CN118247221A

CN118247221A - Fractional flow reserve evaluation method and device based on specific coronary artery blood flow model

Info

Publication number: CN118247221A
Application number: CN202410210575.XA
Authority: CN
Inventors: 刘修健; 张贺晔; 高智凡; 曾德功; 薛晓飞; 梁月
Original assignee: Sun Yat Sen University
Current assignee: Sun Yat Sen University
Priority date: 2024-02-26
Filing date: 2024-02-26
Publication date: 2024-06-25

Abstract

The application provides a fractional flow reserve assessment method and a fractional flow reserve assessment device based on a specific coronary blood flow model, comprising the following steps: acquiring CTP source images of cardiac muscles of a patient and CTA images of target coronary arteries; generating a myocardial model according to the CTP source image; generating a target coronary three-dimensional model according to the CTA image; generating a target coronary blood flow model according to the myocardial model and the target coronary three-dimensional model; and determining the fractional flow reserve of the target coronary artery according to the target coronary blood flow model. The application can generate personalized coronary artery blood flow models based on different patients, can accurately estimate the fractional flow reserve of the coronary artery of the patient, reduce the influence of the outlet diameter on the coronary artery blood flow distribution, improve the repeatability of FFRCT estimation, realize the noninvasive calculation of FFRCT and improve the diagnosis performance.

Description

Fractional flow reserve evaluation method and device based on specific coronary artery blood flow model

Technical Field

The application relates to the field of medical detection, in particular to a fractional flow reserve evaluation method and device based on a specific coronary blood flow model.

Background

Previous studies have shown that Fractional Flow Reserve (FFRCT) is affected by coronary outlet resistance in an equivalent circuit model of coronary blood circulation. In addition, the outlet resistance is also affected by quantification of total coronary congestion blood flow and coronary outlet blood flow distribution, which is critical for accurate estimation of Fractional Flow Reserve (FFRCT) from computed tomography angiography.

Currently, accurate estimation of FFRCT remains a challenging task since clinically noninvasive measurements of coronary outlet resistance are not achieved. The conventional approach is to estimate the outlet resistance by a population-averaged physiological hypothesis model that includes quantifying the total coronary resting blood flow and simulating the maximum congested blood flow. However, the relationship between left ventricular myocardial mass (LVM) and coronary rest total blood flow is not constant. In addition, the degree of vasodilation of coronary microvascular at maximum hyperemia varies from person to person, and individual differences between patients limit the reliability of the model. Therefore, it is not reasonable to simulate the maximum hyperemia for all patients with the same total coronary resistance index hyperemia factor.

On the other hand, a hyperemic coronary arterial total blood flow distribution based on the outlet diameter does not guarantee the accuracy of coronary arterial outlet blood flow. The reason is that the position of the outlet cut-off affects the outlet diameter and coronary blood flow.

In addition, this coronary flow distribution method assumes that the resistance to the hyperemia microcirculation at the distal end of the stenosed vessel is the same as that of the non-diseased coronary arteries. However, in actual cases, especially in patients with microvascular disease, the resistance to hyperemia microcirculation at the distal end of stenosed vessels is higher than that of normal vessels. Thus, the full-blood coronary blood flow and coronary outlet blood flow obtained using the above-described methods may not accurately estimate the outlet resistance and FFRCT.

Disclosure of Invention

In view of the problems, the present application has been made to provide a fractional flow reserve assessment method and apparatus based on a specific coronary flow model that overcomes the problems or at least partially solves the problems, including:

a fractional flow reserve assessment method based on a specific coronary flow model, comprising the steps of:

Acquiring CTP source images of cardiac muscles of a patient and CTA images of target coronary arteries;

Generating a myocardial model according to the CTP source image;

Generating a target coronary three-dimensional model according to the CTA image;

Generating a target coronary blood flow model according to the myocardial model and the target coronary three-dimensional model;

and determining the fractional flow reserve of the target coronary artery according to the target coronary blood flow model.

Further, the step of generating a myocardial model from the CTP source image comprises:

Dividing the CTP source image to generate a myocardial two-dimensional section image;

generating a myocardial model corresponding to the patient according to the myocardial two-dimensional sectional image.

Further, the step of segmenting the CTP source image to generate a myocardial two-dimensional cross-sectional image includes:

correcting the CTP source image, and segmenting left ventricular myocardium from the CTP source image;

Separating the left ventricular myocardium by adopting a blood pool removing method;

generating an arterial input function and a tissue attenuation curve according to the separated left ventricular myocardium;

generating myocardial blood flow from the arterial input function and the tissue attenuation curve.

Further, the step of generating an arterial input function and a tissue attenuation curve from the isolated left ventricular myocardium comprises:

Sampling attenuation values from descending aorta at the head and tail of the image stack of left ventricular myocardium to generate an arterial input function;

generating a tissue attenuation curve according to the attenuation value of each myocardial voxel in the left ventricle myocardium;

fitting the shape of the tissue attenuation curve according to the arterial input function.

Further, the step of generating a three-dimensional model of the target coronary artery from the CTA image includes:

Dividing the CTA image to generate a coronary two-dimensional cross-section image;

Reconstructing the target coronary artery according to the coronary artery two-dimensional section image to generate a target coronary artery three-dimensional model corresponding to the patient.

Further, the step of determining the fractional flow reserve of the target coronary artery according to the target coronary blood flow model comprises the following steps:

cutting off a coronary artery outlet according to the target coronary artery three-dimensional model to generate a cut-off coronary artery tree;

Carrying out myocardial perfusion region segmentation on the target coronary blood flow model; specifically, each voxel of the left ventricular myocardium is allocated to a truncated coronary tree branch nearest to the left ventricle as a corresponding territory for each voxel;

obtaining pressure distribution in a target coronary artery according to voxels in the myocardial perfusion region and a corresponding myocardial blood flow computational fluid dynamics equation;

And determining fractional flow reserve of the target coronary artery according to the pressure distribution in the target coronary artery.

Further, according to the voxels in the myocardial perfusion region and the corresponding myocardial blood flow calculation fluid dynamic equation, the step of obtaining the pressure distribution in the target coronary artery comprises the following steps:

Generating coronary blood flow of a target outlet according to voxels in the myocardial perfusion region and corresponding myocardial blood flow;

And generating the hyperemic outlet resistance of the target coronary artery according to the coronary blood flow of the target outlet and the average arterial pressure of the target coronary artery.

A fractional flow reserve assessment device based on a specific coronary flow model, comprising:

the image acquisition module is used for acquiring CTP source images of cardiac muscles of patients and CTA images of target coronary arteries;

a first model generation module for generating a myocardial model according to the CTP source image;

the second model generation module is used for generating a target coronary three-dimensional model according to the CTA image;

the model matching module is used for generating a target coronary blood flow model according to the myocardial model and the target coronary three-dimensional model;

and the data estimation module is used for determining the fractional flow reserve of the target coronary artery according to the target coronary blood flow model.

An apparatus comprising a processor, a memory and a computer program stored on the memory and capable of running on the processor, which when executed by the processor, implements the steps of a fractional flow reserve assessment method based on a specific coronary flow model as described above.

A computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of a fractional flow reserve assessment method based on a specific coronary flow model as described above.

The application has the following advantages:

In the embodiment of the application, compared with the prior art that the outlet resistance and FFRCT cannot be accurately estimated, the application provides a solution for noninvasively estimating the fractional flow reserve based on a personalized coronary blood flow model, which is specifically as follows: acquiring CTP source images of cardiac muscles of a patient and CTA images of target coronary arteries; generating a myocardial model according to the CTP source image; generating a target coronary three-dimensional model according to the CTA image; generating a target coronary blood flow model according to the myocardial model and the target coronary three-dimensional model; and determining the fractional flow reserve of the target coronary artery according to the target coronary blood flow model. The application can generate personalized coronary artery blood flow models based on different patients, can accurately estimate the fractional flow reserve of the coronary artery of the patient, reduce the influence of the outlet diameter on the coronary artery blood flow distribution, improve the repeatability of FFRCT estimation, realize the noninvasive calculation of FFRCT and improve the diagnosis performance.

Drawings

In order to more clearly illustrate the technical solutions of the present application, the following brief description will be given of the drawings required for the description of the present application, which are merely examples of the present application, and from which other drawings can be obtained without inventive effort for a person skilled in the art.

FIG. 1 is a flow chart of the steps of a fractional flow reserve assessment method based on a specific coronary flow model according to an embodiment of the present application;

FIG. 2 is a flow chart of a fractional flow reserve assessment method based on a specific coronary flow model according to an embodiment of the present application;

FIG. 3 is a schematic flow chart of myocardial blood flow calculation according to an embodiment of the present application;

FIG. 4 is a flow chart illustrating segmentation of myocardial perfusion regions according to an embodiment of the present application;

FIG. 5 is a schematic flow chart of reconstructing a three-dimensional model of a target coronary artery according to an embodiment of the present application;

FIG. 6 is a diagram of FFRCT results calculated based on different models provided by an embodiment of the present application;

FIG. 7 is a block diagram of a fractional flow reserve assessment device based on a specific coronary flow model according to an embodiment of the present application;

Fig. 8 is a schematic structural diagram of a computer device according to an embodiment of the present invention.

Detailed Description

In order that the manner in which the above recited objects, features and advantages of the present application are obtained will become more readily apparent, a more particular description of the application briefly described above will be rendered by reference to the appended drawings. It will be apparent that the described embodiments are some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.

The inventors found by analyzing the prior art that: the identification of patient-specific exit Boundary Conditions (BC) is critical to the calculation FFRCT from computed tomography. In contrast to conventional coronary CT angiography (CTA), FFRCT can evaluate the hemodynamic significance of coronary stenosis, becoming a criterion for non-invasive calculation of Fractional Flow Reserve (FFR). As defined for FFR measurements by Invasive Coronary Angiography (ICA), FFRCT refers to the ratio of the distal to proximal pressure of a coronary stenosis at maximum myocardial hyperemia.

Therefore FFRCT is directly determined by the Computational Fluid Dynamics (CFD) simulated coronary pressure distribution. By solving the fluid dynamics control equation, the pressure distribution in the coronary artery can be calculated. For a given coronary model, a patient-specific outlet BC needs to be defined to solve the control equation. The outlet BC constitutes a mathematical and physiological relationship between variables of interest (e.g. flow, pressure), defined on the boundaries of a mathematical model, defining a specific analytical solution for these variables. Thus, in CFD simulation, the pressure distribution of the coronary arteries depends on the specific outlet BC. For accurate calculation FFRCT, the outlet BC should truly simulate the physiological environment of the coronary arteries according to the patient's specific situation.

However, determining the patient-specific outlet BC still has some challenges, as it is not practical to directly measure the reliable blood flow, pressure or resistance of each outlet.

Dynamic stress Computed Tomography (CTP) imaging has the potential to provide patient-specific exit BC. The reason is that dynamic stress CTP can provide full-blood coronary blood flow and outlet coronary blood flow to estimate outlet resistance. Dynamic stress CTP is a functional imaging method that quantifies Myocardial Blood Flow (MBF) upon pharmacological hyperemia from a pattern of myocardial enhancement following injection of contrast agents. The quantification of MBF is based on the flow of contrast agent from the coronary into the myocardium. Thus, MBF in the myocardial perfusion area may reflect the total coronary blood flow. In addition, CTP is performed at the time of pharmacological hyperemia, which is the same physiological state as when invasive FFR is measured. Thus, we can quantify the total hyperemic coronary blood flow based on CTP. Coronary branches have specific myocardial perfusion areas. Past studies have shown that myocardial segmentation based on Voronoi algorithm can be accurately distributed to perfusion areas of coronary artery branches. From MBF in the myocardial perfusion range, the blood flow of the corresponding coronary artery branches can be obtained. Thus, coronary outlet blood flow may be distributed according to the myocardial perfusion area.

There has been much research devoted to the estimation of FFRCT:

(1) Quantifying whole-blood coronary blood flow based on CTP and assigning a CTPD model of outlet coronary blood flow based on outlet diameter;

(2) The full-blood coronary flow is quantified based on the LVM and the model LVMD of the outlet coronary flow is assigned according to the outlet diameter.

Individual differences between patients limit the reliability of the model.

To this end, the object of the present invention is to develop a CTP-based personalized coronary blood flow model called CTPV to enable non-invasive estimation of outlet resistance and FFRCT. The model can avoid the influence of the outlet diameter on the coronary artery blood flow distribution, improve the repeatability of FFRCT estimation, realize the noninvasive calculation of FFRCT and improve the diagnosis performance.

Referring to FIG. 1, a fractional flow reserve assessment method based on a specific coronary flow model is shown, according to an embodiment of the present application;

The method comprises the following steps:

s110, acquiring CTP source images of cardiac muscles of a patient and CTA images of target coronary arteries;

s120, generating a myocardial model according to the CTP source image;

s130, generating a target coronary three-dimensional model according to the CTA image;

s140, generating a target coronary artery blood flow model according to the myocardial model and the target coronary artery three-dimensional model;

S150, determining the fractional flow reserve of the target coronary artery according to the target coronary artery blood flow model.

Next, a fractional flow reserve evaluation method based on a specific coronary flow model in the present exemplary embodiment will be further described.

As described in the step S110, CTP source images of the patient' S cardiac muscle and CTA images of the target coronary arteries are acquired.

Note that, the computed tomography (CT perfusion imaging, CTP) can assess early brain tissue blood flow perfusion conditions. In this embodiment, referring to fig. 3, the condition of blood perfusion of brain tissue is known by rapidly injecting adenosine perfusion (140 ug/kg/min) into veins, then performing a plurality of continuous CT scans, and reconstructing images with a computer to obtain the change condition of the concentration of the contrast agent in brain tissue.

CT angiography (CT angiography, CTA) can clearly show the arterial blood flow supply state, and the degree of vascular stenosis is obtained. CTA is a reconstruction method performed after an enhanced CT scan, with arterial blood vessels being the main examination. Referring to fig. 3, in this embodiment, by injecting a contrast agent, then CT capturing is performed on the blood circulation of the human body, and if necessary, a nitroglycerin receptor blocker may be injected, so that the condition of blood flow in the blood vessel can be clearly displayed by CTA, and then three-dimensional reconstruction is performed on the CT scan result, so that the running of the blood vessel can be intuitively displayed.

As an example, referring to fig. 3, CTP source images of the myocardium and CTA images of the target coronary arteries of a patient are acquired by scanning of dynamic loading CTPs and coronary arteries CTAs, and 10 to 15 CT complete cardiac datasets are acquired. These datasets contain information about the various angles and levels of the patient's heart, providing the anatomy and lesion condition of the patient's heart.

As described in the step S120, a myocardial model is generated according to the CTP source image.

In one embodiment of the present invention, the specific process of generating a myocardial model from the CTP source image described in step S120 may be further described in conjunction with the following description.

and generating a myocardial model corresponding to the patient according to the myocardial two-dimensional sectional image as follows.

As an example, referring to part a of fig. 4, a schematic flow chart of generating a myocardial model from the CTP source image is shown. The CTP source image of the target patient is obtained, and is subjected to post-processing through commercial software or an automatic algorithm so as to improve the image quality and extract the myocardial area; then segmenting the cardiac muscle from the CTP source image by an image segmentation technology such as threshold segmentation, region growth, level set method and the like to obtain a bull's eye graph of a two-dimensional section of the cardiac muscle; then constructing a myocardial model by utilizing a three-dimensional reconstruction technology (such as surface reconstruction, voxel reconstruction and the like) based on the segmented myocardial region; and finally, optimizing the reconstructed myocardial model, including removing redundancy, smoothing the surface, filling holes and the like, so as to improve the accuracy and reliability of the myocardial model.

In one embodiment of the present invention, the specific process of "segmenting the CTP source image to generate a two-dimensional cross-sectional image of the myocardium" may be further described in conjunction with the following description.

Correcting the CTP source image and segmenting left ventricular myocardium from the CTP source image as follows;

Separating the left ventricular myocardium by a blood pool removal method as follows;

Generating an arterial input function and a tissue attenuation curve from the isolated left ventricular myocardium as described in the following steps; specifically, sampling attenuation values from the descending aorta at the head and tail of the image stack of the left ventricular myocardium, generating an arterial input function; generating a tissue attenuation curve according to the attenuation value of each myocardial voxel in the left ventricle myocardium; fitting the shape of the tissue attenuation curve according to the arterial input function;

Myocardial blood flow is generated from the arterial input function and the tissue attenuation curve as described in the following steps.

In one specific implementation, referring to FIG. 3, a schematic diagram of a dynamic loading CTP image scanning process and a myocardial blood flow calculation process is shown. Part a of fig. 3 is a dynamic loading CTP and coronary CTA scanning scheme; part b of fig. 3 is the acquisition of 10 to 15 CT complete cardiac datasets; part c of fig. 3 is the displacement correction and left myocardial segmentation process; part d of fig. 3 is the definition of the time decay curve (TAC) and Arterial Input Function (AIF); part e of fig. 3 is calculation of Myocardial Blood Flow (MBF) by AIF and TAC; part f of FIG. 3 is a 2D slice of the left myocardial MBF.

Post-processing CTP source images using commercial software or automated algorithms:

(1) As shown in the flow c of fig. 3, the CTP source image is corrected by using a motion correction algorithm so as to remove the influence of the myocardial displacement on the CTP source image.

(2) An automatic segmentation algorithm is adopted to segment the left ventricular myocardium.

The left ventricular myocardium is an important part of the heart and is responsible for pumping blood and circulating. The left ventricular myocardium is isolated for more accurate analysis of the function and condition of the heart. By analyzing the information of the shape, size, position and the like of the left ventricular myocardium, functional parameters such as myocardial perfusion, myocardial contraction, diastole and the like can be obtained, so that the overall health condition of the heart is estimated.

(3) The left ventricular myocardium is separated by a blood pool removal method based on an attenuation value threshold method.

Specifically, in hemodynamic simulation, the Arterial Input Function (AIF) describes the velocity-time or flow-time relationship of arterial blood as it flows from the heart. This function is an important part of the simulation and provides information on the blood velocity and flow rate of all blood vessels flowing into the artery.

In CT imaging, images of blood vessels are obtained by a series of successive image "slices" or "voxels" which constitute an "image stack". As shown in part d of fig. 3, the Arterial Input Function (AIF) samples attenuation values from the descending aorta at the head and tail of the image stack. References herein to "head" and "tail" refer to the beginning and ending portions of this stack. By sampling the attenuation values from these different locations, more information can be obtained, contributing to an improved simulation accuracy of the real hemodynamics. The method can realize double sampling, improves the fitting precision of AIF, can obtain a more complete and detailed arterial input function by sampling at the head, tail and possible middle positions, and can reflect the real hemodynamic condition more accurately, thereby improving the simulation precision.

As shown in part e of fig. 3, the sampling rate of AIF is shown to be twice that of the time decay curve (TAC).

(4) And drawing a TAC chart according to the attenuation value of each myocardial voxel in the left ventricle myocardium.

TAC plots, time-concentration graphs, are used to describe the concentration change of a substance in a body or tissue over time. In medical imaging techniques, a voxel is a smallest unit in an image. By plotting the TAC map by measuring the attenuation values of these voxels, various information about the myocardium can be obtained.

(5) The TAC was fitted using the parametric deconvolution technique. TAC was conformed to the simplified 2-chamber intravascular and extravascular spatial models. This technique utilizes the AIF of high temporal sampling rate as a shape template for organizing TACs.

Specifically, the parameter deconvolution is used to fit or adjust the TAC map of the tissue to extract information from the data. By means of the parametric deconvolution technique, the TAC is adjusted or fitted to match it to a simplified two-compartment model. The two-compartment model includes an intravascular compartment and an extravascular compartment representing the distribution of drugs within and outside of the blood vessel.

An Arterial Input Function (AIF) of high temporal sampling rate is used as a reference or template to direct or adjust the shape of the tissue TAC. AIF describes the velocity-time or flow-time relationship of blood as it flows from the heart, with a high temporal sampling rate meaning that it captures many data points in a very short time interval, thereby providing more accurate information.

(6) TAC was fitted with the following two-chamber model formula:

C(t)＝BV_ivC_A(t-Δt)+K^trans∫₀ ^-ΔtC_A(τ)w(t-Δt-τ)dτ (1)

Wherein, C _A (τ) is AIF, C (t) is TAC, BV _iv is intravascular blood volume, K ^trans is volume transfer constant, MTT _ev is extravascular transport (or residence) time, Δt is delay time, Δt is descending aortic to myocardial travel time.

The above is equivalent to the time-lapse-correction deconvolution of the pulse residual function (IRF), namely:

IRF(τ)＝BV_ivδ(τ)+K^transw(τ) (3)

If the rate constant K _ep is defined as 1/MTT _ev, the triangular extravascular part of IRF is an approximation of the exponential term in the generalized Tofts model:

the TAC conforming to the two-compartment model is obtained by equations (1) - (4).

(7) The ratio of the maximum slope of the TAC to the peak of the AIF was calculated as MBF:

where TAC is the tissue attenuation curve and AIF is the arterial input function (atrial input function).

Referring to section e of fig. 3, TAC is a single myocardial voxel. From the TAC and Eq. for each left myocardial voxel, the MBF for the whole left myocardium can be calculated.

Part f of FIG. 3 is a 2D slice of the left myocardial MBF.

A target coronary three-dimensional model is generated from the CTA image, as described in step S130.

In one embodiment of the present invention, the specific process of generating the three-dimensional model of the target coronary artery from the CTA image in step S130 may be further described in conjunction with the following description.

Dividing the CTA image to generate a coronary two-dimensional cross-sectional image;

and reconstructing the target coronary artery according to the coronary artery two-dimensional section image to generate a target coronary artery three-dimensional model corresponding to the patient.

Referring to parts a-e of fig. 2 and part a of fig. 4, a schematic flow chart of generating a three-dimensional model of the target coronary artery from the CTA image is given. Firstly, acquiring coronary artery CTA image data of a patient, and carrying out two-dimensional section segmentation on the coronary artery CTA image of the patient by adopting a semi-automatic algorithm. Specifically, preprocessing CTA images generally includes: gray scale transformation, noise removal, image enhancement and normalization processes to better extract features and for subsequent analysis. The central line or central point path of the blood vessel is then identified and extracted from the processed CTA image, and the central line is required to be optimized for smoothing the path and ensuring continuity thereof so as to better reflect the actual shape and structure of the blood vessel, because a noisy or intermittent path may be obtained in the central path extraction stage. The vessel can then be planar cut along specific points on the centerline optimized path, and then the vessel region on each plane is segmented, so that the structure and morphology of the vessel can be analyzed and studied on a 2D level. After the two-dimensional section is segmented, reconstructing a target coronary artery of the patient by using a three-dimensional reconstruction technology, and constructing a target coronary artery three-dimensional model.

And (3) carrying out grid division on the three-dimensional model of the coronary artery to generate a high-quality tetrahedron grid unit, so that numerical simulation and analysis can be better carried out later. Meshing is the discretization of a continuous physical space into a finite number of discrete elements that can represent, in a calculation, the shape, mass, velocity, etc. of an object. To determine the appropriate mesh size, the present embodiment performs a mesh sensitivity analysis on the typical model. The main purpose of grid sensitivity analysis is to select proper grid sensitivity, which can effectively represent coronary artery blood flow field and reduce calculation cost. The mesh size ranges from coarse (about 466,878 elements) to coarse (about 63,821 elements), medium (about 892,499 elements) to fine (about 1,318,856 elements), fine (about 1,766,601 elements) to fine (about 2,449,042 elements).

Each grid is then used for simulation. The FFRCT value at the pressure sensor site (at the time of FFR measurement) was chosen as an indicator for grid sensitivity analysis. Grid sensitivity analysis shows that convergence is achieved at a fine grid (about 1318856 units).

A target coronary flow model is generated from the myocardial model and the target coronary three-dimensional model, as described in step S140.

As an example, referring to section e of fig. 2 and section a of fig. 4, the myocardial model and the target coronary model are imported into the same Computational Fluid Dynamics (CFD) software from their source formats, respectively, ensuring that the myocardial model and the target coronary model are precisely spatially aligned so that they are spatially perfectly matched, and the combined coronary CTA and load CT perfusion images ultimately generate a specific coronary blood flow model CTPV.

As described in the step S150, the fractional flow reserve of the target coronary artery is determined according to the target coronary blood flow model.

In one embodiment of the present invention, the specific procedure of "determining fractional flow reserve of the target coronary artery from the target coronary blood flow model" described in step S150 may be further described in connection with the following description.

Performing myocardial perfusion region segmentation on the target coronary blood flow model as follows; specifically, each voxel of the left ventricular myocardium is allocated to a truncated coronary tree branch nearest to the left ventricle as a corresponding territory for each voxel;

according to the voxels in the myocardial perfusion region and the corresponding myocardial blood flow calculation fluid dynamic equation, obtaining the pressure distribution in the target coronary artery;

as described in the following steps, fractional flow reserve of the target coronary artery is determined from the pressure distribution within the target coronary artery.

In an embodiment of the present invention, the specific process of "calculating the fluid dynamics equation from the voxels in the myocardial perfusion region and their corresponding myocardial blood flow to obtain the pressure distribution in the target coronary artery" may be further described in conjunction with the following description.

Generating coronary blood flow of the target outlet according to voxels in the myocardial perfusion region and corresponding myocardial blood flow as follows;

as described in the following steps, the hyperemic outlet resistance of the target coronary artery is generated from the coronary blood flow at the target outlet and the mean arterial pressure of the target coronary artery.

The CTPV model of the present invention utilizes MBF obtained from CTP to quantify total hyperemic coronary blood flow, and proposes a myocardial perfusion area method to distribute coronary outlet blood flow. Then FFRCT is calculated by estimating the reliable outlet resistance. The steps of CFD simulation and FFRCT calculation are as follows:

in numerical simulation of coronary arteries, the conservation of momentum and conservation of mass of the three-dimensional incompressible Navier-Stokes equation are used as control equations:

where → u is the velocity vector of blood; v and ρ represent the viscosity and density of blood, respectively; p is the pressure.

Assuming that blood is an isotropic, uniform, incompressible Newtonian fluid, the dynamic viscosity v is 0.0035Pas and the density ρ is 1050kg/m ³. In the human arteries, the relative displacement of the arterial wall in the blood flow is small, so the material properties of the vessel wall are set as rigid walls at the time of calculation. The container is assumed to be a slip-free rigid wall. In addition, low blood flow velocities within the lumen of the blood vessel may be idealized as laminar flow. The vessel wall deformation caused by the heart cycle is not considered.

Although the hemodynamics of the coronary artery system are complex with periodic time variations, steady state BC can be used to assess the extent of myocardial ischemia in suspected patients. The reason is that the invasive FFR is calculated from the time-averaged pressure measured over several cardiac cycles. Furthermore, as long as the coronary blood flow satisfies the demand of the myocardium in a short time, it is irrelevant whether the blood flow is uniformly or unevenly supplied during this time. Thus, the present embodiment uses steady state BC for FFRCT calculations to reduce time and cost. The pressure generation FFRCT is performed using the average distal coronary pressure to the coronary ostia. The FFRCT positions measured were identical to the invasive FFR.

Before solving the CFD fluid dynamic equation, the inlet boundary condition and the outlet boundary condition of the coronary artery need to be set, so that the simulation can be more accurately performed through CTPV, which is specifically as follows:

the coronary outlet is the location where the coronary artery connects to the surface of the heart, typically the primary outlet for blood flow, and other blood vessels or tissue structures may be present around the coronary outlet, which structures may interfere with the observation and analysis of the coronary outlet itself. Therefore, after the target coronary artery three-dimensional model is reconstructed, a standardized outlet cut-off strategy is adopted, and a myocardial perfusion region is determined through a complete coronary artery tree, so that subsequent analysis and simulation can be better focused on the coronary artery outlet region, the model is simplified, and the myocardial perfusion region is more efficient. The standardized outlet truncation strategy is to truncate on the straight vessel segment of the first generation branch of the coronary aortic arch. The truncation position is about 5 times the diameter of the branch. The truncation method can better estimate FFRCT, and avoid the problem of inaccurate calculation caused by inaccurate segmentation of the distal branch of the coronary artery.

With continued reference to fig. 2 and 4, after the combined coronary CTA and load CT perfusion images finally generate the specific coronary blood flow model CTPV, the Voronoi algorithm is used to determine CTPV the myocardial perfusion region, then the hyperemic outlet resistance is calculated, the computational fluid dynamics Navier-stokes equation is solved, and the final estimate FFRCT is obtained. In FFRCT estimates, the inlet and outlet boundary conditions of the coronary may be used to describe the state of blood flow within the coronary, thereby helping to simulate and analyze hemodynamic conditions within the coronary. By reasonably setting the inlet boundary conditions and the outlet boundary conditions, accurate simulation of the blood flow state within the coronary artery can be facilitated.

The inlet boundary conditions are set as follows:

In clinical measurement of FFR, mean Arterial Pressure (MAP) in the hyperemic state is not significantly different from mean arterial pressure in the resting state. In this embodiment, the inlet boundary condition at the coronary artery inlet is MAP in a resting state, which can be calculated from cuff pressure. The formula is as follows:

MAP＝0.4×(SBP-DBP)+DBP (8)

Wherein the systolic pressure (SBP) and the diastolic pressure (DBP) are the brachial artery systolic pressure and diastolic pressure, respectively.

The outlet boundary conditions are set as follows:

CTP-based exit boundary conditions are local perfusion (representing perfusion volume ml/100 ml/min) of each branch supply region quantified by MBF image intensity. Positron Emission Tomography (PET) is the gold standard for current quantitative measurement of MBF, reporting stress MBF values of normal myocardium between 3-5 mL/min/g. However, in the previous researches, normal cardiac muscle with stress VPCT-mbf of 1.1-1.4ml/min/g is detected by adopting dynamic stress CTP and Volume Perfusion CT (VPCT) software. Thus, the stress VPCTMBF values observed in normal myocardium in previous CT studies were greatly underestimated compared to the stress MBF values obtained in PET studies. The CTP-based VPCT-MBF value was not much related to true absolute myocardial flow and the VPCT-MBF value measured using VPCT software was underestimated by 23% -41% compared to the true value. The model used in VPCT software did not calculate MBF, but rather calculated the hematocrit-myocardial transfer constant K1. K1 is corrected to MBF, and Renkin-Crone formula between K1 and MBF is as follows:

K1＝[1-0.904exp(-1.203/MBF)]MBF (9)

using this formula, the present embodiment corrects VPCT-MBF to MBF. VPCT-MBF (ml/100 ml/min) divided by 105 was converted to ml/g/min.

Myocardial segmentation based on Voronoi algorithm is one of methods for segmenting myocardial perfusion regions by using coronary CTA data, and can accurately evaluate stenosis-specific myocardial perfusion regions. This embodiment utilizes the Voronoi algorithm in combination with CTA and MBF data. Coronary artery CTA data automatically determines the myocardial region corresponding to each coronary artery branch. Briefly, each voxel of the left ventricular myocardium is assigned to the closest coronary branch to the left ventricle as its territory using the Voronoi algorithm, as shown in section a of fig. 4, illustrating the acquired primary imaging dataset, correlation analysis, and key input/output. In particular, the truncation strategy employed in section f of fig. 5 is to truncate the side branch to a maximum reconstructed branch length. The purpose of the truncation strategy is to determine the myocardial perfusion region through a complete coronary artery tree. Section B of fig. 4 is the respective outlet myocardial perfusion areas. In addition, the present example also compared the myocardial perfusion region with the coronary branch vessel network region of ICA, and the results showed good consistency, as shown in part C of fig. 4. The coronary blood flow (Qhypout, i) at the ith outlet is calculated as:

Where Vol _n is the volume of a single voxel in the myocardial perfusion region, N is the number of all voxels in the myocardial perfusion region, and MBF _n is the MBF of a single voxel. Since this embodiment only obtains MBF data of the left ventricle, the right ventricle supplying blood to the right coronary artery cannot be divided into perfusion areas. Thus, the present study calculated FFRCT only for the left coronary artery.

The resistance of the coronary artery branches at the ith outlet is expressed as:

wherein Pv is set to 5mmHg < 30 >, and MAP is the mean arterial pressure.

Experimental example

To verify the accuracy of CTP-based quantification of total coronary arterial hyperemic blood flow of the present invention, we compared the CTPV model of the present invention to FFRCT calculations comparing LVMD model and CTPD model. Table 1 below shows the total coronary blood flow in 5 patients. Qhyp1 and Qhyp are full-blood coronary flows based on LVMD model and CTPD model, respectively. The average values of Qhyp and Qhyp were 135.24ml/min and 260.23ml/min, respectively, in all patients. From the experimental results, the total amount of blood flow in congestion quantified by the CTPD model was about twice that of the LVMD model. As shown in fig. 6, two typical examples of the calculation results are given, which are FFRCT calculated by different methods, FFRCT calculated based on the LVMD model is 0.75 and 0.97, FFRCT calculated based on the CTPD model is 0.66 and 0.91, respectively, and FFRCT calculated based on the CTPV model is 0.68 and 0.90, respectively, and it can be seen that FFRCT calculated based on the CTPV model is closest to 0.69 and 0.89 of the FFR value. From the above experiments, CTPV model can more accurately quantify total coronary congestion flow and distributed coronary outlet flow relative to CTPD and LVMD models, thereby improving the diagnostic performance of FFRCT; the CTPV model was more repeatable than the CTPD model on the estimate FFRCT.

TABLE 1

In summary, the CTPV model of the present invention combines BC estimation and three-dimensional coronary artery reconstruction for CFD simulation, which can estimate FFRCT from non-invasive imaging data; the CTPV model also uses CTP of hyperemic state to quantify total hyperemic coronary flow, which makes estimated BC more reliable, while also achieving coronary outlet flow distribution based on myocardial perfusion range, avoiding the impact of stenotic vessel distal hyperemic microcirculation resistance on flow distribution.

For the device embodiments, since they are substantially similar to the method embodiments, the description is relatively simple, and reference is made to the description of the method embodiments for relevant points.

Referring to FIG. 7, a fractional flow reserve assessment device based on a specific coronary flow model is shown, according to an embodiment of the present application;

The method specifically comprises the following steps:

An image acquisition module 710 for acquiring CTP source images of the patient's myocardium and CTA images of the target coronary arteries;

A first model generating module 720, configured to generate a myocardial model according to the CTP source image;

a second model generation module 730 for generating a target coronary three-dimensional model from the CTA image;

A model matching module 740 for generating a target coronary flow model from the myocardial model and the target coronary three-dimensional model;

A data estimation module 750 for determining a fractional flow reserve of the target coronary artery from the target coronary blood flow model.

In an embodiment of the present invention, the first model generating module 720 includes:

the first segmentation submodule is used for segmenting the CTP source image to generate a myocardial two-dimensional section image;

and the first model reconstruction sub-module is used for generating a myocardial model corresponding to the patient according to the myocardial two-dimensional sectional image.

In an embodiment of the present invention, the first segmentation sub-model includes:

a correction segmentation unit for correcting the CTP source image and segmenting left ventricular myocardium from the CTP source image;

A blood pool removing unit for separating the left ventricular myocardium by a blood pool removing method;

a data generation unit for generating an arterial input function and a tissue attenuation curve according to the separated left ventricular myocardium;

And the MBF calculation unit is used for generating myocardial blood flow according to the arterial input function and the tissue attenuation curve.

In an embodiment of the present invention, the data generating unit includes:

An ALF generation subunit configured to sample attenuation values from descending aorta at the head and tail of the image stack of the left ventricular myocardium, and generate an arterial input function;

The TAC generation subunit is used for generating a tissue attenuation curve according to the attenuation value of each myocardial voxel in the left ventricular myocardium;

And the fitting subunit is used for fitting the shape of the tissue attenuation curve according to the arterial input function.

In an embodiment of the present invention, the second model generating module 730 includes:

the second segmentation submodule is used for segmenting the CTA image and generating a coronary two-dimensional cross-section image;

and the second model reconstruction sub-module is used for reconstructing the target coronary artery according to the coronary artery two-dimensional section image to generate a target coronary artery three-dimensional model corresponding to the patient.

In one embodiment of the present invention, the data estimation module 750 includes:

The outlet truncation submodule is used for truncating the coronary outlet according to the target coronary three-dimensional model to generate a truncated coronary tree;

The myocardial perfusion region segmentation submodule is used for carrying out myocardial perfusion region segmentation on the target coronary blood flow model; specifically, each voxel of the left ventricular myocardium is allocated to a truncated coronary tree branch nearest to the left ventricle as a corresponding territory for each voxel;

the CFD calculation sub-module is used for calculating a fluid dynamics equation according to voxels in the myocardial perfusion region and corresponding myocardial blood flow to obtain pressure distribution in a target coronary artery;

FFRCT a calculation sub-module for determining fractional flow reserve of the target coronary artery from the pressure distribution within the target coronary artery.

In one embodiment of the present invention, the CFD calculation sub-module includes:

a blood flow calculation unit, which comprises coronary artery blood flow of a target outlet generated according to voxels in the myocardial perfusion region and corresponding myocardial blood flow;

and the resistance calculation unit comprises a congestion outlet resistance of the target coronary artery generated according to the coronary artery blood flow of the target outlet and the average arterial pressure of the target coronary artery.

Referring to fig. 8, a computer device for a fractional flow reserve assessment method based on a specific coronary flow model of the present invention is shown, which may specifically include the following:

The computer device 12 described above is embodied in the form of a general purpose computing device, and the components of the computer device 12 may include, but are not limited to: one or more processors or processing units 16, a system memory 28, a bus 18 that connects the various system components, including the system memory 28 and the processing units 16.

Bus 18 represents one or more of several types of bus 18 structures, including a memory bus 18 or memory controller, a peripheral bus 18, an accelerated graphics port, a processor, or a local bus 18 using any of a variety of bus 18 architectures. By way of example, and not limitation, such architectures include Industry Standard Architecture (ISA) bus 18, micro channel architecture (MAC) bus 18, enhanced ISA bus 18, video Electronics Standards Association (VESA) local bus 18, and Peripheral Component Interconnect (PCI) bus 18.

Computer device 12 typically includes a variety of computer system readable media. Such media can be any available media that is accessible by computer device 12 and includes both volatile and nonvolatile media, removable and non-removable media.

The system memory 28 may include computer system readable media in the form of volatile memory, such as Random Access Memory (RAM) 30 and/or cache memory 32. The computer device 12 may further include other removable/non-removable, volatile/nonvolatile computer storage media. By way of example only, storage system 34 may be used to read from or write to non-removable, nonvolatile magnetic media (commonly referred to as a "hard disk drive"). Although not shown in fig. 8, a magnetic disk drive for reading from and writing to a removable non-volatile magnetic disk (e.g., a "floppy disk"), and an optical disk drive for reading from or writing to a removable non-volatile optical disk such as a CD-ROM, DVD-ROM, or other optical media may be provided. In such cases, each drive may be coupled to bus 18 through one or more data medium interfaces. The memory may include at least one program product having a set (e.g., at least one) of program modules 42, the program modules 42 being configured to carry out the functions of embodiments of the invention.

A program/utility 40 having a set (at least one) of program modules 42 may be stored in, for example, a memory, such program modules 42 including, but not limited to, an operating system, one or more application programs, other program modules 42, and program data, each or some combination of which may include an implementation of a network environment. Program modules 42 generally perform the functions and/or methods of the embodiments described herein.

The computer device 12 may also communicate with one or more external devices 14 (e.g., keyboard, pointing device, display 24, camera, etc.), one or more devices that enable a user to interact with the computer device 12, and/or any devices (e.g., network card, modem, etc.) that enable the computer device 12 to communicate with one or more other computing devices. Such communication may occur through an input/output (I/O) interface 22. Moreover, computer device 12 may also communicate with one or more networks such as a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network such as the Internet, through network adapter 20. As shown in fig. 8, the network adapter 20 communicates with other modules of the computer device 12 via the bus 18. It should be appreciated that although not shown in fig. 8, other hardware and/or software modules may be used in connection with computer device 12, including, but not limited to: microcode, device drivers, redundant processing units 16, external disk drive arrays, RAID systems, tape drives, data backup storage systems 34, and the like.

The processing unit 16 executes various functional applications and data processing by running programs stored in the system memory 28, for example, implementing a fractional flow reserve assessment method based on a specific coronary flow model provided by an embodiment of the present invention.

That is, the processing unit 16 realizes when executing the program: acquiring CTP source images of cardiac muscles of a patient and CTA images of target coronary arteries; generating a myocardial model according to the CTP source image; generating a target coronary three-dimensional model according to the CTA image; generating a target coronary blood flow model according to the myocardial model and the target coronary three-dimensional model; and determining the fractional flow reserve of the target coronary artery according to the target coronary blood flow model.

In an embodiment of the present application, the present application further provides a computer-readable storage medium having stored thereon a computer program which, when executed by a processor, implements a fractional flow reserve evaluation method based on a specific coronary flow model as provided in all embodiments of the present application:

That is, the program is implemented when executed by a processor: acquiring CTP source images of cardiac muscles of a patient and CTA images of target coronary arteries; generating a myocardial model according to the CTP source image; generating a target coronary three-dimensional model according to the CTA image; generating a target coronary blood flow model according to the myocardial model and the target coronary three-dimensional model; and determining the fractional flow reserve of the target coronary artery according to the target coronary blood flow model.

Any combination of one or more computer readable media may be employed. The computer readable medium may be a computer-readable signal medium or a computer-readable storage medium. The computer readable storage medium can be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or a combination of any of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In this document, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device.

The computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take any of a variety of forms, including, but not limited to, electro-magnetic, optical, or any suitable combination of the foregoing. A computer readable signal medium may also be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device.

Computer program code for carrying out operations of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, smalltalk, C ++ and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described by differences from other embodiments, and identical and similar parts between the embodiments are all enough to be referred to each other.

While preferred embodiments of the present application have been described, additional variations and modifications in those embodiments may occur to those skilled in the art once they learn of the basic inventive concepts. It is therefore intended that the following claims be interpreted as including the preferred embodiment and all such alterations and modifications as fall within the scope of the embodiments of the application.

Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or terminal that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or terminal. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or terminal device that comprises the element.

The fractional flow reserve assessment method and device based on the specific coronary blood flow model provided by the application are described in detail, and specific examples are applied to illustrate the principle and the implementation of the application, and the description of the examples is only used for helping to understand the method and the core idea of the application; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present application, the present description should not be construed as limiting the present application in view of the above.

Claims

1. A fractional flow reserve assessment method based on a specific coronary flow model, comprising the steps of:

Generating a myocardial model according to the CTP source image;

2. The method of claim 1, wherein the step of generating a myocardial model from said CTP source image comprises:

3. The method of claim 2, wherein the step of segmenting the CTP source image to generate a two-dimensional cross-sectional image of the myocardium comprises:

4. A method according to claim 3, wherein the step of generating an arterial input function and a tissue attenuation curve from the isolated left ventricular myocardium comprises:

5. The method of claim 1, wherein generating a three-dimensional model of a target coronary artery from the CTA image comprises:

6. A method according to claim 3, wherein the step of determining the fractional flow reserve of the target coronary artery from the target coronary blood flow model comprises:

7. The method of claim 6, wherein the step of deriving a pressure distribution within the target coronary artery from the voxel in the myocardial perfusion region and its corresponding myocardial blood flow computational fluid dynamics equation, comprises:

8. A fractional flow reserve assessment device based on a specific coronary flow model, comprising:

9. An apparatus comprising a processor, a memory, and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing a fractional flow reserve assessment method based on a specific coronary flow model as claimed in any one of claims 1 to 7.

10. A computer-readable storage medium, on which a computer program is stored, which computer program, when being executed by a processor, implements the fractional flow reserve assessment method based on a specific coronary flow model according to any one of claims 1 to 7.