinePlus