CN114664455A - Coronary artery blood flow reserve fraction calculation method and device - Google Patents

Coronary artery blood flow reserve fraction calculation method and device Download PDF

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CN114664455A
CN114664455A CN202210246599.1A CN202210246599A CN114664455A CN 114664455 A CN114664455 A CN 114664455A CN 202210246599 A CN202210246599 A CN 202210246599A CN 114664455 A CN114664455 A CN 114664455A
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boundary condition
coronary
vessel
fractional
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熊晓亮
俞益洲
李一鸣
乔昕
吴献鹏
胡新央
王建安
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Beijing Shenrui Bolian Technology Co Ltd
Shenzhen Deepwise Bolian Technology Co Ltd
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Shenzhen Deepwise Bolian Technology Co Ltd
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    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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    • A—HUMAN NECESSITIES
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
    • A61B5/026—Measuring blood flow
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    • G06F18/00—Pattern recognition
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    • G06T2207/30004—Biomedical image processing
    • G06T2207/30101—Blood vessel; Artery; Vein; Vascular
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    • G—PHYSICS
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    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
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    • G06T2210/24—Fluid dynamics
    • G—PHYSICS
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    • G06T2210/41—Medical

Abstract

The invention provides a method and a device for calculating a coronary artery blood flow reserve fraction. The method comprises the following steps: performing feature fusion on the results of vessel segmentation and reconstruction based on the first modality image and the second modality image respectively to obtain a three-dimensional model of the vessel; solving the boundary condition of the fluid dynamic equation by adopting a multi-mode fusion algorithm; solving a fluid dynamics equation based on the boundary condition and the three-dimensional model of the blood vessel to obtain blood flow parameters of different positions of the blood vessel; calculating a fractional coronary flow reserve at different locations based on the blood flow parameter. The invention solves the three-dimensional model of the blood vessel based on multi-modal image fusion, thus improving the blood vessel segmentation precision; modeling a second modal image acquired by invasive surgery in the boundary condition calculation process, so that the accuracy of the boundary condition is improved when only the first modal image exists; the invention can obviously improve the calculation precision of the FFR through the improvement of the two aspects.

Description

Coronary artery blood flow reserve fraction calculation method and device
Technical Field
The invention belongs to the technical field of medical imaging, and particularly relates to a method and a device for calculating coronary artery blood flow reserve fraction.
Background
Coronary heart disease is a heart disease in which myocardial ischemia or necrosis is caused by stenosis or obstruction of a lumen due to atherosclerosis of coronary arteries. At present, the gold standard for clinical diagnosis of coronary heart disease is Coronary Angiography (CAG), and doctors can determine whether there is stenosis in blood vessels, the location and extent of stenosis, etc. by the contour of blood vessels displayed by angiography, and guide further treatment. However, coronary angiography cannot evaluate blood flow from a functional level, and cannot determine the properties of plaque. Fractional Flow Reserve (FFR), a functional assessment of coronary blood Flow, was proposed in 1995 by Nico Pijls, a netherlands scholars, to assess the effect of vascular stenosis on distal blood supply by measuring the pressure in the coronary arteries. Conventional Invasive FFR measurements (ICA-FFR) FFR is calculated by measuring pressure values proximal and distal to the lesion site by surgical implantation of a pressure guidewire in the vessel. Invasive FFR measurements in coronary angiography have become the "gold standard" for assessing coronary vascular physiological function through long-term clinical studies. However, invasive examination has the defects of high operation risk, high cost and the like, and meanwhile, measurement cannot be carried out on patients who are not suitable for vasodilator drugs. The FFR calculation based on the image just solves the problems of ICA-FFR, avoids the risk operations of pressure guide wire intervention in blood vessels, blood vessel expansion medicine taking and the like through algorithms such as fluid dynamics simulation and the like, changes the intervention diagnosis into non-invasive diagnosis on the premise of ensuring the accuracy, and has important significance for reducing the physical injury and the economic burden of patients.
The FFR calculation based on the images mainly obtains the blood vessel images of the patient through different imaging modes, reconstructs a coronary vessel three-dimensional model, then adopts a fluid dynamics algorithm to simulate the dynamic process of blood flowing through the blood vessel, and calculates and obtains the pressure of each position of the blood vessel cavity. Current modes of vascular imaging include Digital Subtraction Angiography (DSA), Coronary CT angiography (CTA), intravascular ultrasound (IVUS), Optical Coherence Tomography (OCT), and the like. The data of different modes are different in convenience, safety and accuracy of scanning, and are suitable for different application scenes. DSA needs invasive surgery and contrast medium injection, and time-series 2D images are acquired and mainly used for judging the degree of vascular stenosis; IVUS requires invasive surgery but does not require injection of contrast media, enabling identification of the precise vessel lumen; OCT has high resolution, needs contrast agent to block blood flow and can be used for analyzing plaque components. The current FFR calculation method based on images respectively calculates the FFR based on a certain mode. The calculation process of the single mode data FFR is as follows: reconstructing a blood vessel three-dimensional lumen model and generating a grid; setting boundary conditions such as flow rate, pressure and the like of an inlet and an outlet; solving a fluid dynamic equation; post-processing and calculating FFR. Wherein the vessel model reconstruction is the basis of the fluid simulation. Meanwhile, the setting of boundary conditions is crucial to the equation solving. Therefore, how to obtain the real blood vessel lumen and more accurate boundary conditions become the key problem of FFR calculation.
Disclosure of Invention
In order to solve the above problems in the prior art, the present invention provides a method and an apparatus for calculating fractional flow reserve of coronary artery.
In order to achieve the above object, the present invention adopts the following technical solutions.
In a first aspect, the present invention provides a method for calculating a fractional coronary flow reserve, comprising the steps of:
performing feature fusion on the results of vessel segmentation and reconstruction based on the first modality image and the second modality image respectively to obtain a three-dimensional model of the vessel;
obtaining a boundary condition for solving a fluid dynamics equation based on the fusion of the first modality image and the second modality image;
solving a fluid dynamics equation based on the boundary condition and the three-dimensional model of the blood vessel to obtain blood flow parameters of different positions of the blood vessel;
calculating a fractional coronary flow reserve at different locations based on the blood flow parameter.
Further, the method comprises a vessel registration step before the feature fusion: and aiming at the result of vessel segmentation and reconstruction based on the first modality image and the second modality image, registering the vessels by adopting a traditional algorithm or a deep learning method so as to align the vessels of the two results.
Further, the method for obtaining the boundary condition further includes:
constructing a neural network model with the first modality image data and the second modality image data as input and the boundary condition as output, and training; and inputting the first mode image into the trained model to obtain the boundary condition.
Further, the fluid dynamics equation is:
Figure BDA0003544948260000031
wherein u is a blood flow rate, p is a pressure, g is a gravitational acceleration, and V is a kinematic viscosity.
Further, the calculation formula of the coronary artery fractional flow reserve is as follows:
Figure BDA0003544948260000032
wherein FFR is the fractional coronary flow reserve, PdThe pressure value, P, distal to the coronary stenosisaThe pressure value proximal to the coronary stenosis.
In a second aspect, the present invention provides a coronary fractional flow reserve calculation apparatus, including:
the multi-modal fusion module is used for performing feature fusion on the result of the vessel segmentation and reconstruction respectively based on the first modal image and the second modal image to obtain a three-dimensional model of the vessel;
the boundary condition obtaining module is used for obtaining a boundary condition for solving a fluid dynamic equation based on the fusion of the first modal image and the second modal image;
the dynamic equation solving module is used for solving a fluid dynamic equation based on the boundary condition and the three-dimensional model of the blood vessel to obtain blood flow parameters of different positions of the blood vessel;
an FFR calculation module to calculate fractional coronary flow reserve at different locations based on the blood flow parameters.
Further, the device further comprises a blood vessel registration module, configured to register, with respect to a result of the blood vessel segmentation and reconstruction based on the first modality image and the second modality image, a blood vessel by using a conventional algorithm or a deep learning method, so that blood vessels of the two results are aligned.
Further, the method for obtaining the boundary condition further includes:
constructing a neural network model with the first modality image data and the second modality image data as input and the boundary condition as output, and training; and inputting the first mode image into the trained model to obtain the boundary condition.
Further, the fluid dynamics equation is:
Figure BDA0003544948260000041
wherein u is a blood flow rate, p is a pressure, g is a gravitational acceleration, and V is a kinematic viscosity.
Further, the calculation formula of the coronary artery fractional flow reserve is as follows:
Figure BDA0003544948260000042
wherein FFR is the fractional coronary flow reserve, PdThe pressure value, P, distal to the coronary stenosisaThe pressure value proximal to the coronary stenosis.
Compared with the prior art, the invention has the following beneficial effects.
According to the method, the three-dimensional model of the blood vessel is obtained by performing feature fusion on the result of the blood vessel segmentation and reconstruction based on the first modality image and the second modality image respectively, the boundary condition for solving the fluid dynamics equation is obtained based on the fusion of the first modality image and the second modality image, the fluid dynamics equation is solved based on the boundary condition and the three-dimensional model of the blood vessel, the blood Flow parameters of different positions of the blood vessel are obtained, and the coronary artery Flow reserve fraction FFR (fractional Flow reserve) is calculated based on the blood Flow parameters, so that the automatic calculation of the FFR is realized. The invention solves the three-dimensional model of the blood vessel based on multi-modal image fusion, thus improving the blood vessel segmentation precision; the boundary condition is calculated based on the second modality image acquired by the invasive surgery, so that the precision of the boundary condition is improved; the invention can obviously improve the calculation precision of the FFR through the improvement of the two aspects.
Drawings
Fig. 1 is a flowchart of a method for calculating fractional coronary flow reserve according to an embodiment of the present invention.
Fig. 2 is a schematic overall flow chart of another embodiment of the present invention.
FIG. 3 is a diagram illustrating the FFR calculation result according to the embodiment of the present invention.
Fig. 4 is a block diagram of a device for calculating fractional coronary flow reserve according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention clearer and more obvious, the present invention is further described below with reference to the accompanying drawings and the detailed description. It should be apparent that the described embodiments are only some embodiments of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
Fig. 1 is a flowchart of a method for calculating fractional flow reserve of coronary artery according to an embodiment of the present invention, including the following steps:
101, performing feature fusion on results of vessel segmentation and reconstruction respectively based on a first modality image and a second modality image to obtain a three-dimensional model of a vessel;
102, obtaining a boundary condition for solving a fluid dynamic equation based on the fusion of the first modal image and the second modal image;
103, solving a fluid dynamics equation based on the boundary condition and the three-dimensional model of the blood vessel to obtain blood flow parameters of different positions of the blood vessel;
based on the blood flow parameters, a fractional coronary flow reserve is calculated for different locations, step 104.
The embodiment provides a coronary artery fractional flow reserve calculation method. At present, the fractional flow reserve FFR calculation method based on medical images generally performs FFR calculation based on a certain modality. For example, because the CTA image scanning process is relatively easy (non-invasively available) and relatively inexpensive, calculating FFR based on CTA images is a way of FFR calculation with relatively large clinical demand. However, CTA is a static image, and cannot acquire information such as blood flow and flow velocity from the image. The existing blood flow velocity method for calculating boundary conditions mainly includes two types: the first type is that the blood flow information is estimated in a data-driven manner, for example, a regression model for FFR calculation is trained by extracting features such as different anatomies, functions, diagnoses and patient information, but factors influencing the blood flow are complex and difficult to comprehensively estimate; the second type is to estimate blood flow information through different phase data, for example, a plurality of phase data are respectively set with different boundary conditions to solve blood flow states and finally integrated. None of these methods results in precise boundary conditions associated with the patient, resulting in an inaccurate FFR calculation. Therefore, the embodiment combines the advantages of data of different mode images (including but not limited to CTA, DSA, IVUS, OCT and the like) (for example, DSA can acquire more accurate blood vessel contour and judge the stenosis rate of the blood vessel, the DSA has time sequence information, IVUS can realize accurate blood vessel lumen, OCT resolution is high), more accurate blood vessel lumen and simulation boundary conditions are established through data fusion, the solution precision of a fluid dynamics equation is improved, and more accurate FFR is obtained.
In this embodiment, step 101 is mainly used to obtain a three-dimensional model of a blood vessel. In the embodiment, the FFR calculation accuracy is improved from two aspects of optimizing the vessel modeling and the boundary condition calculation by mainly fusing multi-modal image data and fully utilizing the advantages of different modal data. In this embodiment, the first-mode image is used as the main mode, the second-mode image is used as the auxiliary mode, for example, the CTA image is used as the main mode (of course, other modes may be used as the main mode), and DSA or IVUS, OCT or other mode data is used as the auxiliary mode (as shown in fig. 2), so as to enhance CTA. Firstly, respectively aiming at two different modal data of CTA and DSA, segmenting and reconstructing blood vessels by adopting a traditional algorithm or a deep learning method; then, the results of segmentation and reconstruction based on the two modality data are fused to obtain a three-dimensional model of the blood vessel. In this embodiment, compared with processing based on only a single modality image, the method can overcome the defects of the single modality image, for example, can solve the problem that the blood vessel segmentation is inaccurate due to different factors such as severe calcification and motion artifact in the CTA image.
In this embodiment, step 102 is mainly used to obtain the boundary condition. The boundary conditions mainly comprise blood flow velocity at the inlet and the outlet of the blood vessel and other relevant parameters which can be used as boundary conditions of the fluid simulation. The boundary condition based on the second modality image can be obtained by establishing a model taking the first modality image and the second modality image as input and the boundary condition as output and utilizing the trained model. In the present embodiment, information such as a blood flow volume and a blood flow velocity is calculated from the second modality image, and compared to other blood flow estimation methods, the blood flow information obtained from the second modality image has a stronger correlation with actual information such as a position and a degree of stenosis of a blood vessel of a patient, so that a boundary condition with higher accuracy can be obtained.
In this embodiment, step 103 is mainly used to calculate the blood flow parameters at different positions of the blood vessel. In this embodiment, based on the three-dimensional model of the blood vessel obtained in step 101 and the boundary conditions obtained in step 102, the fluid dynamics equation is solved to obtain the blood flow parameters, such as the blood flow rate, the pressure, and the like, at each position of the blood vessel, and other relevant physical quantities, such as the wall shear force, can also be calculated for analyzing the blood vessel and the plaque. Solving the fluid dynamics equation according to the three-dimensional model and the boundary conditions of the blood vessel has various solving methods, belongs to the mature prior art, and the specific solving method is not limited in the embodiment.
In this embodiment, step 104 is mainly used to calculate FFR. The present embodiment calculates FFR according to the definition of FFR based on the blood flow parameter obtained in step 103. Since the blood flow parameters at different locations of the blood vessel are also different, it is generally necessary to calculate the FFR at each different location. Fig. 3 shows a distribution diagram of the FFR calculation result, and the general doctor is interested in the position of coronary stenosis, such as two small triangles marked with "0.58" in fig. 3.
The embodiment solves the three-dimensional model of the blood vessel based on multi-modal image fusion, thereby improving the blood vessel segmentation precision; meanwhile, the boundary condition is calculated based on the second modality image acquired by the invasive surgery, so that the precision of the boundary condition is improved. The embodiment can obviously improve the calculation accuracy of the FFR through the improvement of the two aspects.
As an alternative embodiment, the method further comprises, before performing feature fusion, a vessel registration step of: aiming at the result of vessel segmentation and reconstruction based on the first modality image and the second modality image, the vessel is registered by adopting a traditional algorithm or a deep learning method, so that the vessels of the two results are aligned.
The embodiment provides a technical scheme of blood vessel registration. Before multi-modal image feature fusion, the results of segmentation and reconstruction of two modalities of blood vessels generally need to be registered, that is, the blood vessels obtained based on the two modalities are aligned, so as to improve the accuracy of feature fusion. The vessel registration method may adopt a conventional algorithm or a deep learning method, which is not specifically limited in this embodiment.
As an alternative embodiment, the method for obtaining the boundary condition further includes:
constructing a neural network model with the first modality image data and the second modality image data as input and the boundary condition as output, and training; and inputting the first mode image into the trained model to obtain the boundary condition.
This embodiment provides another technical solution for obtaining the boundary condition. The previous embodiment is based on boundary conditions resulting from the fusion of two modality images. In practice, however, second modality images of some patients may sometimes not be available. For this reason, the present embodiment provides another technical solution for obtaining the boundary condition, which can be obtained only from the input first modality image. In this embodiment, the required boundary condition can be obtained by constructing a neural network model using the first modality image data and the second modality image data as input and the boundary condition as output, training the model, and inputting the first modality image into the trained model. Therefore, the calculation of the FFR can be finished without the second modality image, and the convenience and the reliability of the calculation of the FFR are improved.
As an alternative embodiment, the fluid dynamics equation is:
Figure BDA0003544948260000071
wherein u is the blood flow rate, p is the pressure, g is the gravitational acceleration, and V is the kinematic viscosity.
This example gives an expression of the fluid dynamics equation. The fluid dynamic equation is a relational expression which is obtained by applying the law of conservation of mass, momentum and energy to the fluid motion and is related to physical quantities such as fluid speed, pressure, density and the like. The fluid dynamics equations have both an integral form and a derivative form. The former obtains an integral relation between the physical quantities of the fluid by integrating the control body and the control surface; the latter obtains the differential relation between the physical quantities of the fluid at any spatial point by directly establishing an equation for the infinitesimal control body or system. Solving the basic equation of the integral form can obtain the overall performance relation, such as the resultant force acting between the fluid and the object, the total energy exchange and the like; and solving a differential form basic equation or an integral form basic equation established for the infinitesimal control body to obtain the details of the flow field, namely the physical quantity of the fluid on each space point.
As an alternative embodiment, the calculation formula of the coronary artery fractional flow reserve is as follows:
Figure BDA0003544948260000081
wherein FFR is the fractional coronary flow reserve, PdThe pressure value, P, distal to the coronary stenosisaThe pressure value proximal to the coronary stenosis.
This example gives the formula for calculating the fractional coronary flow reserve FFR. FFR was proposed in 1995 by the netherlands scholar Nico Pijls to assess the effect of vascular stenosis on distal blood supply by measuring the pressure in the coronary arteries. F at a certain position of a blood vesselFR is equal to the pressure value P of the distal end of the stenosisdPressure value P of the proximal end of stenosisaThe ratio of (a) to (b). Therefore, P can be obtained at each positiondAnd PaThe ratio is calculated to obtain the FFR here.
Fig. 4 is a schematic composition diagram of an apparatus for calculating fractional coronary flow reserve according to an embodiment of the present invention, the apparatus comprising:
the multi-modal fusion module 11 is configured to perform feature fusion on results of vessel segmentation and reconstruction based on the first modal image and the second modal image, respectively, to obtain a three-dimensional model of a vessel;
a boundary condition obtaining module 12, configured to obtain a boundary condition for solving a fluid dynamics equation based on fusion of the first modality image and the second modality image;
the dynamic equation solving module 13 is used for solving a fluid dynamic equation based on the boundary condition and the three-dimensional model of the blood vessel to obtain blood flow parameters of different positions of the blood vessel;
and the FFR calculation module 14 is used for calculating the coronary artery blood flow reserve fractions at different positions based on the blood flow parameters.
The apparatus of this embodiment may be configured to implement the technical solution of the method embodiment shown in fig. 1, and the implementation principle and the technical effect are similar, which are not described herein again. The same applies to the following embodiments, which are not further described.
As an alternative embodiment, the apparatus further includes a blood vessel registration module, configured to register, for a result of the blood vessel segmentation and reconstruction based on the first modality image and the second modality image, a blood vessel using a conventional algorithm or a deep learning method, so as to align the two resulting blood vessels.
As an optional embodiment, the method for obtaining the boundary condition further includes:
constructing a neural network model with the first modality image data and the second modality image data as input and the boundary condition as output, and training; and inputting the first mode image into the trained model to obtain the boundary condition.
As an alternative embodiment, the fluid dynamics equation is:
Figure BDA0003544948260000091
wherein u is a blood flow rate, p is a pressure, g is a gravitational acceleration, and V is a kinematic viscosity.
As an alternative embodiment, the calculation formula of the coronary artery fractional flow reserve is as follows:
Figure BDA0003544948260000092
wherein FFR is the fractional coronary flow reserve, PdThe pressure value, P, distal to the coronary stenosisaThe pressure value proximal to the coronary stenosis.
The above description is only for the specific embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.

Claims (10)

1. A method for calculating fractional coronary flow reserve, comprising the steps of:
performing feature fusion on the results of vessel segmentation and reconstruction based on the first modality image and the second modality image respectively to obtain a three-dimensional model of the vessel;
obtaining a boundary condition for solving a fluid dynamics equation based on the fusion of the first modality image and the second modality image;
solving a fluid dynamics equation based on the boundary condition and the three-dimensional model of the blood vessel to obtain blood flow parameters of different positions of the blood vessel;
calculating a fractional coronary flow reserve at different locations based on the blood flow parameter.
2. The method of calculating fractional coronary flow reserve according to claim 1, further comprising a vessel registration step prior to feature fusion: and aiming at the result of vessel segmentation and reconstruction based on the first modality image and the second modality image, registering the vessels by adopting a traditional algorithm or a deep learning method so as to align the vessels of the two results.
3. The method of calculating fractional coronary flow reserve of claim 1, wherein the method of obtaining the boundary condition further comprises:
constructing a neural network model with the first modality image data and the second modality image data as input and the boundary condition as output, and training; and inputting the first mode image into the trained model to obtain the boundary condition.
4. The method of calculating fractional coronary flow reserve of claim 1, wherein the fluid dynamics equation is:
Figure FDA0003544948250000011
wherein u is a blood flow rate, p is a pressure, g is a gravitational acceleration, and V is a kinematic viscosity.
5. The method of claim 1, wherein the fractional coronary flow reserve is calculated by the formula:
Figure FDA0003544948250000012
wherein FFR is the fractional coronary flow reserve, PdThe pressure value, P, distal to the coronary stenosisaThe pressure value proximal to the coronary stenosis.
6. A coronary fractional flow reserve calculation apparatus, comprising:
the multi-modal fusion module is used for performing feature fusion on the result of the vessel segmentation and reconstruction respectively based on the first modal image and the second modal image to obtain a three-dimensional model of the vessel;
a boundary condition obtaining module for obtaining a boundary condition for solving a fluid dynamic equation based on the fusion of the first modality image and the second modality image;
the dynamic equation solving module is used for solving a fluid dynamic equation based on the boundary condition and the three-dimensional model of the blood vessel to obtain blood flow parameters of different positions of the blood vessel;
an FFR calculation module to calculate fractional coronary flow reserve at different locations based on the blood flow parameters.
7. The apparatus according to claim 6, further comprising a vessel registration module, configured to, for the result of vessel segmentation and reconstruction based on the first modality image and the second modality image, register vessels by using a conventional algorithm or a deep learning method, so as to align the vessels of the two results.
8. The apparatus according to claim 6, wherein the method for obtaining the boundary condition further comprises:
constructing a neural network model which takes first modality image data and second modality image data as input and takes the boundary condition as output, and training; and inputting the first mode image into the trained model to obtain the boundary condition.
9. The apparatus according to claim 6, wherein the fluid dynamics equation is:
Figure FDA0003544948250000021
wherein u is a blood flow rate, p is a pressure, g is a gravitational acceleration, and V is a kinematic viscosity.
10. The apparatus according to claim 6, wherein the fractional coronary flow reserve is calculated by the formula:
Figure FDA0003544948250000031
wherein FFR is the fractional coronary flow reserve, PdThe pressure value, P, distal to the coronary stenosisaIs the pressure value at the proximal end of the coronary stenosis.
CN202210246599.1A 2022-03-14 2022-03-14 Coronary artery blood flow reserve fraction calculation method and device Pending CN114664455A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115272447A (en) * 2022-09-29 2022-11-01 全景恒升（北京）科学技术有限公司 Multi-modal image-based fractional flow reserve calculation method, device and equipment

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115272447A (en) * 2022-09-29 2022-11-01 全景恒升（北京）科学技术有限公司 Multi-modal image-based fractional flow reserve calculation method, device and equipment
CN115272447B (en) * 2022-09-29 2022-12-20 全景恒升（北京）科学技术有限公司 Multi-modal image-based fractional flow reserve calculation method, device and equipment

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