Method for measuring ultrasonic fractional flow reserve of coronary artery

Abstract

The invention provides a method for measuring ultrasonic fractional flow reserve of coronary artery, and relates to the technical field of ultrasonic imaging. According to the method for measuring the ultrasonic fractional flow reserve of the coronary artery, the blood pressure distribution of the narrow area of the coronary artery is obtained through estimation of the fluid mechanics model on the basis of the ultrasonic contrast video, and then the fractional flow reserve FFRUS and the credibility index sigma are obtained. Based on the acquired ultrasound contrast video data V0, determining the ultrasound contrast video data V0 as coronary artery ultrasound contrast imaging video data V1 by using a sliding window dual filtering strategy; based on V1, determining a narrow area of the coronary artery as a target area omega; based on V1, determining a blood flow velocity field V2 of the target area omega; determining the blood pressure distribution V3 of the target area omega based on the V2 and the near-end blood pressure value Pa of the target area omega; and on the basis of the V3, the fractional flow reserve FFRUS and the credibility index sigma are obtained through calculation by utilizing fractional flow reserve definition.

Description

Technical Field

[0001] The present application relates to the technical field of ultrasonic quantitative imaging, and in particular to a method, electronic equipment, device, system and computer storage medium for measuring coronary blood flow reserve fraction based on ultrasonic imaging. Background Art

[0002] The interventional coronary artery blood flow reserve fraction FFR estimation is an effective way to clinically evaluate coronary atherosclerotic heart disease (CHD) and is also the gold standard for determining whether myocardial ischemia occurs due to coronary artery stenosis. However, this is an invasive and highly ionizing radiation measurement method and is not suitable for patients with impaired liver and kidney function. Moreover, the interventional blood flow reserve fraction measurement method is not convenient and difficult to apply to application scenarios such as physical examinations and screening.

[0003] The blood flow reserve fraction measurement method FFR based on coronary angiography and coronary CT combined with computational fluid dynamics has been proposed in recent years. Angio 、FFR CT . Although the risks brought by intervention are reduced, there is still ionizing radiation. In addition, it is worth pointing out that although the finite element calculation strategy does not require the use of conventional blood pressure monitoring to measure the blood pressure value Pa, it calculates the blood pressure distribution inside the coronary artery by segmenting the geometric boundaries of the coronary artery and using the preset initial conditions of the inlet and outlet and the no-slip geometric boundary conditions; however, the above-mentioned finite element or finite volume method calculation strategies based only on geometric structures use physical models, material parameters, and assumptions that blood vessel boundaries are fixed. It is difficult to effectively reflect individual differences; more studies have pointed out that the presence of plaques will affect FFR. Angio 、FFR CT The results of the measurement; at the same time, the long calculation time also limits the FFR Angio 、FFR CT application scenarios. Summary of the invention

[0004] In order to solve the above technical problems, the present invention discloses a method for measuring coronary blood flow reserve fraction based on ultrasonic imaging, an electronic device, a device, a system and a computer storage medium.

[0005] Specifically, the method for measuring coronary blood flow reserve fraction based on ultrasound imaging includes the following steps: Step 10, acquiring ultrasound contrast video data V0.

[0006] Step 20 is responsible for obtaining the coronary artery ultrasound imaging video data V1 by applying time sliding window double filtering to V0.

[0007] Step 30 is responsible for selecting the stenosis area of ​​the coronary artery as the target area Ω by an automatic recognition algorithm for identifying the boundary of the coronary artery or interactively manually.

[0008] Step 40, based on V1, use the microbubble tracking method to obtain the blood flow velocity field distribution V2 of the target area Ω; in order to ensure the stability of blood flow velocity tracking, the microbubble tracking method adopts a sliding average time domain or frequency domain cross-correlation method developed on the basis of the traditional time domain or frequency domain cross-correlation method.

[0009] Step 50: taking the average pressure result of the conventional blood pressure monitoring measurement of the subject as the proximal blood pressure value Pa of the target area Ω.

[0010] Step 60, based on V2 and Pa, the blood pressure distribution V3 of the target area Ω is estimated by the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by the viscosity term.

[0011] Step 70: Based on V3, the blood flow reserve fraction of each cardiac cycle is obtained by calculating the ratio between the distal blood pressure values ​​Pd and Pa of the target area Ω, and the average value of the above blood flow reserve fraction is used as the blood flow reserve fraction FFR. US The final output is the ratio of the mean to the variance as the credibility σ output.

[0012] The sliding window double filtering strategy is composed of the following steps: for the IQ video data V0 of the Z frame covering the coronary artery area to be observed acquired by step 10, according to the preset time window with a width of T and a preset sliding window step size M, sliding within V0 to obtain Y video sequences V0 k (k=1,2,3…,Y); In order to ensure that the coronary artery area to be observed maintains the minimum amplitude movement within the time window with a width of T during the cardiac cycle, T is selected to be 1 / 5~1 / 2 of the diastolic time; the role of the step size M is to adjust the frame rate of the coronary artery ultrasound angiography video data V1 after double filtering. According to experience, it is usually the time required for 1~10 frames in V0; Y is (ZT) / M.

[0013] V0 k Each pixel point j in the time direction is decomposed into its own time series S k (j,t) (j=1,2,…N; where N is the total number of pixels in each frame, t=1,2,…,T), and S k (j, t) Use a bandpass filter to achieve double filtering; get the double filtered data S k *(j, t); the above-mentioned bandpass filter can be a classical filter or an equivalent bandpass filter based on singular value decomposition; the so-called double filtering refers to filtering out the chaotic high-speed microbubble signals and the slow-moving tissue signals in the heart cavity at the same time.

[0014] Along the time direction S k *(j,t) is averaged and degenerated to reconstruct Y frames of coronary artery ultrasound contrast imaging video data V1.

[0015] The microbubble tracking method is a sliding average time domain or frequency domain cross-correlation method, which consists of the following steps: based on the coronary artery ultrasound contrast imaging video data V1, the microbubble local areas of adjacent images are compared frame by frame in chronological order; specifically, the image is divided into blocks, and the classic time domain or frequency domain cross-correlation method is used for local image matching to estimate the sub-pixel displacement of the microbubble between frames, and then combined with the frame rate, the flow velocity of the microbubble is calculated; since the size of the microbubble is much smaller than the size of the target coronary artery, the flow of the microbubble is almost not affected by the resistance of the blood vessel wall, so the speed of the microbubble is basically the same as the speed of the blood flow, and then the preliminary result V11 (Z*=Y-1) of the blood flow velocity distribution of the target area Ω at Z* time points is obtained.

[0016] For V11 at Z* time points, according to the preset time window of T* and the preset sliding window step size M*, slide within V11 to obtain the blood flow velocity distribution sequence V11 of Y* target areas Ω k (k=1,2,3…,Y*); In order to ensure that the coronary artery area to be observed maintains the minimum amplitude of movement within the time window with a width of T* during the cardiac cycle, T* is selected to be 1 / 10~1 / 2 of the diastolic time; the role of the step size M* is to adjust the time interval of the blood flow velocity field distribution V2 in the stable target area Ω. According to experience, it is usually the time required for 1~10 data points in V11; Y* is (Z*-T*) / M*.

[0017] V11 k (k=1,2,3…,Y*) takes the average value along the time direction for degeneration, and obtains the stable blood flow velocity field distribution V2 of the target area Ω.

[0018] The flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by the viscosity term comprises the following steps: 1) based on the blood flow velocity field distribution V2 of the target area Ω, the following unsteady Bernoulli equation modified by the viscosity term is used to calculate the pressure difference distribution V21 of the target area Ω by the difference method:

[0019] In the formula, the left side of the equation is the pressure difference between the distal and proximal ends of the target area that changes with time. The first two terms on the right side of the equation are the inertia terms in the unsteady Bernoulli equation derived from the Euler formula in fluid mechanics. The third term on the right side is the viscosity term. P is the pressure, ρ is the blood density, and u is s,2 with u s,1are the flow velocities at the far and near ends of the target area Ω along the streamline direction; u s is the velocity of each point in the target area Ω along the streamline direction; s is the streamline position, t is the time, and μ is the viscosity coefficient.

[0020] The above-mentioned difference method refers to difference along the time and space dimensions. The scale of the time difference is the time resolution of the blood flow velocity field distribution V2 of the above-mentioned target area Ω, and the scale of the space difference is the spatial resolution of the above-mentioned V2.

[0021] 2) Based on the proximal blood pressure value Pa, the distal blood pressure value Pd is calculated by the above-mentioned differential method, and V21 is used to finally obtain the blood pressure distribution V3 of the target area Ω.

[0022] The electronic device for ultrasonic coronary blood flow reserve fraction includes a processor, a memory storing executable instructions, and a storage medium; wherein the processor is used to execute a computer program corresponding to the coronary artery ultrasonic blood flow reserve fraction measurement method; and the storage medium is responsible for storing the computer program corresponding to the coronary artery ultrasonic blood flow reserve fraction measurement method and imaging data.

[0023] The ultrasonic coronary blood flow reserve fraction measurement device comprises: a preprocessing module M1, used to obtain coronary artery ultrasonic contrast imaging video data V1; a target area selection module M2, used to select a stenotic area of ​​a coronary artery as a target area Ω; a target area flow velocity estimation module M3, used to obtain a blood flow velocity field distribution V2 of the target area Ω; a target area blood pressure distribution estimation module M4, used to obtain a blood pressure distribution V3 of the target area Ω; and a blood flow reserve fraction estimation module M5, used to obtain a final output of the blood flow reserve fraction.

[0024] The ultrasonic coronary blood flow reserve fraction measurement system comprises: a collection device S1, used to execute step 10; a blood pressure monitoring device S2, used to execute step 50; and the electronic device or the measuring device.

[0025] The computer storage medium of the ultrasonic coronary blood flow reserve fraction includes: a computer program and imaging data corresponding to the coronary blood flow reserve fraction measurement method for storing the computer program and imaging data for the electronic device to execute the ultrasonic coronary blood flow reserve fraction measurement method. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A schematic diagram of a method for measuring coronary blood flow reserve fraction based on ultrasound provided by an exemplary embodiment of the present application.

[0027] Figure 2 A schematic diagram of obtaining coronary artery ultrasound imaging video data V1 by using time sliding window double filtering provided by an exemplary embodiment of the present application.

[0028] Figure 3 A schematic diagram of the target area blood flow velocity field distribution V2 obtained by using a microbubble tracking method is provided as an exemplary embodiment of the present application.

[0029] Figure 4 A schematic diagram of the blood pressure distribution V3 of the target area Ω estimated by the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation is provided as an exemplary embodiment of the present application.

[0030] Figure 5 A schematic diagram of the device structure of a blood flow reserve fraction measurement system based on ultrasound imaging provided by an exemplary embodiment of the present application.

[0031] Figure 6 An electronic device for describing an exemplary embodiment of the present application is provided as an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0032] The following will clearly and completely describe the technical solutions in the embodiments of the present application in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of this application.

[0033] Figure 1 Shown is a schematic diagram of an ultrasound-based coronary blood flow reserve fraction measurement method provided by an exemplary embodiment of the present application.

[0034] The method includes the following acquisition and calculation steps: Step 10, acquiring ultrasound contrast imaging video data V0.

[0035] Specifically, the IQ video data V0 covering the observed coronary artery region, in this step, a total of Z frames of IQ video data V0 covering the observed coronary artery region are collected.

[0036] In an embodiment of the present application, the Z-frame IQ video data V0 covering the observed coronary artery region can be acquired by multi-angle plane wave coherence composite imaging technology and demodulated; it can also be acquired based on spherical wave coherence composite ultrasound imaging technology and demodulated; it can also be acquired based on other ultrasound imaging methods and demodulated. In this exemplary embodiment, 7-angle plane wave coherence composite imaging technology is used to acquire and demodulate 10,000 frames of IQ video data V0, and the frame rate of V0 is 2000 Hz.

[0037] Step 20 is responsible for obtaining the coronary artery ultrasound imaging video data V1 by applying time sliding window double filtering to V0.

[0038] Specifically, the coronary angiography video data V1 mentioned in step 20 provides a reconstructed coronary artery ultrasound angiography video after filtering out the chaotic high-speed microbubble signals and slow-moving tissue signals in the heart cavity.

[0039] Step 30 is responsible for selecting the stenosis area of ​​the coronary artery as the target area Ω by an automatic recognition algorithm for identifying the boundary of the coronary artery or interactively manually.

[0040] Specifically, the algorithm for automatically identifying the coronary artery boundary mentioned in step 30 is a classic image segmentation method; the interactive manual selection method is to select the narrow area of ​​the coronary artery as the target area Ω by point selection in V1.

[0041] Step 40: Based on the coronary angiography video data V1, a microbubble tracking method is used to obtain the blood flow velocity field distribution V2 of the target area Ω.

[0042] Specifically, in order to ensure the accuracy and stability of ultrasonic cardiac cycle measurement, the microbubble tracking method in this embodiment is a strategy of combining the traditional frequency domain-based cross-correlation method with a sliding average.

[0043] Step 50: taking the average pressure result of the conventional blood pressure monitoring measurement of the subject as the proximal blood pressure value Pa of the target area Ω.

[0044] Step 60, based on V2 and Pa, the blood pressure distribution V3 of the target area Ω is estimated by the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by the viscosity term.

[0045] Specifically, in this exemplary embodiment, the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by the viscosity term is selected.

[0046] Step 70: Based on V3, the blood flow reserve fraction of each cardiac cycle is obtained by calculating the ratio between the distal blood pressure values ​​Pd and Pa of the target area Ω, and the average value of the above blood flow reserve fraction is used as the blood flow reserve fraction FFR. US The final output is the ratio of the mean to the variance as the credibility σ output.

[0047] Specifically, the blood flow reserve fraction is defined as FFR=Pd / Pa. In order to evaluate the credibility σ of the blood flow reserve fraction based on ultrasound imaging, in this embodiment, the ratio of the mean and variance in the blood flow reserve fraction measurement based on ultrasound is simultaneously output as the credibility σ output.

[0048] Figure 2 The figure shows a schematic diagram of step 20 provided by an exemplary embodiment of the present application, which is responsible for obtaining the coronary artery ultrasound imaging video data V1 by applying time sliding window double filtering to V0.

[0049] The steps include: Step 21, sliding the IQ video data V0 of the coronary artery region to be observed with the Z frame to obtain Y video sequences V0 k (k=1,2,3…,Y).

[0050] Specifically, for the IQ video data V0 of the Z frame covering the observed coronary artery area obtained in step 10, slide within V0 according to a preset time window with a width of T and a preset sliding window step size M to obtain Y video sequences V0 k (k=1,2,3…,Y).

[0051] Since in the subsequent step 22, it is necessary to k The sliding window filter requires that the tissue covering the observed coronary artery area remains "quasi-static" within the window, so the width T of the time window is as small as possible; at the same time, in order to ensure that the angiography signal is filtered out within the window, the width T of the time window needs to be as large as possible; in this embodiment, in order to balance the above two factors that limit the size of the time window T, T is selected to be 1 / 5 of the diastolic time.

[0052] The role of the step size M is to control the total number of frames Y of the subsequent coronary artery ultrasound angiography video data V1, where Y is (ZT) / M. The smaller M is, the larger Y is. Due to the limitation of computing power, the smaller M is, the longer the processing time of step 11 is. In order to ensure the computing time, according to experience, it is usually the time required for 1 to 10 frames in V0. In this embodiment, Z is 10,000 frames, T is 500 frames, M is the time required for 1 frame in V0, and Y is 9,500 frames.

[0053] Step 22, V0 k Each pixel point j in the time direction is decomposed into its own time series S k (j,t) , and for S k (j, t) Use a bandpass filter to achieve double filtering and obtain the double filtered data S k *(j,t).

[0054] Specifically, V0 k Each pixel point j in the time window T is expanded along the time dimension to obtain the time series S k (j,t) (j=1,2,…N; where N is the total number of pixels in each frame, t=1,2,…,T, k=1,2,…,Y), and in S k In (j, t), double filtering is achieved by using a bandpass filter along the time dimension to obtain the double filtered data S k*(j, t); The above-mentioned bandpass filter can be a classic frequency domain Butterworth filter or a bandpass filter based on singular value decomposition; in this embodiment, a bandpass filter based on SVD singular value decomposition is selected. Double filtering can simultaneously filter out the chaotic high-speed microbubble signals and slow tissue signals in the heart cavity.

[0055] Step 23, for S k *(j, t) takes the average value and degenerates along the time dimension to reconstruct Y frames of coronary artery ultrasound contrast imaging video data V1.

[0056] Specifically, the average value can be selected from various average values ​​such as traditional arithmetic average value and energy average value. In this embodiment, the traditional arithmetic average value is selected. Step 23 reconstructs V1, and the reconstruction step includes multiple classical image reconstruction methods such as multi-level compression, image normalization, and pseudo-colorization.

[0057] Figure 3 It shows step 40 provided by an exemplary embodiment of the present application, which is a schematic diagram of obtaining the blood flow velocity field distribution V2 of the target area Ω using a microbubble tracking method based on the coronary angiography video data V1.

[0058] The method comprises the following steps: Step 41, dividing the coronary artery ultrasound contrast imaging video data V1 into blocks, and using the classic time domain or frequency domain cross-correlation method to perform local image matching, and estimating a preliminary result V11 of the blood flow velocity distribution in the target area Ω.

[0059] Specifically, when V1 is divided into blocks, the higher the overlap between blocks, the more accurate the estimation of the microbubble flow velocity; conversely, the higher the overlap between blocks, the greater the amount of calculation, and the longer the calculation time due to the limitation of computing power; therefore, in practice, it is necessary to balance accuracy and computing power. In this embodiment, the overlap between blocks is 25%. By performing local image matching using the classic time domain or frequency domain cross-correlation method, the displacement of microbubbles between two adjacent frames can be calculated. Combined with the frame rate of V1, the flow velocity of microbubbles can be calculated. By integrating the microbubbles in the entire field, the preliminary result V11 (Z*=Y-1) of the blood flow velocity distribution of the target area Ω at Z* time points can be calculated.

[0060] Since the size of microbubbles is much smaller than the size of the target coronary artery, the microbubbles are hardly affected by the resistance of the blood vessel wall. At this time, the speed of the microbubbles is consistent with the speed of blood flow, and the blood flow speed can be estimated by the speed of the microbubbles.

[0061] Step 42, sliding the initial result V11 of the blood flow velocity distribution at Z* time points to obtain the blood flow velocity distribution sequence V11 of Y* target areas Ω k .

[0062] Specifically, for the preliminary result V11 of the blood flow velocity distribution at Z* time points obtained in step 41, according to the preset time window with a width of T* and a preset sliding window step size M*, slide within V11 to obtain Y* video sequences V11 k (k=1,2,3…,Y*). Since in the subsequent step 43, it is necessary to k This requires that the tissue covering the observed coronary artery area remains almost motionless within the window and the blood flow velocity is stable, so the width T* of the time window is as small as possible; at the same time, in order to ensure that the stability of velocity tracking is improved by the degeneration of the averaging method, the width T* of the time window needs to be as large as possible; in this embodiment, in order to balance the above two factors that limit the size of the time window T*, T* is selected to be 1 / 10 of the diastolic time.

[0063] The role of the step length M* is to control the total number of frames Y* of the subsequent blood flow velocity field distribution V2. Y* is (Z*-T*) / M*. The smaller M* is, the larger Y* is. In principle, the higher the number of frames, the more accurate the pressure distribution of the subsequent target area Ω is. At the same time, due to the limitation of computing power, the smaller M* is, the longer the processing time of step 42 is. In order to ensure the computing time, according to experience, it is usually the time required for 1 to 10 frames in V11. In this embodiment, Z* is 9499, M* is the time required for 100 frames in V11, Y* is 94, and T* is the time required for 99 frames in V11.

[0064] Step 43, V11 k (k=1,2,3…,Y*) is degenerate along the time dimension to obtain a stable blood flow velocity field distribution V2 in the target area Ω.

[0065] Specifically, the degenerate method uses an averaging method, and the averaging method can use various averaging methods such as traditional arithmetic average, energy average, etc. In this embodiment, the traditional arithmetic average is selected.

[0066] Figure 4 The figure shows step 60 provided by an exemplary embodiment of the present application, in which a schematic diagram of the blood pressure distribution V3 of the target area Ω is estimated based on V2 and Pa by the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by the viscosity term.

[0067] The method comprises the following steps: Step 61, based on the blood flow velocity field distribution V2 of the target area Ω, the pressure difference distribution V21 of the target area Ω is calculated by a differential method.

[0068] Specifically, the control equation of the difference method is the unsteady Bernoulli equation modified by the viscosity term:

[0069] In the formula, the left side of the equation is the pressure difference between the distal and proximal ends of the target area Ω that changes with time. The first two terms on the right side of the equation are the inertia terms in the unsteady Bernoulli equation derived from the Euler formula in fluid mechanics. The third term on the right side is the viscosity term. P is the pressure, ρ is the blood density, and u is s,2 with u s,1 are the flow velocities at the far and near ends of the target area Ω along the streamline direction; u s is the velocity of each point in the target area Ω along the streamline direction; s is the streamline position, t is the time, and μ is the viscosity coefficient.

[0070] Specifically, the above-mentioned difference method refers to difference along the time and space dimensions, the scale of the time difference is the time resolution of the blood flow velocity field distribution V2 of the above-mentioned target area Ω, and the scale of the space difference is the spatial resolution of the above-mentioned V2.

[0071] Step 62, based on the proximal blood pressure value Pa, the distal blood pressure value Pd is calculated by the above-mentioned difference method, and V21 is used to finally obtain the blood pressure distribution V3 of the target area Ω.

[0072] Figure 5 A schematic diagram of the device structure of a blood flow reserve fraction measurement system based on ultrasound imaging provided by an exemplary embodiment of the present application.

[0073] It includes: an acquisition device S1, which is responsible for acquiring ultrasound contrast imaging video data V0.

[0074] The preprocessing module M1 is responsible for obtaining the coronary artery ultrasound imaging video data V1 based on V0 by using sliding window double filtering.

[0075] The target region selection module M2 is responsible for selecting the stenosis region of the coronary artery as the target region Ω based on V0.

[0076] The flow velocity estimation module M3 in the target area is responsible for obtaining V2 based on V1 using the microbubble tracking method.

[0077] The blood pressure monitoring device S2 is responsible for collecting the proximal blood pressure value Pa of the target area Ω of the subject.

[0078] The target area blood pressure distribution estimation module M4 is responsible for estimating the blood pressure distribution V3 of the target area Ω based on V2 and Pa by using the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by the viscosity term.

[0079] The blood flow reserve fraction estimation module M5 is responsible for calculating and outputting the blood flow reserve fraction FFR based on V3 US and credibility σ.

[0080] Below, reference Figure 6 An electronic device according to an embodiment of the present application is described.

[0081] As shown in the figure, the electronic device 80 includes one or more processors 801 and a memory 802 .

[0082] The processor 801 may be a central processing unit (CPU) or other forms of processing units having data processing capabilities and / or instruction execution capabilities, and may control other components in the electronic device 80 to perform desired functions.

[0083] Memory 802 may include one or more computer program products, which may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory.

[0084] The volatile memory may include, for example, a random access memory (RAM) and / or a highly buffered memory (cache), etc. The non-volatile memory may include, for example, a read-only memory (ROM), a hard disk, a flash memory, etc. A computer program instruction may be stored or executed on the computer-readable storage medium, and the processor 801 may execute the program instruction to implement the ultrasound-based blood flow reserve fraction measurement method of each embodiment of the present application described above and / or other desired functions.

[0085] The computer storable medium may also store various contents such as IQ video data V0 covering the coronary artery area to be observed, coronary artery ultrasound imaging video data V1, blood flow velocity field distribution V2 of the target area Ω, blood pressure distribution V3 of the target area Ω, blood flow reserve fraction, etc.

[0086] In one example, the electronic device 80 may further include: an input device 803 and an output device 804 , and these components are interconnected via a bus system and / or other forms of connection mechanisms (not shown).

[0087] The input device 803 may include, for example, a keyboard, a mouse, etc.

[0088] The output device 804 can output various information to the outside, including various contents such as IQ video data V0 covering the coronary artery area to be observed, coronary artery ultrasound imaging video data V1, blood flow velocity field distribution V2 of the target area Ω, blood pressure distribution V3 of the target area Ω, blood flow reserve score, etc.

[0089] The output device 804 may include, for example, a display, a speaker, a printer, a communication network and remote output devices connected thereto, and the like.

[0090] Of course, to simplify, Figure 6Only some of the components related to the present application in the electronic device 80 are shown, and components such as a bus, an input / output interface, etc. are omitted. In addition, according to specific application situations, the electronic device 80 may also include any other appropriate components.

[0091] In addition to the above-mentioned methods and devices, an embodiment of the present application may also be a computer program product, which includes computer program instructions, and when the computer program instructions are executed by a processor, the processor executes the steps of the coronary artery imaging method according to various embodiments of the present application described above in this specification.

[0092] The computer program product may be written in any combination of one or more programming languages ​​to write program codes for performing the operations of the embodiments of the present application, including object-oriented programming languages ​​such as Java, C++, etc., and also conventional procedural programming languages ​​such as "C" language or similar programming languages. The program code may be executed entirely on the user computing device, partially on the user device, as an independent software package, partially on the user computing device and partially on a remote computing device, or entirely on a remote computing device or server.

[0093] In addition, an embodiment of the present application may also be a computer-readable storage medium on which computer program instructions are stored. When the computer program instructions are executed by a processor, the processor executes the steps of the coronary artery imaging method according to various embodiments of the present application described above in this specification.

[0094] The computer readable storage medium may take the form of any combination of one or more computer readable media.

[0095] The readable medium may be a readable signal medium or a readable storage medium.

[0096] The readable storage medium may include, for example, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices, or components, or any combination of the above.

[0097] More specific examples (a non-exhaustive list) of readable storage media include: an electrical connection having one or more conductors, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM, an erasable programmable read-only memory (EPROM or flash memory), optical fibers, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0098] The basic principles of the present application are described above in conjunction with specific embodiments. However, it should be pointed out that the advantages, strengths, effects, etc. mentioned in the present application are merely examples and not limitations, and it cannot be considered that these advantages, strengths, effects, etc. are required to be possessed by each embodiment of the present application.

[0099] In addition, the specific details disclosed above are only for the purpose of illustration and facilitating understanding, and are not restrictive. The above details do not limit the present application to being implemented by adopting the above specific details.

[0100] The block diagrams of the devices, apparatuses, equipment, and systems involved in this application are only illustrative examples and are not intended to require or imply that they must be connected, arranged, and configured in the manner shown in the block diagram. As will be appreciated by those skilled in the art, these devices, apparatuses, equipment, and systems can be connected, arranged, and configured in any manner. Words such as "including", "comprising", "having", etc. are open words, referring to "including but not limited to", and can be used interchangeably therewith, and the words "or" and "and" used here refer to the words "and / or", and can be used interchangeably therewith, unless the context clearly indicates otherwise, and the words "such as" used here refer to the phrase "such as but not limited to", and can be used interchangeably therewith.

[0101] It should also be pointed out that in the apparatus, device and method of the present application, each component or each step can be decomposed and / or recombined, and such decomposition and / or recombination should be regarded as an equivalent solution of the present application.

[0102] The above description of the disclosed aspects is provided to enable any person skilled in the art to make or use the present application. Various modifications to these aspects will be readily apparent to those skilled in the art, and the general principles defined herein may be applied to other aspects without departing from the scope of the present application. Therefore, the present application is not intended to be limited to the aspects shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

[0103] The above description has been given for the purpose of illustration and description. In addition, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although multiple example aspects and embodiments have been discussed above, those skilled in the art will recognize certain variations, modifications, changes, additions and sub-combinations thereof.

Claims

1. A method for measuring ultrasonic coronary artery blood flow reserve fraction, characterized in that: Based on ultrasound contrast video, the blood pressure distribution in the coronary artery stenosis area is estimated by fluid dynamics model, and then the blood flow reserve fraction FFR is obtained. US and the credibility index σ; the specific steps include: 1) Acquire ultrasound contrast imaging video data V0, where V0 is ultrasound contrast imaging IQ video data covering the coronary artery region to be measured; 2) Based on the IQ video data V0, the sliding window double filtering strategy is used to calculate the video data V1; wherein V1 is the coronary artery ultrasound angiography video data; 3) Based on V1, the stenosis area of ​​the coronary artery is selected as the target area Ω by automatic recognition algorithm or interactive manual selection; 4) Based on V1, a blood flow velocity field V2 of the target area Ω is obtained using a microbubble tracking method; wherein the microbubble tracking method is a sliding average time domain or frequency domain cross-correlation method; 5) The average pressure result of the subject's routine blood pressure monitoring measurement is used as the proximal blood pressure value Pa of the target area Ω; wherein the average pressure is the average of the systolic pressure and the diastolic pressure; 6) Based on V2 and Pa, the blood pressure distribution V3 of the target area Ω is estimated by the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by the viscosity term; 7) Based on V3, the ratio between the distal blood pressure value Pd and the proximal blood pressure value Pa of the target area Ω is calculated to obtain the blood flow reserve fraction FFR for each cardiac cycle US i (i=1,2,…N; N>=3); Output the average value of blood flow reserve fraction of N cardiac cycles as blood flow reserve fraction FFR US , and output the ratio of the mean to the variance as the credibility σ.

2. The measuring method according to claim 1, characterized in that: Based on the IQ video data V0, the video data V1 is calculated using the sliding window double filtering strategy; the specific steps include: 1) For the IQ video data V0 of frame Z, slide within V0 according to the preset time window width T and the preset sliding window step length M to obtain Y video data sequences V0 k (k=1,…Y); T is 1 / 5~1 / 2 of the diastolic time; the step length M is 1~10; Y is (ZT) / M; 2) V0 k Each pixel point j in (k=1,…Y) is decomposed into its own time series S along the time direction k (j,t) (j=1,2,…N; N is the total number of pixels in each frame; t=1,2,…,T;), and S k (j, t) Use a bandpass filter to achieve double filtering, that is, filter out the messy microbubble signals and slow-moving tissue signals in the heart cavity at the same time, and obtain the double-filtered data S k *(j,t); 3) S along the time direction k *(j, t) are averaged and degenerated respectively to reconstruct Y frames of coronary artery ultrasound contrast imaging video data V1.

3. The measuring method according to claim 1, characterized in that: Based on V1, the blood flow velocity field V2 of the target area Ω is obtained by using a microbubble tracking method, and the specific steps include: 1) In V1, adjacent frame images are compared frame by frame in time sequence, and local image matching is performed based on the classic time domain or frequency domain cross-correlation method to estimate the inter-frame displacement of microbubbles, and then the preliminary result V11 of the blood flow velocity distribution in the target area Ω is calculated according to the frame rate; 2) A multi-frame sliding average strategy is used for V11 to obtain a stable blood flow velocity field distribution V2 in the target area Ω.

4. The measuring method according to claim 1, characterized in that: Based on V2 and Pa, the blood pressure distribution V3 of the target area Ω is obtained by the flow field-blood pressure distribution estimation method. The specific steps include: Based on the blood flow velocity field distribution V2 of the target area Ω, the following unsteady Bernoulli equation modified by viscosity is used to calculate the relative blood pressure distribution V21 of the target area Ω by difference method; finally, the proximal blood pressure value Pa is used as a bias and added to V21 to obtain the absolute blood pressure distribution V3 of the target area Ω The left side of the equation is the pressure difference between the distal and proximal ends of the target area Ω, the first two terms on the right side of the equation are the inertia terms in the unsteady Bernoulli equation, the third term on the right side is the viscosity term, P is the pressure, ρ is the blood density, u s,2 with u s,1 are the flow velocities at the far and near ends of the target area Ω along the streamline direction; u s is the velocity of each point in the target area Ω along the streamline direction; s is the streamline position, t is the time, and μ is the viscosity coefficient.

5. An electronic device, comprising a processor and a computer storage medium, wherein the computer storage medium stores instructions executable by the processor, and the processor is configured to execute the instructions to implement the method according to any one of claims 1 to 4.

6. A measuring device, characterized in that: The measuring device is used to implement the measuring method of claims 1 to 4, comprising: (a) Preprocessing module M1 is responsible for obtaining video data V1 based on V0 using a sliding window double filtering strategy; (b) Target region selection module M2, which is responsible for selecting the stenosis region of the coronary artery as the target region Ω based on V0 using an automatic recognition algorithm or interactively manually; (c) The flow velocity estimation module M3 in the target area is responsible for obtaining V2 based on V1 using the microbubble tracking method; (d) a target area blood pressure distribution estimation module M4, responsible for estimating the blood pressure distribution V3 of the target area Ω based on V2 and Pa by using a flow field-blood pressure distribution estimation method of an unsteady Bernoulli equation modified by a viscosity term; (e) Blood flow reserve fraction estimation module M5, responsible for calculating and outputting blood flow reserve fraction FFR based on V3 US and credibility σ.

7. A measurement system, characterized in that: include: (1) The acquisition device S1 is responsible for acquiring ultrasound contrast imaging video data V0; (2) The blood pressure monitoring device S2 is responsible for collecting the proximal blood pressure value Pa of the target area Ω of the subject; (3) The electronic device according to claim 6 or the measuring device according to claim 7.

8. A computer storage medium, characterized in that The computer storage medium is used to store processor-executable instructions so that the processor executes the measurement method according to any one of claims 1 to 4.

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