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2019-09-20

This paper reviews the methods, advantages and challenges associated with the adoption and translation of computational fluid dynamics (CFD)

Models in cardiovascular medicine.

CFD is a professional field of mathematics and a branch of fluid mechanics, which is often used in different security areas

More and more widely used in key engineering systems of the cardiovascular system.

By promoting fast, economic, low

Risk prototyping and CFD Modeling have revolutionized the development of devices such as the stent, valve prosthesis, and cardiac AIDS.

Combined with cardiovascular imaging, CFD simulations are able to describe complex physiological pressures and flow fields in detail and calculate indicators that cannot be directly measured, such as wall shear stress.

CFD models are now being translated into clinical tools for doctors to use in various areas of coronary artery, Valve, congenital, myocardial and peripheral vascular disease.

CFD Modeling is a minimum

Invasive patient assessment. Patient-specific (

Contains data unique to individuals)and multi-scale (

Combining models of different lengthsand time-scales)

Modeling supports personalized risk prediction and virtual treatment planning.

This is very different from the traditional reliance on the registry --

Population basedaveraged data.

Model integration is gradually developing in the direction of \"digital patients\" or \"virtual physiological people.

Combined with population

These models have the potential to reduce costs, time and risk associated with clinical trials.

The adoption of CFD models marks a new era in cardiovascular medicine.

Some academic and business groups, while likely to be very beneficial, are addressing relevant approaches, regulatory, educational issuesand service-

Related challenges

Introduction to Computational Fluid Dynamics (CFD)is a well-

In many areas, it has become the main method of design and analysis.

Biological engineers have used CFD technology to study complex physiological flows and demonstrate their potential.

There is growing interest in applying these methods in cardiovascular medicine. 2 ,3 CFD-

Technology is used to construct complex computer representation (

In silicon chip)

Cardiovascular system in health and disease.

CFD Modeling is a new area in the field of cardiovascular medicine, which strengthens diagnostic evaluation, equipment design and clinical trials.

It can predict the physiological response to the intervention and calculate the blood flow parameters that were previously impossible to measure.

As CFD Modeling continues to translate into clinical tools, clinicians must understand the principles, advantages and limitations of these technologies.

This article uses state-of-the-

Examples of art in key clinical areas highlight applications that may affect clinical practice in the next 5 years (table 1).

View this table: View inline View pop-up table 1 Overview of CFD Modeling Applications in cardiovascular medicine what is CFD?

CFD is a professional field of mathematics and a branch of fluid mechanics.

It is used in many security designs.

Simulation of fluid flow by solving differential equations, key systems including aircraft and vehicles.

Table 2 provides a glossary of useful terms.

View this table: View inline View pop-up table 2 CFD-

For non-compressed flows, almost all CFD analysis solves Navi-

Stokes and continuity equations that control fluid motion.

These equations are right

Linear partial differential equations based on the principle of conservation of mass and momentum.

The simplification of these equations yields familiar formulas (

For example, these Buruli and posu leaves);

But the analysis solution is impossible for complex geometry, so specialized software applications (CFD solvers)

Approximate numerical solution is calculated. Non-

Because of the acceleration of convection fluid, linearity makes this problem challenging, especially in three dimensions (3D)models;

Therefore, CFD analysis requires a lot of computing power and time.

CFD model complexity the application reviewed in this paper focuses on 3D CFD analysis of local regions of blood vessels, as this is where promising applications begin to transform and influence clinical medicine.

There is a long history of simplifying the control equation to a lower spatial dimension.

Table 3 summarizes the relationship between these methods and provides clinical examples of their use.

View this table: View summary of various orders of CFD Modeling applied to 2d analysis of cardiovascular system in inline View pop-up table 3 generally assume the symmetry of the solution about the central axis, the 1D model only captures the change of the solution along the axial direction, and 0D indicates that the behavior of the vascular region is concentrated in a model with no spatial dimensions, so the term \"concentration\"

Parameter model.

Since the literature covering the application of these techniques in cardiovascular hemodynamic is very extensive, interested readers can refer to recent reviews of the countryof-the-art.

45. 46 model building cfd model building and solving can be described in seven stages (figure 1)

: The clinical image download map is reopened in the case of the new tabDownload powerpoint figure 1 Aorta (A)and coronary (B)

Computational fluid dynamics (CFD)workflows. (A)

Identification of aorta by chest MRI (a)

Segment reconstruction (central image).

A volume grid was created to fit the patient

Specific geometry displayed in detail in the panel (b).

Extract accurate flow measurement from phase

Contrast MRI data to inform the boundary conditions for CFD simulation, such as the inlet (c).

The result is after

After processing, the details of the flow field are displayed in the panel (d).

Export coupling of 0D model in physiological feasibility

Calculate the pressure relationship at the exit (e).

These can be verified by other measurements that may be invasive in pre-clinical situations. (B)(

Online video)

Coronary angiography (a)is segmented (b)

And re-build 3D in electronic models.

A surface and volume was manufactured to fit the patient

Specific geometry (c).

Physiological parameters such as pressure and flow are used to inform boundary conditions for CFD simulation (d). The results (

Pressure and flow here)are post-

Useful physiological data after extraction and processing (e).

Prior to clinical practice, the study sets the simulation results to be validated according to appropriate criteria, e. g. , invasive measurements (f). (

Additional information for video Legends)

: Etiheart is an academic program at the University of Sheffield funded by research (see virtuheart. com).

A range of medical imaging patterns can be used, including ultrasound, CT, MRI, and X-X-ray angiography

Imaging must provide sufficient anatomical and physiological details in an appropriate format and quality to achieve segmentation and data extraction.

49 segmentation and reconstruction segmentation methods convert medical images to electronic geometric shapes that define the physical boundaries of the region of the model of interest.

If the image is obtained in the heart cycle, anatomical movements can be tracked on the segmented area.

50, 51 discrete space discrete or \"grid\" divides the geometry into many discrete volume elements or units.

Time discrete divides the solution into discrete time steps.

The accuracy and numerical stability of the analysis are affected by the refinement of time and space.

The level of grid manufacturing and grid refinement is affected by the caseand context-

Specific factors.

Grid and time step (ie, spatio-

Discrete time)

It must be fine enough to capture the important kinetic behavior of the model Compartment (

The final solution should have nothing to do with the grid parameters)

, But there is no over-refinement, as this negatively affects the time of computing resources and solutions (

See online Supplement Table S1).

Since it is not possible to discrete the entire cardiovascular system, there is at least one entrance and one outlet in the area to be analyzed.

In order to achieve CFD analysis, physiological conditions of the wall and inlet/outlet boundaries must be specified.

The boundary condition is a set of physiological parameters applied (

May vary over time)

Define the physical conditions of the entrance, exit and wall.

They may be based on patients.

Specific data, demographic data, physical models, or assumptions.

39 computer files that simulate the physical parameters of the model are written.

In addition to geometric, discrete, and boundary data, the file must also define attributes, including: blood density and viscosity (

Fluid model)

Initial conditions of the system (

For example, is the fluid initially stationary or moving)

Time discrete information (

Time step and numerical approximation format)

, And expected output data (

For example, the number of heart cycles to be simulated).

This information allows the CFD solver to solve Navi-

Gradually towards the final solution (‘convergence’).

A typical 3D cardiovascular simulation involves running more than 1 million elements in several cardiac cycles, divided into hundreds of individual times per cyclesteps.

Millions of non

Linear partial differential equations are solved repeatedly at the same time on all elements-steps.

So 3D CFD Modeling is time

Consumption and computing requirements. Post-

Typically, CFD solvers generate pressure and velocity fields on all elements each time-step.

Only a small part of this data is of interest to the operator, so some later

Processing is required to extract and display relevant data.

18 it is important to verify the modeling results according to acceptable criteria.

Typically, this includes comparison with values measured in an in vitro model or those obtained in an in vivo evaluation.

4 validation creates confidence in the accuracy and reliability of CFD models.

The above steps are collectively referred to as workflows or toolchains (

See Figure 1 and online supplementary video).

Although there are a number of specialized software applications that contribute to the construction and operation of CFD

Based on the workflow, a considerable amount of skills and experience is required at each stage (

Step 3-5 in particular)

Ensure the reliability of the results.

49 Advanced boundary conditions, not the pressure or flow at the specified boundary, but the attached, lower-

The order model may be coupled with a 3D solver to generate more realistic conditions on the near and far sides of the simulation domain (

See Tables 1 and 2).

This modeling method is effective because it allows detailed analysis in 3D regions without wasting high temporal and spatial refinement in areas outside of it.

In some cases, the model representing the remote boundary also provides the remote boundary condition. These closed-

The loop model or system model needs to be adjusted very carefully.

52 download new tabDownload powerpointFigure2 patient

A specific 3D computational fluid dynamics model of the aorta. Patient-

The specific pressure is close to the boundary conditions. Each outlet (Remote boundary)

Coupling to zero-

Size model. The zero-

Size model representing impedance (Z), resistance (R)

Compliance/capacitance (C)

The loop at the far end of the boundary.

The output data of the 3D domain provides input for the 0D model and vice versa.

The 0D model of algebraic coding is calculated and returned to dynamically notify the parameters of the 3D simulation.

Another option is to coupling the 1D wave transmission model at the outlets, which may provide higher fidelity simulation results, especially in the aorta where physiology is affected by wave reflection.

Many CFD models assume that the split area has a rigid wall.

Although this approximation is not true in the cardiovascular system, it is acceptable in some applications.

53, 54 vascular compliance allows blood to be stored during contraction and released during expansion.

At the system level, this results in a limited speed at which the pressure wave travels and tends to reduce the peak pressure associated with the blood\'s inertial acceleration.

Compliance tends to reduce shear stress as the container is slightly larger when peak flow occurs.

Due to changes in the heart and breathing, deformation of the wall can be simulated in response to pressure changes using fluid

Solid interaction (FSI)models.

These problems are much more complex, boundary conditions are a challenge, and many Wall parameters are still unknown, which increases the number of assumptions.

In addition, a complete FSI method has not been established to improve the accuracy of the application.

Brown et al reported an example of an increase in the computational cost of FSI, in which 3D transient (time-varying)

Analysis of the required aorta 145 Thanh (FSI)

Contrast 6. 6u2005h (CFD).

Another way is to apply Wall movement from imaging data (eg, gated MRI).

The use of data assimilation technologies has been an exciting development in which sparse clinical data, such as 4D imaging, are integrated with the analytical process in order to make progress with the simulation, material properties of individual patient tissues were restored.

55 in the biomedical workflow, it is assumed that the boundary of the fluid geometry is smooth, but medical images may not produce a smooth surface due to poor resolution or imaging artifacts.

On the contrary, after segmentation, the structure can be smooth with electrons.

Typically, cardiovascular simulations assume that blood is present as an uncompressed fluid.

Although no blood

Newton\'s behavior (

See glossary in table 2)

These effects are generally ignored in larger containers, and Newton fluid models are assumed.

Benefits of cardiovascular CFD Modeling is able to study pressure and flow fields at time and space resolutions that cannot be achieved by any clinical approach. Post-

Processing provides additional data, generating new insights into physiological and disease processes.

For example, it is difficult and invasive to measure the wall shear stress of the artery (WSS)

A key factor in development

The CFD model can calculate the WSS and map its spatial distribution.

Work such as 20, 21 has established a link between the flow dynamics disorder and arterial production, and explained the preferred deposition of arterial plaques in the arterial flexion and bifurcation regions.

22 CFD Modeling is critical for our current understanding of the impact of WSS on the inner skin steady state:

Blood flow disorder is associated with an increase in WSS, which inhibits unnecessary endothelial cell activation;

However, turbulent or disturbed blood flow reduces WSS, thus stimulating adverse vascular remodeling.

A series of complex WSS

Behind this phenomenon is the related signaling pathways and interactions.

Being used in these ways

Their complexity requires a better understanding.

The integrated multi-scale CFD model provides a powerful tool to combine fluid dynamics with cell response analysis.

23 recently, the effects of interference with WSS in stent vessels have been simulated to study the effects on vascular endothelial function and new endometrial hyperplasia, which are preferentially accumulated in areas with low interference with WSS (figure 3).

26 This model can be used to develop the bracket to minimize the risk within the bracket

Stent stenosis and thrombosis

24. download the new tabDownload figureOpen powerpointFigure3 to calculate fluid dynamics (CFD)

Model showing the correlation between wall shear stress (WSS)

Re-stenosis of coronary artery disease(A)

Structural model of minimally reconstructed coronary stent insertion in pigs

Stent-arterial coupling obtained after CT and arterial posterior sitting. (B)

Comparison between in vivo histological images (left)

Corresponding section of structural simulation (right)

Good agreement. (C)

Based on the spatial distribution of the WSS magnitude on the arterial wall, the results of CFD simulations. (D)

Correlation between regions characterized by low WSS (orange lines)and in-

After 14 days, the bracket was narrowed again.

The CFD simulation of the WSS has identified areas that reduce shear and re-stenosis and are very consistent.

Under the friendly permission of Springer Science and commercial media, figures reproduced from Morlacchi et al 26.

Electronic device design methods allow rapid prototyping, reducing human risk, so a priority in the medical device industry is to replace expensive and time-consuming-

Consumed in vivo and in vitro experiments with electronic testing.

In response, the US Food and Drug Administration (FDA)

Draft guidance on the use of modeling to support regulatory submissions was released in 2014.

One example is the important role of CFD in the optimization of mechanical heart valve design.

11 although the comparison of the main flow features captured in vitro with CFD predictions shows good consistency, the simulation provides 3D information at a higher resolution in key areas (eg, the hinges)

Than flow visualization, providing valuable insights into design-

Related Potential for tying

10 comprehensive simulation of cardiac valve mechanics, including separation of upstream and downstream fluid regions at shutdown and structural instability and buckle

The dynamics of tissue valves are still computational challenging, but can be achieved.

9, 13 CFD has been used for optimization of several commercial cardiac AIDS (VADs)

, Investigate potential thrombosis possibilities by highlighting the design

Associated stagnation areas and device features that lead to high shear stress.

41. a recent comparative study of two continuously flowing VADs presented a tool to optimize the resistance of thrombosis, which combines experimental and numerical simulations.

40, 57 numerical models also contribute to the catheter implantation process, providing information for the selection of catheter implant sites.

39. in terms of support design, the greatest focus is on simulating the mechanical integrity of the support structure during and after deployment.

However, CFD provides a valuable tool to evaluate the resulting flow dynamics within the stent lesion.

26 This in turn is related to the biological reaction of the blood vessel wall and the development of re-stenosis.

27 diagnostic tools and personalized medical input in-vessel physiology with the aim of minimizing invasive devices are a major interest.

A good example is the fractional flow reserve (FFR)

Physiological indicators (

Coronary artery porridge

Significance of lesion measured by pressure

Sensitive vascular forming guide wire. FFR-

Guided therapy improves patient outcomes, reduces stent insertion, and reduces costs, but is used for

Models in cardiovascular medicine.

CFD is a professional field of mathematics and a branch of fluid mechanics, which is often used in different security areas

More and more widely used in key engineering systems of the cardiovascular system.

By promoting fast, economic, low

Risk prototyping and CFD Modeling have revolutionized the development of devices such as the stent, valve prosthesis, and cardiac AIDS.

Combined with cardiovascular imaging, CFD simulations are able to describe complex physiological pressures and flow fields in detail and calculate indicators that cannot be directly measured, such as wall shear stress.

CFD models are now being translated into clinical tools for doctors to use in various areas of coronary artery, Valve, congenital, myocardial and peripheral vascular disease.

CFD Modeling is a minimum

Invasive patient assessment. Patient-specific (

Contains data unique to individuals)and multi-scale (

Combining models of different lengthsand time-scales)

Modeling supports personalized risk prediction and virtual treatment planning.

This is very different from the traditional reliance on the registry --

Population basedaveraged data.

Model integration is gradually developing in the direction of \"digital patients\" or \"virtual physiological people.

Combined with population

These models have the potential to reduce costs, time and risk associated with clinical trials.

The adoption of CFD models marks a new era in cardiovascular medicine.

Some academic and business groups, while likely to be very beneficial, are addressing relevant approaches, regulatory, educational issuesand service-

Related challenges

Introduction to Computational Fluid Dynamics (CFD)is a well-

In many areas, it has become the main method of design and analysis.

Biological engineers have used CFD technology to study complex physiological flows and demonstrate their potential.

There is growing interest in applying these methods in cardiovascular medicine. 2 ,3 CFD-

Technology is used to construct complex computer representation (

In silicon chip)

Cardiovascular system in health and disease.

CFD Modeling is a new area in the field of cardiovascular medicine, which strengthens diagnostic evaluation, equipment design and clinical trials.

It can predict the physiological response to the intervention and calculate the blood flow parameters that were previously impossible to measure.

As CFD Modeling continues to translate into clinical tools, clinicians must understand the principles, advantages and limitations of these technologies.

This article uses state-of-the-

Examples of art in key clinical areas highlight applications that may affect clinical practice in the next 5 years (table 1).

View this table: View inline View pop-up table 1 Overview of CFD Modeling Applications in cardiovascular medicine what is CFD?

CFD is a professional field of mathematics and a branch of fluid mechanics.

It is used in many security designs.

Simulation of fluid flow by solving differential equations, key systems including aircraft and vehicles.

Table 2 provides a glossary of useful terms.

View this table: View inline View pop-up table 2 CFD-

For non-compressed flows, almost all CFD analysis solves Navi-

Stokes and continuity equations that control fluid motion.

These equations are right

Linear partial differential equations based on the principle of conservation of mass and momentum.

The simplification of these equations yields familiar formulas (

For example, these Buruli and posu leaves);

But the analysis solution is impossible for complex geometry, so specialized software applications (CFD solvers)

Approximate numerical solution is calculated. Non-

Because of the acceleration of convection fluid, linearity makes this problem challenging, especially in three dimensions (3D)models;

Therefore, CFD analysis requires a lot of computing power and time.

CFD model complexity the application reviewed in this paper focuses on 3D CFD analysis of local regions of blood vessels, as this is where promising applications begin to transform and influence clinical medicine.

There is a long history of simplifying the control equation to a lower spatial dimension.

Table 3 summarizes the relationship between these methods and provides clinical examples of their use.

View this table: View summary of various orders of CFD Modeling applied to 2d analysis of cardiovascular system in inline View pop-up table 3 generally assume the symmetry of the solution about the central axis, the 1D model only captures the change of the solution along the axial direction, and 0D indicates that the behavior of the vascular region is concentrated in a model with no spatial dimensions, so the term \"concentration\"

Parameter model.

Since the literature covering the application of these techniques in cardiovascular hemodynamic is very extensive, interested readers can refer to recent reviews of the countryof-the-art.

45. 46 model building cfd model building and solving can be described in seven stages (figure 1)

: The clinical image download map is reopened in the case of the new tabDownload powerpoint figure 1 Aorta (A)and coronary (B)

Computational fluid dynamics (CFD)workflows. (A)

Identification of aorta by chest MRI (a)

Segment reconstruction (central image).

A volume grid was created to fit the patient

Specific geometry displayed in detail in the panel (b).

Extract accurate flow measurement from phase

Contrast MRI data to inform the boundary conditions for CFD simulation, such as the inlet (c).

The result is after

After processing, the details of the flow field are displayed in the panel (d).

Export coupling of 0D model in physiological feasibility

Calculate the pressure relationship at the exit (e).

These can be verified by other measurements that may be invasive in pre-clinical situations. (B)(

Online video)

Coronary angiography (a)is segmented (b)

And re-build 3D in electronic models.

A surface and volume was manufactured to fit the patient

Specific geometry (c).

Physiological parameters such as pressure and flow are used to inform boundary conditions for CFD simulation (d). The results (

Pressure and flow here)are post-

Useful physiological data after extraction and processing (e).

Prior to clinical practice, the study sets the simulation results to be validated according to appropriate criteria, e. g. , invasive measurements (f). (

Additional information for video Legends)

: Etiheart is an academic program at the University of Sheffield funded by research (see virtuheart. com).

A range of medical imaging patterns can be used, including ultrasound, CT, MRI, and X-X-ray angiography

Imaging must provide sufficient anatomical and physiological details in an appropriate format and quality to achieve segmentation and data extraction.

49 segmentation and reconstruction segmentation methods convert medical images to electronic geometric shapes that define the physical boundaries of the region of the model of interest.

If the image is obtained in the heart cycle, anatomical movements can be tracked on the segmented area.

50, 51 discrete space discrete or \"grid\" divides the geometry into many discrete volume elements or units.

Time discrete divides the solution into discrete time steps.

The accuracy and numerical stability of the analysis are affected by the refinement of time and space.

The level of grid manufacturing and grid refinement is affected by the caseand context-

Specific factors.

Grid and time step (ie, spatio-

Discrete time)

It must be fine enough to capture the important kinetic behavior of the model Compartment (

The final solution should have nothing to do with the grid parameters)

, But there is no over-refinement, as this negatively affects the time of computing resources and solutions (

See online Supplement Table S1).

Since it is not possible to discrete the entire cardiovascular system, there is at least one entrance and one outlet in the area to be analyzed.

In order to achieve CFD analysis, physiological conditions of the wall and inlet/outlet boundaries must be specified.

The boundary condition is a set of physiological parameters applied (

May vary over time)

Define the physical conditions of the entrance, exit and wall.

They may be based on patients.

Specific data, demographic data, physical models, or assumptions.

39 computer files that simulate the physical parameters of the model are written.

In addition to geometric, discrete, and boundary data, the file must also define attributes, including: blood density and viscosity (

Fluid model)

Initial conditions of the system (

For example, is the fluid initially stationary or moving)

Time discrete information (

Time step and numerical approximation format)

, And expected output data (

For example, the number of heart cycles to be simulated).

This information allows the CFD solver to solve Navi-

Gradually towards the final solution (‘convergence’).

A typical 3D cardiovascular simulation involves running more than 1 million elements in several cardiac cycles, divided into hundreds of individual times per cyclesteps.

Millions of non

Linear partial differential equations are solved repeatedly at the same time on all elements-steps.

So 3D CFD Modeling is time

Consumption and computing requirements. Post-

Typically, CFD solvers generate pressure and velocity fields on all elements each time-step.

Only a small part of this data is of interest to the operator, so some later

Processing is required to extract and display relevant data.

18 it is important to verify the modeling results according to acceptable criteria.

Typically, this includes comparison with values measured in an in vitro model or those obtained in an in vivo evaluation.

4 validation creates confidence in the accuracy and reliability of CFD models.

The above steps are collectively referred to as workflows or toolchains (

See Figure 1 and online supplementary video).

Although there are a number of specialized software applications that contribute to the construction and operation of CFD

Based on the workflow, a considerable amount of skills and experience is required at each stage (

Step 3-5 in particular)

Ensure the reliability of the results.

49 Advanced boundary conditions, not the pressure or flow at the specified boundary, but the attached, lower-

The order model may be coupled with a 3D solver to generate more realistic conditions on the near and far sides of the simulation domain (

See Tables 1 and 2).

This modeling method is effective because it allows detailed analysis in 3D regions without wasting high temporal and spatial refinement in areas outside of it.

In some cases, the model representing the remote boundary also provides the remote boundary condition. These closed-

The loop model or system model needs to be adjusted very carefully.

52 download new tabDownload powerpointFigure2 patient

A specific 3D computational fluid dynamics model of the aorta. Patient-

The specific pressure is close to the boundary conditions. Each outlet (Remote boundary)

Coupling to zero-

Size model. The zero-

Size model representing impedance (Z), resistance (R)

Compliance/capacitance (C)

The loop at the far end of the boundary.

The output data of the 3D domain provides input for the 0D model and vice versa.

The 0D model of algebraic coding is calculated and returned to dynamically notify the parameters of the 3D simulation.

Another option is to coupling the 1D wave transmission model at the outlets, which may provide higher fidelity simulation results, especially in the aorta where physiology is affected by wave reflection.

Many CFD models assume that the split area has a rigid wall.

Although this approximation is not true in the cardiovascular system, it is acceptable in some applications.

53, 54 vascular compliance allows blood to be stored during contraction and released during expansion.

At the system level, this results in a limited speed at which the pressure wave travels and tends to reduce the peak pressure associated with the blood\'s inertial acceleration.

Compliance tends to reduce shear stress as the container is slightly larger when peak flow occurs.

Due to changes in the heart and breathing, deformation of the wall can be simulated in response to pressure changes using fluid

Solid interaction (FSI)models.

These problems are much more complex, boundary conditions are a challenge, and many Wall parameters are still unknown, which increases the number of assumptions.

In addition, a complete FSI method has not been established to improve the accuracy of the application.

Brown et al reported an example of an increase in the computational cost of FSI, in which 3D transient (time-varying)

Analysis of the required aorta 145 Thanh (FSI)

Contrast 6. 6u2005h (CFD).

Another way is to apply Wall movement from imaging data (eg, gated MRI).

The use of data assimilation technologies has been an exciting development in which sparse clinical data, such as 4D imaging, are integrated with the analytical process in order to make progress with the simulation, material properties of individual patient tissues were restored.

55 in the biomedical workflow, it is assumed that the boundary of the fluid geometry is smooth, but medical images may not produce a smooth surface due to poor resolution or imaging artifacts.

On the contrary, after segmentation, the structure can be smooth with electrons.

Typically, cardiovascular simulations assume that blood is present as an uncompressed fluid.

Although no blood

Newton\'s behavior (

See glossary in table 2)

These effects are generally ignored in larger containers, and Newton fluid models are assumed.

Benefits of cardiovascular CFD Modeling is able to study pressure and flow fields at time and space resolutions that cannot be achieved by any clinical approach. Post-

Processing provides additional data, generating new insights into physiological and disease processes.

For example, it is difficult and invasive to measure the wall shear stress of the artery (WSS)

A key factor in development

The CFD model can calculate the WSS and map its spatial distribution.

Work such as 20, 21 has established a link between the flow dynamics disorder and arterial production, and explained the preferred deposition of arterial plaques in the arterial flexion and bifurcation regions.

22 CFD Modeling is critical for our current understanding of the impact of WSS on the inner skin steady state:

Blood flow disorder is associated with an increase in WSS, which inhibits unnecessary endothelial cell activation;

However, turbulent or disturbed blood flow reduces WSS, thus stimulating adverse vascular remodeling.

A series of complex WSS

Behind this phenomenon is the related signaling pathways and interactions.

Being used in these ways

Their complexity requires a better understanding.

The integrated multi-scale CFD model provides a powerful tool to combine fluid dynamics with cell response analysis.

23 recently, the effects of interference with WSS in stent vessels have been simulated to study the effects on vascular endothelial function and new endometrial hyperplasia, which are preferentially accumulated in areas with low interference with WSS (figure 3).

26 This model can be used to develop the bracket to minimize the risk within the bracket

Stent stenosis and thrombosis

24. download the new tabDownload figureOpen powerpointFigure3 to calculate fluid dynamics (CFD)

Model showing the correlation between wall shear stress (WSS)

Re-stenosis of coronary artery disease(A)

Structural model of minimally reconstructed coronary stent insertion in pigs

Stent-arterial coupling obtained after CT and arterial posterior sitting. (B)

Comparison between in vivo histological images (left)

Corresponding section of structural simulation (right)

Good agreement. (C)

Based on the spatial distribution of the WSS magnitude on the arterial wall, the results of CFD simulations. (D)

Correlation between regions characterized by low WSS (orange lines)and in-

After 14 days, the bracket was narrowed again.

The CFD simulation of the WSS has identified areas that reduce shear and re-stenosis and are very consistent.

Under the friendly permission of Springer Science and commercial media, figures reproduced from Morlacchi et al 26.

Electronic device design methods allow rapid prototyping, reducing human risk, so a priority in the medical device industry is to replace expensive and time-consuming-

Consumed in vivo and in vitro experiments with electronic testing.

In response, the US Food and Drug Administration (FDA)

Draft guidance on the use of modeling to support regulatory submissions was released in 2014.

One example is the important role of CFD in the optimization of mechanical heart valve design.

11 although the comparison of the main flow features captured in vitro with CFD predictions shows good consistency, the simulation provides 3D information at a higher resolution in key areas (eg, the hinges)

Than flow visualization, providing valuable insights into design-

Related Potential for tying

10 comprehensive simulation of cardiac valve mechanics, including separation of upstream and downstream fluid regions at shutdown and structural instability and buckle

The dynamics of tissue valves are still computational challenging, but can be achieved.

9, 13 CFD has been used for optimization of several commercial cardiac AIDS (VADs)

, Investigate potential thrombosis possibilities by highlighting the design

Associated stagnation areas and device features that lead to high shear stress.

41. a recent comparative study of two continuously flowing VADs presented a tool to optimize the resistance of thrombosis, which combines experimental and numerical simulations.

40, 57 numerical models also contribute to the catheter implantation process, providing information for the selection of catheter implant sites.

39. in terms of support design, the greatest focus is on simulating the mechanical integrity of the support structure during and after deployment.

However, CFD provides a valuable tool to evaluate the resulting flow dynamics within the stent lesion.

26 This in turn is related to the biological reaction of the blood vessel wall and the development of re-stenosis.

27 diagnostic tools and personalized medical input in-vessel physiology with the aim of minimizing invasive devices are a major interest.

A good example is the fractional flow reserve (FFR)

Physiological indicators (

Coronary artery porridge

Significance of lesion measured by pressure

Sensitive vascular forming guide wire. FFR-

Guided therapy improves patient outcomes, reduces stent insertion, and reduces costs, but is used for

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