Method and system for calculating heart output state through ultrasonic color image

Abstract

The invention provides a method and system for calculating a heart output state through an ultrasonic color image, and the method comprises the steps: obtaining a first image of a Doppler blood flow velocity and a second image of a Doppler blood flow intensity signal, and obtaining the variance of a blood flow signal through the calculation of the first image and the second image; calculating each pixel point in the ultrasonic color blood flow image frame, extracting the variance, speed and energy of a blood flow signal corresponding to the pixel point, and determining whether the current pixel point is a blood flow point; and for the color blood flow signal variance in the time dimension, according to an effective blood flow proportion, calculating a time curve of a blood flow point in the effective blood flow proportion to obtain a Doppler blood flow curve, and calculating the Doppler blood flow curve to obtain an average heart rate, a blood flow peak value frame and a blood flow valley value frame of the current ultrasonic color blood flow image. The system comprises an image processing module, a parameter calculation module and a curve analysis module. According to the invention, the output state of the heart can be comprehensively understood and evaluated.

Description

Technical Field

[0001] The present invention relates to the technical field of ultrasonic image processing, and in particular to a method and system for calculating cardiac output status using ultrasonic color images. Background Art

[0002] Ultrasonic color flow images are very helpful for evaluating cardiac function and overall hemodynamic status. Especially for examinations involving the carotid artery, as one of the main pathways for cardiac output, the blood flow characteristics of the carotid artery directly reflect the heart's pumping efficiency and the state of the surrounding blood vessels; by analyzing the ultrasonic blood flow images of the carotid artery, doctors can indirectly evaluate the health of the heart's contraction. Common in ultrasonic color flow images is the pulsatile blood flow waveform in the carotid artery, which is synchronized with the heart's pulsation. During cardiac systole, the blood flow velocity in the carotid artery reaches its maximum, forming the so-called systolic peak. The peak value of the systolic peak reflects the heart's contractile force and blood output. The blood flow waveform of the carotid artery can provide indirect information about cardiac function; for example, the sharpness of the waveform and the height of the peak can help determine the heart's pumping efficiency. Abnormalities in the carotid waveform, such as waveform attenuation or the two-peak phenomenon, may indicate problems such as reduced cardiac function or stenosis of the heart outlet.

[0003] The above information is helpful in assessing the risk of cardiovascular disease. Carotid ultrasound is an important means of assessing the risk of cardiovascular disease, especially for the early diagnosis of atherosclerosis. Monitoring patients with heart disease. For patients with known heart disease, regular examination of carotid blood flow can monitor the progression of the patient's disease and the effectiveness of treatment. By analyzing the ultrasonic blood flow images of the carotid artery, doctors can obtain important information about the heart's pumping efficiency and its impact on systemic hemodynamics, thereby helping to assess heart health. This examination method is simple, non-invasive, and contains a large amount of information. It is one of the commonly used techniques in the diagnosis and monitoring of cardiovascular disease.

[0004] In the current practice of clinically relevant technologies, Doppler ultrasound technology, especially spectral Doppler technology, is often used to evaluate the functional status of the heart for ultrasonic carotid artery blood flow examinations, which can show in detail the changes in blood flow velocity over time and provide important insights into cardiac output and hemodynamics. Spectral Doppler ultrasound uses the Doppler effect to detect and measure the velocity and direction of blood flow in the body. When the ultrasound beam encounters moving blood cells, the frequency changes (Doppler frequency shift) due to the movement of blood cells. The frequency change can be detected by the instrument and converted into visual images and sound signals to analyze blood flow characteristics. Although spectral Doppler ultrasound is widely used in clinical practice to evaluate blood flow characteristics, especially in the fields of cardiology and angiology, the technology itself has some limitations: 1. Spectral Doppler mainly provides blood flow velocity information at a single point or through a small area, and cannot provide dynamic images of blood flow in the entire blood vessel or large area at the same time. If the selected measurement point does not represent the overall situation, it may lead to misdiagnosis or missed diagnosis. 2. Spectral Doppler cannot display the complete blood flow morphology. Spectral Doppler cannot provide a complete two-dimensional blood flow morphology image and cannot display the distribution of blood flow in the entire blood vessel cross-section. This is a limitation when evaluating certain vascular diseases (such as uneven blood flow distribution caused by hemangiomas or atherosclerotic plaques). 3. Spectral Doppler may be interfered by surrounding tissue structures, especially in deep blood vessels or when surrounding tissue density is high. 4. Noise and electronic interference of spectral Doppler ultrasound equipment may also affect the accuracy of the measurement.

[0005] Spectral Doppler ultrasound provides detailed blood flow velocity information in cardiac function assessment, but its main limitation is that it can only obtain data from a single location and cannot provide visual information about the distribution of blood flow in a larger area. Therefore, in actual clinical diagnosis, doctors often use two-dimensional color blood flow images in combination to obtain more comprehensive diagnostic information. The advantages of two-dimensional color blood flow images include comprehensive visual information. Two-dimensional color blood flow images can show the blood flow status of the entire heart or blood vessels, including blood flow direction and relative velocity, allowing doctors to observe the distribution of blood flow in the entire heart or valve area at a glance. More intuitive diagnosis, color coding helps distinguish blood flow toward the probe and away from the probe, simplifying the identification of abnormal blood flow patterns (such as turbulence or reverse flow), which is crucial for diagnosing conditions such as heart valve disease. In cardiac ultrasound diagnosis, spectral Doppler and two-dimensional color blood flow images are often used in combination: comprehensive evaluation, spectral Doppler is used to obtain detailed blood flow velocity and waveform data at key points, while two-dimensional color blood flow images are used to evaluate the overall blood flow pattern and identify potential abnormal areas. Diagnostic efficiency: By combining these two technologies, the accuracy and efficiency of diagnosis can be improved, especially in complex cardiac cases, which can quickly guide clinical decision-making. The combination of spectral Doppler and two-dimensional color blood flow images can easily increase the system load and reduce the frame rate. The color blood flow image is prone to jamming, making it impossible for the operator to observe the flow of continuous blood flow more intuitively. There is also a certain time delay between the two images, and they cannot be completely matched. At the same time, in cardiac examinations, a higher display frame rate is generally required. The use of spectral Doppler and two-dimensional color blood flow duplex will reduce the display frame rate, and the frame rate will drop more with the increase of blood flow range, which is caused by switching between multi-task modes.

[0006] Prior art 1, application number: CN 202110795613.9 discloses a spectral Doppler measurement method and ultrasonic imaging equipment for the heart, including: determining the section type of the ultrasonic B image of the target heart; determining the spectral measurement item corresponding to the section type of the ultrasonic B image according to the mapping relationship between the preset section type and the spectral measurement item; determining the structure to be measured corresponding to the spectral measurement item on the ultrasonic B image, and setting the sampling volume according to the structure to be measured; obtaining the spectral Doppler data at the sampling volume; analyzing the spectral Doppler data to obtain the measurement parameters corresponding to the determined spectral measurement item. Although the time of cardiac spectral Doppler measurement is reduced and the efficiency is improved by intelligently recommending spectral measurement items. However, it is not possible to provide a dynamic image of blood flow in the entire blood vessel or a large area at the same time, and the selection of measurement points is difficult, which can easily lead to large errors in the spectral measurement results.

[0007] Prior art 2, application number: CN 202311375975.8 discloses a method for calculating cardiac function indicators, a computer-readable medium and an ultrasonic device, including: identifying a first cardiac section according to cardiac ultrasonic image data; sampling the ultrasonic image data corresponding to the first cardiac section in Doppler imaging mode to obtain a spectrum image to be processed corresponding to the first cardiac section; adjusting the spectrum image parameters corresponding to the spectrum image to be processed to obtain a target spectrum image whose spectrum image parameters meet the requirements; calculating the cardiac function indicators corresponding to the first cardiac section according to the cardiac function parameters represented by the target spectrum image. Although by adjusting the spectrum image parameters corresponding to the spectrum image to be processed to obtain a target spectrum image whose spectrum image parameters meet the requirements, the calculation of cardiac function indicators can refer to the cardiac function parameters represented by the high-quality target spectrum image, thereby improving the accuracy of the calculation of cardiac function indicators. However, it is impossible to display the distribution of blood flow in the entire cross-section of the blood vessel, which affects the accuracy of the calculation of cardiac function indicators.

[0008] Prior art three, application number: CN 202110629090.0 discloses a cardiac ultrasound imaging method, terminal equipment and computer-readable storage medium, including: transmitting a scanning pulse to a preset area to be tested, wherein the scanning pulse includes an M-type scanning pulse and a Doppler scanning pulse; receiving echo data based on scanning pulse feedback; converting the echo data into an M-type ultrasound image and Doppler spectrum data; inputting both the M-type ultrasound image and the Doppler spectrum data into a display device for real-time synchronous display. Although by transmitting a scanning pulse including an M-type scanning pulse and a Doppler scanning pulse, receiving echo data based on scanning pulse feedback to obtain an M-type ultrasound image and Doppler spectrum data, by inputting the M-type ultrasound image and the Doppler spectrum data into the display device for synchronous display, so that the doctor can accurately judge the phase of the heart according to the synchronously displayed M-type ultrasound image and Doppler spectrum, it is convenient for the doctor to make an accurate diagnosis of the heart of the person being examined. However, the spectrum Doppler ultrasound device is susceptible to noise and electronic interference, resulting in poor results of ultrasound images.

[0009] At present, the existing technologies 1, 2 and 3 cannot provide the dynamic blood flow images of the entire blood vessel or a large area at the same time, cannot display the distribution of blood flow in the entire blood vessel cross section, are susceptible to noise and electronic interference, resulting in incomplete ultrasonic color blood flow images, and low accuracy and efficiency. Therefore, the present invention provides a method and system for calculating the cardiac output state by ultrasonic color images, so that the ultrasonic cardiogram is not limited to static anatomical evaluation, but can dynamically monitor the functional state of the heart, providing a powerful tool for the early detection and management of heart disease. Summary of the invention

[0010] In order to solve the above technical problems, the present invention provides a method for calculating cardiac output state from ultrasonic color images, comprising the following steps:

[0011] Acquire an ultrasonic color blood flow image, perform post-processing on the ultrasonic color blood flow image to obtain a first image of Doppler blood flow velocity and a second image of Doppler blood flow intensity signal, and calculate the variance of the blood flow signal from the first image and the second image;

[0012] Calculate each pixel in the ultrasonic color blood flow image frame, extract the variance, velocity and energy of the blood flow signal corresponding to the pixel, compare them with the set variance threshold, velocity threshold and energy threshold respectively, confirm whether the current pixel is a blood flow point, and calculate the blood flow intensity and blood flow velocity according to the signal of the blood flow point;

[0013] The variance of the color blood flow signal in the time dimension is analyzed over time. According to the effective blood flow ratio, the time curve of the blood flow point within the effective blood flow ratio is calculated, and then the Doppler blood flow curve is obtained. The Doppler blood flow curve is calculated to obtain the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasound color blood flow image.

[0014] Optionally, the process of post-processing the ultrasound color blood flow image includes the following steps:

[0015] The ultrasonic radio frequency signal, RF signal, collected by the ultrasonic probe is obtained through the front-end circuit of the ultrasonic imaging device. The step length of the ultrasonic radio frequency signal is time T. The collected ultrasonic radio frequency signal is uploaded to the host computer through the PCIe interface and rearranged to form a three-dimensional data structure.

[0016] The three-dimensional data structure is m*n*T, where m represents the number of acquisition channels, n represents the number of sub-time periods within time T, and the length of each sub-time period is t;

[0017] The collected ultrasonic RF signal is orthogonally demodulated to obtain an IQ signal in complex form; the obtained IQ signal is beamformed to generate a synthesized new IQ signal, bfIQ, and the synthesized new IQ signal is passed to a high-pass filter to remove low-frequency noise and DC components and enhance key signal characteristics;

[0018]

[0019] Among them, f s represents the sampling frequency, j represents the imaginary unit;

[0020] IQ{filtered}=H{hp}(IQ)

[0021] Where H{hp} represents the transfer function of the high-pass filter;

[0022] A fixed-delay correlation calculation is performed on the filtered new IQ signal to extract the expected blood flow signal characteristics; an arctangent calculation is performed on the result R1 of the fixed-delay correlation calculation to obtain the Doppler blood flow velocity; a compression process is performed on the result R0 without delay to generate a Doppler blood flow intensity signal image, and the expected result is processed through the processing flow to finally obtain a Doppler blood flow velocity image and a Doppler blood flow intensity signal image;

[0023]

[0024] Wherein, V represents the obtained Doppler blood flow velocity image.

[0025] Optionally, the process of performing fixed delay correlation calculation on the filtered new IQ signal includes the following steps:

[0026] The fixed delay correlation is calculated using the product of the complex signal; the zero delay correlation is calculated to calculate the energy or intensity of the IQ signal at the same time point. After the correlation calculation is completed, the zero delay correlation and the fixed delay correlation are obtained for processing to obtain the final image result;

[0027] Performing arctangent function calculation on the calculated fixed delay correlation to obtain Doppler blood flow velocity image, the output of the arctangent function will represent the blood flow velocity;

[0028] The Doppler blood flow intensity signal image is obtained by modulo zero-delay correlation and threshold compression. The modulo zero-delay correlation calculates the amplitude of the zero-delay correlation result, which represents the intensity of the return signal; threshold compression is used to enhance the image quality and remove background noise.

[0029] Optionally, the calculation expression related to the fixed delay is:

[0030]

[0031] The calculation expression related to zero delay is:

[0032]

[0033] Wherein, WFIQ represents the IQ signal after high-pass filtering, EL represents the length of Ensemble, the number of times data is periodically collected within a period of time, i and j represent the row coordinates and column coordinates in space respectively, R0 represents zero-delay correlation, and R1 represents fixed-delay correlation; the obtained R1 is subjected to arc tangent function calculation and velocity conversion to obtain the Doppler blood flow velocity image, and R0 is subjected to modulus calculation and threshold compression to obtain the Doppler blood flow intensity signal blood flow image;

[0034] Among them, the inverse tangent function is calculated for the calculated R1, and the formula is:

[0035]

[0036] Where imag(R1) represents the imaginary part of the R1 result, and real(R1) represents its real part; the output V of the inverse tangent function will represent the blood flow velocity in cm / s or m / s.

[0037] Optionally, the expression for the variance of the blood flow signal calculated from the first image and the second image is:

[0038]

[0039] Among them, r0 represents the energy signal, r1 represents the velocity signal, PRF is the pulse repetition frequency when collecting data, T is the variance of color blood flow, and abs is the absolute value.

[0040] Optional, the setting method of variance threshold, speed threshold and energy threshold, the variance includes high threshold T max With low threshold T min , energy threshold P threshold With speed threshold V threshold ;

[0041] Among them, the variance high threshold T max Set the normalized variance range to 70%-90% and the variance low threshold T min The energy threshold P is set between 40% and 60% of the normalized variance range. threshold Set the speed threshold V in the range of 40% to 60% of the normalized energy. threshold The normalized velocity range is set between 20% and 40%. The set value is related to the background grayscale of the current ultrasound color blood flow image and is set within a variance / energy / velocity threshold range close to the expected display effect of the current blood flow.

[0042] Optionally, the process of calculating each pixel point in the ultrasonic color blood flow image frame includes the following steps:

[0043] Obtain processed ultrasound color blood flow images, and correct the acquired images, including noise removal and artifact correction, etc.; in the post-processed images, for each pixel point, extract the position of the pixel in the image and the change of the pixel point in the time series;

[0044] For each pixel, the blood flow velocity is calculated according to the Doppler effect principle; the corresponding blood flow energy is obtained by normalizing the signal intensity at the pixel; the blood flow signal values ​​of adjacent time frames are collected and the signal variance of the pixel is calculated;

[0045] The calculated variance, velocity and energy values ​​are compared with the preset variance threshold, velocity threshold and energy threshold to confirm whether the pixel point belongs to the actual blood flow point.

[0046] Optionally, if the speed of the pixel is greater than V threshold , the energy is less than P threshold , the variance is greater than the variance threshold T max Determine the point as a noise point; if the speed of the pixel point is greater than the speed threshold V threshold , the energy is greater than the energy threshold P threshold , the variance is less than the variance threshold T min The point is determined to be a blood flow point; the signal located as the blood flow point is used to calculate the maximum blood flow intensity P per unit time. max With the minimum blood flow intensity P min , the maximum blood flow velocity V max With the minimum blood flow velocity V min .

[0047] Optionally, the process of obtaining the average heart rate, blood flow peak value frame and blood flow valley value frame of the current ultrasonic color blood flow image comprises the following steps:

[0048] The effective continuous ratio N% is set as the ratio of effective blood flow points that continue to be effective signals in the time direction; based on the pulsation frequency of the carotid artery, the effective continuous ratio is set to 30%-50%; for each effective blood flow pixel point, the data of its intensity changing with time is extracted, and the time curve is formed by plotting the jump of the intensity value of the pixel point with time;

[0049] Among all the pixels within the effective ratio, the corresponding average values ​​of their time curves are calculated to obtain the average Doppler average blood flow curve of the sample data segment; the average Doppler average blood flow energy curve and the average Doppler average blood flow velocity curve are calculated to provide key blood flow characteristics;

[0050] Perform specific time analysis on the average Doppler energy curve, calculate the three time parameters of peak-to-peak time, valley-to-valley time and peak-to-valley time; convert the three time parameters into heart rate HR, average the three calculated heart rate values ​​to obtain the average heart rate of the current blood flow image; extract the blood flow peak value frame from the velocity curve, and extract the blood flow valley value frame from the energy curve.

[0051] The present invention provides a system for calculating cardiac output status using ultrasonic color images, comprising:

[0052] An image processing module is responsible for acquiring an ultrasonic color blood flow image, performing post-processing on the ultrasonic color blood flow image to obtain a first image of Doppler blood flow velocity and a second image of Doppler blood flow intensity signal, and calculating the variance of the blood flow signal from the first image and the second image;

[0053] The parameter calculation module is responsible for calculating each pixel point in the ultrasonic color blood flow image frame, extracting the variance, velocity and energy of the blood flow signal corresponding to the pixel point, comparing them with the set variance threshold, velocity threshold and energy threshold respectively, confirming whether the current pixel point is a blood flow point, and calculating the blood flow intensity and blood flow velocity according to the signal of the blood flow point;

[0054] The curve analysis module is responsible for analyzing the variance of the color blood flow signal in the time dimension and its changes over time. According to the effective blood flow ratio, the time curve of the blood flow point within the effective blood flow ratio is calculated, and then the Doppler blood flow curve is obtained. The Doppler blood flow curve is calculated to obtain the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasonic color blood flow image.

[0055] The present invention acquires and processes ultrasonic color blood flow images, acquires color blood flow images through ultrasonic equipment, and truly reflects the blood flow situation; post-processes the acquired images, extracts Doppler blood flow velocity and intensity signals, and obtains a first image (velocity image) and a second image (intensity image); calculates the variance of the blood flow signal by processing the velocity and signal intensity, which provides basic data for subsequent analysis. Data basis: This step is the basis of the entire process and ensures the accuracy of subsequent analysis. Acquiring high-quality data is a prerequisite for successfully evaluating the cardiac output state; blood flow dynamic feature analysis: The variance calculation of the blood flow signal helps to understand the dynamic changes of blood flow, and then helps to identify abnormal situations. Pixel point analysis and threshold comparison calculate the variance, velocity, energy and other information of each pixel point in the ultrasonic color blood flow image frame; compares the calculated variance, velocity and energy with the set threshold to confirm whether the current pixel point is a valid blood flow point; extracts information on blood flow rate and intensity from the confirmed blood flow point signal. Accurately identify blood flow points: Through high-precision pixel analysis and threshold comparison, blood flow points can be effectively identified from the image, reducing noise and erroneous judgments, and ensuring the reliability of the analysis results; Provide clinical data: The calculated blood flow intensity and speed provide important indicators for clinicians, which help to judge the health of the heart and blood vessels. Variance analysis and Doppler blood flow curve extraction in the time dimension analyze the changes in the variance of blood flow signals in the time dimension to observe the dynamic characteristics of blood flow; calculate the proportion of effective blood flow based on the identified blood flow points, and extract the time curve associated with these blood flow points; obtain the Doppler blood flow curve by calculating the time series, and further calculate the heart rate and blood flow peak frame and valley frame. Dynamic monitoring of heart status: Analysis of variance changes over time and effective blood flow ratio provides an in-depth understanding of the dynamic changes of heart output status, helping doctors monitor heart function in real time; Clinical decision support: The calculated average heart rate and blood flow peak and valley values ​​provide important dynamic indicators for clinicians, which can assist in judging the patient's heart health status and hemodynamic characteristics.

[0056] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures particularly pointed out in the written description and the accompanying drawings.

[0057] The technical solution of the present invention is further described in detail below through the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0058] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0059] Figure 1 This is a flow chart of a method for calculating cardiac output status from an ultrasonic color image in Embodiment 1 of the present invention;

[0060] Figure 2 This is a process diagram of post-processing an ultrasonic color blood flow image in Embodiment 2 of the present invention;

[0061] Figure 3 This is a process diagram of performing fixed delay correlation calculation on the filtered new IQ signal in Embodiment 3 of the present invention;

[0062] Figure 4 This is a diagram of the process of calculating each pixel point in an ultrasonic color blood flow image frame in Embodiment 6 of the present invention;

[0063] Figure 5 This is a process diagram of obtaining the average heart rate, blood flow peak value frame and blood flow valley value frame of the current ultrasonic color blood flow image in Example 7 of the present invention;

[0064] Figure 6 This is a system block diagram of calculating cardiac output status from ultrasonic color images in Example 8 of the present invention. DETAILED DESCRIPTION

[0065] The preferred embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be understood that the preferred embodiments described herein are only used to illustrate and explain the present invention, and are not used to limit the present invention.

[0066] The terms used in the embodiments of the present application are only for the purpose of describing specific embodiments, and are not intended to limit the embodiments of the present application. The singular forms of "a", "said" and "the" used in the embodiments of the present application are also intended to include plural forms, unless the context clearly indicates other meanings. It should also be understood that the term "and / or" used in this article refers to and includes any or all possible combinations of one or more associated listed items.

[0067] When the following description refers to the accompanying drawings, unless otherwise indicated, the same numbers in different drawings represent the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present application. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present application as described in detail. In the description of the present application, it should be understood that the terms "first", "second", "third", etc. are only used to distinguish similar objects, and do not have to be used to describe a specific order or sequence, nor can they be understood as indicating or implying relative importance. For those of ordinary skill in the art, the specific meanings of the above terms in the present application can be understood according to the specific circumstances.

[0068] Example 1: Figure 1 As shown, the embodiment of the present invention provides a method for calculating cardiac output state from ultrasound color images, comprising the following steps:

[0069] S100: Acquire an ultrasonic color blood flow image, perform post-processing on the ultrasonic color blood flow image to obtain a first image of Doppler blood flow velocity and a second image of Doppler blood flow intensity signal, and calculate the variance of the blood flow signal from the first image and the second image;

[0070] S200: Calculate each pixel point in the ultrasonic color blood flow image frame, extract the variance, velocity and energy of the blood flow signal corresponding to the pixel point, compare them with the set variance threshold, velocity threshold and energy threshold respectively, confirm whether the current pixel point is a blood flow point, and calculate the blood flow intensity and blood flow velocity according to the signal of the blood flow point;

[0071] S300: Analyze the variance of the color blood flow signal in the time dimension over time, calculate the time curve of the blood flow point within the effective blood flow ratio according to the effective blood flow ratio, and then obtain the Doppler blood flow curve, calculate the Doppler blood flow curve, and obtain the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasound color blood flow image.

[0072] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment first acquires an ultrasonic color blood flow image, performs post-processing on the ultrasonic color blood flow image, obtains a first image of Doppler blood flow velocity and a second image of Doppler blood flow intensity signal, and calculates the variance of the blood flow signal from the first image and the second image; secondly, each pixel point in the ultrasonic color blood flow image frame is calculated, and the variance, velocity and energy of the blood flow signal corresponding to the pixel point are extracted, and the variance, velocity and energy of the blood flow signal corresponding to the pixel point are respectively compared with the set variance threshold, velocity threshold and energy threshold to confirm whether the current pixel point is a blood flow point, and calculate the blood flow intensity and blood flow velocity according to the signal of the blood flow point; finally, the variance of the color blood flow signal in the time dimension is analyzed over time, and according to the effective blood flow ratio, the time curve of the blood flow point within the effective blood flow ratio is calculated, and then the Doppler blood flow curve is obtained, and the Doppler blood flow curve is calculated to obtain the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasonic color blood flow image. Step S100 of the above scheme acquires and processes the ultrasonic color blood flow image, acquires the color blood flow image through the ultrasonic device, and truly reflects the blood flow situation; post-processes the acquired image, extracts the Doppler blood flow velocity and intensity signal, and obtains the first image (velocity image) and the second image (intensity image); calculates the variance of the blood flow signal by processing the velocity and signal intensity, which provides basic data for subsequent analysis. Data basis: This step is the basis of the entire process to ensure the accuracy of subsequent analysis. Acquiring high-quality data is a prerequisite for successfully evaluating the cardiac output state; blood flow dynamic feature analysis: The variance calculation of the blood flow signal helps to understand the dynamic changes of blood flow, and then helps to identify abnormal conditions. Step S200 pixel point analysis and threshold comparison, calculates the variance, velocity, energy and other information of each pixel point in the ultrasonic color blood flow image frame; compares the calculated variance, velocity and energy with the set threshold to confirm whether the current pixel point is a valid blood flow point; extracts blood flow rate and intensity information from the confirmed blood flow point signal. Accurately identify blood flow points: Through high-precision pixel analysis and threshold comparison, blood flow points can be effectively identified from the image, reducing noise and erroneous judgments, and ensuring the reliability of the analysis results; provide clinical data: the calculated blood flow intensity and speed provide important indicators for clinicians, which help to judge the health of the heart and blood vessels. Step S300 Variance analysis and Doppler blood flow curve extraction in the time dimension, analyze the changes in the variance of blood flow signals in the time dimension to observe the dynamic characteristics of blood flow; calculate the proportion of effective blood flow based on the identified blood flow points, and extract the time curves associated with these blood flow points; obtain the Doppler blood flow curve by calculating the time series, and further calculate the heart rate and blood flow peak frame and valley frame.Dynamic monitoring of cardiac status: Analysis of variance changes over time and effective blood flow ratio provides an in-depth understanding of the dynamic changes in cardiac output status, helping doctors monitor cardiac function in real time; Clinical decision support: The calculated average heart rate and blood flow peak and valley values ​​provide important dynamic indicators for clinicians, which can assist in judging the patient's cardiac health status and hemodynamic characteristics.

[0073] In summary, in the overall process of this embodiment, each step is not only independent, but also interconnected, and together constitutes a complete cardiac function evaluation system; by comprehensively analyzing the speed, intensity and variance in the ultrasonic color blood flow image, it is possible to more comprehensively understand and evaluate the output state of the heart, and provide support for clinical decision-making. It will enhance the role of ultrasonic imaging technology in the diagnosis of cardiovascular diseases.

[0074] Example 2: Figure 2 As shown, based on Example 1, the process of post-processing the ultrasonic color blood flow image provided by the embodiment of the present invention includes the following steps:

[0075] S101: Obtain an ultrasonic radio frequency signal (RF signal) collected by an ultrasonic probe through a front-end circuit of an ultrasonic imaging device, wherein the step length of the ultrasonic radio frequency signal is time T, upload the collected ultrasonic radio frequency signal to a host computer through a PCIe interface, and rearrange it to form a three-dimensional data structure;

[0076] The three-dimensional data structure is m*n*T, where m represents the number of acquisition channels, n represents the number of sub-time periods within time T, and the length of each sub-time period is t;

[0077] S102: performing orthogonal demodulation on the collected ultrasonic radio frequency signal to obtain an IQ signal in a complex form; performing beam synthesis on the obtained IQ signal to generate a synthesized new IQ signal (bfIQ), and transmitting the synthesized new IQ signal to a high-pass filter to remove low-frequency noise and DC components and enhance key signal features;

[0078]

[0079] Among them, f s represents the sampling frequency, j represents the imaginary unit;

[0080] IQ{filtered}=H{hp}(IQ)

[0081] Where H{hp} represents the transfer function of the high-pass filter;

[0082] S103: performing a fixed-delay correlation calculation on the filtered new IQ signal to extract the expected blood flow signal features; performing an arctangent calculation on the result R1 of the fixed-delay correlation calculation to obtain the Doppler blood flow velocity; performing compression processing on the result R0 without delay to generate a Doppler blood flow intensity signal image, and the expected results are processed through the process to finally obtain a Doppler blood flow velocity image and a Doppler blood flow intensity signal image;

[0083]

[0084] Wherein, V represents the obtained Doppler blood flow velocity image.

[0085] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the ultrasonic radio frequency signal (RF signal) collected by the ultrasonic probe is first obtained through the front-end circuit of the ultrasonic imaging device, and the step length of the ultrasonic radio frequency signal is time T. The collected ultrasonic radio frequency signal is uploaded to the host computer through the PCIe interface and rearranged to form a three-dimensional data structure; secondly, the collected ultrasonic radio frequency signal is orthogonally demodulated to obtain an IQ signal in a complex form; the obtained IQ signal is beamformed to generate a synthesized new IQ signal (bfIQ), and the synthesized new IQ signal is passed to a high-pass filter to remove low-frequency noise and DC components and enhance key signal features; finally, a fixed delay correlation calculation is performed on the filtered new IQ signal to extract the desired blood flow signal features; the result R1 of the fixed delay correlation calculation is subjected to an inverse tangent calculation to obtain the Doppler blood flow velocity; for the result R0 without delay, compression processing is performed to generate a Doppler blood flow intensity signal image, and the expected result is processed through the processing flow to finally obtain a Doppler blood flow velocity image and a Doppler blood flow intensity signal image. The above scheme includes step S101 of ultrasonic radio frequency signal acquisition and three-dimensional data rearrangement. The ultrasonic radio frequency signal (RF signal) generated by the ultrasonic probe is acquired through the front-end circuit of the ultrasonic imaging device. The signal represents the reflected ultrasonic wave; the acquired RF signal is uploaded to the host computer through the PCIe interface, thereby entering a more advanced processing stage; the RF signal is rearranged into a three-dimensional data structure according to the number of acquisition channels m, the number of time period divisions n and the time length T. Data foundation: This step provides a basic data structure for subsequent analysis to ensure that subsequent processing can extract valuable information from the rich acquisition data; multi-dimensional data analysis: the formation of a three-dimensional data structure enables subsequent processing to effectively perform multi-dimensional signal analysis, reflecting the signal changes in time and space. Step S102 orthogonal demodulation and beam synthesis: orthogonal demodulation of the RF signal is performed and converted into an IQ signal in complex form, so that the signal can be used for subsequent processing in a more effective way; beam synthesis is performed on the IQ signal to generate a synthesized IQ signal (bfIQ) to enhance image quality and resolution; the new IQ signal is passed through a high-pass filter to remove low-frequency noise and DC components and improve the key features of the signal. Signal quality improvement: Through orthogonal demodulation and beamforming, the coherence and spatial resolution of the signal are significantly improved, laying a foundation for higher quality for subsequent analysis; Noise suppression: High-pass filtering reduces the impact of low-frequency noise, making important dynamic features in the image more prominent, which helps to accurately analyze blood flow information. Step S103 Fixed delay correlation calculation and Doppler blood flow velocity extraction, fixed delay correlation calculation is performed on the filtered IQ signal to extract the desired blood flow signal features; the inverse tangent function is used to calculate the fixed delay result to obtain the Doppler blood flow velocity; the delay-free result is compressed to generate a Doppler blood flow intensity signal image.Velocity information acquisition: Doppler blood flow velocity is obtained by calculation, providing an objective quantitative indicator for clinical evaluation of blood flow dynamics, which helps to judge cardiovascular health status; Intensity analysis: The generation of Doppler blood flow intensity signal images further enriches the understanding of blood flow status and helps clinicians evaluate blood flow volume and direction.

[0086] In summary, the Doppler blood flow velocity image and Doppler blood flow intensity signal image finally obtained by the processing of the above steps in this embodiment provide important basic data and clinical reference for the detection and treatment of cardiovascular diseases. It not only improves the signal quality, but also ensures the accuracy and credibility of the data, providing strong support for medical imaging and diagnosis.

[0087] Example 3: Figure 3 As shown, based on Example 2, the process of performing fixed delay correlation calculation on the filtered new IQ signal provided in the embodiment of the present invention includes the following steps:

[0088] S1031: using the product of the complex signal, calculating the fixed delay correlation; calculating the zero delay correlation, calculating the energy or intensity of the IQ signal at the same time point, after completing the correlation calculation, obtaining the zero delay correlation and the fixed delay correlation for processing to obtain the final image result;

[0089] S1032: performing arctangent function calculation on the calculated fixed delay correlation to obtain a Doppler blood flow velocity image, wherein the output of the arctangent function represents the blood flow velocity;

[0090] S1033: Obtaining a Doppler blood flow intensity signal image by modulo zero-delay correlation and threshold compression. The modulo zero-delay correlation operation calculates the amplitude of the zero-delay correlation result, which represents the intensity of the return signal; the threshold compression is used to enhance the image quality and remove background noise;

[0091] The calculation expression related to the fixed delay is:

[0092]

[0093] The calculation expression related to zero delay is:

[0094]

[0095] Wherein, WFIQ represents the IQ signal after high-pass filtering, EL represents the integration length (k is the number of times data is periodically collected within a period of time), i and j represent the row coordinates and column coordinates in space respectively, R0 represents zero delay correlation, and R1 represents fixed delay correlation; in this embodiment, the R1 obtained is subjected to arc tangent function calculation and velocity conversion to obtain a Doppler blood flow velocity image, and R0 is subjected to modulus calculation and threshold compression to obtain a Doppler blood flow intensity signal blood flow image;

[0096] Among them, the inverse tangent function is calculated for the calculated R1, and the formula is:

[0097]

[0098] Where imag(R1) represents the imaginary part of the R1 result, and real(R1) represents its real part; the output V of the inverse tangent function will represent the blood flow velocity in cm / s or m / s.

[0099] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment first uses the product of complex signals to calculate the fixed delay correlation; calculates the zero delay correlation, and calculates the energy or intensity of the IQ signal at the same time point. After completing the correlation calculation, the zero delay correlation and the fixed delay correlation are obtained for processing and obtaining the final image result; secondly, the calculated fixed delay correlation is calculated by the inverse tangent function to obtain the Doppler blood flow velocity image, and the output of the inverse tangent function will represent the blood flow velocity; finally, the Doppler blood flow intensity signal image is obtained by modulo and threshold compression of the zero delay correlation. The modulo operation of the zero delay correlation calculates the amplitude of the zero delay correlation result, which represents the intensity of the return signal; the threshold compression is used to enhance the image quality and remove background noise. Step S1031 of the above solution calculates the fixed delay correlation and the zero delay correlation, calculates the correlation between adjacent time periods, and extracts the dynamic information of the signal through product and averaging; the zero delay correlation calculates the energy of the signal at the same time, that is, the signal intensity, which can reflect the total energy of the echo signal. Dynamic feature extraction: Fixed delay correlation provides a basis for visualization of the dynamic changes of blood flow, providing information for subsequent blood flow velocity analysis; Signal strength confirmation: Zero delay correlation provides quantification of signal strength, which can effectively identify and evaluate the strength of echo signals, and is crucial for clinical diagnosis. Step S1032: Calculate the inverse tangent function, and obtain the blood flow velocity at a specific time point by calculating the ratio between the imaginary part and the real part. Velocity estimation: The output of the inverse tangent function directly represents the blood flow velocity, usually in units of cm / s or m / s; this information is clinically significant for evaluating blood flow, diagnosing cardiovascular diseases, and monitoring treatment effects. Step S1033: Modulus and threshold compression: Threshold processing is performed on the signal after modulus, and information below a certain threshold is removed to enhance the image quality and reduce the impact of background noise. Acquisition of intensity information: By modulo the zero delay correlation, the intensity map of the blood flow signal is extracted, and the activity of the blood flow can be judged. This is of great value for clinicians to assess vascular conditions and decide on treatment plans; Improvement of suggestive image quality: Threshold compression further enhances image clarity, making the lesions and surrounding tissues more distinct, improving doctors' visualization analysis capabilities, and helping to make accurate diagnoses.

[0100] In summary, this embodiment provides comprehensive and accurate information for the post-processing of ultrasound color blood flow images by calculating fixed delay correlation and zero delay correlation, combined with arc tangent processing and threshold compression, which can not only obtain quantitative indicators of blood flow velocity, but also generate clear blood flow intensity images, thereby helping clinicians to better analyze, evaluate and diagnose cardiovascular health conditions. Each step plays a vital role in the process of signal processing and image generation.

[0101] Embodiment 4: Based on Embodiment 1, the expression for calculating the variance of the blood flow signal obtained by calculating the first image and the second image provided by the embodiment of the present invention is:

[0102]

[0103] Among them, r0 represents the energy signal, r1 represents the velocity signal, PRF (Pulse Repetition Frequency) is the pulse repetition frequency when collecting data, T is the variance of color blood flow, and abs is the absolute value;

[0104] The working principle and beneficial effects of the above technical solution are as follows: This embodiment can effectively evaluate and analyze the dynamic characteristics of blood flow by calculating the variance of blood flow signals, and the evaluation is of great significance for understanding the state of the cardiovascular system and diagnosing potential diseases (such as thrombosis, vascular stenosis, etc.); by combining energy signals with velocity signals, the ability to analyze signals is improved, especially in complex blood flow environments, the variance can help eliminate noise interference and more clearly reveal the true changes of signals. This embodiment considers PRF so that the technology can adapt to signal analysis under different acquisition conditions and has a wider applicability. For example, at high PRF, faster blood flow changes can be obtained, while low PRF can be used to obtain detailed signal patterns. In clinical practice, understanding the variance of blood flow can help doctors make more accurate diagnostic decisions and provide a basis for subsequent treatment plans; it has great application potential in heart disease monitoring, vascular disease diagnosis, etc.; the calculation of variance is relatively simple, and the required data comes from the previous signal processing steps, which can achieve efficient signal analysis without increasing medical costs.

[0105] In summary, this embodiment provides an effective and meaningful evaluation index for ultrasound image processing by calculating the variance of blood flow signals, which helps clinicians better understand blood flow conditions and enhances the application effectiveness of ultrasound technology in the diagnosis of cardiovascular diseases.

[0106] Embodiment 5: Based on Embodiment 1, the embodiment of the present invention provides a setting method for the variance threshold, speed threshold and energy threshold, wherein the variance includes a high threshold T max With low threshold T min , energy threshold Pthreshold With speed threshold V threshold ;

[0107] Among them, the variance high threshold T max Set the normalized variance range to 70%-90% and the variance low threshold T min The energy threshold P is set between 40% and 60% of the normalized variance range. threshold Set the speed threshold V in the range of 40% to 60% of the normalized energy. threshold The normalized velocity range is set between 20% and 40%. The set value is related to the background grayscale of the current ultrasound color blood flow image and is set within a variance / energy / velocity threshold range close to the expected display effect of the current blood flow.

[0108] The working principle and beneficial effects of the above technical solution are as follows: This embodiment can effectively reduce the interference of background noise on signal extraction and improve the accuracy of the effective signal actually detected by scientifically setting the thresholds of variance, energy and speed, so that the blood flow image has a higher signal-to-noise ratio. The threshold setting method can optimize the quality of the color blood flow map and can more clearly observe the dynamic changes and distribution of blood flow; especially in the case of uneven blood flow, a reasonable threshold can help filter out unnecessary low-quality signals and emphasize important blood flow characteristics. Combined with the grayscale of the current image background and the actual image situation, the threshold setting can be dynamically adjusted to enhance the adaptability of the system, which is crucial for processing images of different patients or different parts of the body. By associating the threshold setting with the grayscale of the image background, partial automated processing can be achieved, manual intervention can be reduced, work efficiency can be improved, and clinical applications can be promoted. More accurate blood flow signal detection and analysis results can provide better decision support for clinicians and help achieve more personalized diagnosis and treatment plans, especially in the monitoring and evaluation of cardiovascular diseases.

[0109] In summary, this embodiment improves the effectiveness and accuracy of ultrasonic color blood flow imaging by scientifically setting the variance high and low thresholds, energy thresholds, and speed thresholds, which can not only enhance the distinction between signals and noise, but also meet the needs of clinical diagnosis to the extent of ensuring image quality. This series of improvement measures is of great significance for enhancing the value of ultrasonic imaging technology in practical applications.

[0110] Example 6: Figure 4 As shown, based on Example 1, the process of calculating each pixel point in the ultrasonic color blood flow image frame provided by the embodiment of the present invention includes the following steps:

[0111] S201: Obtaining a processed ultrasonic color blood flow image, and correcting the acquired image, including noise removal and artifact correction, etc.; in the post-processed image, for each pixel point, extracting the position of the pixel in the image, and the change of the pixel point in the time series;

[0112] S202: for each pixel point, the blood flow velocity is calculated according to the Doppler effect principle; the corresponding blood flow energy is obtained by normalizing the signal intensity at the pixel point; the blood flow signal values ​​of adjacent time frames are collected, and the signal variance of the pixel point is calculated;

[0113] Blood flow velocity uses the Doppler shift formula:

[0114] Among them, c represents the speed of sound, Δf represents the frequency shift, and f0 represents the transmitting frequency;

[0115] Blood flow energy usage equation: E = |WFIQ| 2 ;

[0116] Variance calculation;

[0117]

[0118] Among them, x i represents the signal value of the pixel in the continuous time frame, represents the signal average, textVar(x) represents the variance of the random variable x, N represents the number of data samples, that is, the sum of sample points considered when calculating the variance; i ranges from 1 to N, indicating that the calculation of the variance is based on all these independent observations;

[0119] S203: comparing the calculated variance, velocity and energy values ​​with the preset variance threshold, velocity threshold and energy threshold to confirm whether the pixel point belongs to an actual blood flow point;

[0120] Among them, if the speed of the pixel is greater than V threshold , the energy is less than P threshold , the variance is greater than the variance threshold T max Determine the point as a noise point; if the speed of the pixel point is greater than the speed threshold V threshold , the energy is greater than the energy threshold P threshold , the variance is less than the variance threshold T min The point is determined to be a blood flow point; the signal located as the blood flow point is used to calculate the maximum blood flow intensity P per unit time. max With the minimum blood flow intensity P min , the maximum blood flow velocity V max With the minimum blood flow velocity V min .

[0121] The working principle and beneficial effects of the above technical solution are as follows: the present embodiment first obtains the processed ultrasonic color blood flow image, and corrects the obtained image, including noise removal and artifact correction, etc.; in the post-processed image, for each pixel point, extracts the position of the pixel in the image, and the change of the pixel point in the time series; secondly, for each pixel point, according to the principle of Doppler effect, calculates the blood flow velocity; by normalizing the signal intensity at the pixel point, obtains the corresponding blood flow energy; collects the blood flow signal values ​​of adjacent time frames, and calculates the signal variance of the pixel point; finally, S203 compares the calculated variance, velocity and energy values ​​with the preset variance threshold, velocity threshold and energy threshold to confirm whether the pixel point belongs to the actual blood flow point. Step S201 of the above scheme obtains and corrects the image, removes noise and corrects the artifact, which can significantly improve the clarity and readability of the image and reduce the misjudgment caused by low-quality images; by extracting the position of each pixel point in the image, collecting the information of the change in the time series, laying the foundation for subsequent analysis. Ensure that the subsequent data analysis is based on more reliable image information, so that the calculated parameters such as speed, energy and variance reflect the real blood flow characteristics; provide necessary spatiotemporal information for pixel-by-pixel analysis, and enhance the scientific basis of analysis. Step S202 calculates speed, energy and variance, and obtains a more accurate blood flow velocity value through the Doppler effect, which helps to judge the dynamic characteristics of blood flow; the method of normalizing signal intensity generates the corresponding energy value, which is convenient for comparison and analysis with the standard; by analyzing the background noise, the variance is calculated, and the stability and change of each pixel signal can be evaluated. These calculations provide basic data for subsequent analysis, allowing further threshold comparison, and then effectively distinguishing blood flow points from noise points; accurate calculation of blood flow velocity and energy is a necessary means to evaluate cardiovascular health and its dynamic behavior. Step S203 threshold comparison and classification, by comparing with the set threshold, can effectively distinguish effective blood flow points from noise points, and make judgments according to different conditions to ensure that the extracted signal reflects the real blood flow information; by analyzing the signal located as the blood flow point, the maximum and minimum blood flow intensity and speed can be obtained, which helps to understand the changes in blood flow. This classification method enhances the clinical application value of ultrasound imaging, allowing doctors and researchers to focus more intuitively on important physiological indicators; extreme blood flow indicators provide effective data on cardiac output function, which can help doctors evaluate cardiac function and identify potential cardiovascular problems or diseases.

[0122] In summary, the design and implementation of the entire process of this embodiment not only improves the accuracy and reliability of ultrasonic color blood flow image analysis, but also provides a deep understanding of heart health monitoring and disease diagnosis. Each step of technical operation provides a solid foundation for subsequent analysis and clinical application, ensuring that the data obtained is scientific and reasonable, and can reflect the real physiological state, thereby promoting the optimization of clinical decision-making.

[0123] Example 7: Figure 5 As shown, based on Example 1, the process of obtaining the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasonic color blood flow image provided by the embodiment of the present invention includes the following steps:

[0124] S301: Set the effective continuous ratio (N%) as the ratio of effective blood flow points that continue to be effective signals in the time direction (T direction); based on the pulsation frequency of the carotid artery, the effective continuous ratio is set to about 40%; for each effective blood flow pixel point, extract the data of its intensity change with time, and form a time curve by plotting the jump of the intensity value of the pixel point with time;

[0125] S302: Calculate the corresponding average values ​​of the time curves of all pixels within the effective ratio to obtain the average Doppler mean flow curve (Average Doppler Mean Flow Curve) of the sample data segment; calculate the average Doppler mean energy curve (Average Doppler Mean Energy Curve) and the average Doppler mean velocity curve (Average Doppler Mean Velocity Curve) to provide key blood flow characteristics;

[0126] S303: performing a specific time analysis on the average Doppler energy curve, calculating three time parameters, namely, peak-to-peak time, valley-to-valley time, and peak-to-valley time; converting the three time parameters into heart rate (HR), averaging the three calculated heart rate values, and obtaining the average heart rate of the current blood flow image; extracting the blood flow peak value frame from the velocity curve, and extracting the blood flow valley value frame from the energy curve;

[0127] Among them, Peak-to-Peak Time (PPT): the time interval between two adjacent peaks;

[0128] Trough-to-Trough Time (TTT): the time interval between two adjacent trough values;

[0129] Peak-to-Trough Time (PTT): The time interval between the peak value and the first trough value after it;

[0130] The heart rate can be calculated according to the formula Wherein, T is the corresponding time parameter, and the three calculated heart rate values ​​are averaged to obtain the average heart rate (Average Heart Rate) of the current blood flow image;

[0131] The peak systolic frame is the frame with the maximum blood flow velocity value during cardiac contraction; the end systole frame is the frame with the lowest blood flow energy value at the end of cardiac contraction.

[0132] The working principle and beneficial effects of the above technical solution are as follows: In this embodiment, the effective continuous ratio (N%) is first set as the ratio of effective blood flow points that continue to be effective signals in the time direction (T direction); based on the pulsation frequency of the carotid artery, the effective continuous ratio is set to about 40%; for each effective blood flow pixel point, the data of its intensity changing with time is extracted, and a time curve is formed by plotting the intensity value of the pixel point changing with time; secondly, among all the pixels within the effective ratio, the corresponding average value of the time curve is calculated to obtain the average Doppler mean flow curve (Average Doppler Mean Flow Curve) of the sample data segment; the average Doppler mean energy curve (Average Doppler Mean Energy Curve) and the average Doppler mean velocity curve (Average Doppler Mean Velocity Curve) are calculated. Curve), providing key blood flow characteristics; finally, a specific time analysis is performed on the average Doppler energy curve, and three time parameters, peak-to-peak time, valley-to-valley time, and peak-to-valley time, are calculated; the three time parameters are converted into heart rate (HR), and the three calculated heart rate values ​​are averaged to obtain the average heart rate of the current blood flow image; the blood flow peak frame is extracted from the velocity curve, and the blood flow valley frame is extracted from the energy curve; step S301 of the above scheme sets the effective continuous ratio and the time curve formation, and by setting the effective continuous ratio (N%) as the continuous signal of the effective blood flow point in the time direction, it is ensured that the selected data represents a real and stable blood flow signal and effectively eliminates the influence of noise; the intensity change data of each effective blood flow pixel point over time is extracted, and a time curve is constructed to provide basic data for describing the dynamic changes of blood flow for subsequent analysis. Setting the effective continuous ratio ensures the reliability of the analysis process, thereby improving the quality of the data and ensuring accurate judgment of the blood flow state; the construction of the time curve provides an intuitive form of expression for obtaining the dynamic characteristics of blood flow, which helps to identify and analyze the volatility of flow. Step S302 calculates the average Doppler curve and blood flow characteristics, averages the time curves of all pixels within the effective ratio, and obtains the average Doppler average blood flow curve, which can effectively summarize the overall blood flow characteristics in the data segment; the average Doppler average blood flow energy curve and velocity curve obtained by calculation provide key indicators that can reflect the hemodynamic characteristics, which is convenient for subsequent physiological analysis. The averaging process eliminates the influence of individual outliers and noise, so that the final average curve can truly reflect the hemodynamic state and facilitate clinical decision-making; clear blood flow characteristics (i.e., blood flow energy and velocity) provide important data references for diagnosing and monitoring cardiovascular health status, helping doctors make more accurate assessments.Step S303: Time analysis and heart rate calculation, calculate the peak-to-peak time, valley-to-valley time and peak-to-valley time, and provide a quantitative basis for the timing analysis of the Doppler energy curve; convert these three time parameters into heart rate (HR), and obtain more stable heart rate data by averaging these values, reflecting the rhythm and functional state of the heart; extract the blood flow peak frame and blood flow valley frame from the velocity curve and energy curve respectively, and effectively capture the peak and end of the heart contraction. The accurate calculation of time parameters provides a physiological basis for cardiovascular health status, and can help determine the heart's pulsation characteristics and frequency; the extracted peak and valley frames provide clinically operable parameters, which help doctors make accurate judgments when interpreting hemodynamic changes, and are of great significance for the monitoring and diagnosis of heart diseases.

[0133] In summary, through the implementation of these steps, this embodiment systematically integrates the ultrasonic color blood flow image data throughout the entire process, from noise removal and feature extraction to heart rate analysis and cardiac function parameter extraction, providing a scientific basis for cardiovascular health monitoring. It not only improves the accuracy of data analysis, but also provides an important reference for clinical decision-making and promotes a comprehensive understanding of the hemodynamic state.

[0134] Example 8: Figure 6 As shown, based on Embodiments 1 to 7, the system for calculating cardiac output state using ultrasound color images provided by the embodiment of the present invention comprises:

[0135] An image processing module is responsible for acquiring an ultrasonic color blood flow image, performing post-processing on the ultrasonic color blood flow image to obtain a first image of Doppler blood flow velocity and a second image of Doppler blood flow intensity signal, and calculating the variance of the blood flow signal from the first image and the second image;

[0136] The parameter calculation module is responsible for calculating each pixel point in the ultrasonic color blood flow image frame, extracting the variance, velocity and energy of the blood flow signal corresponding to the pixel point, comparing them with the set variance threshold, velocity threshold and energy threshold respectively, confirming whether the current pixel point is a blood flow point, and calculating the blood flow intensity and blood flow velocity according to the signal of the blood flow point;

[0137] The curve analysis module is responsible for analyzing the variance of the color blood flow signal in the time dimension and its changes over time. According to the effective blood flow ratio, the time curve of the blood flow point within the effective blood flow ratio is calculated, and then the Doppler blood flow curve is obtained. The Doppler blood flow curve is calculated to obtain the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasonic color blood flow image.

[0138] The working principle and beneficial effects of the above technical solution are as follows: the image processing module provided in this embodiment obtains an ultrasonic color blood flow image, performs post-processing on the ultrasonic color blood flow image, obtains a first image of Doppler blood flow velocity and a second image of Doppler blood flow intensity signal, and calculates the variance of the blood flow signal from the first image and the second image; the parameter calculation module calculates each pixel point in the ultrasonic color blood flow image frame, extracts the variance, velocity and energy of the blood flow signal corresponding to the pixel point, and compares them with the set variance threshold, velocity threshold and energy threshold respectively to confirm whether the current pixel point is a blood flow point, and calculates the blood flow intensity and blood flow velocity according to the signal of the blood flow point; the curve analysis module analyzes the variance of the color blood flow signal in the time dimension, and calculates the time curve of the blood flow point within the effective blood flow ratio according to the effective blood flow ratio, and then obtains the Doppler blood flow curve, and calculates the Doppler blood flow curve to obtain the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasonic color blood flow image. The image processing module of the above scheme analyzes the processed image, generates the first image of Doppler blood flow velocity and the second image of Doppler blood flow intensity signal, and extracts the key indicators representing the blood flow dynamics; by calculating the first image and the second image, the variance of the blood flow signal is obtained, and the stability and intensity change of the signal are evaluated. The accuracy and effectiveness of this module ensure the basis for subsequent data analysis. High-quality image processing reduces the interference of noise and artifacts, making the analysis more accurate; by obtaining Doppler images of velocity and intensity signals, it can provide intuitive data for the analysis of blood flow characteristics and promote the understanding of blood flow dynamics. The parameter calculation module calculates the corresponding blood flow intensity and velocity based on the confirmed blood flow point signal and provides key physiological parameters. This module is the core of the system and directly provides a quantitative analysis basis for the heart output state. Through the precise analysis of each pixel point, the characteristics of the blood flow can be carefully understood, which helps to discover potential cardiovascular health problems; by effectively detecting and classifying blood flow points, the accuracy of the analysis results can be ensured, making the subsequent extraction of parameters such as heart rate more reliable. The curve analysis module calculates the Doppler blood flow curve and obtains important physiological parameters such as average heart rate, blood flow peak frame and blood flow valley frame. This module realizes comprehensive monitoring of the dynamic output state of the heart through comprehensive analysis of time series data, helping doctors better understand the patient's blood flow characteristics; by calculating the heart rate and key blood flow frame points, it can provide valuable information for improving treatment plans and monitoring effects, providing a basis for clinical decision support.

[0139] In summary, the various modules of the entire system of this embodiment cooperate with each other, from image acquisition and processing to signal parameter extraction, and then to time curve analysis, to form a complete set of ultrasound color image analysis processes. Each module directly serves to improve the accuracy and timeliness of cardiac output status assessment, thereby providing reliable support for early detection of cardiovascular diseases, monitoring of treatment effects, and clinical decision-making.

[0140] Embodiment 9: Based on Embodiment 8, the image processing module provided by the embodiment of the present invention comprises:

[0141] The structure rearrangement submodule is responsible for obtaining the ultrasonic radio frequency signal (RF signal) collected by the ultrasonic probe through the front-end circuit of the ultrasonic imaging device. The step length of the ultrasonic radio frequency signal is time T. The collected ultrasonic radio frequency signal is uploaded to the host computer through the PCIe interface and rearranged to form a three-dimensional data structure.

[0142] The signal demodulation submodule is responsible for orthogonal demodulation of the collected ultrasonic RF signal to obtain an IQ signal in complex form; beamforming is performed on the obtained IQ signal to generate a synthesized new IQ signal (bfIQ), and the synthesized new IQ signal is passed to a high-pass filter to remove low-frequency noise and DC components and enhance key signal characteristics;

[0143] The correlation calculation submodule is responsible for performing fixed-delay correlation calculation on the filtered new IQ signal to extract the expected blood flow signal characteristics; performing arctangent calculation on the result R1 of the fixed-delay correlation calculation to obtain the Doppler blood flow velocity; performing compression processing on the result R0 without delay to generate a Doppler blood flow intensity signal image, and the expected result is processed through the processing flow to finally obtain the Doppler blood flow velocity image and the Doppler blood flow intensity signal image;

[0144] The working principle and beneficial effects of the above technical solution are as follows: the structure rearrangement submodule of this embodiment obtains the ultrasonic radio frequency signal (RF signal) collected by the ultrasonic probe through the front-end circuit of the ultrasonic imaging device, the step length of the ultrasonic radio frequency signal is time T, and the collected ultrasonic radio frequency signal is uploaded to the host computer through the PCIe interface, and rearranged to form a three-dimensional data structure; the signal demodulation submodule performs orthogonal demodulation on the collected ultrasonic radio frequency signal to obtain an IQ signal in complex form; the obtained IQ signal is beamformed to generate a synthesized new IQ signal (bfIQ), and the synthesized new IQ signal is passed to a high-pass filter to remove low-frequency noise and DC components and enhance key signal features; the correlation calculation submodule performs fixed-delay correlation calculation on the filtered new IQ signal to extract the expected blood flow signal features; the result R1 of the fixed-delay correlation calculation is subjected to inverse tangent calculation to obtain the Doppler blood flow velocity; for the result R0 without delay, compression processing is performed to generate a Doppler blood flow intensity signal image, and the expected result is processed through the processing flow to finally obtain a Doppler blood flow velocity image and a Doppler blood flow intensity signal image. The structural rearrangement submodule of the above scheme obtains ultrasonic radio frequency signals (RF signals) from the ultrasonic probe through the front-end circuit of the ultrasonic imaging device. These signals contain information about blood flow and tissue and reflect the echo characteristics of the wave; the collected ultrasonic radio frequency signals with a time step of T are rearranged to form a three-dimensional data structure (data body) for subsequent processing, which is composed of time and space (x, y, z coordinates). The processing of this submodule ensures the effective organization of the signal in the spatial and temporal dimensions, and provides structured data support for subsequent image generation and signal analysis; the formation of the three-dimensional data structure enables the subsequent processing steps to more effectively utilize the signal information distributed in the three-dimensional space, thereby improving the accuracy and efficiency of subsequent analysis. The signal demodulation submodule performs orthogonal demodulation on the collected ultrasonic radio frequency signals to obtain IQ signals in complex form. This process converts the RF signal into an IQ signal that can be analyzed more intuitively, and separates the phase and amplitude information; the demodulated IQ signal is beamformed to generate a synthesized new IQ signal (bfIQ). Beamforming aggregates signals at different angles to enhance the spatial resolution of the signal; high-pass filtering: the synthesized new IQ signal is passed to the high-pass filter to remove low-frequency noise and DC components, retaining and enhancing important signal features. The orthogonal demodulation method can help better analyze the signal, provide basic data for subsequent processing, and make the analysis of phase and amplitude information simpler; the application of the high-pass filter makes the final signal cleaner, retaining only the most critical components for Doppler measurement, thereby improving the quality and accuracy of signal processing.The correlation calculation submodule performs fixed-delay correlation calculation on the filtered new IQ signal to extract the expected blood flow signal features. This process is used to capture the motion information of the blood flow signal; the fixed-delay correlation result R1 is calculated by arctangent to obtain the Doppler blood flow velocity and provide blood flow velocity information; the non-delay correlation result R0 is compressed to generate a Doppler blood flow intensity signal image to represent the distribution of blood flow intensity. This submodule directly converts the acquired signal features into the Doppler blood flow velocity data required for clinical use, providing the necessary quantitative analysis tools for cardiovascular function assessment; the generated Doppler blood flow velocity and intensity images can intuitively display the dynamic characteristics of blood flow, helping doctors to better analyze blood flow conditions in clinical practice and improve the ability to diagnose and monitor diseases.

[0145] In summary, the image processing module of this embodiment realizes a comprehensive signal processing flow from signal acquisition to Doppler image generation through the mutual cooperation of various submodules. The technical effect and practical significance of each submodule together make the analysis of ultrasound images more efficient and accurate, providing important support for subsequent medical diagnosis and treatment. It not only improves the quality of the image, but also makes the understanding of blood flow characteristics clearer, providing a reliable data basis for the evaluation of cardiovascular diseases.

[0146] Embodiment 10: Based on Embodiment 8, the parameter calculation module provided in the embodiment of the present invention comprises:

[0147] The image correction submodule is responsible for obtaining the processed ultrasound color blood flow image and correcting the obtained image, including noise removal and artifact correction. In the post-processed image, for each pixel point, the position of the pixel in the image and the change of the pixel point in the time series are extracted;

[0148] The variance calculation submodule is responsible for calculating the blood flow velocity for each pixel point according to the Doppler effect principle; obtaining the corresponding blood flow energy by normalizing the signal intensity at the pixel point; collecting the blood flow signal values ​​of adjacent time frames and calculating the signal variance of the pixel point;

[0149] The threshold comparison submodule is responsible for comparing the calculated variance, velocity and energy values ​​with the preset variance threshold, velocity threshold and energy threshold to confirm whether the pixel point belongs to the actual blood flow point.

[0150] The working principle and beneficial effects of the above technical solution are as follows: the image correction submodule of this embodiment obtains the processed ultrasonic color blood flow image, and corrects the obtained image, including noise removal and artifact correction, etc.; in the post-processed image, for each pixel point, the position of the pixel in the image and the change of the pixel point in the time series are extracted; the variance calculation submodule calculates the blood flow velocity for each pixel point according to the Doppler effect principle; the corresponding blood flow energy is obtained by normalizing the signal intensity at the pixel point; the blood flow signal values ​​of adjacent time frames are collected, and the signal square of the pixel point is calculated. The threshold comparison submodule compares the calculated variance, velocity and energy values ​​with the preset variance threshold, velocity threshold and energy threshold to confirm whether the pixel belongs to the actual blood flow point; the image correction submodule of the above scheme optimizes the ultrasound image through a specific algorithm to reduce the random noise in the image to improve the image quality and the accuracy of information; identifies and corrects artifacts caused by equipment or environmental factors to ensure that the image reflects the true blood flow state; for each pixel in the processed image, obtains its spatial position in the image and the change of the pixel in the time series to form spatiotemporal data. Image correction is the basis for subsequent analysis. Denoising and artifact correction ensure the reliability of the analysis results and make the extraction of blood flow information more reliable; by extracting the spatiotemporal information of the pixel points, it provides the necessary data support for the subsequent speed and energy calculations, which helps to accurately analyze the dynamic changes of blood flow. The variance calculation submodule uses the Doppler effect to process the signal acquisition results of the pixel point, calculate the blood flow velocity corresponding to the pixel point, and reflect the dynamic characteristics of the blood flow; normalize the signal intensity of the pixel point, calculate the corresponding blood flow energy, and use it to evaluate the intensity of the blood flow; collect the blood flow signal values ​​of adjacent time frames, calculate the signal variance of the pixel point, and quantify the stability and consistency of the signal change. The processing of this module is the core of analyzing the dynamic state of blood flow. The calculation of velocity and energy provides key data for the evaluation of blood flow conditions; the calculation of variance helps to evaluate the changes in blood flow signals. A stable variance value may mean the stability of the blood flow state, thereby providing a basis for clinical decision-making. The threshold comparison submodule compares the calculated variance, velocity and energy values ​​with the preset threshold to determine whether the current pixel point belongs to an actual blood flow point; through comparative analysis, the effective blood flow point is confirmed to avoid misjudging noise or artifacts as effective blood flow signals, thereby improving the accuracy of data processing. This submodule is equivalent to a filter for the signal. Its main function is to ensure the accuracy of the analysis and avoid noise interference affecting the diagnostic results. Determining which pixels are actual blood flow points lays a clear foundation for subsequent flow and dynamic analysis, which helps to more accurately monitor and analyze cardiovascular conditions.

[0151] In summary, the parameter calculation module of this embodiment forms a complete set of ultrasonic blood flow signal analysis process through the collaborative work of submodules such as image correction, variance calculation and threshold comparison. Each submodule ensures the accurate extraction and analysis of the signal, provides reliable data support for clinicians, and improves the understanding of dynamic changes in blood flow and the ability to diagnose cardiovascular diseases. The efficiency and accuracy of the overall process are crucial to improving the success rate of clinical diagnosis and reducing the misdiagnosis rate.

[0152] Embodiment 11: Based on Embodiment 8, the curve analysis module provided in the embodiment of the present invention comprises:

[0153] The curve making submodule is responsible for setting the effective continuous ratio (N%) as the ratio of effective blood flow points that continue to be effective signals in the time direction (T direction); based on the pulsation frequency of the carotid artery, the effective continuous ratio is set to about 40%; for each effective blood flow pixel point, the data of its intensity change with time is extracted, and the time curve is formed by plotting the jump of the intensity value of the pixel point with time;

[0154] The mean calculation submodule is responsible for calculating the corresponding average values ​​of the time curves of all pixels within the effective ratio to obtain the average Doppler mean flow curve (Average Doppler Mean Flow Curve) of the sample data segment; the average Doppler mean energy curve (Average Doppler Mean Energy Curve) and the average Doppler mean velocity curve (Average Doppler Mean Velocity Curve) are calculated to provide key blood flow characteristics;

[0155] The information extraction submodule is responsible for performing specific time analysis on the average Doppler energy curve, calculating three time parameters: peak-to-peak time, valley-to-valley time, and peak-to-valley time; converting the three time parameters into heart rate (HR), averaging the three calculated heart rate values, and obtaining the average heart rate of the current blood flow image; extracting the blood flow peak value frame from the velocity curve, and extracting the blood flow valley value frame from the energy curve.

[0156] The working principle and beneficial effects of the above technical solution are as follows: the curve making submodule of this embodiment sets the effective continuous ratio (N%) as the ratio of effective blood flow points that continue to be effective signals in the time direction (T direction); based on the pulsation frequency of the carotid artery, the effective continuous ratio is set to about 40%; for each effective blood flow pixel point, the data of its intensity changing with time is extracted, and the time curve is formed by plotting the intensity value of the pixel point changing with time; the mean calculation submodule calculates the corresponding average value of the time curve of all pixel points within the effective ratio to obtain the average Doppler mean flow curve (Average Doppler Mean Flow Curve) of the sample data segment; the average Doppler mean energy curve (Average Doppler Mean Energy Curve) and the average Doppler mean velocity curve (Average Doppler Mean Velocity Curve) are calculated. Curve), providing key blood flow characteristics; the information extraction submodule performs specific time analysis on the average Doppler energy curve, calculates three time parameters: peak-to-peak time, valley-to-valley time, and peak-to-valley time; converts the three time parameters into heart rate (HR), averages the three calculated heart rate values, and obtains the average heart rate of the current blood flow image; extracts blood flow peak frames from the velocity curve, and extracts blood flow valley frames from the energy curve. The curve making submodule of the above scheme sets the effective continuous ratio (N%), that is, the proportion of effective blood flow signals that are continuous in the time direction. Based on the pulsation frequency of the carotid artery, it is usually set to about 40%; for each effective blood flow pixel point, extract its intensity change data over time to form a time curve representing the pixel point. By setting the effective continuous ratio, unstable or short-term signals can be filtered out to ensure that only real and continuous blood flow signals are analyzed, thereby improving the accuracy of the analysis; drawing the time curve provides an intuitive data representation for the subsequent reflection of the dynamic changes of blood flow, which helps doctors observe and understand the fluctuations of blood flow intensity over time. The mean calculation submodule calculates the average value of the time curve of all pixels within the effective ratio to generate the average Doppler mean flow curve (Average Doppler Mean Flow Curve); calculates the average Doppler mean energy curve (Average Doppler Mean Energy Curve) and the average Doppler mean velocity curve (Average Doppler Mean Velocity Curve), providing comprehensive blood flow characteristic data. The generation of the average curve provides a quantitative description of the overall blood flow state, which can not only help evaluate the intensity and speed of blood flow, but also improve the clinical understanding of blood flow characteristics; through these average curves, doctors can compare the blood flow status of different patients or the same patient at different time points, thereby providing a basis for diagnosis and treatment.The information extraction submodule calculates the peak-to-peak time, valley-to-valley time, and time parameters of the peak-to-valley time for the average Doppler energy curve; converts the three time parameters into heart rate (HR) and calculates the average heart rate of the current image; extracts the blood flow peak frame from the velocity curve and the blood flow valley frame from the energy curve to provide detailed blood flow information. By calculating the time parameters, the periodicity and regularity of blood flow dynamics can be understood, providing important information for the analysis of the cardiac cycle and helping doctors determine the functional state of the heart; the calculation of the average heart rate provides a convenient indicator for clinical evaluation and can quickly reflect the patient's cardiovascular health status. Extracting the peak and valley frame information can deeply analyze the hemodynamic characteristics and guide more precise clinical intervention.

[0157] In summary, the curve analysis module of this embodiment fully reflects and analyzes the dynamic characteristics of the ultrasonic color blood flow signal through the coordinated work of the curve making, mean calculation and information extraction submodules. Each submodule forms a set of scientific and reasonable analysis processes. To a certain extent, the overall process can not only enhance the understanding of blood flow conditions, but also provide important data support for the detection and diagnosis of cardiovascular diseases, thereby improving the treatment effect and health management of patients.

[0158] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention belong to the scope of the present invention and its equivalent technologies, the present invention is also intended to include these changes and variations.

Claims

1. A method for calculating cardiac output state using ultrasonic color images, characterized in that: The following steps are involved: Acquire an ultrasonic color blood flow image, perform post-processing on the ultrasonic color blood flow image to obtain a first image of Doppler blood flow velocity and a second image of Doppler blood flow intensity signal, and calculate the variance of the blood flow signal from the first image and the second image; Calculate each pixel in the ultrasonic color blood flow image frame, extract the variance, velocity and energy of the blood flow signal corresponding to the pixel, compare them with the set variance threshold, velocity threshold and energy threshold respectively, confirm whether the current pixel is a blood flow point, and calculate the blood flow intensity and blood flow velocity according to the signal of the blood flow point; The variance of the color blood flow signal in the time dimension is analyzed over time. According to the effective blood flow ratio, the time curve of the blood flow point within the effective blood flow ratio is calculated, and then the Doppler blood flow curve is obtained. The Doppler blood flow curve is calculated to obtain the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasound color blood flow image.

2. The method for calculating cardiac output state using ultrasound color images according to claim 1, characterized in that: The process of post-processing ultrasound color blood flow images includes the following steps: The ultrasonic radio frequency signal, RF signal, collected by the ultrasonic probe is obtained through the front-end circuit of the ultrasonic imaging device. The step length of the ultrasonic radio frequency signal is time T. The collected ultrasonic radio frequency signal is uploaded to the host computer through the PCIe interface and rearranged to form a three-dimensional data structure. The three-dimensional data structure is m*n*T, where m represents the number of acquisition channels, n represents the number of sub-time periods within time T, and the length of each sub-time period is t; The collected ultrasonic RF signal is orthogonally demodulated to obtain an IQ signal in complex form; the obtained IQ signal is beamformed to generate a synthesized new IQ signal, bfIQ, and the synthesized new IQ signal is passed to a high-pass filter to remove low-frequency noise and DC components and enhance key signal characteristics; Among them, f s represents the sampling frequency, j represents the imaginary unit; IQ{filtered}=H{hp}(IQ) Where H{hp} represents the transfer function of the high-pass filter; A fixed-delay correlation calculation is performed on the filtered new IQ signal to extract the expected blood flow signal characteristics; an arctangent calculation is performed on the result R1 of the fixed-delay correlation calculation to obtain the Doppler blood flow velocity; a compression process is performed on the result R0 without delay to generate a Doppler blood flow intensity signal image, and the expected result is processed through the processing flow to finally obtain a Doppler blood flow velocity image and a Doppler blood flow intensity signal image; Wherein, V represents the obtained Doppler blood flow velocity image.

3. The method for calculating cardiac output state using ultrasound color images as claimed in claim 2, characterized in that: The process of performing fixed delay correlation calculation on the filtered new IQ signal includes the following steps: Using the product of complex signals, calculate the fixed delay correlation; Calculate the zero-delay correlation, which calculates the energy or intensity of the IQ signal at the same time point. After completing the correlation calculation, the zero-delay correlation and the fixed-delay correlation are obtained for processing to obtain the final image result; Performing arctangent function calculation on the calculated fixed delay correlation to obtain Doppler blood flow velocity image, the output of the arctangent function will represent the blood flow velocity; The Doppler blood flow intensity signal image is obtained by modulo zero-delay correlation and threshold compression. The modulo zero-delay correlation calculates the amplitude of the zero-delay correlation result, which represents the intensity of the return signal; threshold compression is used to enhance the image quality and remove background noise.

4. The method for calculating cardiac output state using ultrasound color images as claimed in claim 3, characterized in that: The calculation expression related to the fixed delay is: The calculation expression related to zero delay is: Wherein, WFIQ represents the IQ signal after high-pass filtering, EL represents the integration length, the number of times data is periodically collected within a period of time, i, j represent the row coordinates and column coordinates in space respectively, R0 represents zero delay correlation, and R1 represents fixed delay correlation; the obtained R1 is subjected to arc tangent function calculation and velocity conversion to obtain the Doppler blood flow velocity image, and R0 is subjected to modulus calculation and threshold compression to obtain the Doppler blood flow intensity signal blood flow image; Among them, the inverse tangent function is calculated for the calculated R1, and the formula is: Where imag(R1) represents the imaginary part of the R1 result, and real(R1) represents its real part; the output V of the inverse tangent function will represent the blood flow velocity in cm / s or m / s.

5. The method for calculating cardiac output state using ultrasound color images as claimed in claim 1, characterized in that: The expression of the variance of the blood flow signal calculated from the first image and the second image is: Among them, r0 represents the energy signal, r1 represents the velocity signal, PRF is the pulse repetition frequency when collecting data, T is the variance of color blood flow, and abs is the absolute value.

6. The method for calculating cardiac output state using ultrasound color images according to claim 1, characterized in that: The setting method of variance threshold, speed threshold and energy threshold, the variance includes the high threshold T max With low threshold T min , energy threshold P threshold With speed threshold V threshold ; Among them, the variance high threshold T max Set the normalized variance range to 70%-90% and the variance low threshold T min The energy threshold P is set between 40% and 60% of the normalized variance range. threshold Set the speed threshold V in the range of 40% to 60% of the normalized energy. threshold The normalized velocity range is set between 20% and 40%. The set value is related to the background grayscale of the current ultrasound color blood flow image and is set within a variance / energy / velocity threshold range close to the expected display effect of the current blood flow.

7. The method for calculating cardiac output state using ultrasound color images according to claim 1, characterized in that: The process of calculating each pixel in the ultrasound color blood flow image frame includes the following steps: Obtain processed ultrasound color blood flow images, and correct the acquired images, including noise removal and artifact correction; in the post-processed images, for each pixel point, extract the position of the pixel in the image and the change of the pixel point in the time series; For each pixel point, the blood flow velocity is calculated according to the Doppler effect principle; the corresponding blood flow energy is obtained by normalizing the signal intensity at the pixel point; Collect the blood flow signal values ​​of adjacent time frames and calculate the signal variance of the pixel point; The calculated variance, velocity and energy values ​​are compared with the preset variance threshold, velocity threshold and energy threshold to confirm whether the pixel point belongs to the actual blood flow point.

8. The method for calculating cardiac output status using ultrasound color images as claimed in claim 7, characterized in that: in, If the speed of the pixel is greater than V threshold , the energy is less than P threshold , the variance is greater than the variance threshold T max Determine the point as a noise point; If the speed of the pixel is greater than the speed threshold V threshold , the energy is greater than the energy threshold P threshold , the variance is less than the variance threshold T min The point is determined to be a blood flow point; the signal located as the blood flow point is used to calculate the maximum blood flow intensity P per unit time. max With the minimum blood flow intensity P min , the maximum blood flow velocity V max With the minimum blood flow velocity V min .

9. The method for calculating cardiac output state using ultrasound color images according to claim 1, characterized in that: The process of obtaining the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasound color blood flow image includes the following steps: The effective continuous ratio N% is set as the ratio of effective blood flow points that continue to be effective signals in the time direction; based on the pulsation frequency of the carotid artery, the effective continuous ratio is set to 30%-50%; for each effective blood flow pixel point, the data of its intensity changing with time is extracted, and the time curve is formed by plotting the jump of the intensity value of the pixel point with time; Among all the pixels within the effective ratio, the corresponding average values ​​of their time curves are calculated to obtain the average Doppler average blood flow curve of the sample data segment; the average Doppler average blood flow energy curve and the average Doppler average blood flow velocity curve are calculated to provide key blood flow characteristics; Perform specific time analysis on the average Doppler energy curve, calculate the three time parameters of peak-to-peak time, valley-to-valley time and peak-to-valley time; convert the three time parameters into heart rate HR, average the three calculated heart rate values ​​to obtain the average heart rate of the current blood flow image; extract the blood flow peak value frame from the velocity curve, and extract the blood flow valley value frame from the energy curve.

10. A system for calculating cardiac output status using ultrasound color images, characterized in that: Include: An image processing module is responsible for acquiring an ultrasonic color blood flow image, performing post-processing on the ultrasonic color blood flow image to obtain a first image of Doppler blood flow velocity and a second image of Doppler blood flow intensity signal, and calculating the variance of the blood flow signal from the first image and the second image; The parameter calculation module is responsible for calculating each pixel point in the ultrasonic color blood flow image frame, extracting the variance, velocity and energy of the blood flow signal corresponding to the pixel point, comparing them with the set variance threshold, velocity threshold and energy threshold respectively, confirming whether the current pixel point is a blood flow point, and calculating the blood flow intensity and blood flow velocity according to the signal of the blood flow point; The curve analysis module is responsible for analyzing the variance of the color blood flow signal in the time dimension and its changes over time. According to the effective blood flow ratio, the time curve of the blood flow point within the effective blood flow ratio is calculated, and then the Doppler blood flow curve is obtained. The Doppler blood flow curve is calculated to obtain the average heart rate, blood flow peak frame and blood flow valley frame of the current ultrasonic color blood flow image.

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