Method, device and computer equipment for determining carotid artery blood flow based on ultrasound

Abstract

The present invention relates to an ultrasound-based method, device, computer equipment and storage medium for determining carotid artery blood flow. The method converts a carotid artery ultrasound signal into a power spectral density signal, and simultaneously determines the forward envelope and reverse envelope corresponding to the carotid artery ultrasound spectrum based on the power spectral density signal, thereby obtaining a waveform composed of the forward envelope and the reverse envelope. The blood flow per unit area per beat can then be determined more conveniently based on the waveform. The method has the advantages of a simple calculation process and high calculation efficiency.

Description

Technical Field

[0001] The present invention relates to the technical field of medical signal processing, and in particular to a method, device, computer equipment and computer-readable storage medium for determining carotid artery blood flow based on ultrasound. Background Art

[0002] Currently, hypotension and shock are common manifestations in critically ill patients. For example, hypotension can lead to reduced oxygen delivery and inadequate organ perfusion, which, if left untreated, can result in adverse outcomes and death. Increasing stroke volume typically results in increased organ perfusion and oxygen delivery. Fluid resuscitation is a common initial therapy for increasing stroke volume. However, up to half of patients who receive intravenous fluids will be "non-responders." Fluid resuscitation not only provides no benefit but can actually increase morbidity and mortality in these non-responders following infusion.

[0003] Because fluid resuscitation can be helpful or harmful in hypotensive patients, current approaches to predict fluid responsiveness include intravenous fluid administration or passive leg raises (PLR). Fluid responsiveness measures include central venous pressure, inferior vena cava distensibility, and invasive blood pressure monitoring. A 10% to 15% increase in stroke volume after intervention is considered a responder. Although effective, these approaches are invasive and cannot be applied in most settings.

[0004] Carotid Doppler ultrasound can noninvasively, rapidly, and continuously measure the presence of fluid responsiveness. Currently, carotid blood flow volume, a parameter of carotid ultrasound, is commonly used to assess fluid responsiveness. However, carotid blood flow volume has certain limitations and is easily affected by vessel diameter. While carotid blood flow can accurately reflect polytrauma and hemorrhagic shock, there is currently no method to directly assess carotid blood flow. Summary of the Invention

[0005] Based on this, it is necessary to provide an ultrasound-based carotid artery blood flow determination method, apparatus, computer device and computer-readable storage medium that can directly evaluate carotid artery blood flow in order to address the above technical problems.

[0006] The present application provides a method for determining carotid artery blood flow based on ultrasound. The method comprises:

[0007] Obtain the power spectrum density signal corresponding to each column of signals in the carotid artery ultrasound spectrum;

[0008] Performing integration processing on the power spectrum density signal to obtain a forward maximum frequency point and a reverse maximum frequency point in a column signal corresponding to the power spectrum density signal;

[0009] Connecting the forward maximum frequency point and the reverse maximum frequency point corresponding to each column of signals in the carotid artery ultrasound spectrum to obtain a waveform corresponding to the carotid artery ultrasound spectrum;

[0010] The blood flow per unit area per beat is determined based on the waveform.

[0011] Furthermore, the method further comprises: calculating the blood flow per unit area per minute and the shock index based on the blood flow per unit area per beat.

[0012] Furthermore, determining the blood flow per unit area per beat based on the waveform includes: determining, based on the waveform, the lowest trough between two adjacent peaks in a cycle as a dividing point; dividing the two adjacent peaks in the first coordinate direction according to the dividing point to obtain a systolic region and a diastolic region after division; obtaining a first integral value of the systolic blood flow corresponding to the systolic region over time, and a second integral value of the diastolic blood flow corresponding to the diastolic region over time; calculating the sum of the first integral value and the second integral value, and determining the sum as the blood flow per unit area per beat.

[0013] Furthermore, determining the blood flow per unit area per beat based on the waveform includes: determining, based on the waveform, the lowest trough between two adjacent peaks in a cycle as a first dividing point; segmenting the two adjacent peaks in a first coordinate direction based on the first dividing point to obtain a systolic region and a diastolic region after segmentation; segmenting the waveform of the cycle in a second coordinate direction based on the starting point of the systolic region and the end point of the diastolic region to obtain a basal blood flow region and a pulsating blood flow region after segmentation; obtaining a third integral value of the basal blood flow over time corresponding to the basal blood flow region, and a fourth integral value of the pulsating blood flow over time corresponding to the pulsating blood flow region; calculating the sum of the third integral value and the fourth integral value, and determining the sum as the blood flow per unit area per beat.

[0014] Furthermore, the integration processing of the power spectrum density signal to obtain the forward maximum frequency point and the reverse maximum frequency point in the column signal corresponding to the power spectrum density signal includes: performing a first integration processing on the power spectrum density signal based on a first frequency interval corresponding to the power spectrum density signal to obtain a corresponding first integral curve; determining the maximum energy point of the power spectrum density signal according to the first integral curve; determining two minimum energy points of the power spectrum density signal according to the maximum energy point; performing a second integration processing on the power spectrum density signal of the corresponding interval based on a second frequency interval corresponding to the two minimum energy points to obtain a corresponding second integral curve; and determining the forward maximum frequency point and the reverse maximum frequency point in the column signal corresponding to the power spectrum density signal according to the second integral curve.

[0015] Furthermore, determining the maximum energy point of the power spectrum density signal based on the first integral curve includes: obtaining the first and last endpoints of the first integral curve, connecting the two endpoints through a first reference line, and the first reference line is a straight line; obtaining a first intersection point between the first reference line and the first integral curve, and using the first intersection point as the maximum energy point of the power spectrum density signal.

[0016] Furthermore, determining the two minimum energy points of the power spectrum density signal based on the maximum energy point includes: dividing the power spectrum density signal into two signal intervals according to the maximum energy point; and obtaining the point with the smallest vertical coordinate in each signal interval as the minimum energy point of the power spectrum density signal.

[0017] Furthermore, determining the forward maximum frequency point and the reverse maximum frequency point in the column signal corresponding to the power spectrum density signal according to the second integral curve includes: obtaining a first endpoint and a second endpoint of the second integral curve, connecting the first endpoint and the second endpoint through a second reference line, where the second reference line is a straight line; determining a second intersection point, a forward frequency point, and a reverse frequency point between the second reference line and the second integral curve, the forward frequency point being the frequency point corresponding to the forward maximum distance from the second integral curve to the second reference line, and the reverse frequency point being the frequency point corresponding to the reverse maximum distance from the second integral curve to the second reference line; obtaining a first average value difference corresponding to the signals on both sides of the signal segmented by the forward frequency point between the first endpoint and the second intersection point, and when the first average value difference is greater than or equal to a preset threshold, determining the forward frequency point as the forward maximum frequency point; obtaining a second average value difference corresponding to the signals on both sides of the signal segmented by the reverse frequency point between the second intersection point and the second endpoint, and when the second average value difference is greater than or equal to the preset threshold, determining the reverse frequency point as the reverse maximum frequency point.

[0018] Furthermore, the method also includes: when the first average value difference is less than a preset threshold, moving the forward frequency point in the direction of the second intersection point, and returning to execute the step of obtaining the first average value difference corresponding to the signals on both sides of the first endpoint to the second intersection point divided by the forward frequency point; when the second average value difference is less than the preset threshold, moving the reverse frequency point in the direction of the second intersection point, and returning to execute the step of obtaining the second average value difference corresponding to the signals on both sides of the second intersection point to the second endpoint divided by the reverse frequency point.

[0019] The present application also provides an ultrasound-based carotid artery blood flow determination device, comprising:

[0020] A power spectrum density signal acquisition module is used to obtain the power spectrum density signal corresponding to each column of the signal in the carotid artery ultrasound spectrum;

[0021] a frequency point determination module, configured to perform integration processing on the power spectrum density signal to obtain a forward maximum frequency point and a reverse maximum frequency point in a column signal corresponding to the power spectrum density signal;

[0022] a waveform acquisition module, configured to connect the forward maximum frequency point and the reverse maximum frequency point corresponding to each column of signals in the carotid artery ultrasound spectrum to obtain a waveform corresponding to the carotid artery ultrasound spectrum;

[0023] The blood flow determination module is used to determine the blood flow per unit area per beat based on the waveform.

[0024] The present application also provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0025] The present application also provides a computer-readable storage medium having a computer program stored thereon, which implements the steps of the above method when executed by a processor.

[0026] The above-mentioned ultrasound-based carotid artery blood flow determination method, apparatus, computer device, and computer-readable storage medium obtain a power spectral density signal corresponding to each column of signals in a carotid artery ultrasound spectrum, integrate the power spectral density signal to obtain the forward maximum frequency point and the reverse maximum frequency point in the column signal corresponding to the power spectral density signal, and connect the forward maximum frequency point and the reverse maximum frequency point corresponding to each column of signals in the carotid artery ultrasound spectrum, thereby obtaining a waveform corresponding to the carotid artery ultrasound spectrum, and then determining the blood flow per unit area per beat based on the waveform. Because this embodiment converts the carotid artery ultrasound signal into a power spectral density signal and simultaneously determines the forward envelope and reverse envelope corresponding to the carotid artery ultrasound spectrum based on the power spectral density signal, thereby obtaining a waveform consisting of a forward envelope and a reverse envelope, the blood flow per unit area per beat can be more conveniently determined based on the waveform, and has the advantages of a simple calculation process and high computational efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 Schematic diagram of the process of the ultrasound-based carotid artery blood flow determination method in this application.

[0028] Figure 2A Schematic diagram of the internal structure of the ultrasonic Doppler probe in this application.

[0029] Figure 2B This is a schematic diagram of signal conversion in this application.

[0030] Figure 3 Schematic diagram of the flow of the steps of integrating the power spectrum density signal in this application.

[0031] Figure 4 This is a schematic diagram of the first integral curve in this application.

[0032] Figure 5 This is a schematic diagram of determining the maximum energy point and the minimum energy point in the power spectrum density signal in this application.

[0033] Figure 6 This is a schematic diagram of the second integral curve in this application.

[0034] Figure 7 Schematic diagram of the waveform envelope in this application.

[0035] Figure 8 This is a flow chart of the steps for determining the blood flow per unit area per beat in this application.

[0036] Figure 9 Schematic diagram of the forward waveform corresponding to the carotid artery ultrasound spectrum in this application.

[0037] Figure 10 This is a flow chart of the steps for determining the blood flow per unit area per beat in this application.

[0038] Figure 11 This is a structural block diagram of the ultrasound-based carotid artery blood flow determination device in this application.

[0039] Figure 12 This is a diagram of the internal structure of the computer device in this application. DETAILED DESCRIPTION

[0040] To make the technical solutions and beneficial effects of the present invention more clearly understood, the following detailed description is given by way of specific embodiments. The accompanying drawings are not necessarily drawn to scale, and local features may be enlarged or reduced to more clearly illustrate the details of the local features. Unless otherwise defined, the technical and scientific terms used herein have the same meanings as those in the technical field to which this application belongs.

[0041] This application provides a method for determining carotid artery blood flow based on ultrasound. This embodiment uses the method applied to a terminal as an example for illustration. It is understandable that the method can also be applied to a server, or to a system including a terminal and a server, and implemented through the interaction between the terminal and the server. Specifically, Figure 1 As shown, the above method includes the following steps:

[0042] Step 102: Obtain the power spectrum density signal corresponding to each column of signals in the carotid artery ultrasound spectrum.

[0043] The carotid artery ultrasound spectrum is a spectrum formed by collecting ultrasound signals from the carotid artery using an ultrasonic Doppler probe. Each column of the carotid artery ultrasound spectrum consists of several signal points. Each signal point can be uniquely represented in the power spectrum based on its corresponding frequency and energy.

[0044] like Figure 2A As shown, the internal structure of the ultrasonic Doppler probe includes a processor, an ultrasonic transmitting circuit, an ADC (Analog-to-Digital Converter), a data communication interface, and a display module connected to the processor respectively, wherein the other end of the ultrasonic transmitting circuit is also connected to an ultrasonic transmitting chip, and the other end of the ADC is connected in sequence to an ultrasonic receiving signal module and an ultrasonic receiving chip, and also includes a power supply system that provides working voltage for the above-mentioned components. Specifically, when working, the processor is powered on and triggers the ultrasonic transmitting circuit to transmit scanning ultrasound to the ultrasonic transmitting chip to perform scanning, while the ultrasonic receiving chip uses the reverse effect of the ultrasonic transmitting chip to work. Therefore, when the ultrasonic wave scans the target (such as the carotid artery) and acts on the ultrasonic receiving chip, the ultrasonic receiving chip generates a corresponding piezoelectric effect or piezomagnetic effect, which enables the ultrasonic receiving signal module to detect the piezoelectric effect or piezomagnetic effect of the ultrasonic receiving chip, thereby generating an alternating electric potential. The ADC performs analog-to-digital conversion on the alternating potential of the ultrasound receiving signal module and transmits the result of the analog-to-digital conversion to the processor. The processor processes the result to obtain the target ultrasound signal (such as the carotid artery ultrasound map) and displays the ultrasound signal through the display module. At the same time, the ultrasound signal can also be transmitted to other devices for processing through the data communication interface.

[0045] In this embodiment, if Figure 2B As shown in the figure, for a number of signal points corresponding to any column of signals n in the carotid artery ultrasound spectrum, they can be converted into a power spectrum density signal S(n) represented by a power spectrum according to the frequency and energy corresponding to each signal point in the column of signals, where the horizontal axis of the power spectrum represents the frequency and the vertical axis represents the energy.

[0046] Step 104 : performing integration processing on the power spectrum density signal to obtain a forward maximum frequency point and a reverse maximum frequency point in the column signal corresponding to the power spectrum density signal.

[0047] The forward maximum frequency point represents the maximum velocity point of the corresponding column signal, and the reverse maximum frequency point represents the minimum velocity point of the corresponding column signal. In this embodiment, the forward maximum frequency point and reverse maximum frequency point of each column signal are obtained by integrating the power spectrum density signal S(n) corresponding to each column signal n in the carotid artery ultrasound spectrum.

[0048] Step 106 , connecting the forward maximum frequency point and the reverse maximum frequency point corresponding to each column of signals in the carotid artery ultrasound spectrum to obtain a waveform corresponding to the carotid artery ultrasound spectrum.

[0049] Specifically, by connecting the positive maximum frequency points corresponding to each column of signals in the carotid artery ultrasound spectrum in sequence, the corresponding positive envelope line is obtained. By connecting the negative maximum frequency points corresponding to each column of signals in the carotid artery ultrasound spectrum in sequence, the corresponding negative envelope line is obtained. The waveform composed of the positive envelope line and the negative envelope line is the waveform corresponding to the carotid artery ultrasound spectrum.

[0050] Step 108: Determine the blood flow per unit area per beat based on the waveform.

[0051] Since the waveform corresponding to the carotid artery ultrasound spectrum can represent diastolic blood flow and systolic blood flow in the horizontal axis direction and basal blood flow and pulsatile blood flow in the vertical axis direction, the blood flow per unit area per beat can be obtained by analyzing the waveform corresponding to the carotid artery ultrasound spectrum. Specifically, the blood flow per unit area per beat can be the sum of the diastolic blood flow and systolic blood flow corresponding to the waveform within a cycle, or the blood flow per unit area per beat can be the sum of the basal blood flow and pulsatile blood flow corresponding to the waveform within a cycle.

[0052] In the above embodiment, a power spectrum density signal corresponding to each column of signals in the carotid artery ultrasound spectrum is obtained, the power spectrum density signal is integrated to obtain the forward maximum frequency point and the reverse maximum frequency point in the column signal corresponding to the power spectrum density signal, and the forward maximum frequency point and the reverse maximum frequency point corresponding to each column of signals in the carotid artery ultrasound spectrum are connected to obtain a waveform corresponding to the carotid artery ultrasound spectrum, and then the blood flow per unit area per beat is determined based on the waveform. Because this embodiment converts the carotid artery ultrasound signal into a power spectrum density signal and simultaneously determines the forward envelope and reverse envelope corresponding to the carotid artery ultrasound spectrum based on the power spectrum density signal, thereby obtaining a waveform consisting of a forward envelope and a reverse envelope, the blood flow per unit area per beat can be more conveniently determined based on the waveform, and has the advantages of a simple calculation process and high computational efficiency.

[0053] In one embodiment, Figure 3As shown, the above-mentioned step of integrating the power spectrum density signal to obtain the forward maximum frequency point and the reverse maximum frequency point in the column signal corresponding to the power spectrum density signal may specifically include:

[0054] Step 302 : Perform a first integration process on the power spectrum density signal based on a first frequency interval corresponding to the power spectrum density signal to obtain a corresponding first integral curve.

[0055] The first frequency interval refers to the frequency interval corresponding to the power spectrum density signal. The first integral process is essentially the accumulation process of the grayscale of the column signal in the ultrasound spectrum from low frequency to high frequency. The first integral curve is the integral result obtained after the first integral process is performed on the power spectrum density signal.

[0056] In this embodiment, Figure 4 As shown, through Figure 2B A first integral process is performed on the power spectral density signal S(n) in the ultrasound spectrum, thereby obtaining a corresponding first integral curve P(n). Specifically, the first integral curve P(n) is a discrete data point curve obtained by integrating the power spectral density signal S(n) corresponding to the n-th column signal in the carotid artery ultrasound spectrum, i.e., a discrete data point curve obtained by integrating the power spectral density signal S(n) with increasing frequency. It will be understood that for different column signals n in the ultrasound spectrum, the corresponding power spectral density signal S(n) may be different, and thus the first integral curve P(n) obtained after the integral process may also be different.

[0057] Step 304: Determine the maximum energy point of the power spectrum density signal according to the first integral curve.

[0058] Since the vertical axis of the power spectrum is energy, the maximum energy point is Figure 2B The point with the largest vertical coordinate in the power spectrum shown. Since the first integral curve P(n) is Figure 2B The power spectrum density signal S(n) shown in FIG is obtained by performing integration processing. Therefore, in this embodiment, by Figure 4 The first integral curve P(n) shown can more conveniently determine the maximum energy point of the power spectrum density signal S(n).

[0059] Specifically, if Figure 4 As shown, the first and last endpoints of the first integral curve P(n) are first determined, and then the two endpoints are connected by a first reference line K1, where the first reference line K1 is a straight line. A first intersection point m1 between the first reference line K1 and the first integral curve P(n) is then obtained. This first intersection point m1 is the maximum energy point of the power spectral density signal.

[0060] Step 306: Determine two minimum energy points of the power spectrum density signal according to the maximum energy point.

[0061] Since the maximum energy point is the point with the largest ordinate in the power spectrum, the minimum energy point is the point with the smallest ordinate in the power spectrum.

[0062] In this embodiment, the power spectrum density signal S(n) is divided into two signal intervals based on the maximum energy point determined in the above steps, that is, the first intersection point m1, and the point with the smallest ordinate in each signal interval is obtained as the minimum energy point of the power spectrum density signal.

[0063] Specifically, if Figure 5 As shown, the power spectrum density signal S(n) is divided into regions by the position of the first intersection point m1 in the horizontal coordinate direction, and the signal interval from S(1) to S(m1) and the signal interval from S(m1) to S(n) are obtained (where S(1) is the signal point corresponding to the coordinate origin, S(m1) is the horizontal coordinate corresponding to the m1 signal point, and S(n) is the horizontal coordinate corresponding to the signal point n far away from the origin). The point with the smallest vertical coordinate in the signal interval from S(1) to S(m1) is searched to obtain lowest_p, and the point with the smallest vertical coordinate in the signal interval from S(m1) to S(n) is searched to obtain lowest_r, thereby obtaining two minimum energy points lowest_p and lowest_r.

[0064] Step 308 : Perform a second integral process on the power spectrum density signal in the corresponding interval based on the second frequency interval corresponding to the two minimum energy points to obtain a corresponding second integral curve.

[0065] In this embodiment, after determining the two minimum energy points through the above steps, the interval formed by the horizontal coordinates corresponding to the two minimum energy points is used as the second frequency interval, and the power spectrum density signal corresponding to the interval is subjected to a second integration process to obtain a corresponding second integral curve.

[0066] Specifically, if Figure 5 As shown, if the power spectrum density signal corresponding to the interval of the minimum energy point lowest_p to lowest_r is S(p_r) (S(p_r) is the signal corresponding to the signal interval from lowest_p to lowest_r intercepted from S(n)), then the second frequency interval composed of lowest_p to lowest_r is used as the integration interval to perform the second integration processing on S(p_r), so as to obtain the following: Figure 6 The second integral curve P(m) is shown.

[0067] Step 310 : Determine the forward maximum frequency point and the reverse maximum frequency point in the column signal corresponding to the power spectrum density signal according to the second integral curve.

[0068] Specifically, if Figure 6As shown, first obtain the first endpoint and the second endpoint of the second integral curve P(m), and then connect the first endpoint and the second endpoint via a second reference line K2, where the second reference line K2 is a straight line. The first endpoint is the endpoint of the second integral curve P(m) closest to the origin, and the second endpoint is the endpoint of the second integral curve P(m) farthest from the origin.

[0069] Then, the second intersection point m2, the forward frequency point locate_p, and the reverse frequency point locate_r between the second reference line K2 and the second integral curve P(m) are determined. The forward frequency point locate_p is the frequency point corresponding to the maximum forward distance between the second integral curve P(m) and the second reference line K2, and the reverse frequency point locate_r is the frequency point corresponding to the maximum reverse distance between the second integral curve P(m) and the second reference line K2.

[0070] Obtain the first average value difference corresponding to the signals on both sides of the signal segmented by the forward frequency point locate_p between the first endpoint and the second intersection point m2, that is, obtain the first average value of the frequencies corresponding to all signal points between the first endpoint and the forward frequency point locate_p, and obtain the second average value of the frequencies corresponding to all signal points between the forward frequency point locate_p and the second intersection point m2, and calculate the difference between the first average value and the second average value, which is the first average value difference. When the first average value difference is greater than or equal to the preset threshold, the forward frequency point locate_p is determined as the forward maximum frequency point. The preset threshold can be any value between 0 and 255.

[0071] Similarly, the second average value difference between the second intersection point m2 and the second endpoint divided by the reverse frequency point locate_r is obtained. When the second average value difference is greater than or equal to the preset threshold, the reverse frequency point locate_r is determined as the reverse maximum frequency point.

[0072] When the first average value difference is less than the preset threshold, the positive frequency point is moved in the direction of the second intersection point, that is, locate_(p+1) is determined to be the positive frequency point, and the step of obtaining the first average value difference corresponding to the two side signals between the first endpoint and the second intersection point divided by the positive frequency point is returned to execute until the first average value difference is greater than or equal to the preset threshold, then the corresponding positive frequency point can be determined as the positive maximum frequency point.

[0073] Similarly, when the second average value difference is less than the preset threshold, the reverse frequency point is moved in the direction of the second intersection point, that is, locate_(r-1) is determined to be the reverse frequency point, and the step of obtaining the second average value difference corresponding to the two side signals divided by the reverse frequency point between the second intersection point and the second endpoint is returned to execute until the second average value difference is greater than or equal to the preset threshold. In this case, the corresponding reverse frequency point can be determined as the reverse maximum frequency point.

[0074] The power spectrum density signal corresponding to each column of the ultrasonic spectrum is respectively Figure 3 By the processing shown, the forward maximum frequency point and the reverse maximum frequency point corresponding to each column of signals can be obtained. By sequentially connecting the forward maximum frequency points corresponding to each column of signals in the ultrasonic spectrum, the corresponding forward envelope can be obtained. By sequentially connecting the reverse maximum frequency points corresponding to each column of signals in the ultrasonic spectrum, the corresponding reverse envelope can be obtained. The waveform composed of the forward envelope and the reverse envelope is the waveform envelope corresponding to the ultrasonic spectrum (such as Figure 7 shown).

[0075] In the above embodiment, a first integral process is performed on the power spectrum density signal to determine the maximum energy point and the minimum energy point, so as to achieve the purpose of filtering and denoising the signal, and then a second integral process is performed on the filtered and denoised signal, so that the positive maximum frequency point and the reverse maximum frequency point of the corresponding column signal in the ultrasonic spectrum can be determined more accurately. The degree of waveform adhesion can also be flexibly adjusted by presetting the threshold.

[0076] In one embodiment, Figure 8 As shown, the above determination of the blood flow per unit area according to the waveform may specifically include:

[0077] Step 802: Determine the lowest trough between two adjacent peaks in a cycle as a dividing point based on the waveform.

[0078] Specifically, Figure 7 The waveform obtained is a waveform including a positive envelope and a negative envelope. Since the carotid blood flow is only related to the positive waveform (i.e. the waveform composed of the positive envelope and the horizontal coordinate), in this embodiment, the coordinate origin is used as the starting point for the Figure 7 The waveform shown in the figure is adjusted and the reverse waveform below the horizontal axis is filtered out to obtain the following Figure 9 The carotid artery ultrasound image shown corresponds to the positive waveform.

[0079] Depend on Figure 9 As can be seen from the waveform shown, the waveform changes periodically. Therefore, we first determine each period in the waveform, for example, Figure 9Then, for each cycle, the lowest trough between two adjacent peaks is determined as the dividing point. For example, for cycle T1, the lowest point G1 between peaks F1 and F2 (i.e., the point with the smallest vertical coordinate) is determined as the dividing point. This is then analyzed in subsequent steps.

[0080] Step 804 : Segment two adjacent peaks in the first coordinate direction according to the dividing point to obtain segmented systolic and diastolic regions.

[0081] The first coordinate direction refers to the horizontal coordinate direction. Specifically, based on the aforementioned dividing point G1, the waveform in period T1 is segmented on the horizontal coordinate (segmented along the dashed line in the figure), thereby obtaining the waveform region from the starting point of T1 to the dividing point G1 (the region to the left of the dashed line in T1), i.e., the systolic region, and the waveform region from the dividing point G1 to the end point of T1 (the region to the right of the dashed line in T1), i.e., the diastolic region.

[0082] Step 806 : Obtain a first integral value of the systolic blood flow over time corresponding to the systolic region, and a second integral value of the diastolic blood flow over time corresponding to the diastolic region.

[0083] Specifically, velocity time integration is performed on each of the divided regions. For example, in the systolic region, the integral of the systolic blood flow corresponding to the region over time is calculated to obtain a first integral value. In the diastolic region, the integral of the diastolic blood flow corresponding to the region over time is also calculated to obtain a second integral value.

[0084] Step 808: Calculate the sum of the first integral value and the second integral value, and determine the sum as the blood flow per unit area per beat.

[0085] Specifically, by accumulating the first integral and the second integral, the accumulated result is the blood flow per unit area per beat.

[0086] In the above embodiment, the waveform corresponding to the carotid artery ultrasound spectrum is analyzed to obtain the blood flow per unit area per beat. Since it is not affected by the blood vessel diameter, the accuracy of the blood flow can be improved and the fluid responsiveness can be better reflected.

[0087] In one embodiment, Figure 10 As shown, the above determination of the blood flow per unit area according to the waveform may specifically include:

[0088] Step 1002: Determine, based on the waveform, the lowest trough between two adjacent peaks in a cycle as the first dividing point.

[0089] Step 1004 : Segment two adjacent peaks in the first coordinate direction according to the first dividing point to obtain a segmented systolic region and a segmented diastolic region.

[0090] It should be noted that the first demarcation point in this embodiment has the same meaning as the demarcation point in the above embodiment, and the determination method is also the same. For details, please refer to the above steps 802 and 804, and this will not be repeated in this embodiment.

[0091] Step 1006 : Segment the periodic waveform in the second coordinate direction based on the starting point of the systolic region and the end point of the diastolic region to obtain segmented basal blood flow regions and pulsating blood flow regions.

[0092] Specifically, the waveform in period T1 is segmented in the second coordinate direction based on the starting point S1 of the systolic region and the end point E1 of the diastolic region, thereby obtaining the region from the origin to point S1, i.e., the basal blood flow region ( Figure 9 The black rectangular area of ​​the middle period T1) and the waveform area from point S1 to the highest point of the peak F1, that is, the pulsating blood flow area ( Figure 9 The gray waveform area composed of peaks F1 and F2).

[0093] Step 1008: Obtain a third integral value of the basal blood flow corresponding to the basal blood flow region over time, and a fourth integral value of the pulsating blood flow corresponding to the pulsating blood flow region over time.

[0094] Specifically, velocity-time integration is performed on each of the above-mentioned divided areas. For example, taking the basal blood flow area as an example, the integral of the basal blood flow corresponding to the area over time is calculated to obtain the corresponding third integral value. For the pulsating blood flow area, the integral of the pulsating blood flow corresponding to the area over time is also calculated to obtain the corresponding fourth integral value.

[0095] Step 1010: Calculate the sum of the third integral value and the fourth integral value, and determine the sum as the blood flow per unit area per beat.

[0096] Specifically, by accumulating the third integral and the fourth integral, the accumulated result is the blood flow per unit area per beat.

[0097] In the above embodiment, the waveform corresponding to the carotid artery ultrasound spectrum is analyzed to obtain the blood flow per unit area per beat. Since it is not affected by the blood vessel diameter, the accuracy of the blood flow can be improved and the fluid responsiveness can be better reflected.

[0098] In one embodiment, the method further comprises: calculating the blood flow per unit area per minute and the shock index based on the blood flow per unit area per beat.

[0099] Specifically, the blood flow per unit area per minute is the product of the blood flow per unit area per beat and the heart rate. The shock index can be the quotient of the heart rate and the blood flow per unit area per beat. Therefore, based on the blood flow per unit area per beat and the corresponding heart rate, the blood flow per unit area per minute and the shock index can be easily calculated, and the calculation process is simple and portable.

[0100] In one embodiment, the shock index may also be the quotient of the heart rate and the first integral, or the quotient of the heart rate and the second integral, or the quotient of the heart rate and the third integral, or the quotient of the heart rate and the fourth integral, and this embodiment is not limited thereto. As can be seen from the above embodiments, the first integral, the second integral, the third integral, and the fourth integral are velocity-time integrals for different regions of the waveform within one cycle.

[0101] Since the common carotid artery blood flow can be used as a window to the left ventricle, the above-mentioned calculated data has a high reference value for the study of aorta, myocardial function, multiple injuries and hemorrhagic shock.

[0102] It should be understood that, although the various steps in the flowcharts involved in the various embodiments described above are displayed in sequence according to the instructions of the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be executed in other orders. Moreover, at least a portion of the steps in the flowcharts involved in the various embodiments described above can include multiple steps or multiple stages, and these steps or stages are not necessarily executed and completed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a portion of steps or stages in other steps.

[0103] Based on the same inventive concept, embodiments of the present application also provide an ultrasound-based carotid artery blood flow determination device for implementing the aforementioned ultrasound-based carotid artery blood flow determination method. The solution provided by this device is similar to the solution described in the aforementioned method. Therefore, the specific limitations of one or more of the following embodiments of the ultrasound-based carotid artery blood flow determination device can be found in the above-described limitations of the ultrasound-based carotid artery blood flow determination method and are not further elaborated here.

[0104] In one embodiment, Figure 11 As shown, an ultrasound-based carotid artery blood flow determination device is provided, comprising: a power spectrum density signal acquisition module 1102, a frequency point determination module 1104, a waveform acquisition module 1106, and a blood flow determination module 1108, wherein:

[0105] The power spectrum density signal acquisition module 1102 is used to acquire the power spectrum density signal corresponding to each column of the signal in the carotid artery ultrasound spectrum;

[0106] A frequency point determination module 1104 is configured to perform integration processing on the power spectrum density signal to obtain a forward maximum frequency point and a reverse maximum frequency point in a column signal corresponding to the power spectrum density signal;

[0107] A waveform acquisition module 1106 is configured to connect the forward maximum frequency point and the reverse maximum frequency point corresponding to each column of signals in the carotid artery ultrasound spectrum to obtain a waveform corresponding to the carotid artery ultrasound spectrum;

[0108] The blood flow determination module 1108 is configured to determine the blood flow per unit area per beat based on the waveform.

[0109] In one embodiment, the blood flow determination module may further be configured to calculate the blood flow per unit area per minute and the shock index based on the blood flow per unit area per beat.

[0110] In one embodiment, the blood flow determination module can also be specifically used to: determine, based on the waveform, the lowest trough between two adjacent peaks in a cycle as a dividing point; segment the two adjacent peaks in the first coordinate direction according to the dividing point to obtain the segmented systolic region and diastolic region; obtain a first integral value of the systolic blood flow over time corresponding to the systolic region, and a second integral value of the diastolic blood flow over time corresponding to the diastolic region; calculate the sum of the first integral value and the second integral value, and determine the sum as the blood flow per unit area per beat.

[0111] In one embodiment, the blood flow determination module can also be specifically used to: determine, based on the waveform, the lowest trough between two adjacent peaks in a cycle as the first dividing point; segment the two adjacent peaks in the first coordinate direction based on the first dividing point to obtain the segmented systolic region and diastolic region; segment the waveform of the cycle in the second coordinate direction based on the starting point of the systolic region and the end point of the diastolic region to obtain the segmented basal blood flow region and pulsating blood flow region; obtain the third integral value of the basal blood flow corresponding to the basal blood flow region over time, and the fourth integral value of the pulsating blood flow corresponding to the pulsating blood flow region over time; calculate the sum of the third integral value and the fourth integral value, and determine the sum as the blood flow per unit area per beat.

[0112] In one embodiment, the frequency point determination module may also include: a first integral processing unit, used to perform a first integral processing on the power spectrum density signal based on the first frequency interval corresponding to the power spectrum density signal, to obtain a corresponding first integral curve; a maximum energy point determination unit, used to determine the maximum energy point of the power spectrum density signal according to the first integral curve; a minimum energy point determination unit, used to determine the two minimum energy points of the power spectrum density signal according to the maximum energy point; a second integral processing unit, used to perform a second integral processing on the power spectrum density signal of the corresponding interval based on the second frequency interval corresponding to the two minimum energy points, to obtain a corresponding second integral curve; a frequency point determination unit, used to determine the forward maximum frequency point and the reverse maximum frequency point in the column signal corresponding to the power spectrum density signal according to the second integral curve.

[0113] In one embodiment, the maximum energy point determination unit is further specifically used to: obtain the first and last endpoints of the first integral curve, connect the two endpoints through a first reference line, and the first reference line is a straight line; obtain a first intersection point between the first reference line and the first integral curve, and use the first intersection point as the maximum energy point of the power spectrum density signal.

[0114] In one embodiment, the minimum energy point determination unit is further configured to: divide the power spectrum density signal into two signal intervals according to the maximum energy point; and obtain the point with the smallest ordinate in each signal interval as the minimum energy point of the power spectrum density signal.

[0115] In one embodiment, the frequency point determination unit is further configured to: obtain a first endpoint at the head and a second endpoint at the tail of the second integral curve, connect the first endpoint and the second endpoint through a second reference line, where the second reference line is a straight line; determine a second intersection point, a forward frequency point, and a reverse frequency point between the second reference line and the second integral curve, where the forward frequency point is a frequency point corresponding to the maximum forward distance from the second integral curve to the second reference line, and the reverse frequency point is a frequency point corresponding to the maximum reverse distance from the second integral curve to the second reference line; obtain a first average value difference between the two sides between the first endpoint and the second intersection point separated by the forward frequency point, and when the first average value difference is greater than or equal to a preset threshold, determine the forward frequency point as the forward maximum frequency point; obtain a second average value difference between the two sides between the second intersection point and the second endpoint separated by the reverse frequency point, and when the second average value difference is greater than or equal to the preset threshold, determine the reverse frequency point as the reverse maximum frequency point.

[0116] In one embodiment, the frequency point determination unit is further specifically used to: when the first average value difference is less than a preset threshold, move the forward frequency point toward the second intersection point, and return to execute the step of obtaining the first average value difference between the two sides between the first endpoint and the second intersection point divided by the forward frequency point; when the second average value difference is less than a preset threshold, move the reverse frequency point toward the second intersection point, and return to execute the step of obtaining the second average value difference between the two sides between the second intersection point and the second endpoint divided by the reverse frequency point.

[0117] Each module in the aforementioned ultrasound-based carotid artery blood flow determination device may be implemented in whole or in part via software, hardware, or a combination thereof. Each module may be embedded in or independent of a processor within a computer device in the form of hardware, or may be stored in a memory within the computer device in the form of software, so that the processor can call and execute the corresponding operations of each module.

[0118] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as follows: Figure 12 As shown. The computer device includes a processor, a memory, a communication interface, a display screen and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be achieved through WIFI, a mobile cellular network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a method for determining carotid artery blood flow based on ultrasound is implemented. The display screen of the computer device can be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0119] Those skilled in the art will understand that Figure 12 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than shown in the figure, or combine certain components, or have a different component arrangement.

[0120] In one embodiment, a computer device is provided, including a memory and a processor, wherein a computer program is stored in the memory, and when the processor executes the computer program, the following steps are implemented:

[0121] Obtain the power spectrum density signal corresponding to each column of signals in the carotid artery ultrasound spectrum;

[0122] Performing integration processing on the power spectrum density signal to obtain a forward maximum frequency point and a reverse maximum frequency point in a column signal corresponding to the power spectrum density signal;

[0123] Connecting the forward maximum frequency point and the reverse maximum frequency point corresponding to each column of signals in the carotid artery ultrasound spectrum to obtain a waveform corresponding to the carotid artery ultrasound spectrum;

[0124] The blood flow per unit area per beat is determined based on the waveform.

[0125] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: calculating the blood flow per unit area per minute and the shock index based on the blood flow per unit area per beat.

[0126] In one embodiment, when the processor executes the computer program, the following steps are also implemented: based on the waveform, the lowest trough between two adjacent peaks in a cycle is determined as a dividing point; based on the dividing point, the two adjacent peaks are divided in the first coordinate direction to obtain the divided systolic region and diastolic region; a first integral value of the systolic blood flow corresponding to the systolic region over time and a second integral value of the diastolic blood flow corresponding to the diastolic region over time are obtained; the sum of the first integral value and the second integral value is calculated, and the sum is determined as the blood flow per unit area per beat.

[0127] In one embodiment, when the processor executes the computer program, the following steps are also implemented: based on the waveform, the lowest trough between two adjacent peaks in a cycle is determined as the first dividing point; based on the first dividing point, the two adjacent peaks are divided in the first coordinate direction to obtain the divided systolic region and diastolic region; based on the starting point of the systolic region and the end point of the diastolic region, the waveform of the cycle is divided in the second coordinate direction to obtain the divided basal blood flow region and pulsating blood flow region; the third integral value of the basal blood flow corresponding to the basal blood flow region over time and the fourth integral value of the pulsating blood flow corresponding to the pulsating blood flow region are obtained; the sum of the third integral value and the fourth integral value is calculated, and the sum is determined as the blood flow per unit area per beat.

[0128] In one embodiment, when the processor executes the computer program, it also implements the following steps: performing a first integral processing on the power spectrum density signal based on a first frequency interval corresponding to the power spectrum density signal to obtain a corresponding first integral curve; determining the maximum energy point of the power spectrum density signal based on the first integral curve; determining two minimum energy points of the power spectrum density signal based on the maximum energy point; performing a second integral processing on the power spectrum density signal of the corresponding interval based on a second frequency interval corresponding to the two minimum energy points to obtain a corresponding second integral curve; and determining the forward maximum frequency point and the reverse maximum frequency point in the column signal corresponding to the power spectrum density signal based on the second integral curve.

[0129] In one embodiment, when the processor executes the computer program, it further implements the following steps: obtaining the first and last endpoints of the first integral curve, connecting the two endpoints through a first reference line, where the first reference line is a straight line; obtaining a first intersection point between the first reference line and the first integral curve, and using the first intersection point as the maximum energy point of the power spectrum density signal.

[0130] In one embodiment, when the processor executes the computer program, it further implements the following steps: dividing the power spectrum density signal into two signal intervals according to the maximum energy point; and obtaining the point with the smallest vertical coordinate in each signal interval as the minimum energy point of the power spectrum density signal.

[0131] In one embodiment, when the processor executes the computer program, the processor further implements the following steps: obtaining a first endpoint and a second endpoint of the second integral curve, connecting the first endpoint and the second endpoint via a second reference line, where the second reference line is a straight line; determining a second intersection point, a forward frequency point, and a reverse frequency point between the second reference line and the second integral curve, where the forward frequency point is a frequency point corresponding to the maximum forward distance from the second integral curve to the second reference line, and the reverse frequency point is a frequency point corresponding to the maximum reverse distance from the second integral curve to the second reference line; obtaining a first average value difference corresponding to signals on both sides of the line between the first endpoint and the second intersection point, separated by the forward frequency point; when the first average value difference is greater than or equal to a preset threshold, determining the forward frequency point as the forward maximum frequency point; obtaining a second average value difference corresponding to signals on both sides of the line between the second intersection point and the second endpoint, separated by the reverse frequency point; when the second average value difference is greater than or equal to the preset threshold, determining the reverse frequency point as the reverse maximum frequency point.

[0132] In one embodiment, when the processor executes the computer program, the following steps are further implemented: when the first average value difference is less than a preset threshold, the forward frequency point is moved toward the second intersection point, and the processor returns to execute the step of obtaining the first average value difference corresponding to the signals on both sides of the first endpoint and the second intersection point separated by the forward frequency point; when the second average value difference is less than the preset threshold, the reverse frequency point is moved toward the second intersection point, and the processor returns to execute the step of obtaining the second average value difference corresponding to the signals on both sides of the second intersection point and the second endpoint separated by the reverse frequency point.

[0133] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps of the method provided in any of the above embodiments are implemented.

[0134] Those skilled in the art will appreciate that all or part of the processes in the above-mentioned embodiment methods can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, database or other media used in the embodiments provided in this application may include at least one of non-volatile and volatile memory. Non-volatile memory may include read-only memory (ROM), magnetic tape, floppy disk, flash memory, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetic random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory may include random access memory (RAM) or external cache memory, etc. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in the various embodiments provided herein may include at least one of a relational database and a non-relational database. Non-relational databases may include, but are not limited to, distributed databases based on blockchains. The processor involved in the various embodiments provided herein may be, but are not limited to, a general-purpose processor, a central processing unit, a graphics processing unit, a digital signal processor, a programmable logic unit, a data processing logic unit based on quantum computing, and the like.

[0135] It should be understood that the above embodiments are exemplary and are not intended to encompass all possible implementations of the claims. Various modifications and variations may be made to the above embodiments without departing from the scope of this disclosure. Similarly, the various technical features of the above embodiments may be arbitrarily combined to form additional embodiments of the present invention that may not be explicitly described. Therefore, the above embodiments merely illustrate several implementations of the present invention and do not limit the scope of protection of the patent of this invention.

Claims

1. A method for determining carotid artery blood flow based on ultrasound, characterized in that the method include: Obtaining the power spectrum density signal of each column signal in the carotid artery ultrasound spectrum; performing a first integral processing on the power spectrum density signal based on a first frequency interval of the power spectrum density signal to obtain a first integral curve; Obtain the first and last endpoints of the first integral curve, and connect the two endpoints with a first reference line; Obtaining a first intersection point of the first reference line and the first integral curve as a maximum energy point of the power spectrum density signal; The power spectrum density signal is divided into two signal intervals according to the maximum energy point; Obtain the points with the smallest ordinate in each signal interval as the two minimum energy points of the power spectrum density signal; Performing a second integral process on the power spectrum density signal of the corresponding interval based on the second frequency interval of the two minimum energy points to obtain a second integral curve; Obtain a first endpoint at the beginning and a second endpoint at the end of the second integral curve, and connect the first endpoint and the second endpoint with a second reference line; Determine a second intersection point of the second reference line and the second integral curve, a forward frequency point, and a reverse frequency point, where the forward frequency point is a point corresponding to the maximum forward distance from the second integral curve to the second reference line, and the reverse frequency point is a point corresponding to the maximum reverse distance from the second integral curve to the second reference line; Obtain a first average value difference between the signals on both sides of the first endpoint and the second intersection point, which are divided by the forward frequency point. When the first average value difference is greater than or equal to a preset threshold, the forward frequency point is determined as the forward maximum frequency point. Obtain a second average value difference between the signals on both sides of the second intersection point and the second endpoint, which are divided by the reverse frequency point. When the second average value difference is greater than or equal to the preset threshold, the reverse frequency point is determined as the reverse maximum frequency point. Connect the forward maximum frequency point and the reverse maximum frequency point of each column signal in the carotid artery ultrasound spectrum to obtain a waveform; Determine the blood flow per unit area per beat based on the waveform.

2. The method according to claim 1, characterized in that The method further comprises: The blood flow per unit area per minute and the shock index are calculated based on the blood flow per unit area per beat.

3. The method according to claim 1, characterized in that Determining the blood flow per unit area according to the waveform includes: According to the waveform, determining the lowest trough between two adjacent peaks in one cycle as the dividing point; Segmenting the two adjacent peaks in the first coordinate direction according to the dividing point to obtain a segmented systolic region and a segmented diastolic region; Obtaining a first integral value of the systolic blood flow over time corresponding to the systolic region, and a second integral value of the diastolic blood flow over time corresponding to the diastolic region; The sum of the first integral value and the second integral value is calculated, and the sum is determined as the blood flow per unit area per beat.

4. The method according to claim 1, wherein Determining the blood flow per unit area according to the waveform includes: According to the waveform, determining the lowest trough between two adjacent peaks in one cycle as the first dividing point; Segmenting the two adjacent peaks in the first coordinate direction according to the first dividing point to obtain a segmented systolic region and a segmented diastolic region; Segmenting the waveform of the cycle in a second coordinate direction based on the starting point of the systolic region and the end point of the diastolic region to obtain segmented basal blood flow regions and pulsating blood flow regions; Obtaining a third integral value of the basal blood flow corresponding to the basal blood flow region over time, and a fourth integral value of the pulsating blood flow corresponding to the pulsating blood flow region over time; The sum of the third integrated value and the fourth integrated value is calculated, and the sum is determined as the blood flow per unit area per beat.

5. The method according to claim 1, wherein The method further comprises: When the first average value difference is less than a preset threshold, moving the forward frequency point toward the second intersection point, and returning to the step of obtaining the first average value difference corresponding to the signals on both sides of the line between the first endpoint and the second intersection point, which are divided by the forward frequency point; When the second average value difference is less than a preset threshold, the reverse frequency point is moved toward the second intersection point, and the step of obtaining the second average value difference corresponding to the signals on both sides divided by the reverse frequency point between the second intersection point and the second endpoint is returned.

6. An ultrasound-based carotid artery blood flow determination device, characterized in that: The device comprises: A power spectrum density signal acquisition module is used to acquire the power spectrum density signal of each column signal in the carotid artery ultrasound spectrum; a frequency point determination module, configured to perform a first integral processing on the power spectrum density signal based on a first frequency interval of the power spectrum density signal to obtain a first integral curve; Obtain the first and last endpoints of the first integral curve, and connect the two endpoints with a first reference line; Obtaining a first intersection point of the first reference line and the first integral curve as a maximum energy point of the power spectrum density signal; The power spectrum density signal is divided into two signal intervals according to the maximum energy point; Obtain the points with the smallest ordinate in each signal interval as the two minimum energy points of the power spectrum density signal; Performing a second integral process on the power spectrum density signal of the corresponding interval based on the second frequency interval of the two minimum energy points to obtain a second integral curve; Obtain a first endpoint at the beginning and a second endpoint at the end of the second integral curve, and connect the first endpoint and the second endpoint with a second reference line; Determine a second intersection point of the second reference line and the second integral curve, a forward frequency point, and a reverse frequency point, where the forward frequency point is a point corresponding to the maximum forward distance from the second integral curve to the second reference line, and the reverse frequency point is a point corresponding to the maximum reverse distance from the second integral curve to the second reference line; Obtain a first average value difference between the signals on both sides of the first endpoint and the second intersection point, which are divided by the forward frequency point. When the first average value difference is greater than or equal to a preset threshold, the forward frequency point is determined as the forward maximum frequency point. Obtain a second average value difference between the signals on both sides of the second intersection point and the second endpoint, which are divided by the reverse frequency point. When the second average value difference is greater than or equal to the preset threshold, the reverse frequency point is determined as the reverse maximum frequency point. a waveform acquisition module, configured to connect the forward maximum frequency point and the reverse maximum frequency point of each column signal in the carotid artery ultrasound spectrum to obtain a waveform corresponding to the carotid artery ultrasound spectrum; The blood flow determination module is used to determine the blood flow per unit area per beat based on the waveform.

7. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 5 are implemented.

8. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 5 are implemented.

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