Method and device for determining fractional flow reserve, electronic equipment and storage medium

Abstract

The invention discloses a fractional flow reserve determination method and device, electronic equipment and a storage medium. The method comprises the following steps: acquiring a coronary angiography image containing a target blood vessel segment, generating an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determining first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model; acquiring an optical coherence tomography image containing the target blood vessel segment, and determining second key point information of the target blood vessel segment based on the optical coherence tomography image; determining a model scaling coefficient based on the first key point information and the second key point information, and scaling the initial three-dimensional blood vessel model based on the model scaling coefficient to obtain a target three-dimensional blood vessel model; and determining the fractional flow reserve based on the target three-dimensional blood vessel model. According to the technical scheme, by introducing the optical coherence tomography image, correction of the three-dimensional blood vessel model is achieved, the precision of the three-dimensional blood vessel model is improved, and then the accuracy of the fractional flow reserve is improved.

Description

Technical Field

[0001] The present invention relates to the field of image processing technology, and in particular to a method, device, electronic device and storage medium for determining a blood flow reserve fraction. Background Art

[0002] Fractional Flow Reserve (FFR) has become the gold standard for evaluating myocardial ischemia. Currently, the methods for obtaining FFR in clinical practice include invasive and non-invasive methods.

[0003] In the process of implementing the present invention, the inventors found that there are at least the following technical problems in the prior art: the existing method for obtaining the blood flow reserve fraction has the problem of inaccurate blood flow reserve fraction. Summary of the invention

[0004] The present invention provides a method, device, electronic device and storage medium for determining a blood flow reserve fraction, so as to improve the accuracy of the blood flow reserve fraction.

[0005] According to one aspect of the present invention, a method for determining a blood flow reserve fraction is provided, comprising:

[0006] Acquire a coronary angiography image containing a target blood vessel segment, generate an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determine first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model;

[0007] Acquire an optical coherence tomography image containing a target blood vessel segment, and determine second key point information of the target blood vessel segment based on the optical coherence tomography image;

[0008] determining a model scaling factor based on the first key point information and the second key point information, and scaling the initial three-dimensional blood vessel model based on the model scaling factor to obtain a target three-dimensional blood vessel model;

[0009] A blood flow reserve fraction is determined based on the target three-dimensional blood vessel model.

[0010] According to another aspect of the present invention, there is provided a device for determining a blood flow reserve fraction, comprising:

[0011] a first key point information determination module, configured to acquire a coronary angiography image including a target blood vessel segment, generate an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determine first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model;

[0012] a second key point information determining module, configured to acquire an optical coherence tomography image containing a target blood vessel segment, and determine second key point information of the target blood vessel segment based on the optical coherence tomography image;

[0013] a three-dimensional blood vessel model scaling module, configured to determine a model scaling factor based on the first key point information and the second key point information, and scale the initial three-dimensional blood vessel model based on the model scaling factor to obtain a target three-dimensional blood vessel model;

[0014] The blood flow reserve fraction determination module is used to determine the blood flow reserve fraction based on the target three-dimensional blood vessel model.

[0015] According to another aspect of the present invention, an electronic device is provided, the electronic device comprising:

[0016] at least one processor;

[0017] and a memory communicatively coupled to the at least one processor;

[0018] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for determining the blood flow reserve fraction described in any embodiment of the present invention.

[0019] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the blood flow reserve fraction described in any embodiment of the present invention when executed.

[0020] The technical solution of the embodiment of the present invention obtains a coronary angiography image containing a target vascular segment, and then generates an initial three-dimensional vascular model of the target vascular segment based on the coronary angiography image, and then determines first key point information of the target vascular segment based on the initial three-dimensional vascular model; further, obtains an optical coherence tomography image containing the target vascular segment, and then determines second key point information of the target vascular segment based on the optical coherence tomography image; further, determines a model scaling factor based on the first key point information and the second key point information, and then scales the initial three-dimensional vascular model according to the model scaling factor to obtain a target three-dimensional vascular model, thereby realizing correction of the three-dimensional vascular model and improving the accuracy of the three-dimensional vascular model, and then determines a blood flow reserve fraction based on the corrected target three-dimensional vascular model, thereby improving the accuracy of the blood flow reserve fraction.

[0021] It should be understood that the contents described in this section are not intended to identify the key or important features of the embodiments of the present invention, nor are they intended to limit the scope of the present invention. Other features of the present invention will become easily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0023] Figure 1 is a flow chart of a method for determining a blood flow reserve fraction according to Embodiment 1 of the present invention;

[0024] Figure 2 is a schematic diagram of a three-dimensional blood vessel model reconstruction provided according to an embodiment of the present invention;

[0025] Figure 3 is a flow chart of a method for determining a blood flow reserve fraction according to Embodiment 2 of the present invention;

[0026] Figure 4 is a flow chart of a method for determining a blood flow reserve fraction according to Embodiment 3 of the present invention;

[0027] Figure 5 is a schematic structural diagram of a device for determining a blood flow reserve fraction according to a fourth embodiment of the present invention;

[0028] Figure 6 It is a schematic diagram of the structure of an electronic device for implementing the method for determining the blood flow reserve fraction according to an embodiment of the present invention. DETAILED DESCRIPTION

[0029] In order to enable those skilled in the art to better understand the scheme of the present invention, the technical scheme in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0030] It should be noted that the terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device that includes a series of steps or units is not necessarily limited to those steps or units that are clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices. The acquisition, storage, use, processing, etc. of data in the technical solution of this application comply with the relevant provisions of national laws and regulations.

[0031] Embodiment 1

[0032] Figure 1 This is a flow chart of a method for determining a blood flow reserve fraction provided in Embodiment 1 of the present invention. This embodiment is applicable to the case of determining a blood flow reserve fraction based on multimodal data. The method can be performed by a blood flow reserve fraction determination device. The blood flow reserve fraction determination device can be implemented in the form of hardware and / or software. The blood flow reserve fraction determination device can be configured in an electronic device such as a terminal or a server. Figure 1 As shown, the method includes:

[0033] S110, acquiring a coronary angiography image including a target blood vessel segment, generating an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determining first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model.

[0034] In this embodiment, the coronary angiography image refers to medical imaging data containing a target blood vessel segment, and the number of the medical imaging images may be multiple, wherein the target blood vessel segment refers to a coronary artery with a stenotic lesion.

[0035] Specifically, a plurality of coronary angiography images including the target blood vessel segment may be reconstructed to obtain an initial three-dimensional blood vessel model of the target blood vessel segment.

[0036] Optionally, obtaining a coronary angiography image containing a target blood vessel segment includes: obtaining coronary angiography images containing the target blood vessel segment at at least two angles; correspondingly, generating an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography images includes: annotating the coronary angiography images containing the target blood vessel segment at at least two angles to obtain blood vessel contour images containing the target blood vessel segment at at least two angles; and generating the initial three-dimensional blood vessel model based on the blood vessel contour images containing the target blood vessel segment at at least two angles.

[0037] Exemplarily, a coronary angiography image containing the target blood vessel segment at a first angle and a coronary angiography image containing the target blood vessel segment at a second angle are obtained from a preset storage path, and the first angle and the second angle may differ by greater than or equal to 25 degrees; further, the coronary angiography image containing the target blood vessel segment at the first angle and the coronary angiography image containing the target blood vessel segment at the second angle are annotated to obtain a blood vessel contour image at the first angle ( Figure 2 Upper left) and the vascular contour image at the second angle ( Figure 2 The vascular contour image at the first angle and the vascular contour image at the second angle are then used to generate an initial three-dimensional vascular model according to the morphological imaging method ( Figure 2 right).

[0038] In this embodiment, the first key point information refers to information associated with the key point in the initial three-dimensional blood vessel model, which can be information such as length or area, and is not specifically limited here. The key point may include but is not limited to the location of the blood vessel opening, the location of the blood vessel bifurcation, and the narrowest location of the blood vessel.

[0039] Specifically, the first key point information of the initial three-dimensional blood vessel model can be measured and obtained by using a software measurement tool or other measurement methods.

[0040] S120: Acquire an optical coherence tomography image containing a target blood vessel segment, and determine second key point information of the target blood vessel segment based on the optical coherence tomography image.

[0041] In this embodiment, the optical coherence tomography (OCT) image refers to an image containing a target blood vessel segment, and the number of the image can be multiple. The second key point information refers to information associated with the key point in the optical coherence tomography image, which can be information such as length or area, and is not specifically limited here. It should be noted that the key points in the optical coherence tomography image can correspond one-to-one to the key points in the initial three-dimensional blood vessel model, that is, the key points in the two are the same blood vessel position.

[0042] Exemplarily, a plurality of optical coherence tomography images including the target blood vessel segment may be acquired from a preset storage path, and then the second key point information of the optical coherence tomography images may be measured by a software measurement tool or other measurement methods.

[0043] S130: Determine a model scaling factor based on the first key point information and the second key point information, and scale the initial three-dimensional blood vessel model based on the model scaling factor to obtain a target three-dimensional blood vessel model.

[0044] In this embodiment, the ratio of the first key point information to the second key point information can be used as a model scaling factor, and the initial three-dimensional blood vessel model can be scaled based on the scaling factor, thereby correcting the three-dimensional blood vessel model and improving the accuracy of the three-dimensional blood vessel model.

[0045] S140: Determine a blood flow reserve fraction based on the target three-dimensional blood vessel model.

[0046] In this embodiment, the blood flow reserve fraction is determined based on the modified three-dimensional blood vessel model, thereby improving the accuracy of the blood flow reserve fraction.

[0047] The technical solution of the embodiment of the present invention obtains a coronary angiography image containing a target vascular segment, and then generates an initial three-dimensional vascular model of the target vascular segment based on the coronary angiography image, and then determines first key point information of the target vascular segment based on the initial three-dimensional vascular model; further, obtains an optical coherence tomography image containing the target vascular segment, and then determines second key point information of the target vascular segment based on the optical coherence tomography image; further, determines a model scaling factor based on the first key point information and the second key point information, and then scales the initial three-dimensional vascular model according to the model scaling factor to obtain a target three-dimensional vascular model, thereby realizing correction of the three-dimensional vascular model and improving the accuracy of the three-dimensional vascular model, and then determines a blood flow reserve fraction based on the corrected target three-dimensional vascular model, thereby improving the accuracy of the blood flow reserve fraction.

[0048] Embodiment 2

[0049] Figure 3A flow chart of a method for determining a blood flow reserve fraction provided in the second embodiment of the present invention, the method of this embodiment can be combined with the various optional schemes in the method for determining a blood flow reserve fraction provided in the above embodiments. The method for determining a blood flow reserve fraction provided in this embodiment is further optimized. Optionally, the first key point information includes the distance between the key point at the beginning of the target blood vessel segment and the key point at the end of the target blood vessel segment in the initial three-dimensional blood vessel model, and the blood vessel cross-sectional areas corresponding to multiple key points on the target blood vessel segment in the initial three-dimensional blood vessel model; the second key point information includes the distance between the key point at the beginning of the target blood vessel segment and the key point at the end of the target blood vessel segment in the optical coherence tomography image, and the blood vessel cross-sectional areas corresponding to multiple key points on the target blood vessel segment in the optical coherence tomography image; accordingly, the model scaling coefficient is determined based on the first key point information and the second key point information, and the initial three-dimensional blood vessel model is scaled based on the model scaling coefficient. The method comprises: determining a length scaling factor based on a distance between a key point at a start end of a target blood vessel segment and a key point at a terminal end of the target blood vessel segment in the initial three-dimensional blood vessel model and a distance between a key point at a start end of the target blood vessel segment and a key point at a terminal end of the target blood vessel segment in the optical coherence tomography image; determining an area scaling factor based on blood vessel cross-sectional areas respectively corresponding to a plurality of key points on the target blood vessel segment in the initial three-dimensional blood vessel model and blood vessel cross-sectional areas respectively corresponding to a plurality of key points on the target blood vessel segment in the optical coherence tomography image; and scaling the initial three-dimensional blood vessel model based on the length scaling factor and the area scaling factor to obtain a target three-dimensional blood vessel model.

[0050] like Figure 3 As shown, the method includes:

[0051] S210. Acquire a coronary angiography image including a target blood vessel segment, generate an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determine first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model, wherein the first key point information includes a distance between a starting key point of the target blood vessel segment and an ending key point of the target blood vessel segment in the initial three-dimensional blood vessel model and blood vessel cross-sectional areas corresponding to a plurality of key points on the target blood vessel segment in the initial three-dimensional blood vessel model.

[0052] S220, acquiring an optical coherence tomography image including a target blood vessel segment, and determining second key point information of the target blood vessel segment based on the optical coherence tomography image, wherein the second key point information includes a distance between a key point at a starting end of the target blood vessel segment and a key point at an ending end of the target blood vessel segment in the optical coherence tomography image, and blood vessel cross-sectional areas corresponding to a plurality of key points on the target blood vessel segment in the optical coherence tomography image.

[0053] S230: Determine a length scaling factor based on a distance between a key point at the start end of the target blood vessel segment and a key point at the end end of the target blood vessel segment in the initial three-dimensional blood vessel model and a distance between a key point at the start end of the target blood vessel segment and a key point at the end end of the target blood vessel segment in the optical coherence tomography image.

[0054] Exemplarily, the ratio of the distance between the target blood vessel segment start key point and the target blood vessel segment end key point in the initial three-dimensional blood vessel model and the distance between the target blood vessel segment start key point and the target blood vessel segment end key point in the optical coherence tomography image can be used as the length scaling factor.

[0055] S240: Determine an area scaling factor based on the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the initial three-dimensional blood vessel model and the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the optical coherence tomography image.

[0056] Exemplarily, the number of key points may be n, and the vascular cross-sectional areas corresponding to the multiple key points on the target vascular segment in the initial three-dimensional vascular model are recorded as S1, S2...Sn; the vascular cross-sectional areas corresponding to the multiple key points on the target vascular segment in the optical coherence tomography image are recorded as S1', S2'...Sn'; further, the ratio of the n key points may be R1, R2...Rn; wherein, Rn=Sn / Sn'; further, the area scaling factor=(R1+R2+...+Rn) / n.

[0057] S250 , scaling the initial three-dimensional blood vessel model based on the length scaling factor and the area scaling factor to obtain a target three-dimensional blood vessel model.

[0058] Specifically, the length scaling and area scaling are performed on the initial three-dimensional blood vessel model as a whole through the length scaling coefficient and the area scaling coefficient, thereby improving the accuracy of the three-dimensional blood vessel model.

[0059] S260: Determine a blood flow reserve fraction based on the target three-dimensional blood vessel model.

[0060] The technical solution of the embodiment of the present invention performs overall length scaling and area scaling on the initial three-dimensional blood vessel model through the length scaling coefficient and the area scaling coefficient, thereby improving the accuracy of the three-dimensional blood vessel model.

[0061] Embodiment 3

[0062] Figure 4A flow chart of a method for determining a blood flow reserve fraction provided in Embodiment 3 of the present invention, the method of this embodiment can be combined with the various optional schemes in the method for determining a blood flow reserve fraction provided in the above embodiments. The method for determining a blood flow reserve fraction provided in this embodiment is further optimized. Optionally, determining the blood flow reserve fraction based on the target three-dimensional vascular model includes: determining a target vascular segment pressure drop of the target three-dimensional vascular model; determining the blood flow reserve fraction based on the target vascular segment pressure drop of the target three-dimensional vascular model.

[0063] like Figure 4 As shown, the method includes:

[0064] S310, acquiring a coronary angiography image including a target blood vessel segment, generating an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determining first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model.

[0065] S320: Acquire an optical coherence tomography image containing the target blood vessel segment, and determine second key point information of the target blood vessel segment based on the optical coherence tomography image.

[0066] S330: Determine a model scaling factor based on the first key point information and the second key point information, and scale the initial three-dimensional blood vessel model based on the model scaling factor to obtain a target three-dimensional blood vessel model.

[0067] S340: Determine the pressure drop of the target blood vessel segment of the target three-dimensional blood vessel model.

[0068] In this embodiment, the pressure drop of the target blood vessel segment can be calculated by a pressure drop calculation formula or computational fluid dynamics simulation, etc., which is not specifically limited here.

[0069] Optionally, determining the target blood vessel segment pressure drop of the target three-dimensional blood vessel model includes: determining the vascular viscosity resistance coefficient, vascular inertial resistance coefficient and vascular flow of the target three-dimensional blood vessel model; and determining the target blood vessel segment pressure drop of the target three-dimensional blood vessel model based on the vascular viscosity resistance coefficient, vascular inertial resistance coefficient and the vascular flow.

[0070] Exemplarily, the target blood vessel segment pressure drop is calculated as follows:

[0071] ΔP=C1×Q+C2×Q 2 ;

[0072] Among them, ΔP represents the pressure drop of the target blood vessel segment, C1 represents the blood vessel viscosity resistance coefficient, C2 represents the blood vessel inertia resistance coefficient, Q represents the blood vessel flow rate, K1 represents the coefficient related to the blood vessel diameter, μ is the blood viscosity coefficient, d0 represents the blood vessel diameter, and A0 represents the blood vessel area; ρ represents blood density, A s represents the area of ​​vascular stenosis, k e Indicates a coefficient with a value range of 0.8-1.4; Q = αV m β , where α and β are empirical coefficients, α ranges from 1.2 to 1.5, β ranges from 0.5 to 1.5, V m Represents the blood vessel volume.

[0073] Optionally, determining the target blood vessel segment pressure drop of the target three-dimensional blood vessel model includes: performing computational fluid dynamics simulation on the target three-dimensional blood vessel model to obtain the target blood vessel segment pressure drop of the target three-dimensional blood vessel model.

[0074] In this embodiment, the core of computational fluid dynamics simulation is to solve the Navier-Stokes equations. Exemplarily, the Navier-Stokes equations include the continuity equation and the momentum equation, which are as follows:

[0075] Continuity equation:

[0076]

[0077] Momentum equation:

[0078]

[0079] Among them, u, v and w represent the velocity components in three directions respectively, x, y and z represent the coordinate components in three directions of any point in the target three-dimensional vascular model respectively, t represents time, U represents the velocity vector, div represents the divergence, which is used to characterize the strength of the divergence of the vector field at each point in space, τ represents shear stress, ρ represents blood density, p represents pressure, and Fx, Fy and Fz represent the volume forces in three directions respectively.

[0080] By solving the above Navier-Stokes equations, the pressure and velocity of each point in the target three-dimensional vascular model at any time can be obtained, and the pressure drop of the target blood vessel segment can be calculated based on the pressure and velocity of each point in the target three-dimensional vascular model.

[0081] S350: Determine a blood flow reserve fraction based on a target blood vessel segment pressure drop in the target three-dimensional blood vessel model.

[0082] Specifically, the mean coronary inlet pressure may be obtained; and the blood flow reserve fraction may be determined based on the mean coronary inlet pressure and the target vessel segment pressure drop of the target three-dimensional vessel model.

[0083] Exemplarily, the calculation formula of blood flow reserve fraction is as follows:

[0084]

[0085] Among them, P a It represents the average pressure at the coronary entrance, which can be measured by a pressure guidewire.

[0086] The technical solution of the embodiment of the present invention calculates the target blood vessel segment pressure drop through a pressure drop calculation formula or computational fluid dynamics simulation, and then determines the blood flow reserve fraction based on the target blood vessel segment pressure drop. Compared with the prior art, the pressure drop is taken into account when calculating the blood flow reserve fraction, thereby improving the accuracy of the blood flow reserve fraction.

[0087] Embodiment 4

[0088] Figure 5 This is a schematic diagram of the structure of a device for determining blood flow reserve fraction provided in Embodiment 4 of the present invention. Figure 5 As shown, the device comprises:

[0089] A first key point information determination module 410 is used to obtain a coronary angiography image including a target blood vessel segment, generate an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determine first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model;

[0090] A second key point information determining module 420, configured to obtain an optical coherence tomography image including a target blood vessel segment, and determine second key point information of the target blood vessel segment based on the optical coherence tomography image;

[0091] A three-dimensional blood vessel model scaling module 430, configured to determine a model scaling factor based on the first key point information and the second key point information, and scale the initial three-dimensional blood vessel model based on the model scaling factor to obtain a target three-dimensional blood vessel model;

[0092] The blood flow reserve fraction determination module 440 is configured to determine the blood flow reserve fraction based on the target three-dimensional blood vessel model.

[0093] The technical solution of the embodiment of the present invention obtains a coronary angiography image containing a target vascular segment, and then generates an initial three-dimensional vascular model of the target vascular segment based on the coronary angiography image, and then determines first key point information of the target vascular segment based on the initial three-dimensional vascular model; further, obtains an optical coherence tomography image containing the target vascular segment, and then determines second key point information of the target vascular segment based on the optical coherence tomography image; further, determines a model scaling factor based on the first key point information and the second key point information, and then scales the initial three-dimensional vascular model according to the model scaling factor to obtain a target three-dimensional vascular model, thereby realizing correction of the three-dimensional vascular model and improving the accuracy of the three-dimensional vascular model, and then determines a blood flow reserve fraction based on the corrected target three-dimensional vascular model, thereby improving the accuracy of the blood flow reserve fraction.

[0094] In some optional embodiments, the first key point information includes the distance between the key point at the start end of the target blood vessel segment and the key point at the end end of the target blood vessel segment in the initial three-dimensional blood vessel model, and the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the initial three-dimensional blood vessel model; the second key point information includes the distance between the key point at the start end of the target blood vessel segment and the key point at the end end of the target blood vessel segment in the optical coherence tomography image, and the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the optical coherence tomography image;

[0095] Accordingly, the three-dimensional blood vessel model scaling module 430 is specifically used for:

[0096] Determine a length scaling factor based on a distance between a key point at the start end of the target blood vessel segment and a key point at the end end of the target blood vessel segment in the initial three-dimensional blood vessel model and a distance between a key point at the start end of the target blood vessel segment and a key point at the end end of the target blood vessel segment in the optical coherence tomography image;

[0097] Determine an area scaling factor based on the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the initial three-dimensional blood vessel model and the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the optical coherence tomography image;

[0098] The initial three-dimensional blood vessel model is scaled based on the length scaling factor and the area scaling factor to obtain a target three-dimensional blood vessel model.

[0099] In some optional implementations, the first key point information determination module 410 may be specifically used to:

[0100] Acquire coronary angiography images containing the target blood vessel segment at at least two angles;

[0101] Annotating the coronary angiography images containing the target blood vessel segment at the at least two angles to obtain blood vessel contour images containing the target blood vessel segment at the at least two angles;

[0102] An initial three-dimensional blood vessel model is generated based on the blood vessel contour images including the target blood vessel segment at the at least two angles.

[0103] In some optional embodiments, the blood flow reserve fraction determination module 440 includes:

[0104] a blood vessel segment pressure drop determination unit, used to determine a target blood vessel segment pressure drop of the target three-dimensional blood vessel model;

[0105] The blood flow reserve fraction determination unit is used to determine the blood flow reserve fraction based on the target blood vessel segment pressure drop of the target three-dimensional blood vessel model.

[0106] In some optional implementations, the blood vessel segment pressure drop determination unit may be specifically used to:

[0107] Determining a vascular viscosity resistance coefficient, a vascular inertial resistance coefficient, and a vascular flow rate of the target three-dimensional vascular model;

[0108] A target blood vessel segment pressure drop of the target three-dimensional blood vessel model is determined based on the blood vessel viscosity resistance coefficient, the blood vessel inertia resistance coefficient, and the blood vessel flow rate.

[0109] In some optional implementations, the blood vessel segment pressure drop determination unit may be specifically used to:

[0110] Computational fluid dynamics simulation is performed on the target three-dimensional blood vessel model to obtain a target blood vessel segment pressure drop of the target three-dimensional blood vessel model.

[0111] In some optional embodiments, the blood flow reserve fraction determination unit may be specifically configured to:

[0112] Obtain the mean coronary inlet pressure;

[0113] The blood flow reserve fraction is determined based on the mean coronary inlet pressure and the target vessel segment pressure drop of the target three-dimensional vessel model.

[0114] The device for determining the blood flow reserve fraction provided in the embodiment of the present invention can execute the method for determining the blood flow reserve fraction provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0115] Embodiment 5

[0116] Figure 6A schematic diagram of the structure of an electronic device 10 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital assistants, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or required herein.

[0117] like Figure 6 As shown, the electronic device 10 includes at least one processor 11, and a memory connected to the at least one processor 11, such as a read-only memory (ROM) 12, a random access memory (RAM) 13, etc., wherein the memory stores a computer program that can be executed by at least one processor, and the processor 11 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 12 or the computer program loaded from the storage unit 18 to the random access memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 can also be stored. The processor 11, the ROM 12, and the RAM 13 are connected to each other through a bus 14. The I / O interface 15 is also connected to the bus 14.

[0118] A number of components in the electronic device 10 are connected to the I / O interface 15, including: an input unit 16, such as a keyboard, a mouse, etc.; an output unit 17, such as various types of displays, speakers, etc.; a storage unit 18, such as a disk, an optical disk, etc.; and a communication unit 19, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks.

[0119] The processor 11 may be a variety of general and / or special processing components with processing and computing capabilities. Some examples of the processor 11 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The processor 11 executes the various methods and processes described above, such as a method for determining a blood flow reserve fraction, which includes:

[0120] Acquire a coronary angiography image containing a target blood vessel segment, generate an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determine first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model;

[0121] Acquire an optical coherence tomography image containing a target blood vessel segment, and determine second key point information of the target blood vessel segment based on the optical coherence tomography image;

[0122] determining a model scaling factor based on the first key point information and the second key point information, and scaling the initial three-dimensional blood vessel model based on the model scaling factor to obtain a target three-dimensional blood vessel model;

[0123] A blood flow reserve fraction is determined based on the target three-dimensional blood vessel model.

[0124] In some embodiments, the method for determining the blood flow reserve fraction may be implemented as a computer program, which is tangibly contained in a computer-readable storage medium, such as a storage unit 18. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 10 via the ROM 12 and / or the communication unit 19. When the computer program is loaded into the RAM 13 and executed by the processor 11, one or more steps of the method for determining the blood flow reserve fraction described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the method for determining the blood flow reserve fraction in any other appropriate manner (e.g., by means of firmware).

[0125] Various implementations of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0126] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, so that when the computer program is executed by the processor, the functions / operations specified in the flow chart and / or block diagram are implemented. The computer program may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0127] In the context of the present invention, a computer-readable storage medium may be a tangible medium that may contain or store a computer program for use by or in combination with an instruction execution system, device or equipment. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, 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 disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0128] To provide interaction with a user, the systems and techniques described herein may be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and a pointing device (e.g., a mouse or trackball) through which the user can provide input to the electronic device. Other types of devices may also be used to provide interaction with the user; for example, the feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form (including acoustic input, voice input, or tactile input).

[0129] The systems and techniques described herein may be implemented in a computing system that includes backend components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes frontend components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such backend components, middleware components, or frontend components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0130] A computing system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The client and server relationship is generated by computer programs running on the corresponding computers and having a client-server relationship with each other. The server may be a cloud server, also known as a cloud computing server or cloud host, which is a host product in the cloud computing service system to solve the defects of difficult management and weak business scalability in traditional physical hosts and VPS services.

[0131] It should be understood that the various forms of processes shown above can be used to reorder, add or delete steps. For example, the steps described in the present invention can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution of the present invention can be achieved, and this document does not limit this.

[0132] The above specific implementations do not constitute a limitation on the protection scope of the present invention. It should be understood by those skilled in the art that various modifications, combinations, sub-combinations and substitutions can be made according to design requirements and other factors. Any modification, equivalent substitution and improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A method for determining blood flow reserve fraction, characterized in that: include: Acquire a coronary angiography image containing a target blood vessel segment, generate an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determine first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model; Acquire an optical coherence tomography image containing a target blood vessel segment, and determine second key point information of the target blood vessel segment based on the optical coherence tomography image; determining a model scaling factor based on the first key point information and the second key point information, and scaling the initial three-dimensional blood vessel model based on the model scaling factor to obtain a target three-dimensional blood vessel model; A blood flow reserve fraction is determined based on the target three-dimensional blood vessel model.

2. The method according to claim 1, characterized in that The first key point information includes the distance between the starting key point of the target blood vessel segment and the ending key point of the target blood vessel segment in the initial three-dimensional blood vessel model and the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the initial three-dimensional blood vessel model; the second key point information includes the distance between the starting key point of the target blood vessel segment and the ending key point of the target blood vessel segment in the optical coherence tomography image and the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the optical coherence tomography image; Accordingly, determining a model scaling factor based on the first key point information and the second key point information, scaling the initial three-dimensional blood vessel model based on the model scaling factor to obtain a target three-dimensional blood vessel model includes: Determine a length scaling factor based on a distance between a key point at the start end of the target blood vessel segment and a key point at the end end of the target blood vessel segment in the initial three-dimensional blood vessel model and a distance between a key point at the start end of the target blood vessel segment and a key point at the end end of the target blood vessel segment in the optical coherence tomography image; Determine an area scaling factor based on the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the initial three-dimensional blood vessel model and the blood vessel cross-sectional areas corresponding to the multiple key points on the target blood vessel segment in the optical coherence tomography image; The initial three-dimensional blood vessel model is scaled based on the length scaling factor and the area scaling factor to obtain a target three-dimensional blood vessel model.

3. The method according to claim 1, characterized in that: The step of acquiring a coronary angiography image containing a target blood vessel segment comprises: Acquire coronary angiography images containing the target blood vessel segment at at least two angles; Accordingly, the generating of the initial three-dimensional vascular model of the target vascular segment based on the coronary angiography image includes: Annotating the coronary angiography images containing the target blood vessel segment at the at least two angles to obtain blood vessel contour images containing the target blood vessel segment at the at least two angles; An initial three-dimensional blood vessel model is generated based on the blood vessel contour images including the target blood vessel segment at the at least two angles.

4. The method according to claim 1, characterized in that The determining of the blood flow reserve fraction based on the target three-dimensional blood vessel model comprises: determining a target blood vessel segment pressure drop of the target three-dimensional blood vessel model; The blood flow reserve fraction is determined based on the target blood vessel segment pressure drop of the target three-dimensional blood vessel model.

5. The method according to claim 4, characterized in that The step of determining the target blood vessel segment pressure drop of the target three-dimensional blood vessel model comprises: Determining a vascular viscosity resistance coefficient, a vascular inertial resistance coefficient, and a vascular flow rate of the target three-dimensional vascular model; A target blood vessel segment pressure drop of the target three-dimensional blood vessel model is determined based on the blood vessel viscosity resistance coefficient, the blood vessel inertia resistance coefficient, and the blood vessel flow rate.

6. The method according to claim 4, characterized in that The step of determining the target blood vessel segment pressure drop of the target three-dimensional blood vessel model comprises: Computational fluid dynamics simulation is performed on the target three-dimensional blood vessel model to obtain a target blood vessel segment pressure drop of the target three-dimensional blood vessel model.

7. The method according to claim 4, characterized in that The determining of the blood flow reserve fraction based on the target blood vessel segment pressure drop of the target three-dimensional blood vessel model comprises: Obtain the mean coronary inlet pressure; The blood flow reserve fraction is determined based on the mean coronary inlet pressure and the target vessel segment pressure drop of the target three-dimensional vessel model.

8. A device for determining blood flow reserve fraction, characterized in that: include: a first key point information determination module, configured to acquire a coronary angiography image including a target blood vessel segment, generate an initial three-dimensional blood vessel model of the target blood vessel segment based on the coronary angiography image, and determine first key point information of the target blood vessel segment based on the initial three-dimensional blood vessel model; a second key point information determining module, configured to acquire an optical coherence tomography image containing a target blood vessel segment, and determine second key point information of the target blood vessel segment based on the optical coherence tomography image; a three-dimensional blood vessel model scaling module, configured to determine a model scaling factor based on the first key point information and the second key point information, and scale the initial three-dimensional blood vessel model based on the model scaling factor to obtain a target three-dimensional blood vessel model; The blood flow reserve fraction determination module is used to determine the blood flow reserve fraction based on the target three-dimensional blood vessel model.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can perform the method for determining the blood flow reserve fraction according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for determining the blood flow reserve fraction according to any one of claims 1 to 7 when executed.

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