Systems and methods for processing electronic images

Abstract

A noninvasive patient-specific method is provided to aid in the analysis, diagnosis, prediction or treatment of hemodynamics of the cardiovascular system of a patient. Coronary blood flow and pressure can be predicted using a 3-D patient image-based model that is implicitly coupled with a model of at least a portion of the remaining cardiovascular system. The 3-D patient image-based model includes at least a portion of the thoracic aorta and epicardial coronaries of the patient. The shape of one or more velocity profiles at the interface of the models is enforced to control complex flow features of recirculating or retrograde flow thereby minimizing model instabilities and resulting in patient-specific predictions of coronary flow rate and pressure. The invention allows for patient-specific predictions of the effect of different or varying physiological states and hemodynamic benefits of coronary medical interventions, percutaneous coronary interventions and surgical therapies.

Description

FIELD OF THE INVENTION

This invention relates to computer-implemented methods of quantifying and predicting hemodynamic aspects of the cardiovascular system to aid in the diagnosis and treatment of coronary diseases.

BACKGROUND

The number of patients with coronary artery disease continues to rise resulting in a third of global deaths and afflicting over seventeen million individuals in the United States alone. Once patients are diagnosed with coronary artery disease using medical imaging techniques including angiography, ultrasound, and computed tomography, they are treated with medications, lifestyle changes, interventional procedures, or open heart surgery depending on the severity of their disease. Flow rate and pressure in coronary arteries are measured invasively during interventional procedures or open heart surgery. However,, the information obtainable from the medical imaging techniques and the invasive flow and pressure measurement techniques are limited because the resolution of the medical imaging techniques are sufficient to visualize only the larger arteries, and the flow and pressure measurement techniques are highly invasive and restricted by time and the physical condition of the patient. However, information on coronary flow rate and the pressure of the coronary vascular beds of a patient is crucial to select treatments.

Computational simulations have proven to be useful in studying blood flow in the cardiovascular system and include research on the hemodynamics of healthy and diseased blood vessels, the design and evaluation of cardiovascular medical devices, planning of cardiovascular surgeries, and the prediction of the outcomes of interventions. However, computational simulations have been rarely used to predict pulsatile velocity and pressure fields of three-dimensional coronary vascular beds, in part because the flow rate and pressure in the coronary vascular beds are highly related to the interactions between the heart and the arterial system. Unlike flow in other parts of the arterial system, coronary flow decreases when the ventricles contract, increasing the intramyocardial pressure. Coronary flow increases when the ventricles relax, thereby, decreasing the intramyocardial pressure. Therefore, to model coronary flow and pressure realistically, it is necessary to have a model of the heart and a model of the arterial system with consideration of the interactions between the two models.

Because of this complexity in modeling coronary flow and pressure, most three-dimensional computational studies have been conducted with coronary arteries only, ignoring the interactions between the heart and the arterial system and prescribing, not predicting, coronary flow. Further, these studies have not modeled realistic pressures and generally use traction-free outlet boundary conditions. The analytic models used as boundary conditions were coupled explicitly, necessitating either sub-iterations within the same time step or a small time step size bounded by the stability of an explicit time integration scheme. To predict the flow rate and the pressure in the coronary arterial, trees of a patient realistically, computational simulations should be robust and stable enough to handle complex flow characteristics, and the coupling should be efficient and versatile to different scales of computer models.

In view of the above, there remains a need in the art for new and improved techniques for more realistic computer models of coronary flow rate and pressure.

SUMMARY OF THE INVENTION

The invention provides a noninvasive patient-specific method for aiding in the analysis, diagnosis, prediction or treatment of hemodynamics of the cardiovascular system of a patient. Coronary blood flow and pressure can be predicted using a 3-D patient image-based model that is implicitly coupled with a model of at least a portion of the remaining cardiovascular system (e.g. a lumped parameter heart model, a lumped parameter systemic vascular model, a lumped parameter pulmonary vascular model, or any combination thereof). Implicit coupling between the models in this invention is defined as the simultaneous solution at each instant of time of the equations of blood flow in both the 3-D patient image-based model and the model for at least a portion of the remainder of the cardiovascular system. The 3-D patient image-based model includes at least a portion of the thoracic aorta of the patient and at least a portion of the epicardial coronary arteries of the patient. 3-D anatomical and/or physiological data for the 3-D patient-image based model is preferably obtained via non-invasive imaging techniques or systems such as, but not limited to, computed tomography or magnetic resonance imaging. The shape of one or more shape velocity profiles at the interface of the models is enforced to have a predetermined form to control recirculating flow features or retrograde flow to, minimize model instabilities and resulting in patient-specific predictions of coronary flow rate and pressure. The invention, which is implemented in a computer system, allows for patient-specific predictions of coronary flow rate, and pressure under different or varying physiological states (e.g. rest, exercise, pharmacologic-induced stress, or the like) and by simulating one or more hemodynamic benefits of coronary medical interventions, percutaneous coronary interventions and surgical therapies.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a method and system according to an embodiment of the invention including a patient imaging system either directly or indirectly coupled to a computer system providing 3-D patient images for the 3-D patient image-based model. The 3-D patient image-based model is implicitly coupled to a model of at least a portion of the remaining cardiovascular (CV) system. The shape of at least one of the velocity profiles is enforced at the interface between the models. Coronary blood flow rate and pressure is calculated using the computer-implemented models and displayed or outputted in computer graphics or data.

FIG. 2 is a schematic of a model of the cardiovascular system according to an embodiment of the invention including lumped parameter models of the right and left atria and ventricles. The aortic inlet is coupled to the lumped parameter model of the left ventricle at (A). All the outlets of the three-dimensional computational model feed back in the lumped model at (V). The lumped parameter models coupled to the inlet, upper branch vessels, the descending thoracic aorta, and coronary outlets for simulations of blood flow in a normal thoracic aorta model with coronary outlets under rest and exercise conditions are shown on the right. Note that all the outlets of the three-dimensional computational model, feed back in the lumped model at (V).

FIG. 3 shows inlet A coupled to a lumped parameter heart model according to an embodiment of the invention.

FIG. 4 shows outlets B-H coupled to three-element Windkessel models according to an embodiment of the invention.

FIG. 5 shows coronary outlets a-k coupled to lumped parameter coronary vascular models according to an embodiment of the invention.

DETAILED DESCRIPTION

Methods to calculate flow and pressure in three-dimensional coronary vascular beds are provided by considering a hybrid numerical/analytic closed loop system. For each coronary outlet of the three-dimensional finite element model, a lumped parameter coronary vascular bed model was coupled and the impedance of downstream coronary vascular networks not modeled explicitly in the computational domain was approximated. Similarly, Windkessel models were assigned to the upper branch vessels and the descending thoracic aorta to represent the rest of the arterial system. For the inlet, a lumped parameter heart model was coupled that completes a closed-loop description of the system. Using the heart model, it is possible to compute the compressive forces acting on the coronary vascular beds throughout the cardiac cycle. Further, the shape of velocity profiles of the inlet and outlet boundaries with retrograde flow was enforced to minimize numerical instabilities. The computer models solved for coronary flow and pressure as well as aortic flow and pressure in subject-specific models by considering the interactions between these model of the heart, the impedance of the systemic arterial system and the pulmonary system, and the impedance of coronary vascular beds.

Three-Dimensional Finite Element Model of Blood Flow and Vessel Wall Dynamics

Blood flow in the large vessels of the cardiovascular system can be approximated by a Newtonian fluid. Blood flow can then be solved using the incompressible Navier-Stokes equations and the motion of the vessel wall was modeled using the elastodynamics equations.

For a fluid domain Ω with boundary Γ and solid domain Ω^s with boundary Γ^s, we solve for velocity {right arrow over (v)}({right arrow over (x)}, t), pressure p({right arrow over (x)}, t), and wall displacement {right arrow over (u)}({right arrow over (x)}^s, t) as follows:

Given {right arrow over (f)}:Ω×(0, T)→³, {right arrow over (f)}^s:Ω^s×(0, T)→³, {right arrow over (g)}:Γ_g×(0, T)→³,{right arrow over (g)}^s:Γ_g^s×(0, T)→³, {right arrow over (v)}₀:Ω→³, {right arrow over (u)}₀:Ω^s→³, and {right arrow over (u)}_0,t:Ω^s→³, find {right arrow over (v)}({right arrow over (x)}, t), p({right arrow over (x)}, t), and {right arrow over (u)}({right arrow over (x)}^s, t) for ∀{right arrow over (x)}ϵΩ, ∀{right arrow over (x)}^sϵΩ^s, and ∀tϵ(0, T) such that the following conditions are satisfied:ρ{right arrow over (v)}_,t+ρ{right arrow over (v)}·∇{right arrow over (v)}=−∇p+div({tilde under (τ)})+{right arrow over (f)} for ({right arrow over (x)}, t)ϵΩ×(0, T)div({right arrow over (v)})=0 for ({right arrow over (x)}, t)ϵΩ×(0, T)ρ^s{right arrow over (u,)}_tt=∇·{tilde under (σ)}^s+{right arrow over (f)}^s for ({right arrow over (x)}^s, t)ϵΩ^s×(0, T)where {tilde under (σ)}=μ(∇{right arrow over (v)}+(∇{right arrow over (v)})^T) and {tilde under (σ)}^s={tilde under (C)}:½(∇{right arrow over (u)}+(∇{right arrow over (u)})^T)   (1)with the Dirichlet boundary conditions,{right arrow over (v)}({right arrow over (x)}, t)={right arrow over (g)}({right arrow over (x)}, t) for ({right arrow over (x)}, t)ϵΓ_g×(0, T){right arrow over (u)}({right arrow over (x)}^s, t)={right arrow over (g)}^s({right arrow over (x)}^s, t) for ({right arrow over (x)}^s, t)ϵΓ_g^s×(0, T)   (2)the Neumann boundary conditions,{right arrow over (t)}_{{right arrow over (n)}}={−p{tilde under (I)}+{tilde under (τ)}}{right arrow over (n)}={right arrow over (h)}({right arrow over (v)}, p, {right arrow over (x)}, t) for {right arrow over (x)}ϵΓ_h   (3)and the initial conditions,{right arrow over (v)}({right arrow over (x)}, 0)={right arrow over (v)}₀({right arrow over (x)}) for {right arrow over (x)}ϵΩ{right arrow over (u)}({right arrow over (x)}^s, 0)={right arrow over (u)}₀({right arrow over (x)}^s) for {right arrow over (x)}^sϵΩ^s {right arrow over (u)}_,t({right arrow over (x)}^s, 0)={right arrow over (u)}_0,t({right arrow over (x)}^s) for {right arrow over (x)}^sϵΩ^s   (4)

For fluid-solid interface conditions, the conditions implemented in the coupled momentum method were used with a fixed fluid mesh assuming small displacements of the vessel wall.

The density ρ and the dynamic viscosity μ of the fluid, and the density ρ^s of the vessel walls are assumed to be constant. The external body force on the fluid domain is represented by {right arrow over (f)}. Similarly, {right arrow over (f)}^s is the external body force on the solid domain, {tilde under (C)} is a fourth-order tensor of material constants, and {tilde under (σ)}^s is the vessel wall stress tensor.

A stabilized semi-discrete finite element method was utilized to use the same order piecewise polynomial spaces for velocity and pressure variables.

Boundary Conditions

The boundary Γ of the fluid domain is divided into a Dirichlet boundary portion Γ_g and a Neumann boundary portion Γ_h. Further, the Neumann boundary portion Γ_h is divided into coronary surfaces Γ_hcor, inlet surface Γ_in, and the set of other outlet surfaces Γ′_h, such that (Γ_hcor∩Γ_in∩Γ′_h)=Γ_h and Γ_hcor∩Γ_in∩Γ′_h=ϕ. Note that in this example, when the aortic valve is open, the inlet surface is included in the Neumann boundary portion Γ_h, not in the Dirichlet boundary portion Γ_g, to enable coupling with a lumped parameter heart model. Therefore, the Dirichlet boundary portion Γ_g only includes the inlet and outlet rings of the computational domain when the aortic valve is open. These rings are fixed in time and space.

Boundary Conditions for Coronary Outlets

To represent the coronary vascular beds absent in the computational domain, a lumped parameter coronary vascular model was used (FIGS. 2-5). The coronary venous microcirculation compliance was eliminated from the original model to simplify the numerics without affecting the shape of the flow and pressure waveforms significantly. Each coronary vascular bed model includes coronary arterial resistance R_a, coronary arterial compliance C_a, coronary arterial microcirculation resistance R_a-micro, myocardial compliance C_im, coronary venous microcirculation resistance R_v-micro, coronary venous resistance R_v, and intramyocardial pressure P_im(t).

For each coronary outlet

${\Gamma }_{h_{{\mathrm{cor}}_k}}$ of the three-dimensional finite element model where

${\Gamma }_{h_{{\mathrm{cor}}_k}}\subseteq {\Gamma }_{h_{\mathrm{cor}}},$ the lumped parameter coronary vascular model was implicitly coupled using the continuity of mass and momentum operators of the coupled multidomain method as follows:

$\begin{array}{cc}\begin{array}{c}\left[{\underset{~}{M}}_m\left(\overrightarrow{\upsilon },p\right)+{\underset{~}{H}}_m\right]=-\left(\begin{array}{c}R{\int }_{{\Gamma }_{h{\mathrm{cor}}_k}}\overrightarrow{\upsilon }\left(t\right)·\overrightarrow{n}d\Gamma +\\ {\int }_0^t{e}^{{\lambda }_1\left(t-s\right)}{Z}_1{\int }_{{\Gamma }_{h{\mathrm{cor}}_k}}\overrightarrow{\upsilon }\left(s\right)·\overrightarrow{n}d\Gamma \mathrm{ds}\end{array}\right)\underset{~}{I}+\\ \left({\int }_0^t{e}^{{\lambda }_2\left(t-s\right)}{Z}_2{\int }_{{\Gamma }_{h{\mathrm{cor}}_k}}\overrightarrow{\upsilon }\left(s\right)·\overrightarrow{n}d\Gamma \mathrm{ds}-\overrightarrow{n}·\underset{~}{\tau }·\overrightarrow{n}\right)\underset{~}{I}+\\ \underset{~}{\tau }-\left({\mathrm{Ae}}^{{\lambda }_{1}t}+{\mathrm{Be}}^{{\lambda }_{2}t}\right)\underset{~}{I}-\\ ({\int }_0^t{e}^{{\lambda }_1\left(t-s\right)}·Y_1{P}_{\mathrm{im}}\left(s\right)\mathrm{ds}+{\int }_0^t{e}^{{\lambda }_2\left(t-s\right)}·\\ Y_2{P}_{\mathrm{im}}\left(s\right)\mathrm{ds})\underset{~}{I}\\ \left[{\overrightarrow{M}}_c\left(\overrightarrow{\upsilon },p\right)+{\overrightarrow{H}}_c\right]=\overrightarrow{\upsilon }\end{array}& \left(5\right)\end{array}$ where the parameters R, Z₁, Z₂, A, B, Y₁, Y₂, λ₁, λ₂ are derived from the lumped parameter coronary vascular models.

The intramyocardial pressure P_im representing the compressive force acting on the coronary vessels due to the contraction and relaxation of the left and right ventricles was modeled with either the left or right ventricular pressure depending on the location of the coronary arteries. Both the left and right ventricular pressures were computed from two lumped parameter heart models of the closed loop system (FIGS. 2-3).

Boundary Conditions for the Inlet

The left and right sides of the heart were modeled using a lumped parameter heart model. Each heart model includes a constant atrial elastance E_A, atrio-ventricular valve, atrio-ventricular valvular resistance R_A-V, atrio-ventricular inductance L_A-V, ventriculo-arterial valve, ventriculo-arterial valvular resistance R_V-art, ventriculo-arterial inductance L_V-art, and time-varying ventricular elastance E(t). An atrio-ventricular inductance L_A-V and ventriculo-arterial inductance L_V-art were added, to the model to approximate the inertial effects of blood flow.

The time-varying elastance E(t) models the contraction and relaxation of the left and right ventricles. Elastance is the instantaneous ratio of ventricular pressure P_v(t) and ventricular volume V_v(t) according to the following equation:P_v(t)=E(t)·{V_v(t)−V₀}  (6)

Here, V₀ is a constant correction volume, which is recovered when the ventricle is unloaded.

Each elastance function is derived by scaling a normalized elastance function, which remains unchanged regardless of contractility, vascular loading, heart rate and heart disease to approximate the measured cardiac output, pulse pressure and contractility of each subject.

The left side of the heart lumped parameter model is coupled to the inlet of the finite element model using a coupled multidomain method when the aortic valve is open as follows:

$\begin{array}{cc}\begin{array}{c}{\left[{\underset{~}{M}}_m\left(\overrightarrow{\upsilon },p\right)+{\underset{~}{H}}_m\right]}_{{\Gamma }_{in}}=-E\left(t\right)·\left\{\begin{array}{c}{V}_{\upsilon }\left(t_{\mathrm{ao},\mathrm{LV}}\right)+\\ {\int }_{t_{\mathrm{ao},\mathrm{LV}}}^t{\int }_{{\Gamma }_{in}}\overrightarrow{\upsilon }·\overrightarrow{n}d\Gamma \mathrm{ds}-V_{\mathrm{LV},0}\end{array}\right\}\underset{~}{I}-\\ \left(R_{\mathrm{LV}-\mathrm{art}}+L_{\mathrm{LV}-\mathrm{art}}\frac{d}{\mathrm{dt}}\right)\\ {\int }_{{\Gamma }_{in}}\overrightarrow{\upsilon }·\overrightarrow{n}d\Gamma \underset{~}{I}+\underset{~}{\tau }-\left(\overrightarrow{n}·\underset{~}{\tau }·\overrightarrow{n}\right)\underset{~}{I}\\ {\left[{\overrightarrow{M}}_c\left(\overrightarrow{\upsilon },p\right)+{\overrightarrow{H}}_c\right]}_{{\Gamma }_{in}}=\overrightarrow{\upsilon }{|}_{{\Gamma }_{in}}\end{array}& \left(7\right)\end{array}$

Here, t_ao,LV is the time the aortic valve opens. When the valve is closed, the inlet boundary is switched to a Dirichlet boundary and assigned a zero velocity condition.

Boundary Conditions for Other Outlets

For the other boundaries Γ^′_h, we used the same method to couple three-element Windkessel models and modeled the continuity of momentum and mass using the following operators:

$\begin{array}{c}{\left[{\underset{~}{M}}_m\left(\overrightarrow{\upsilon },p\right)+{\underset{~}{H}}_m\right]}_{{\Gamma }_h^{\prime }}=-\left\{\begin{array}{c}{R}_p{\int }_{{\Gamma }_h^{\prime }}\overrightarrow{\upsilon }·\overrightarrow{n}\mathrm{ds}+\left(R_p+R_d\right)\\ {\int }_0^t\frac{e^{-(t-t_1}/\tau }{C}{\int }_{{\Gamma }_h^{\prime }}\overrightarrow{\upsilon }·\overrightarrow{n}d{\mathrm{sdt}}_1\end{array}\right\}\underset{~}{I}+\\ \left\{\begin{array}{c}\left(P\left(0\right)-R{\int }_{{\Gamma }_h^{\prime }}\upsilon \left(0\right)·\overrightarrow{n}ds-P_d\left(0\right)\right)e^{-t/r}-\\ P_d\left(t\right)\end{array}\right\}\underset{~}{I}+\\ \underset{~}{\tau }-\overrightarrow{n}·\underset{~}{\tau }·\overrightarrow{n}\underset{~}{I}\\ {\left[{\overrightarrow{M}}_c\left(\overrightarrow{\upsilon },p\right)+{\overrightarrow{H}}_c\right]}_{{\Gamma }_h^{\prime }}=\overrightarrow{\upsilon }\end{array}$

Closed Loop Model

The boundary conditions combined with the three-dimensional finite element model of the aorta may include a closed loop model of the cardiovascular system. In most cases, the closed loop model has two lumped parameter heart models representing the left and right sides of the heart, a three-dimensional finite element model of the aorta with coronary arteries, three-element Windkessel models and lumped parameter coronary vascular models that represent the rest of the systemic circulation, and a lumped parameter model to approximate the pulmonary circulation. This closed loop model can be used to compute the right ventricular pressure, which is used to approximate the intramyocardial pressure acting on the right coronary arteries.

Parameter Values

Choice of the Parameter Values for Coronary Boundary Conditions

The boundary condition parameters determining the mean flow to each primary branch of the coronary arteries can be'determined using morphology data and data from the literature. In one example, the mean coronary flow was assumed to be 4.0% of the cardiac output. For each coronary outlet surface, coronary venous resistance was calculated on the basis of the mean flow and assigned venous pressure according to literature data. The coronary arterial resistance and coronary arterial microcirculation resistance was obtained on the basis of mean flow, mean arterial pressure, and the coronary impedance spectrum using literature data. The capacitance values were adjusted to give physiologically realistic coronary flow and pressure waveforms.

In an example during simulated exercise, the mean flow to the coronary vascular bed was, increased to maintain the mean flow at 4.0% of the cardiac output. The coronary parameter values for each coronary outlet surface were modified by increasing the capacitances, and the ratio of the coronary arterial resistance to the total coronary resistance.

Choice of the Parameter Values for the Inflow Boundary Condition

The parameter values of the lumped parameter heart model according to one example were determined as follows:

$t_{\mathrm{ma}x,\mathrm{LV}}=t_{\mathrm{ma}x,\mathrm{RV}}=\{\begin{array}{cc}\frac{T}{3},& \mathrm{at}\mathrm{rest},\mathrm{where}T\mathrm{is}\mathrm{the}\mathrm{measured}\mathrm{cardiac}\mathrm{cycle}.\\ 0.5T,& \mathrm{during}\mathrm{exercise}.\end{array}{E}_{\mathrm{ma}x,\mathrm{LV}}=\frac{\gamma ·R_S}{T},$ where R_S is total resistance of systemic circulation and 1≤γ≤2.

$E_{\mathrm{ma}x,\mathrm{RV}}=\frac{\gamma ·R_P}{T},$ where R_P is total resistance of pulmonary circulation and 1≤γ≤2.

$V_{\mathrm{LV},0}=V_{\mathrm{LV},\mathrm{esv}}-\frac{0.9P_{\mathrm{sys}}}{E_{\mathrm{ma}x,\mathrm{LV}}},$ where V_LV,esv is an end-systolic volume of left ventricle and P_sys is an aortic systolic pressure.

Choice of the Parameter Values for Other Outlet Boundary Conditions

For the upper branch vessels and the descending thoracic aorta in one model, three-element Windkessel models were adjusted to match mean flow distribution and the measured brachial artery pulse pressure by modifying the total resistance, capacitance, and the ratio between the proximal resistance and distal resistance based on literature data.

Constraining Shape of Velocity Profiles to Stabilize Blood Flow Simulations

Using these sets of boundary conditions, in one example, the physiologic coronary flow of subject-specific computer models were simulated. When we first simulated blood flow in complex subject-specific models with high mesh resolutions, however, we encountered instabilities in the outlet boundaries caused by complex flow structures; such as retrograde flow or complex flow propagating to the outlets from the interior domain due to vessel curvature or branches.

To resolve these instabilities, the invention further provides an augmented Lagrangian method to enforce the shape of the velocity profiles of the inlet boundary and the outlet boundaries with complex flow features or retrograde flow. The constraint functions enforce a shape of the velocity profile on a part of Neumann partition Γ_hk and minimize in-plane velocity components:

$\begin{array}{cc}{c}_{k1}\left(\stackrel{->}{v},\stackrel{->}{x},t\right)={\alpha }_k{\int }_{{\Gamma }_{h_k}}^{}{\left(\stackrel{->}{v}\left(\stackrel{->}{x},t\right)·\stackrel{->}{n}-{\Phi }_k\left(\stackrel{->}{v}\left(\stackrel{->}{x},t\right),\stackrel{->}{x},t\right)\right)}^2\mathrm{ds}=0\stackrel{->}{x}\in {\Gamma }_{h_k}{c}_{\mathrm{ki}}\left(\stackrel{->}{v},\stackrel{->}{x},t\right)={\alpha }_k{\int }_{{\Gamma }_{h_k}}^{}{\left(\stackrel{->}{v}\left(\stackrel{->}{x},t\right)·\stackrel{->}{t_i}\right)}^2\mathrm{ds}=0\mathrm{for}i=2,3& \left(8\right)\end{array}$

Here, Φ_k({right arrow over (v)}({right arrow over (x)}, t), {right arrow over (x)}, t) defines the shape of the normal velocity profile, {right arrow over (n)} is the unit normal vector of face Γ_hk. {right arrow over (t)}₂ and {right arrow over (t)}₃ are unit in-plane vectors which are orthogonal to each other and to the unit normal vector {right arrow over (n)} at face Γ_hk. α_k is used to nondimensionalize the constraint functions.

The boxed terms below are added to the weak form of the governing equations of blood flow and wall dynamics. The weak form becomes:

Find {right arrow over (v)}ϵ, pϵ and {right arrow over (λ)}₁, {right arrow over (λ)}₂, . . . , {right arrow over (λ)}_ncϵ(L²(0, T))^nsd, {right arrow over (K)}_kϵ^nsd+, Penalty numbers where k=1 . . . , n_c, and {right arrow over (σ)}_kϵ^nsd+, Regularization parameters such that |{right arrow over (σ)}_k|<<1, k=1, . . . , n_c such that for any {right arrow over (w)}ϵ, qϵ and δ{right arrow over (λ)}₁, δ{right arrow over (λ)}₂, . . . , δ{right arrow over (λ)}_ncϵ(L²(0, T))^nsd, the following is satisfied:

$\begin{array}{cc}{B}_G\left(\overrightarrow{w},q,\delta {\overrightarrow{\lambda }}_1,\dots ,\delta {\overrightarrow{\lambda }}_{n_c};\overrightarrow{\upsilon },p,{\overrightarrow{\lambda }}_1,\dots ,{\overrightarrow{\lambda }}_{n_c}\right)={\int }_{\Omega }\{\overrightarrow{w}·\left(\rho {\overrightarrow{\upsilon }}_{,t}+\rho \overrightarrow{\upsilon }·\nabla \overrightarrow{\upsilon }-\overrightarrow{f}\right)+\nabla \overrightarrow{w}:\left(-p\underset{~}{I}+\underset{~}{\tau }\right)\}d\overrightarrow{x}-{\int }_{\Omega }\nabla q·\overrightarrow{\upsilon }d\overrightarrow{x}-{\int }_{{\Gamma }_h}\overrightarrow{w}·\left(-p\underset{~}{I}+\underset{~}{\tau }\right)·\overrightarrow{n}ds+{\int }_{\Gamma }q\overrightarrow{\upsilon }·\overrightarrow{n}ds+\xi {\int }_{{\Gamma }_h^s}\{\overrightarrow{w}·{\rho }^s{\overrightarrow{\upsilon }}_{,t}+\nabla \overrightarrow{w}:{\underset{~}{\sigma }}^2\left(\overrightarrow{u}\right)-\overrightarrow{w}·{\overrightarrow{f}}^s\}\mathrm{ds}-\xi {\int }_{\partial {\Gamma }_h^s}\overrightarrow{w}·{\overrightarrow{h}}^s\mathrm{dl}+\begin{array}{c}\sum _{i=1}^{n_{\mathrm{sd}}}\sum _{k=1}^{n_c}\left\{{\lambda }_{\mathrm{ki}}·\left({\sigma }_{\mathrm{ki}}\delta {\lambda }_{\mathrm{ki}}-\delta c_{\mathrm{ki}}\left(\overrightarrow{w};\overrightarrow{\upsilon },\overrightarrow{x},t\right)\right)\right\}+\\ \sum _{i=1}^{n_{\mathrm{sd}}}\sum _{k=1}^{n_c}\delta {\lambda }_{\mathrm{ki}}·\left({\sigma }_{\mathrm{ki}}{\lambda }_{\mathrm{ki}}-c_{\mathrm{ki}}\left(\overrightarrow{\upsilon },\overrightarrow{x},t\right)\right)\end{array}+\begin{array}{c}\sum _{i=1}^{n_{\mathrm{sd}}}\sum _{k=1}^{n_c}{\kappa }_{\mathrm{ki}}·c_{\mathrm{ki}}\left(\overrightarrow{\upsilon },\overrightarrow{x},t\right)\delta c_{\mathrm{ki}}\left(\overrightarrow{w};\overrightarrow{\upsilon },\overrightarrow{x},t\right)\end{array}=0\mathrm{where}\delta c_{\mathrm{ki}}\left(\overrightarrow{w}:\overrightarrow{\upsilon },\overrightarrow{x},t\right)=\underset{ϵ->0}{\lim }\frac{d{c}_{\mathrm{ki}}\left(\overrightarrow{\upsilon }+ϵ\overrightarrow{w},\overrightarrow{x},t\right)}{dϵ}& \left(9\right)\end{array}$

Here, L²(0, T) represents the Hilbert space of functions that are square-integrable in time [0, T]. Here n_sp is the number of spatial dimensions and is assumed to be three and n_c is the number of constrained surfaces. Here, in addition to the terms required to impose the augmented Lagrangian method, the regularization term

$\begin{array}{cc}\sum _{i=1}^{n_{\mathrm{sd}}}\sum _{k=1}^{n_c}2{\sigma }_{\mathrm{ki}}{\lambda }_{\mathrm{ki}}\delta {\lambda }_{\mathrm{ki}}& \end{array}$ is added to obtain a system of equations with a non-zero diagonal block for the Lagrange multiplier degrees of freedom. This method was shown not to alter the solution significantly except in the immediate vicinity of the constrained outlet boundaries and stabilize problems that previously diverged without constraints.

TABLE 1
Examples of parameter values of the closed loop system at rest and during
exercise for the simulations of coronary flow and pressure with normal
coronary anatomy. The examples are non-limiting to the invention
	Rest	Exercise		Rest	Exercise
Parameter values of the left and right sides of the heart
RLA-V(dynes ·	5	5	RRA-V(dynes ·	5	5
s/cm5)			s/cm5)
LLA-V(dynes ·	5	5	LRA-V(dynes ·	1	1
s2/cm5)			s2/cm5)
RLV-art(dynes ·	10	10	RRV-art(dynes ·	10	10
s/cm5)			s/cm5)
LLV-art(dynes ·	0.69	0.69	LRV-art(dynes ·	0.55	0.55
s2/cm5)			s2/cm5)
ELV, max(mmHg/	2.0	2.0	ERV, max(mmHg/	0.5	0.5
cc)			cc)
VLV, 0(cc)	0	0	VRV, 0(cc)	0	0
VLA, 0(cc)	−60	−60	VRA, 0(cc)	−60	−60
ELA(mmHg/cc)	270	350	ERA(mmHg/cc)	60	80
Other parameter values
tmax(s)	0.33	0.25	Cardiac cycle (s)	1.0	0.5
Rpp(dynes · s/cm5)	16	16	Cp(cm5/dynes)	0.022	0.022
Rpd(dynes · s/cm5)	144	144

TABLE 2
Examples of parameter values of the three-element Windkessel models
at rest and during exercise for the simulations of coronary flow and
pressure with normal coronary anatomy.
Parameter values of the Windkessel models
	B: Right	C: Right	D: Right
	subclavian	carotid	vertebral
Rp(103dynes · s/cm5)	1.49	1.41	10.7
C(10−6cm5/dynes)	235	248	32.9
Rd(103dynes · s/cm5)	15.1	14.3	108
		F: Left	G: Left
	E: Left carotid	vertebral	subclavian
Rp(103dynes · s/cm5)	1.75	7.96	1.80
C(10−6cm5/dynes)	201	44.0	195
Rd(103dynes · s/cm5)	17.6	80.5	18.2
	H: Aorta	H: Aorta
	(Rest)	(Exercise)
Rp(103dynes · s/cm5)	0.227	0.180
C(10−6cm5/dynes)	1540	1600
Rd(103dynes · s/cm5)	2.29	0.722
Note
that the parameter values of the upper branch vessels are the same for the light exercise condition. The examples are non-limiting to the invention

TABLE 3
Examples of parameter values of the lumped parameter models of the
coronary vascular beds for the simulations of coronary flow and pressure
with normal coronary anatomy. The examples are non-limiting to the
invention.
	Ra	Ra-micro	Rv + Rv-micro	Ca	Cim
Parameter values of the coronary models at rest
(Resistance in 103dynes · s/cm5 and capacitance in 10−6cm5/dynes)
a: LAD1	183	299	94	0.34	2.89
b: LAD2	131	214	67	0.48	4.04
c: LAD3	91	148	65	0.49	4.16
d: LAD4	55	90	40	0.80	6.82
e: LCX1	49	80	25	1.28	10.8
f: LCX2	160	261	82	0.39	3.31
g: LCX3	216	353	111	0.29	2.45
h: LCX4	170	277	87	0.37	3.12
i: RCA1	168	274	86	0.37	3.15
j: RCA2	236	385	121	0.26	2.24
k: RCA3	266	435	136	0.23	1.99
Parameter values of the coronary models at exercise
(Resistance in 103dynes · s/cm5 and capacitance in 10−6cm5/dynes)
a: LAD1	76	24	18	0.75	6.88
b: LAD2	52	16	13	1.02	9.34
c: LAD3	51	16	12	1.07	9.74
d: LAD4	31	10	7	0.74	15.9
e: LCX1	20	6.2	5	2.79	25.4
f: LCX2	65	20	15	0.85	7.78
g: LCX3	87	27	21	0.63	5.74
h: LCX4	68	21	16	0.80	7.29
i: RCA1	71	22	16	0.83	7.60
j: RCA2	98	31	23	0.59	5.38
k: RCA3	110	35	25	0.52	4.72

Claims

1. A method for processing images of a patient, the method comprising: receiving, using at least one computer system, patient-specific image data of at least a portion of at least one vessel of a patient;determining, using the at least one computer system, a patient-specific geometric model of a geometry of a first vessel portion of a patient's vessel, the patient-specific geometric model being generated based on the received patient-specific image data;determining, using the at least one computer system, fluid dynamics equations governing blood flow through the patient-specific geometric model of the geometry of the first vessel portion of the patient's vessel;determining, using the at least one computer system, a lower-order patient- specific model of blood flow through a second vessel portion, the second vessel portion corresponding to a second vessel of the patient and/or a second portion of the patient's vessel, the lower-order patient-specific model having fewer dimensions than the patient-specific geometric model, the lower-order patient-specific model being associated with one or more fluid dynamics equations; anddetermining, using the at least one computer system, a value of a characteristic of blood flow through the first vessel portion and/or the second vessel portion by implicit closed-loop coupling and solving both the fluid dynamics equations governing blood flow through the patient-specific geometric model and the fluid dynamics equations of the lower-order patient-specific model of blood flow through the second vessel portion.

2. The method of claim 1, wherein the patient-specific geometric model comprises a three-dimensional model.

3. The method of claim 1, wherein the lower-order patient-specific model of blood flow comprises a lumped parameter model.

4. The method of claim 1, wherein the lower-order patient-specific model of blood flow comprises a lumped parameter heart model, a lumped parameter systemic vascular model, a lumped parameter pulmonary vascular model, or any combination thereof.

5. The method of claim 1, wherein the characteristic of blood flow comprises a blood flow rate and/or blood pressure.

6. The method of claim 1, wherein the second vessel portion is downstream from the first vessel portion.

7. The method of claim 1, wherein the fluid dynamics equations governing blood flow through the patient-specific geometric model and the fluid dynamics equations governing blood flow of the lower-order patient-specific model of blood flow through the second vessel portion are solved simultaneously.

8. A system for processing images of a patient, the system comprising: a data storage device storing instructions for performing a method of determining cardiovascular information for a patient; anda processor configured to execute the instructions including: receiving patient-specific image data of at least a portion of at least one vessel of a patient;determining a patient-specific geometric model of a geometry of a first vessel portion of a patient's vessel, the patient-specific geometric model being generated based on the received patient-specific image data;determining, using the at least one computer system, fluid dynamics equations governing blood flow through the patient-specific geometric model of the geometry of the first vessel portion of the patient's vessel;determining a lower-order patient-specific model of blood flow through a second vessel portion, the second vessel portion corresponding to a second vessel of the patient and/or a second portion of the patient's vessel, the lower-order patient-specific model having fewer dimensions than the patient-specific geometric model, the lower-order patient-specific model being associated with one or more fluid dynamics equations; anddetermining a value of a characteristic of blood flow through the first vessel portion and/or the second vessel portion by implicit closed-loop coupling and solving both the fluid dynamics equations governing blood flow through the patient-specific geometric model and the fluid dynamics equations of the lower-order patient-specific model of blood flow through the second vessel portion.

9. The system of claim 8, wherein the patient-specific geometric model comprises a three-dimensional model.

10. The system of claim 8, wherein the lower-order patient-specific model of blood flow comprises a lumped parameter model.

11. The system of claim 8, wherein the lower-order patient-specific model of blood flow comprises a lumped parameter heart model, a lumped parameter systemic vascular model, a lumped parameter pulmonary vascular model, or any combination thereof.

12. The system of claim 8, wherein the characteristic of blood flow comprises a blood flow rate and/or blood pressure.

13. The system of claim 8, wherein the second vessel portion is downstream from the first vessel portion.

14. The system of claim 8, wherein the fluid dynamics equations governing blood flow through the patient-specific geometric model and the fluid dynamics equations governing blood flow of the lower-order patient-specific model of blood flow through the second vessel portion are solved simultaneously.

15. A non-transitory computer readable medium for use on a computer system containing computer-executable programming instructions for performing a method of processing images of a patient, the method comprising: receiving, using at least one computer system, patient-specific image data of at least a portion of at least one vessel of a patient;determining, using the at least one computer system, a patient-specific geometric model of a geometry of a first vessel portion of a patient's vessel, the patient-specific geometric model being generated based on the received patient-specific image data;determining, using the at least one computer system, fluid dynamics equations governing blood flow through the patient-specific geometric model of the geometry of the first vessel portion of the patient's vessel;determining, using the at least one computer system, a lower-order patient-specific model of blood flow through a second vessel portion, the second vessel portion corresponding to a second vessel of the patient and/or a second portion of the patient's vessel, the lower-order patient-specific model having fewer dimensions than the patient-specific geometric model, the lower-order patient-specific model being associated with one or more fluid dynamics equations; anddetermining, using the at least one computer system, a value of a characteristic of blood flow through the first vessel portion and/or the second vessel portion by implicit closed-loop coupling and solving both the fluid dynamics equations governing blood flow through the patient-specific geometric model and the fluid dynamics equations of the lower-order patient-specific model of blood flow through the second vessel portion.

16. The computer readable medium of claim 15, wherein the patient-specific geometric model comprises a three-dimensional model.

17. The computer readable medium of claim 15, wherein the lower-order patient-specific model of blood flow comprises a lumped parameter model.

18. The computer readable medium of claim 15, wherein the lower-order patient-specific model of blood flow comprises a lumped parameter heart model, a lumped parameter systemic vascular model, a lumped parameter pulmonary vascular model, or any combination thereof.

19. The computer readable medium of claim 15, wherein the second vessel portion is downstream from the first vessel portion.

20. The computer readable medium of claim 15, wherein the fluid dynamics equations governing blood flow through the patient-specific geometric model and the fluid dynamics equations governing blood flow of the lower-order patient-specific model of blood flow through the second vessel portion are solved simultaneously.