A method of measuring fractional flow reserve of coronary ultrasound

Abstract

The application provides a method for measuring an ultrasound coronary artery flow reserve fraction, and relates to the technical field of ultrasound imaging.The method for measuring the ultrasound coronary artery flow reserve fraction provided by the application estimates the blood pressure distribution of a coronary artery stenosis region through a fluid mechanics model based on ultrasound contrast video, and then obtains a flow reserve fraction FFRUS and a reliability index sigma.Based on the ultrasound contrast video data V0 collected, the coronary artery ultrasound contrast imaging video data V1 is determined by using a sliding window double filtering strategy;based on V1, the stenosis region of the coronary artery is determined as a target region Omega;based on V1, the blood flow velocity field V2 of the target region Omega is determined;based on V2 and the proximal blood pressure value Pa of the target region Omega, the blood pressure distribution V3 of the target region Omega is determined;based on V3, the flow reserve fraction FFRUS and the reliability index sigma are calculated by using the flow reserve fraction definition.

Description

TECHNICAL FIELD

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

[0002] Coronary flow reserve fraction FFR estimation based on intervention is an effective way to evaluate coronary atherosclerotic heart disease (coronary heart disease) in clinic, and is also the gold standard for judging whether myocardial ischemia is caused by coronary stenosis. However, it is an invasive and high ionizing radiation measurement method, and is not suitable for patients with liver and kidney dysfunction. Moreover, the intervention-based flow reserve fraction measurement method is not convenient and difficult to apply to physical examination and screening application scenarios.

[0003] In recent years, the flow reserve fraction measurement method FFR Angio , FFR CT based on coronary angiography and coronary CT combined with computational fluid dynamics has been proposed. Although the risk brought by intervention is reduced, it still has ionizing radiation. Moreover, it is worth pointing out that the finite element calculation strategy, although it does not need to use the conventional monitoring blood pressure to measure the blood pressure value Pa, but through the segmentation of the geometric boundary of the coronary artery, the blood pressure distribution in the coronary artery is calculated through the preset initial condition of the inlet and outlet, the no-slip geometric boundary condition; however, the physical model, material parameter and blood vessel boundary fixed assumption used in the above finite element or finite volume method calculation strategy based only on geometric structure are difficult to effectively reflect individual differences; moreover, studies have pointed out that the existence of plaques will affect the results of FFR Angio , FFR CT measurement; at the same time, long-time calculation also limits the application scenarios of FFR Angio , FFR CT . SUMMARY

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

[0005] Specifically, the method for measuring coronary flow reserve fraction based on ultrasonic imaging comprises the following steps: step 10, acquiring ultrasonic contrast video data V0.

[0006] Step 20 is responsible for adopting time sliding window double filtering on V0 to obtain coronary ultrasonic imaging video data V1.

[0007] Step 30 involves selecting the narrowed area of ​​the coronary artery as the target region Ω using an automatic recognition algorithm that identifies the boundaries of the coronary artery or through interactive manual selection.

[0008] Step 40: Based on V1, the blood flow velocity field distribution V2 of the target region Ω is obtained using the microbubble tracking method. In order to ensure the stability of blood flow velocity tracking, the microbubble tracking method adopts the moving 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: The average blood pressure result of the subject's routine monitoring blood pressure measurement is used as the proximal blood pressure value Pa of the target region Ω.

[0010] Step 60: Based on V2 and Pa, the blood pressure distribution V3 of the target region Ω is estimated using the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation with viscosity term correction.

[0011] Step 70: Based on V3, the fractional flow reserve (FFR) for each cardiac cycle is obtained by calculating the ratio between the distal blood pressure values ​​Pd and Pa in the target region Ω, and the average of the above FFRs is taken as the fractional flow reserve (FFR). US The final output is the ratio of the mean to the variance, which is then used as the confidence level σ.

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

[0013] V0 k Each pixel j in the dataset is decomposed into its own time series S along the time direction. k (j,t) (j=1,2,…N; where N is the total number of pixels in each frame, t=1,2,…,T), and for S k (j,t) uses a bandpass filter to implement double filtering; the double-filtered data S is obtained. k *(j,t); The above bandpass filter can be a classic filter or an equivalent bandpass filter based on singular value decomposition; the so-called dual filtering refers to simultaneously filtering out the chaotic high-speed microbubble signals and the slow-moving tissue signals in the heart chambers.

[0014] S k The Y frame coronary ultrasound contrast imaging video data V1 is reconstructed by averaging and degenerating (j, t).

[0015] The microbubble tracking method is a sliding average time domain or frequency domain cross-correlation method, which comprises the following steps: based on the coronary ultrasound contrast imaging video data V1, the microbubble local regions of adjacent images are compared frame by frame in time sequence; specifically, the images are blocked, the classic time domain or frequency domain cross-correlation method is used for image local matching, the sub-pixel level displacement of the microbubble between frames is estimated, and then the flow velocity of the microbubble is calculated in combination with the frame rate; since the size of the microbubble is far smaller than the size of the target coronary artery, the flow of the microbubble is almost not resisted by the blood vessel wall, so the velocity of the microbubble is basically consistent with the velocity of the blood flow, and then the preliminary result V11 of the blood flow velocity distribution of the target region Ω at Z* time points (Z*=Y-1) is obtained.

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

[0017] V11 k (k=1, 2, 3…, Y*) is averaged in the time direction to obtain the stable blood flow velocity field distribution V2 of the target region Ω.

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

[0019]

[0020] In the formula, the left side of the equation is the pressure difference between the distal end and the proximal end of the target region with time, the first two terms on the right side are the inertia term in the unsteady Bernoulli equation derived based on the Euler formula in fluid mechanics, the third term on the right side is the viscous term, P is the pressure, ρ is the blood density, u s,2 and us,1 respectively, are the far-end and near-end flow velocities of the target region Ω along the streamline direction; u s is the velocity of each point in the target region Ω along the streamline direction; s is the streamline position, t is the time, and μ is the viscosity coefficient.

[0021] The above difference method refers to the difference along the space-time dimension, the scale of the time difference is the time resolution of the blood flow velocity field distribution V2 of the target region Ω, and the scale of the spatial difference is the spatial resolution of V2.

[0022] 2) Based on the near-end blood pressure value Pa, the far-end blood pressure value Pd is calculated by the above difference method, and V21 is used to finally obtain the blood pressure distribution V3 of the target region Ω.

[0023] The electronic device of the ultrasound coronary artery flow reserve fraction includes a processor, a memory storing executable instructions, and a storage medium; wherein the processor is used to execute the computer program corresponding to the coronary artery ultrasound flow reserve fraction measurement method; the storage medium is responsible for storing the computer program corresponding to the coronary artery ultrasound flow reserve fraction measurement method and imaging data.

[0024] The measurement device of the ultrasound coronary artery flow reserve fraction includes: a preprocessing module M1 for obtaining coronary artery ultrasound contrast imaging video data V1; a target region selection module M2 for selecting a stenosis region of the coronary artery as a target region Ω; a flow velocity estimation module M3 of the target region for obtaining a blood flow velocity field distribution V2 of the target region Ω; a blood pressure distribution estimation module M4 of the target region for obtaining a blood pressure distribution V3 of the target region Ω; and a flow reserve fraction estimation module M5 for obtaining the final output of the flow reserve fraction.

[0025] The measurement system of the ultrasound coronary artery flow reserve fraction includes: an acquisition device S1 for performing step 10; a blood pressure monitoring device S2 for performing step 50; and the electronic device or the measurement device.

[0026] The computer storage medium of the ultrasound coronary artery flow reserve fraction includes: responsible for storing the computer program corresponding to the coronary artery ultrasound flow reserve fraction measurement method and imaging data, used for the electronic device to execute the ultrasound coronary artery flow reserve fraction measurement method. BRIEF DESCRIPTION OF DRAWINGS

[0027] Figure 1 The schematic diagram of the ultrasound-based coronary artery flow reserve fraction measurement method provided by an exemplary embodiment of the present application.

[0028] Figure 2A schematic diagram of ultrasound imaging video data V1 obtained by time sliding window double filtering for an exemplary embodiment of the present application.

[0029] Figure 3 A schematic diagram of blood flow velocity field distribution V2 obtained by a microbubble tracking method for an exemplary embodiment of the present application.

[0030] Figure 4 A schematic diagram of blood pressure distribution V3 of the target region Ω estimated by a flow field-blood pressure distribution estimation method of unsteady Bernoulli equation for an exemplary embodiment of the present application.

[0031] Figure 5 A schematic diagram of the device structure of an ultrasound imaging based blood flow reserve fraction measurement system for an exemplary embodiment of the present application.

[0032] Figure 6 An electronic device for describing an embodiment of the present application is provided for an exemplary embodiment of the present application. DETAILED DESCRIPTION

[0033] The technical solutions in the embodiments of the present application will be described clearly and completely in the embodiments of the present application in combination with the drawings. Obviously, the described embodiments are only some of the embodiments of the present application, but not all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0034] Figure 1 A schematic diagram of an ultrasound based coronary blood flow reserve fraction measurement method is provided for an exemplary embodiment of the present application.

[0035] The acquisition and calculation steps include: step 10, acquiring ultrasound contrast video data V0.

[0036] Specifically, the IQ video data V0 covering the observed coronary artery region is acquired. In this step, Z frames of IQ video data V0 covering the observed coronary artery region are acquired.

[0037] 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 coherent compounding imaging technology and demodulated; or can be acquired based on spherical wave coherent compounding ultrasound imaging technology and demodulated; or can be acquired based on other ultrasound imaging methods and demodulated. In the exemplary embodiment, 7-angle plane wave coherent compounding imaging technology is used to acquire and demodulate 10000 frames of IQ video data V0, and the frame rate of V0 is 2000 Hz.

[0038] Step 20 is responsible for adopting time sliding window double filtering to V0 to obtain coronary ultrasound imaging video data V1.

[0039] Specifically, the coronary angiography imaging video data V1 mentioned in step 20 provides filtered high-speed microbubble signals and slow-moving tissue signals in the heart chamber, and reconstructs the coronary ultrasound contrast video.

[0040] Step 30 is responsible for identifying the stenosis region of the coronary artery as the target region Ω by an automatic identification algorithm for identifying the boundary of the coronary artery or an interactive manual selection method.

[0041] Specifically, the algorithm for automatically identifying the boundary of the coronary artery mentioned in step 30 is a classic image segmentation method; the interactive manual selection method is to select the stenosis region of the coronary artery as the target region Ω in V1 by point selection.

[0042] Step 40 is to obtain the blood flow velocity field distribution V2 of the target region Ω using the microbubble tracking method based on the coronary angiography imaging video data V1.

[0043] Specifically, in order to ensure the accuracy and stability of the ultrasonic cardiac cycle measurement, the microbubble tracking method in this embodiment is a traditional frequency domain-based cross-correlation method combined with a sliding average strategy.

[0044] Step 50 takes the mean pressure result of the conventional monitoring blood pressure measurement of the subject as the proximal blood pressure value Pa of the target region Ω.

[0045] Step 60 estimates the blood pressure distribution V3 of the target region Ω based on V2 and Pa by using the flow field-blood pressure distribution estimation method of the non-steady Bernoulli equation with viscosity correction.

[0046] Specifically, in this exemplary embodiment, the flow field-blood pressure distribution estimation method of the non-steady Bernoulli equation with viscosity correction is selected.

[0047] Step 70 obtains the blood flow reserve fraction of each cardiac cycle based on V3 by calculating the ratio between the distal blood pressure value Pd of the target region Ω and Pa, and takes the average value of the above blood flow reserve fraction as the final output of the blood flow reserve fraction FFR US , and takes the ratio of the mean value and the variance as the confidence σ output.

[0048] Specifically, the definition of the blood flow reserve fraction is FFR=Pd / Pa, and in order to evaluate the confidence σ of the ultrasound imaging-based blood flow reserve fraction, the ratio of the mean value and the variance in the ultrasound-based blood flow reserve fraction measurement is output as the confidence σ output in this embodiment.

[0049] Figure 2The step 20 provided by an exemplary embodiment of the present application is responsible for adopting time sliding window double filtering on V0 to obtain a coronary ultrasound imaging video data V1.

[0050] The step 21 is to slide a Z-frame covering the IQ video data V0 of the coronary artery region to be observed to obtain Y video sequences V0 k (k=1,2,3…,Y).

[0051] Specifically, for the Z-frame covering the IQ video data V0 of the coronary artery region to be observed obtained in the step 10, a time window with a preset width T is adopted to slide in V0 with a preset sliding window step M to obtain Y video sequences V0 k (k=1,2,3…,Y).

[0052] Since the video sequence V0 k needs to be filtered by sliding window in the subsequent step 22, it is required that the tissue covering the coronary artery region to be observed keeps “quasi-static” in the window, so the width T of the time window is as small as possible; meanwhile, in order to ensure that the contrast signal can be filtered out in the window, the width T of the time window needs to be as large as possible; in the present embodiment, in order to balance the two factors limiting the size of the time window T, T is selected as 1 / 5 of the diastolic time.

[0053] The step M is used to control the total frame number Y of the subsequent coronary ultrasound contrast imaging video data V1, Y is (Z-T) / M, the smaller M is, the larger Y is; limited by the computing power, the smaller M is, the longer the processing time of the step 11 is, in order to ensure the calculation time, according to experience, it is usually 1 to 10 frames required in V0, in the present embodiment, Z is 10000 frames, T is 500 frames, M is the time required for 1 frame in V0, and Y is 9500 frames.

[0054] The step 22 is to decompose each pixel point j in V0 k into a respective time sequence S k (j,t) in the time direction, and to use a band-pass filter to realize double filtering on S k (j,t) to obtain double filtered data S k *(j,t).

[0055] Specifically, each pixel point j in V0 k is unfolded in the time dimension within the time window T to obtain a time sequence S k (j,t) (j=1,2,…N; wherein N is the total number of pixels of each frame image, t=1,2,…,T, and k=1,2,…,Y), and a band-pass filter is used to realize double filtering on S k (j,t) along the time dimension to obtain double filtered data S k(j, t); the aforementioned band-pass filter can be a classical frequency domain Butterworth filter or a band-pass filter based on singular value decomposition; in the embodiment, a band-pass filter based on singular value decomposition is selected. The double filtering can filter out the chaotic high-speed microbubble signals and slow tissue signals in the heart chamber.

[0056] Step 23, reconstructing V1 from S k (j, t) along the time dimension and degenerate, and reconstructing to obtain Y-frame coronary ultrasound contrast imaging video data V1.

[0057] Specifically, the average value can be selected from various average value methods such as traditional arithmetic average value, energy average value, etc. In the embodiment, the traditional arithmetic average value is selected. The reconstruction of V1 in step 23 includes various classical image reconstruction means such as multi-level compression, image normalization, and pseudo-colorization.

[0058] Figure 3 Step 40 provided by an exemplary embodiment of the application is shown, which uses a microbubble tracking method to obtain a blood flow velocity field distribution V2 of the target region Ω based on the coronary angiography imaging video data V1.

[0059] The step 41 includes the following steps: Step 41, block V1, and use a classical time domain or frequency domain cross-correlation method for image local matching to estimate a preliminary result V11 of the blood flow velocity distribution of the target region Ω.

[0060] Specifically, when V1 is blocked, the higher the overlap degree between blocks, the more accurate the estimation of the microbubble flow velocity; otherwise, the higher the overlap degree between blocks, the greater the calculation amount, and the longer the calculation time is limited by the computing power; therefore, in practice, the accuracy and computing power need to be balanced, and in the embodiment, the overlap degree between blocks is 25%. Through the classical time domain or frequency domain cross-correlation method for image local matching, the displacement of the microbubble between adjacent two frames can be calculated, and the frame rate of V1 can be combined to calculate the flow velocity of the microbubble. The preliminary result V11 of the blood flow velocity distribution of the target region Ω at Z* time points can be calculated (Z* = Y-1).

[0061] Since the microbubble size is much smaller than the target coronary size, the microbubble is almost not affected by the resistance of the blood vessel wall, at this time, the velocity of the microbubble is consistent with the velocity of the blood flow, and then the blood flow velocity can be estimated by the velocity of the microbubble.

[0062] Step 42, sliding windowing the preliminary result V11 of the blood flow velocity distribution at Z* time points to obtain Y* blood flow velocity distribution sequences V11 k .

[0063] Specifically, the preliminary result V11 of the blood flow velocity distribution of Z* time points obtained in step 41 is slid in V11 according to a preset time window with a width of T* and a preset sliding window step M*, to obtain Y* video sequences V11 k (k=1, 2, 3…, Y*). Since in subsequent step 43, it is required to be degenerated in the video sequence V11 k , it is required that the tissues covering the observed coronary artery region remain almost stationary in 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 the velocity tracking is improved by relying on the degeneration method, the width T* of the time window needs to be as large as possible; in this embodiment, in order to balance the two factors limiting the size of the time window T*, T* is selected as 1 / 10 of the diastolic time.

[0064] The step M* controls 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 frame number is, the more accurate the pressure distribution of the subsequent target region Ω is; at the same time, limited by the computing power, the smaller M* is, the longer the processing time of step 42 is, in order to ensure the calculation time, according to experience, it is usually 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.

[0065] Step 43, V11 k (k=1, 2, 3…, Y*) is degenerated along the time dimension, to obtain the stable blood flow velocity field distribution V2 of the target region Ω.

[0066] Specifically, the degeneration method selects the average value method, and various average value methods such as traditional arithmetic average value and energy average value can be selected, and in this embodiment, the traditional arithmetic average value is selected.

[0067] Figure 4 The step 60 provided by an exemplary embodiment of the application is shown, and the blood pressure distribution V3 of the target region Ω is estimated by a flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by a viscous term based on V2 and Pa.

[0068] The step 60 provided by an exemplary embodiment of the application is shown, and the blood pressure distribution V3 of the target region Ω is estimated by a flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by a viscous term based on V2 and Pa.

[0069] Specifically, the difference method control equation is the unsteady Bernoulli equation modified by a viscous term:

[0070]

[0071] In the formula, the left side of the equation is the pressure difference between the distal and proximal ends of the target region Ω over time, and the first two terms on the right side of the equation are the inertia terms in the unsteady Bernoulli equation derived based on 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, u s,2 and u s,1 are the flow velocity magnitudes of the distal and proximal ends of the target region Ω along the streamline direction, respectively; u s is the velocity magnitude of each point in the target region Ω along the streamline direction; s is the streamline position, t is the time, and μ is the viscosity coefficient.

[0072] Specifically, the above difference method refers to difference along the space-time dimension, the scale of time difference is the time resolution of the blood flow velocity field distribution V2 of the target region Ω, and the scale of space difference is the spatial resolution of V2.

[0073] In step 62, based on the proximal blood pressure value Pa, the distal blood pressure value Pd is calculated by the above difference method, and V21 is used to finally obtain the blood pressure distribution V3 of the target region Ω.

[0074] Figure 5 The device structure schematic diagram of the blood flow reserve fraction measurement system based on ultrasonic imaging provided by an exemplary embodiment of the present application is shown.

[0075] It includes an acquisition device S1 responsible for acquiring the ultrasonic contrast video data V0.

[0076] A preprocessing module M1 is responsible for obtaining the coronary artery ultrasonic imaging video data V1 by using a sliding window double filtering based on V0.

[0077] A target region selection module M2 is responsible for selecting a stenosis region of the coronary artery as a target region Ω based on V0.

[0078] A flow velocity estimation module M3 of the target region is responsible for obtaining V2 by using a microbubble tracking method based on V1.

[0079] A blood pressure monitoring device S2 is responsible for acquiring the proximal blood pressure value Pa of the target region Ω of the subject to be measured.

[0080] A blood pressure distribution estimation module M4 of the target region is responsible for estimating the blood pressure distribution V3 of the target region Ω by a flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation modified by a viscosity term based on V2 and Pa.

[0081] A blood flow reserve fraction estimation module M5 is responsible for calculating and outputting the blood flow reserve fraction FFR US and the confidence σ based on V3.

[0082] Next, the electronic device of the embodiment of the present application is described with reference to Figure 6 the accompanying drawings.

[0083] As shown, the electronic device 80 includes one or more processors 801 and a memory 802.

[0084] The processor 801 can be a central processing unit (CPU) or other form of processing unit that has data processing and / or instruction execution capabilities, and can control other components in the electronic device 80 to perform desired functions.

[0085] The memory 802 can include one or more computer program products that can include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory.

[0086] The volatile memory, for example, can include random access memory (RAM), and / or cache memory, etc. The non-volatile memory, for example, can include read-only memory (ROM), hard disk, flash memory, etc. One or more computer program instructions can be stored in the computer-readable storage media, and the processor 801 can run the program instructions to implement the ultrasound-based blood flow reserve fraction measurement method of various embodiments of the present application described above and / or other desired functions.

[0087] Various contents, such as the IQ video data V0 covering the coronary artery region to be observed, the coronary ultrasound imaging video data V1, the blood flow velocity field distribution V2 of the target region Ω, the blood pressure distribution V3 of the target region Ω, the blood flow reserve fraction, etc. can also be stored in the computer-readable storage media.

[0088] In one example, the electronic device 80 can further include an input device 803 and an output device 804, which are interconnected through a bus system and / or other forms of connection mechanisms (not shown).

[0089] The input device 803 can include, for example, a keyboard, a mouse, etc.

[0090] The output device 804 can output various information to the outside, including various contents such as the IQ video data V0 covering the coronary artery region to be observed, the coronary ultrasound imaging video data V1, the blood flow velocity field distribution V2 of the target region Ω, the blood pressure distribution V3 of the target region Ω, the blood flow reserve fraction, etc.

[0091] The output device 804 can include, for example, a display, a speaker, a printer, a communication network and a remote output device connected thereto, etc.

[0092] Of course, in order to simplify, Figure 6Only some of the components of the electronic device 80 that are relevant to the present application are shown in FIG. 8. Components such as a bus, input / output interface, and the like have been omitted for the sake of clarity. The electronic device 80 can also include any other suitable components according to particular needs.

[0093] In addition to the methods and devices described above, embodiments of the present application can also be a computer program product that includes computer program instructions that when run by a processor cause the processor to perform steps of the coronary imaging method according to various embodiments of the present application described above in the specification.

[0094] The computer program product can be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C++, etc., and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code can execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device or entirely on the remote computing device or server.

[0095] In addition, embodiments of the present application can also be a computer readable storage medium having stored thereon computer program instructions that when run by a processor cause the processor to perform steps of the coronary imaging method according to various embodiments of the present application described above in the specification.

[0096] The computer readable storage medium can be any combination of one or more computer readable media.

[0097] The computer readable medium can be a computer readable signal medium or a computer readable storage medium.

[0098] The computer readable storage medium can include, for example but not limited to, electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing.

[0099] More specific examples (a non-exhaustive list) of the computer readable storage medium include an electrical connection having one or more wires, a portable disc, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.

[0100] The above generally describes the basic principles of the application in conjunction with specific embodiments, but it should be noted that the advantages, benefits, effects and the like mentioned in the present application are only examples and are not limiting, and these advantages, benefits, effects and the like cannot be considered as necessary for each embodiment of the present application.

[0101] In addition, the above specific details of the disclosure are only for the purpose of example and understanding, and are not limiting, and the above details do not limit the present application to be necessarily implemented with the above specific details.

[0102] The block diagrams of the devices, apparatuses, equipment, systems involved in the present application are only illustrative examples and are not intended to require or imply the connection, arrangement, configuration as shown in the block diagram. As those skilled in the art will recognize, these devices, apparatuses, equipment, systems can be connected, arranged, configured in any manner. Words such as "include", "contain", "have" and the like are open-ended words, which mean "including but not limited to", and can be used interchangeably, and the word "or" and "and" used herein means the word "and / or", and can be used interchangeably, unless the context clearly indicates otherwise. The word "such as" used herein means the phrase "such as but not limited to", and can be used interchangeably.

[0103] It should also be noted that in the devices, apparatuses and methods of the present application, each component or each step can be decomposed and / or recombined, and these decompositions and / or recombinations should be considered as equivalent solutions of the present application.

[0104] The above description of the disclosed aspects is provided so that any person skilled in the art can make or use the present application. Various modifications to these aspects will be apparent to those skilled in the art, and the general principles defined herein can 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 is intended to be accorded the widest scope consistent with the principles and novel features disclosed herein.

[0105] The above description has been given for the purpose of illustration and description. Furthermore, this description is not intended to limit the embodiments of the present application to the forms disclosed herein. Although a number of 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 fractional flow reserve in coronary arteries using ultrasound, characterized in that, Based on ultrasound angiography video, the blood pressure distribution in the stenotic region of the coronary artery was estimated using a hydrodynamic model, and the fractional flow reserve (FFR) was then obtained. US With 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 area of ​​the coronary artery to be tested; 2) Based on the IQ video data V0, video data V1 is calculated using a sliding window dual filtering strategy; where V1 is the coronary artery ultrasound angiography video data; the specific steps include: (1) For the IQ video data V0 of frame Z, slide within V0 according to the preset window width T and preset sliding window step size M to obtain Y video data sequences V0. k , where k=1,…Y; where T is 1 / 5 to 1 / 2 of the diastolic time; step size M is 1 to 10; Y is (ZT) / M; (2) V0 k Where k=1,…Y, and each pixel j is decomposed into its own time series S along the time direction. k (j,t), where j=1,2,…N1, N1 is the total number of pixels in each frame, t=1,2,…,T, and S k (j,t) uses a bandpass filter to achieve double filtering, that is, simultaneously filtering out the chaotic microbubble signals and slowly moving tissue signals in the heart chambers, to obtain the double-filtered data S. k * (j,t); (3) Along the time direction, S k * The average values ​​of (j,t) are taken and simplified to reconstruct the Y-frame coronary ultrasound angiography video data V1. 3) Based on V1, the narrowed area of ​​the coronary artery is selected as the target area Ω through automatic identification algorithm or interactive manual selection; 4) Based on V1, the blood flow velocity field V2 of the target region Ω is obtained using the microbubble tracking method; wherein, the microbubble tracking method is a time-domain or frequency-domain cross-correlation method based on moving average; 5) The average blood pressure measurement result of the subject's routine monitoring is taken as the proximal blood pressure value Pa of the target area Ω; wherein, the average pressure is the average of systolic and diastolic blood pressure; 6) Based on V2 and Pa, the blood pressure distribution V3 of the target region Ω is estimated by the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation with viscosity term correction; 7) Based on V3, calculate the ratio between the distal blood pressure value Pd and the proximal blood pressure value Pa in the target region Ω to obtain the fractional flow reserve (FFR) for each cardiac cycle. US i Where i = 1, 2, ..., N2, N2 >= 3; the average value of the fractional flow reserve over N2 cardiac cycles is output as the fractional flow reserve (FFR). US The ratio of the mean to the variance is output as the confidence level σ.

2. The measurement method according to claim 1, characterized in that, Based on V1, the blood flow velocity field V2 of the target region Ω is obtained using the microbubble tracking method. The specific steps include: 1) In V1, adjacent frames are compared frame by frame in chronological order. 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. Then, the preliminary result V11 of the blood flow velocity distribution in the target region Ω is calculated based on the frame rate. 2) A multi-frame moving average strategy is used for V11 to obtain a stable blood flow velocity field distribution V2 in the target region Ω.

3. The measurement method according to claim 1, characterized in that, Based on V2 and Pa, the blood pressure distribution V3 of the target region Ω is obtained through the flow field-blood pressure distribution estimation method. The specific steps include: Based on the blood flow velocity field distribution V2 of the target region Ω, the relative blood pressure distribution V21 of the target region Ω is calculated using the following unsteady Bernoulli equation modified with a viscosity term and the finite 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 region Ω. In this equation, the left side represents the pressure difference between the distal and proximal ends of the target region Ω; the first two terms on the right side are inertial terms in the unsteady Bernoulli equation, which are functions of s and t; the third term on the right side is the viscous term, where P is pressure, ρ is blood density, and u... s,2 with u s,1 These represent the magnitudes of the flow velocities along the streamline direction at the far and near ends of the target region Ω, respectively; u s is the velocity of each point within the target region Ω along the streamline direction; s is the streamline position, t is time, and μ is the viscosity coefficient.

4. An electronic device comprising a processor and a computer storage medium storing processor-executable instructions, the processor being configured to execute the executable instructions to implement the method as claimed in any one of claims 1 to 3.

5. A measuring device, characterized in that, The measuring device is used to implement any one of the measuring methods of claims 1 to 3, comprising: The preprocessing module M1 is responsible for obtaining video data V1 based on V0 using a sliding window dual filtering strategy; The target region selection module M2 is responsible for selecting the stenotic area of ​​the coronary artery as the target region Ω based on V1, using an automatic recognition algorithm or interactive manual selection. The velocity estimation module M3 for the target region is responsible for obtaining V2 based on V1 using the microbubble tracking method. The target region blood pressure distribution estimation module M4 is responsible for estimating the blood pressure distribution V3 of the target region Ω based on V2 and Pa using the flow field-blood pressure distribution estimation method of the unsteady Bernoulli equation with viscosity term correction. The fractional flow reserve estimation module M5 is responsible for calculating and outputting the fractional flow reserve (FFR) based on V3. US And credibility σ.

6. A measurement system, characterized in that, include: 1) Acquisition device S1 is responsible for acquiring ultrasound contrast imaging video data V0; 2) 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 as described in claim 4 or the measuring device as described in claim 5.

7. A computer storage medium, characterized in that, The computer storage medium is used to store processor-executable instructions so that the processor performs the measurement method according to any one of claims 1 to 3.

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