CN118680598A - Blood flow velocity measurement method and related device based on intravascular ultrasound - Google Patents

Abstract

The application provides a blood flow velocity measurement method and a related device based on intravascular ultrasound, wherein the method comprises the following steps: acquiring radio frequency signals of the blood vessel segment to be tested under different blood flow rates; calculating the lag time between adjacent radio frequency sequences according to the radio frequency signals; obtaining the axial velocity of blood flow in a blood vessel based on an axial velocity formula according to the lag time, and obtaining the transverse velocity of the blood flow based on a pre-constructed transverse velocity measurement model; the transverse velocity measurement model comprises the steps of carrying out alignment processing on ultrasonic sequences among radio frequency signals, calculating sequence correlation after the alignment processing, obtaining a correlation change curve according to the correlation, extracting characteristic values of the correlation change curve, and carrying out data fitting based on the characteristic values and the real transverse velocity. The application can obtain the blood flow velocity distribution conditions in the directions parallel to and perpendicular to the sound beam at the same time, thereby realizing the measurement of blood flow and avoiding shearing force.

Description

Blood flow velocity measurement method and related device based on intravascular ultrasound

Technical Field

The present application belongs to the technical field of blood flow velocity measurement, and in particular, relates to a blood flow velocity measurement method based on intravascular ultrasound and a related device.

Background Art

Vascular disease is one of the leading causes of death worldwide, affecting individual health while also placing a significant burden on the socio-economic system. Blood flow velocity measurement plays a vital role in the diagnosis and treatment of vascular diseases. By accurately monitoring the flow rate of blood in blood vessels, physicians can obtain key physiological data to help evaluate and diagnose a variety of cardiovascular diseases. In addition, blood flow velocity measurement can help doctors detect lesions in the early stages of disease formation and take preventive measures to reduce the incidence of acute events.

Although extracorporeal ultrasound imaging and magnetic resonance imaging are currently commonly used methods for measuring blood flow velocity in clinical practice, these methods have their limitations. Extracorporeal ultrasound imaging is greatly affected by the operator's experience and has limited measurement capabilities for tiny blood vessels buried deep in the body (such as the renal artery and coronary artery). Although magnetic resonance imaging can provide images of the distribution of blood flow velocity in blood vessels, it takes a long time to image, is expensive, and has low resolution.

In order to quickly and accurately evaluate the intravascular blood flow velocity, scholars have proposed a variety of invasive measurement methods, such as myocardial infarction thrombolysis frame counting method based on X-ray coronary angiography, intravascular optical coherence tomography Doppler, and intravascular ultrasound Doppler guidewire. Although these technologies have advantages in some aspects, they also have their own shortcomings. Myocardial infarction thrombolysis frame counting method is relatively subjective during operation and may be affected by the operator's skills and experience, and can only be used for qualitative analysis. Optical coherence tomography Doppler technology is highly sensitive to small movements of the sample, which may lead to a decrease in imaging quality, and requires diluted blood as a contrast agent, which increases the complexity of clinical application and the risk to patients. The ultrasound Doppler guidewire method is greatly affected by the geometry of the stenosis and the placement of the Doppler guidewire, and can only obtain the average value of the coronary blood flow velocity, and cannot obtain the velocity distribution in the radial or cross-sectional area of the blood vessel.

Summary of the invention

In view of this, the present application aims to propose a blood flow velocity measurement method and related devices based on intravascular ultrasound to solve the problem of being unable to obtain the velocity distribution in the radial direction or cross section of the blood vessel.

To achieve the above purpose, the technical solution of this application is implemented as follows:

In a first aspect, the present application provides a method for measuring blood flow velocity based on intravascular ultrasound, comprising:

Acquiring radio frequency signals at different blood flow rates of the blood vessel segment to be tested, and performing data preprocessing on the radio frequency signals;

Calculating the lag time between adjacent radio frequency sequences according to the processed radio frequency signal;

According to the lag time, the axial velocity of the blood flow in the blood vessel is obtained based on the axial velocity formula, and the lateral velocity of the blood flow is obtained based on a pre-constructed lateral velocity measurement model;

Among them, the lateral velocity measurement model includes aligning the ultrasonic sequences between the radio frequency signals, calculating the sequence correlation after the alignment processing, obtaining a correlation change curve according to the correlation, extracting the characteristic value of the correlation change curve, and performing data fitting based on the characteristic value and the actual lateral velocity.

In the second aspect, based on the same inventive concept, the present application also provides a blood flow velocity measurement system based on intravascular ultrasound, comprising:

A signal acquisition module is configured to acquire radio frequency signals at different blood flow rates of the blood vessel segment to be tested, and perform data preprocessing on the radio frequency signals;

a lag time calculation module, configured to calculate the lag time between adjacent RF sequences according to the processed RF signal;

a blood flow velocity calculation module, configured to obtain the axial velocity of the blood flow in the blood vessel based on the lag time and the axial velocity formula, and to obtain the lateral velocity of the blood flow based on a pre-built lateral velocity measurement model;

The lateral velocity measurement model includes aligning the ultrasonic sequences between the radio frequency signals, calculating the sequence correlation after the alignment, obtaining a correlation change curve according to the correlation, extracting the characteristic value of the correlation change curve, and performing data fitting based on the characteristic value and the actual lateral velocity.

In the third aspect, based on the same inventive concept, the present application also provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the blood flow velocity measurement method based on intravascular ultrasound as described in the first aspect is implemented.

In the fourth aspect, based on the same inventive concept, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the blood flow velocity measurement method based on intravascular ultrasound as described in the first aspect.

Compared with the prior art, the blood flow velocity measurement method based on intravascular ultrasound and the related device described in this application have the following beneficial effects:

The blood flow velocity measurement method and related devices based on intravascular ultrasound described in this application do not rely on contrast agents and have a wider range of application scenarios. Compared with the ultrasonic Doppler guidewire method, the proposed method can simultaneously obtain the blood flow velocity distribution in two directions parallel and perpendicular to the sound beam, thereby realizing the measurement of blood flow volume and avoiding shear force.

BRIEF DESCRIPTION OF THE DRAWINGS

The drawings constituting a part of the present application are used to provide a further understanding of the present application. The illustrative embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

FIG1 is a flow chart of a blood flow velocity measurement method based on intravascular ultrasound according to an embodiment of the present application;

FIG2 is a specific schematic diagram of the blood flow velocity measurement method based on intravascular ultrasound described in an embodiment of the present application;

FIG3 is a schematic diagram of the geometric relationship between blood flow and transducer beam according to an embodiment of the present application;

FIG4 is a schematic diagram of a feature extraction and calibration process according to an embodiment of the present application;

FIG5 is a comparison diagram of axial velocity distribution according to an embodiment of the present application;

FIG6 is a schematic diagram showing a comparison of axial velocity estimation results according to an embodiment of the present application;

FIG7 is a comparison diagram of the lateral velocity distribution according to the embodiment of the present application;

FIG8 is a schematic diagram of cross-sectional flow velocity results at different flow velocities described in an embodiment of the present application;

FIG9 is a comparison diagram of the profile flow rate forming results described in the embodiment of the present application;

FIG10 is a schematic structural diagram of a blood flow velocity measurement device based on intravascular ultrasound according to an embodiment of the present application;

FIG. 11 is a schematic diagram of the hardware structure of the electronic device according to an embodiment of the present application.

DETAILED DESCRIPTION

In order to make the objectives, technical solutions and advantages of the present application more clearly understood, the present application is further described in detail below in combination with specific embodiments and with reference to the accompanying drawings.

It should be noted that, unless otherwise defined, the technical terms or scientific terms used in the embodiments of the present application should be the usual meanings understood by people with ordinary skills in the field to which the present application belongs. The "first", "second" and similar words used in the embodiments of the present application do not represent any order, quantity or importance, but are only used to distinguish different components. "Including" or "comprising" and similar words mean that the elements or objects appearing in front of the word cover the elements or objects listed after the word and their equivalents, without excluding other elements or objects. "Connect" or "connected" and similar words are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. "Up", "down", "left", "right" and the like are only used to indicate relative positional relationships. When the absolute position of the described object changes, the relative positional relationship may also change accordingly.

The embodiments of the present application are described in detail below with reference to the accompanying drawings.

Intravascular Ultrasound (IVUS) uses an invasive ultrasound microprobe to obtain a cross-sectional image sequence of a blood vessel segment by rotating and retracting. It is a commonly used minimally invasive examination method for vascular diseases in clinical practice. IVUS provides high-resolution vascular structure imaging, providing effective support for vascular stenosis assessment, vascular wall structure analysis, and plaque stability analysis.

Referring to FIG. 1 and FIG. 2 , this embodiment provides a blood flow velocity measurement method based on intravascular ultrasound, including:

Step S101, obtaining radio frequency signals of a blood vessel segment to be tested at different blood flow rates, and performing data preprocessing on the radio frequency signals.

Specifically, in this embodiment, the ultrasonic microprobe is inserted into the blood vessel segment to be measured, and the ultrasonic probe generates ultrasonic waves in the direction of the sound source under the action of the pulse voltage to form a sound beam. During the propagation process, the ultrasonic waves encounter scatterers in the blood or sound reflections from vascular tissues to form reflected waves, which are received by the ultrasonic probe to form radio frequency (RF) signals. The RF signals are collected by the data acquisition circuit, recorded in the storage device, and analyzed by the computer.

Figure 3 shows the geometric relationship between blood flow and transducer beam. Due to factors such as the complex manufacturing process of the transducer, the emitted ultrasonic beam has a slight deviation in angle, usually less than 90 degrees. The actual blood flow velocity v is decomposed into the axial velocity v _y parallel to the acoustic beam axis; the longitudinal velocity v _z in the imaging plane and perpendicular to v _y ; and the transverse velocity v _y perpendicular to both v _y and v _x . In general, the v _z component mainly appears in situations involving local turbulence or probe rotation, _and can usually be ignored compared to other components. The angle of the ultrasonic beam (represented by θ) is determined by the ratio of v _y to v _x .

The data preprocessing described in this embodiment uses median filtering to filter out noise in the radio frequency signal.

Step S102: Calculate the lag time between adjacent radio frequency sequences according to the processed radio frequency signal.

Specifically, in this embodiment, the ultrasound probe is in a self-transmitting and self-receiving mode, and uses an external pulse to trigger the signal and collect the radio frequency signal. During the blood flow process, the axial displacement of the scatterer in the blood will cause a time lag between adjacent radio frequency sequences.

The DTW algorithm with triangle constraints is used to calculate the lag time, which includes the following steps:

(1) The core of the DTW algorithm is to construct a cumulative cost function related to distance. For the echo signals X∈R ^m×1 and Y∈R ^n×1 , the cumulative distance matrix D∈R ^m×n can be constructed by calculating the Euclidean distance between sample points;

The initial conditions can be expressed as:

D _i,1 =D _i-1,1 ++||X _i -Y ₁ || ²

D _1,j =D _1,j-1 +||X ₁ -Y _j || ²

The calculation formula for the remaining elements in the matrix can be expressed as:

D _i,j =min(D _i-1,j ,D _i,j-1 ,D _i-1,j-1 )+||X _i -Y _j || ²

Specific code implementation: Given the RF sequences X and Y, calculate the distance matrix D according to Algorithm 1.

(2) According to the following triangle constraint conditions, the corrected cumulative distance matrix Calculate according to the following formula

(3) Use the dynamic path search algorithm to calculate the optimal path P.

The search process starts from the cumulative distance matrix The first element of the matrix Start, go to the last element End. For an element in the search path Determines the next element in the search path as as well as The minimum value in , where α is the set weight coefficient, which is mainly used to overcome the distortion alignment phenomenon of the DTW algorithm. Let P∈R ^L×2 represent the position set of points on the optimal path, P _l,1 represents the horizontal coordinate of the point on the path, and P _l,2 represents the vertical coordinate of the point on the path, then the lag time It can be expressed as the difference between the horizontal and vertical coordinates.

Specific code implementation: Calculate the optimal path P according to Algorithm 2.

(4) Calculate the lag time according to the following formula:

Wherein, fs is the sampling frequency of the RF signal, and P _l,1 -P _l,2 represents the difference between the horizontal and vertical coordinates of the points on the optimal path, which actually represents the phase difference between the two RF sequences.

It should be noted that in the process of estimating the time delay caused by the axial velocity component, a method of averaging the time delay results of multiple sequences in a specific time period is used, but due to the low similarity between individual sequences and the suboptimal phase matching of the model, the calculation results have potential oscillations. To address this problem, a detection algorithm based on the Median Absolute Deviation (MAD) is used to smooth the data.

Among them, MAD represents the absolute deviation from the median and is defined as ω=bM _i (| _xi - _Mj ( _xj )|), where _xj is a point on the original data, _Mj is the median of _xj , _xi is the i-th data sorted from small to large, _Mi is the median of | _xi - _Mj ( _xj )|, and the coefficient b is a constant tailored for the normal assumption of the data, usually set to 1.4826.

Assume that x is the sample value and M is the median of the sample. When MAD is determined, a rejection criterion is established based on the characteristics of the data to retain the data in The data points outside the range are replaced by the values obtained by linear interpolation of adjacent data, thereby excluding points with large fluctuations and obtaining smoother and more reliable results.

Compared with mean-based detection methods, the median method has several advantages: it is applicable to a wider range of data distributions and does not require a normal distribution; it is not affected by sample size and is suitable for the analysis of small data sets; and it is more robust because it is less sensitive to outliers.

Step S103, according to the lag time, the axial velocity of the blood flow in the blood vessel is obtained based on the axial velocity formula, and the lateral velocity of the blood flow is obtained based on the pre-constructed lateral velocity measurement model;

Among them, the lateral velocity measurement model includes aligning the ultrasonic sequences between the RF signals, calculating the correlation of the sequences after the alignment, obtaining the correlation change curve according to the correlation, extracting the eigenvalue of the correlation change curve, and performing data fitting based on the eigenvalue and the actual lateral velocity.

Specifically, in this embodiment, for the calculation of the axial velocity, it is assumed that the lag time of the RF sequence Y relative to X is Then the axial velocity v _y can be calculated by the following formula:

Among them, Δy represents the phase shift distance of the signal, τ is the pulse repetition interval, and c is the propagation speed of the sound wave in the blood.

For multiple continuous ultrasound sequences X, Y ₁ ...Y _n collected at intervals of τ, the lag time between the sequences is calculated using the method in step 2. According to the lag time, Y ₁ ...Y _n is aligned to X, and the correlation ρ(y,t) between the aligned sequence Y ₁ ′ ...Y _n ′ and X is calculated. The correlation ρ is arranged in the time sequence of Y ₁ ...Y _n to obtain a correlation curve over time. Within the same time interval τ, the faster the blood flow velocity and the greater the displacement distance of the scatterer, the more obvious the decline in the correlation curve of the ultrasound sequence.

The correlation change curve is feature extracted and recorded as ξ(y), where y represents the position in the lumen. ξ(y) can be expressed as the characteristics of the integral, differential, and derivative average of the correlation ρ(t) with respect to time. As shown in Figure 4(a), the correlation between sequences gradually decreases with time and stops changing after reaching a certain minimum value. For example, a linear function is used to fit the sample points during this period, and the slope of the straight line is the derivative average of the correlation curve. The time integral of the correlation curve can be expressed as the sum of the similarities of all sample points during this period.

A mapping model v _x = f (ξ) between the extracted features and the blood flow velocity is constructed. The mapping model is shown in FIG4(b). The model can be determined by theoretical derivation and experimental calibration. Then, when calculating the transverse velocity, the lumen is first divided into multiple windows according to different positions, the correlation change curve in each window is calculated, the feature ξ (y) is extracted, and the transverse velocity v _x is solved by substituting it into the mapping model v _x = f (ξ).

In some embodiments, the method further comprises:

Based on the quantitative results of axial velocity and transverse velocity, physiological parameters were calculated;

The physiological parameters include at least cross-sectional flow, average flow velocity, beam angle and wall shear force.

Specifically, in this embodiment, the method can obtain quantitative results of axial and transverse velocity, which can be used for clinical blood flow analysis. During the analysis process, some basic parameters are involved, and the calculation method is as follows:

(1) Cross-sectional flow

The average flow rate can be obtained by calculating the flow rate Q of the cross section and dividing it by the cross-sectional area A. The flow rate of a small annular area with a radius of r and a width of dr is dq = 2πrv _x dr. Since the flow rate of the front half of the cross section will be affected by the position of the probe, this embodiment selects the flow rate v _x (r) of the rear half of the cross section for flow calculation. Therefore, the total flow rate Q through the cross section can be expressed as:

(2) Average flow velocity within the cross section

Average velocity of the cross section It can be expressed as:

(3) Beam angle

The beam angle θ can be calculated from the average velocity of the transverse velocity component and the average velocity of the axial velocity component The ratio between is determined as:

(3) Wall shear force

The wall shear rate can be calculated by taking the derivative of the blood flow velocity at the avoidance surface along the radial direction of the blood vessel:

Here, μ represents the blood viscosity coefficient.

When this method is implemented:

The center frequency of the intravascular ultrasound probe used in the embodiment is 50MHz, the sampling frequency is 200MHz, the axial resolution is about 50μm, and 4096 RF signals can be collected per second. The probe is placed inside the vascular phantom (a plastic tube with an inner diameter of 3mm), close to the inner wall of the pipe. The experimental device mainly consists of an ultrasonic acquisition device and a syringe pump. The experiment is carried out at a controlled temperature of 38°C. The plastic tube is placed horizontally on a splint to avoid bending of the pipeline. The liquid in the pipeline is healthy sterile pig blood with EDTA as an anticoagulant. By adjusting the parameters of the syringe pump, different blood flow velocities are generated in the pipeline, and the resulting velocity range is 2.36 to 37.75cm/s.

According to step 1, the IVUS system is used to collect RF signals at different blood flow rates. Median filtering is used to filter out noise in the signal.

According to step 2, the lag time between signals is extracted and the axial velocity is calculated.

According to step 3, calculate the lateral velocity. It mainly includes the following steps:

(1) For a plurality of continuous ultrasonic sequences X, Y ₁ ...Y _n collected at intervals of τ, the lag time between the sequences is calculated using the method in step 2.

(2) Align Y ₁ …Y _n to X according to the lag time, and calculate the correlation ρ(y,t) between the aligned sequence and X.

(3) Extract features from the constructed correlation change curve and record it as ξ(y). In this embodiment, the time accumulation is used as the feature value:

(4) Using the correlation feature ξ(y) and its corresponding true lateral velocity v _x , a velocity measurement model v _x =f(ξ) is fitted. This embodiment uses a cubic function for fitting.

This embodiment compares the performance of a windowed cross-correlation (WCC) algorithm and a proposed triangle-constrained dynamic warping (Triangle-DTW) algorithm.

Figure 5 shows the distribution of axial velocity v _y of multiple sequences calculated by several algorithms at different flow rates. By averaging the time dimension, the axial velocity estimation result shown in Figure 6 is obtained. Although the velocity direction calculated by the WCC algorithm is correct, the calculated axial velocity is not proportional to the change in flow velocity and does not match the theoretical waveform distribution. This phenomenon is due to the fact that the WCC algorithm ignores the contextual information between sequences when processing the delay of each time window, resulting in localized results. The proposed Triangle-DTW algorithm provides a more stable flow velocity distribution. The algorithm can not only accurately estimate the axial velocity, but also obtain a velocity distribution consistent with theoretical expectations.

Figure 7 shows the distribution of transverse blood flow velocity _vx obtained by different algorithms at six speeds. The velocity distribution matrix is averaged in the time dimension, and Figure 8 shows the calculation results of cross-sectional velocity at different flow rates. It can be seen that the WCC method has a large error, which is close to 40% overall. The Triangle-DTW calculation accuracy is high, and the error fluctuates within the range of 18%. The calculation errors of the three different methods at 2.36cm/s and 4.72cm/s are much higher than the average error, which is caused by the jitter of the injection pump at low flow rates.

Figure 9 shows the comparison between the cross-sectional velocity v imaging results of different methods and the ideal situation within 4.8 ms. The ideal velocity distribution in the figure is obtained by the ideal model of blood flow. It can be seen from the figure that the imaging results of the constrained DTW method are better than those of the WCC method. Both different constraint methods can form relatively stable and reliable cross-sectional velocity maps. Thanks to the unique shape of the triangle constraint, Triangle-DTW performs best at the lumen boundary.

The blood flow velocity measurement method based on intravascular ultrasound described in this embodiment does not rely on contrast agents and has a wider range of application scenarios. Compared with the ultrasonic Doppler guidewire method, the proposed method can simultaneously obtain the blood flow velocity distribution in two directions parallel and perpendicular to the sound beam, thereby realizing the measurement of blood flow volume and avoiding shear force.

It should be noted that the above describes some embodiments of the present application. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recorded in the claims can be performed in an order different from that in the above embodiments and still achieve the desired results. In addition, the processes depicted in the accompanying drawings do not necessarily require the specific order or continuous order shown to achieve the desired results. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present application further provides a blood flow velocity measurement system based on intravascular ultrasound.

As shown in FIG10 , the blood flow velocity measurement system based on intravascular ultrasound includes:

The signal acquisition module 11 is configured to acquire radio frequency signals at different blood flow rates of the blood vessel segment to be measured, and perform data preprocessing on the radio frequency signals;

A lag time calculation module 12, configured to calculate the lag time between adjacent RF sequences according to the processed RF signal;

The blood flow velocity calculation module 13 is configured to obtain the axial velocity of the blood flow in the blood vessel based on the lag time and the axial velocity formula, and obtain the lateral velocity of the blood flow based on a pre-constructed lateral velocity measurement model;

The lateral velocity measurement model includes aligning the ultrasonic sequences between the radio frequency signals, calculating the sequence correlation after the alignment, obtaining a correlation change curve according to the correlation, extracting the characteristic value of the correlation change curve, and performing data fitting based on the characteristic value and the actual lateral velocity.

For the convenience of description, the above system is described by dividing the functions into various modules. Of course, when implementing the embodiments of the present application, the functions of each module can be implemented in the same or multiple software and/or hardware.

The system of the above embodiment is used to implement the corresponding blood flow velocity measurement method based on intravascular ultrasound in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

Based on the same inventive concept, corresponding to any of the above-mentioned embodiments and methods, an embodiment of the present application also provides an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the blood flow velocity measurement method based on intravascular ultrasound as described in any of the above embodiments is implemented.

FIG11 shows a more specific schematic diagram of the hardware structure of an electronic device provided in this embodiment, and the device may include: a processor 1010, a memory 1020, an input/output interface 1030, a communication interface 1040, and a bus 1050. The processor 1010, the memory 1020, the input/output interface 1030, and the communication interface 1040 are connected to each other in communication within the device through the bus 1050.

The processor 1010 can be implemented by a general-purpose CPU (Central Processing Unit), a microprocessor, an application-specific integrated circuit (ASIC), or one or more integrated circuits, and is used to execute relevant programs to implement the technical solutions provided in the embodiments of this specification.

The memory 1020 may be implemented in the form of ROM (Read Only Memory), RAM (Random Access Memory), static storage device, dynamic storage device, etc. The memory 1020 may store an operating system and other application programs. When the technical solutions provided in the embodiments of this specification are implemented by software or firmware, the relevant program codes are stored in the memory 1020 and are called and executed by the processor 1010.

The input/output interface 1030 is used to connect the input/output module to realize information input and output. The input/output module can be configured in the device as a component (not shown in the figure), or it can be externally connected to the device to provide corresponding functions. The input device may include a keyboard, a mouse, a touch screen, a microphone, various sensors, etc., and the output device may include a display, a speaker, a vibrator, an indicator light, etc.

The communication interface 1040 is used to connect a communication module (not shown) to realize communication interaction between the device and other devices. The communication module can realize communication through a wired mode (such as USB, network cable, etc.) or a wireless mode (such as mobile network, WIFI, Bluetooth, etc.).

The bus 1050 includes a path that transmits information between the various components of the device (eg, the processor 1010, the memory 1020, the input/output interface 1030, and the communication interface 1040).

It should be noted that, although the above device only shows the processor 1010, the memory 1020, the input/output interface 1030, the communication interface 1040 and the bus 1050, in the specific implementation process, the device may also include other components necessary for normal operation. In addition, it can be understood by those skilled in the art that the above device may also only include the components necessary for implementing the embodiments of the present specification, and does not necessarily include all the components shown in the figure.

The electronic device of the above embodiment is used to implement the corresponding blood flow velocity measurement method based on intravascular ultrasound in any of the above embodiments, and has the beneficial effects of the corresponding method embodiment, which will not be repeated here.

Based on the same inventive concept, corresponding to any of the above-mentioned embodiment methods, the present application also provides a non-transitory computer-readable storage medium, wherein the non-transitory computer-readable storage medium stores computer instructions, and the computer instructions are used to enable the computer to execute the blood flow velocity measurement method based on intravascular ultrasound as described in any of the above embodiments.

The computer-readable medium of this embodiment includes permanent and non-permanent, removable and non-removable media, and information storage can be achieved by any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technology, read-only compact disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, tape disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device.

The computer instructions stored in the storage medium of the above embodiment are used to enable the computer to execute the blood flow velocity measurement method based on intravascular ultrasound as described in any of the above embodiments, and have the beneficial effects of the corresponding method embodiments, which will not be repeated here.

Those skilled in the art should understand that the discussion of any of the above embodiments is merely illustrative and is not intended to imply that the scope of the present application (including the claims) is limited to these examples. In line with the concept of the present application, the technical features in the above embodiments or different embodiments may be combined, the steps may be implemented in any order, and there are many other variations of the different aspects of the embodiments of the present application as described above, which are not provided in detail for the sake of simplicity.

In addition, to simplify the description and discussion, and in order not to make the embodiments of the present application difficult to understand, the known power supply/ground connection with the integrated circuit (IC) chip and other components may or may not be shown in the provided drawings. In addition, the device can be shown in the form of a block diagram to avoid making the embodiments of the present application difficult to understand, and this also takes into account the fact that the details of the implementation of these block diagram devices are highly dependent on the platform to be implemented in the embodiments of the present application (that is, these details should be fully within the scope of understanding of those skilled in the art). In the case of elaborating specific details (e.g., circuits) to describe exemplary embodiments of the present application, it is obvious to those skilled in the art that the embodiments of the present application can be implemented without these specific details or when these specific details are changed. Therefore, these descriptions should be considered to be illustrative rather than restrictive.

Although the present application has been described in conjunction with specific embodiments of the present application, many replacements, modifications and variations of these embodiments will be apparent to those skilled in the art from the foregoing description. For example, other memory architectures (e.g., dynamic RAM (DRAM)) may use the embodiments discussed.

The embodiments of the present application are intended to cover all such substitutions, modifications and variations that fall within the broad scope of the appended claims. Therefore, any omissions, modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the embodiments of the present application should be included in the scope of protection of the present application.

Claims (9)

1. A method for measuring blood flow velocity based on intravascular ultrasound, comprising:

Acquiring radio frequency signals of a blood vessel segment to be detected under different blood flow rates, and carrying out data preprocessing on the radio frequency signals;

Calculating the lag time between adjacent radio frequency sequences according to the processed radio frequency signals;

obtaining the axial velocity of blood flow in a blood vessel based on an axial velocity formula according to the lag time, and obtaining the transverse velocity of the blood flow based on a pre-constructed transverse velocity measurement model;

the transverse velocity measurement model comprises the steps of carrying out alignment processing on ultrasonic sequences among radio frequency signals, calculating sequence correlation after the alignment processing, obtaining a correlation change curve according to the correlation, extracting characteristic values of the correlation change curve, and carrying out data fitting based on the characteristic values and real transverse velocity.

2. The intravascular ultrasound-based blood flow velocity measurement method according to claim 1, wherein calculating a lag time between adjacent radio frequency sequences using a triangle-constrained DTW algorithm comprises:

Constructing an accumulated distance matrix according to the radio frequency sequence X and the radio frequency sequence Y by using the Euclidean distance between samples, wherein X epsilon R ^m×1,Y∈R^n×1 and the accumulated distance matrix D epsilon R ^m×n;

defining an initial condition, the initial condition being expressed as:

D_i,1＝D_i-1,1+‖X_i-Y₁‖²；

D_1,j＝D_1,j-1+‖X₁-Y_j‖²；

the calculation formula of the rest elements in the matrix is expressed as follows:

D_i,j＝min(D_i-1,j,D_i,j-1,D_i-1,j-1)+‖X_i-Y_j‖²；

according to the triangle constraint condition, the corrected accumulated distance matrix The formula is:

Computing matrices by dynamic path search algorithm According to the optimal path and through a lag time formula, the lag time between adjacent radio frequency sequences is obtained.

3. The intravascular ultrasound-based blood flow velocity measurement method of claim 2, wherein the lag time formula is:

Where f _s is the sampling frequency of the radio frequency signal, P _l,1 represents the abscissa of the point on the optimal path, and P _l,2 represents the ordinate of the point on the optimal path.

4. The intravascular ultrasound-based blood flow velocity measurement method of claim 3, wherein the axial velocity formula is:

Where deltay represents the phase shift distance of the signal, τ is the pulse repetition interval, c is the propagation velocity of the acoustic wave in the blood, Is the lag time between adjacent radio frequency sequences.

5. The intravascular ultrasound-based blood flow velocity measurement method of claim 2, wherein the deriving the lateral velocity of the blood flow based on the pre-constructed lateral velocity measurement model comprises:

For a plurality of continuous radio frequency sequences X, Y ₁…Y_n acquired at intervals tau, aligning Y ₁…Y_n to X according to the lag time, calculating the correlation degree rho (Y, t) between the aligned sequences Y' ₁…Y′_n and X, and arranging the correlation degree rho according to the time sequence of Y ₁…Y_n to obtain a correlation degree change curve;

Extracting the characteristics of the correlation change curve and marking the characteristic as xi (y);

A mapping model v _x =f (ζ) is constructed regarding the extracted features and the blood flow velocity.

6. The intravascular ultrasound-based blood flow velocity measurement method according to claim 1, further comprising:

calculating to obtain physiological parameters according to the quantitative results of the axial speed and the transverse speed;

wherein the physiological parameters include at least cross-sectional flow, average flow velocity, beam angle, and wall shear.

7. An intravascular ultrasound-based blood flow velocity measurement system, comprising:

The signal acquisition module is configured to acquire radio frequency signals of the blood vessel segment to be detected at different blood flow rates and perform data preprocessing on the radio frequency signals;

A lag time calculation module configured to calculate a lag time between adjacent radio frequency sequences from the processed radio frequency signals;

The blood flow velocity calculation module is configured to obtain the axial velocity of blood flow in a blood vessel based on an axial velocity formula according to the lag time, and obtain the transverse velocity of the blood flow based on a pre-constructed transverse velocity measurement model;

the transverse velocity measurement model comprises the steps of carrying out alignment processing on ultrasonic sequences among radio frequency signals, calculating sequence correlation after the alignment processing, obtaining a correlation change curve according to the correlation, extracting characteristic values of the correlation change curve, and carrying out data fitting based on the characteristic values and real transverse velocity.

8. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the intravascular ultrasound based blood flow velocity measurement method according to any one of claims 1-6 when executing the program.

9. A non-transitory computer readable storage medium, wherein the non-transitory computer readable storage medium stores computer instructions for causing a computer to perform the intravascular ultrasound-based blood flow velocity measurement method of any one of claims 1-6.