Blood flow image processing method and device, and storage medium

Abstract

The application relates to the technical field of ultrasonic imaging, and discloses a blood flow image processing method and device and a storage medium. The method comprises the following steps: acquiring variance data of effective sampling points in a current frame of blood flow image; comparing each variance data with a preset variance threshold to determine a target data point in the effective sampling points; determining a variance proportion based on the total number of the effective sampling points and the target point number of the target data point; comparing the variance proportion with a preset variance proportion threshold to determine a gain adjustment strategy corresponding to the current frame of blood flow image; determining a target gain parameter value corresponding to the current frame of blood flow image based on the gain adjustment strategy; and performing image processing on a next frame of blood flow image according to the target gain parameter value to obtain a target blood flow image. Through implementation of the technical scheme of the application, the gain adjustment can be automatically determined and applied based on the variance statistical characteristics of the current frame of blood flow image, and adaptive optimization of the next frame of blood flow image is realized.

Description

Technical Field

[0001] This application relates to the field of ultrasound imaging technology, specifically to a method, apparatus, and storage medium for processing blood flow images. Background Technology

[0002] Ultrasound color Doppler blood flow imaging visually presents the dynamic information of tissue blood flow morphology, velocity, and direction through color atlas mapping, making it an important tool for clinical diagnosis. During the imaging process, the setting of the blood flow gain parameter directly affects the image quality: if the gain is too low, the blood flow signal will be weak, which may lead to the loss of diagnostic information; if the gain is too high, noise will be introduced into the image, interfering with clinical diagnosis.

[0003] Because patients have significant individual differences in hemodynamics, doctors often need to manually adjust the blood flow gain repeatedly based on the current image quality when performing color Doppler examinations. This process not only depends on the operator's experience but is also time-consuming, reducing the efficiency of clinical screening. Summary of the Invention

[0004] This application provides a method, apparatus, and storage medium for processing blood flow images to solve the problem of low processing efficiency of blood flow images.

[0005] In a first aspect, this application provides a method for processing blood flow images, comprising: acquiring target variance data of valid sampling points in a current frame of blood flow image; comparing each target variance data with a preset variance threshold to determine target data points among the valid sampling points; determining the variance proportion corresponding to the current frame of blood flow image based on the total number of valid sampling points and the number of target data points; comparing the variance proportion with a preset variance proportion threshold to determine a gain adjustment strategy corresponding to the current frame of blood flow image based on the comparison result; determining a target gain parameter value corresponding to the current frame of blood flow image based on the gain adjustment strategy; and performing image processing on the next frame of blood flow image according to the target gain parameter value to obtain a target blood flow image.

[0006] In one optional implementation, the variance percentage is compared with a preset variance percentage threshold, and a gain adjustment strategy corresponding to the current frame blood flow image is determined based on the comparison result. This includes: if the variance percentage exceeds the variance percentage threshold, the blood flow state of the current frame blood flow image is determined to be a first state, and a first gain adjustment strategy corresponding to the first state is determined; if the variance percentage does not exceed the variance percentage threshold, the blood flow state of the current frame blood flow image is determined to be a second state, and a second gain adjustment strategy corresponding to the second state is determined.

[0007] In one optional implementation, the target gain parameter value corresponding to the current frame blood flow image is determined based on the second gain adjustment strategy, including: acquiring the target energy data of the effective sampling points in the current frame blood flow image, and the mapping relationship between the gain parameter value and the energy value; fitting the target variance data and the target energy data to obtain the fitting relationship corresponding to the current frame blood flow image; determining the reference energy value corresponding to the current frame blood flow image based on the preset variance threshold and the fitting relationship; and determining the target gain parameter value corresponding to the reference energy value based on the mapping relationship.

[0008] In one optional implementation, fitting the target variance data and target energy data to obtain the fitting relationship corresponding to the blood flow image of the current frame includes: performing logarithmic processing on the target variance data and target energy data to obtain logarithmic variance data and logarithmic energy data; performing linear fitting on the logarithmic variance data and logarithmic energy data to obtain slope parameters and intercept parameters; and constructing the fitting relationship between the target variance data and target energy data based on the slope parameters and intercept parameters.

[0009] In one optional implementation, the target gain parameter value corresponding to the blood flow image of the current frame is determined based on the first gain adjustment strategy, including: obtaining the initial gain parameter value corresponding to the blood flow image of the current frame; and determining the target gain parameter value based on the fusion result of the initial gain parameter value and the preset gain step value.

[0010] In one optional implementation, the preset variance threshold and the preset variance percentage threshold are predetermined based on the analysis results of blood flow velocity data, energy data, and variance data in historical blood flow images of different diagnostic sites.

[0011] In one optional implementation, obtaining the target variance data of valid sampling points in the current frame blood flow image includes: obtaining the original variance data of all sampling points in the current frame blood flow image; quantizing the original variance data to obtain quantized variance data; and removing invalid data from the quantized variance data to obtain the target variance data of valid sampling points.

[0012] In one optional implementation, each target variance data is compared with a preset variance threshold to determine the target data point among the valid sampling points, including: determining the sampling points whose target variance data in the valid sampling points does not exceed the preset variance threshold as target data points.

[0013] Secondly, this application provides a blood flow image processing apparatus, comprising: an acquisition module for acquiring target variance data of valid sampling points in a current frame blood flow image; a first comparison module for comparing each target variance data with a preset variance threshold to determine target data points among multiple sampling points; a first determination module for determining the variance proportion corresponding to the current frame blood flow image based on the total number of sampling points and the number of target data points; a second comparison module for comparing the variance proportion with a preset variance proportion threshold and determining a gain adjustment strategy corresponding to the current frame blood flow image based on the comparison result; a second determination module for determining a target gain parameter value corresponding to the current frame blood flow image based on the gain adjustment strategy; and a processing module for performing image processing on the next frame blood flow image according to the target gain parameter value to obtain a target blood flow image.

[0014] Thirdly, this application provides a computer device, including: a memory and a processor, which are communicatively connected to each other. The memory stores computer instructions, and the processor executes the computer instructions to perform the blood flow image processing method of the first aspect or any corresponding embodiment described above.

[0015] Fourthly, this application provides a computer-readable storage medium storing computer instructions for causing a computer to execute the blood flow image processing method of the first aspect or any corresponding embodiment described above.

[0016] Fifthly, this application provides a computer program product, including computer instructions for causing a computer to execute the blood flow image processing method of the first aspect or any corresponding embodiment described above.

[0017] The blood flow image processing method provided in this application obtains the target variance data of effective sampling points in the current frame blood flow image and compares each target variance data with a preset variance threshold to determine the target data points, thus achieving objective screening of sampling points; calculates the variance ratio based on the total number of points and the target number of points, and converts the screening result into an overall quantitative index; determines the gain adjustment strategy by comparing the variance ratio with a preset variance ratio threshold, so that the direction of gain adjustment has a clear basis; determines the target gain parameter value based on the strategy, and processes the next frame blood flow image according to the parameter value to obtain the target blood flow image, thus realizing automatic inter-frame adjustment of gain, enabling the image processing result to adaptively optimize according to the blood flow state of the current frame. Attached Figure Description

[0018] To more clearly illustrate the technical solutions in the specific embodiments or related technologies of this application, the drawings used in the description of the specific embodiments or related technologies will be briefly introduced below. Obviously, the drawings described below are some embodiments of this application. For those skilled in the art, other drawings can be obtained from these drawings without creative effort.

[0019] Figure 1 This is a schematic flowchart of a first method for processing blood flow images according to an embodiment of this application; Figure 2 This is a schematic diagram of a second process for processing blood flow images according to an embodiment of this application; Figure 3 This is a variance energy distribution diagram according to an embodiment of this application; Figure 4 This is a structural block diagram of a blood flow image processing apparatus according to an embodiment of this application; Figure 5 This is a schematic diagram of the hardware structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0021] The terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of this application, "multiple" means two or more, unless otherwise explicitly specified.

[0022] Color Doppler ultrasound flow imaging is a medical imaging technique based on the Doppler principle. It calculates blood flow velocity and energy components and maps them to a color spectrum to obtain a two-dimensional color blood flow image, which is widely used in clinical diagnosis. During color Doppler flow imaging, the gain parameter setting has a significant impact on image quality: too low a gain will result in a weak blood flow signal and missing information, while too high a gain will introduce noise into the image; both situations will affect the doctor's diagnostic judgment.

[0023] In related technologies, due to individual patient differences and different examination sites, blood flow status varies greatly. Operators usually need to manually adjust the blood flow gain multiple times based on the current image effect to achieve the best image display state. This process is cumbersome and inefficient.

[0024] The blood flow image processing method provided in this application obtains the target variance data of effective sampling points in the current frame blood flow image and compares each target variance data with a preset variance threshold to determine the target data points. Then, based on the total number of points and the target number of points, the variance ratio is calculated to form a statistical index for overall quantification of the current frame image. Subsequently, the variance ratio is compared with a preset variance ratio threshold, and the corresponding gain adjustment strategy is automatically determined based on the comparison result, thereby determining the target gain parameter value. The next frame blood flow image is automatically processed according to the target gain parameter value, replacing the operator's manual adjustment of gain multiple times based on the current image effect, thus solving the problem of cumbersome operation and low efficiency caused by reliance on manual adjustment in related technologies.

[0025] According to an embodiment of this application, a method for processing blood flow images is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0026] This embodiment provides a method for processing blood flow images, which can be used in electronic devices, such as ultrasound imaging equipment. Figure 1 This is a flowchart of a blood flow image processing method according to an embodiment of this application, such as... Figure 1 As shown, the process includes the following steps: Step S101: Obtain the target variance data of the effective sampling points in the current frame blood flow image.

[0027] The current frame blood flow image is a color Doppler blood flow image acquired and processed by an electronic device at the current moment. Each sampling point in the image carries multi-dimensional information such as blood flow velocity, energy, and variance components detected at the corresponding location. Valid sampling points refer to data points in the current frame blood flow image that, after preprocessing, are deemed to carry valid information and participate in subsequent statistical analysis. Target variance data refers to the variance component values ​​extracted from all valid sampling points. Specifically, after completing the blood flow imaging scan of the current frame, the electronic device generates a raw blood flow image containing multiple sampling points. Each sampling point carries multi-dimensional blood flow information detected at that location, including a feature data reflecting the stability or velocity dispersion of the blood flow signal at that point—namely, variance data. To focus on meaningful blood flow regions and eliminate interference from invalid backgrounds, these raw sampling points undergo a screening process, removing invalid points that clearly do not represent any blood flow or tissue information. The remaining points are the valid sampling points. The numerical information corresponding to these retained valid sampling points, which reflects the characteristics of signal fluctuations, is the target variance data that needs to be obtained.

[0028] It should be noted that the target variance data covers all variance components at different spatial locations and at different time points, that is, all variance components at different locations and at different time points in the current frame are used as the basis for statistical analysis.

[0029] Step S102: Compare each target variance data with a preset variance threshold to determine the target data points among the valid sampling points.

[0030] The preset variance threshold (VarThrd) is a critical value for the variance component that effectively distinguishes between blood flow and noise signals, determined through statistical analysis based on the probability distribution of blood flow and noise signals. Target data points refer to data points among all valid sampling points that, after being filtered using the preset variance threshold, are initially identified as blood flow signals. Specifically, after obtaining the target variance data of all valid sampling points, it is necessary to initially distinguish these points by their properties, determining whether each point is more likely to represent a true blood flow signal or randomly occurring noise. To achieve this distinction, a pre-set reference standard, the preset variance threshold, is used. This threshold is an empirical or statistical boundary value used to define the difference between blood flow and noise based on variance characteristics. The target variance data of each valid sampling point is read one by one and compared numerically with the preset variance threshold. Based on the comparison results, valid sampling points whose characteristics conform to the blood flow signal pattern are selected and marked as target data points.

[0031] Step S103: Based on the total number of valid sampling points and the target number of target data points, determine the variance percentage of the blood flow image in the current frame.

[0032] The total number of points (Nall) refers to the total number of valid sampling points in the current frame of the blood flow image. The number of target points (N0) refers to the number of data points initially identified as blood flow signals. The variance percentage (VarPer) refers to the percentage of target data points to the total number of valid sampling points. Specifically, after classifying the valid sampling points, two key counting results are obtained: one is the total number of all samples involved in the analysis, i.e., the total number of valid sampling points; the other is the number of those identified as blood flow signals, i.e., the number of target data points. To comprehensively assess the blood flow fullness or signal-to-noise ratio level of the current frame of the image, the proportion of target data points identified as blood flow among all valid sampling points is calculated. This proportion is the variance percentage, a dimensionless statistical indicator that macroscopically reflects whether the blood flow signal dominates or is merely sparsely present in the current image relative to the overall analyzable area, thus aggregating the local point-by-point judgment results into a holistic quantitative indicator that can describe the blood flow state of the entire image. The variance percentage can be calculated using the formula VarPer = (N0 / Nall) × 100%.

[0033] Step S104: Compare the variance percentage with the preset variance percentage threshold, and determine the gain adjustment strategy corresponding to the blood flow image of the current frame based on the comparison result.

[0034] The preset variance percentage threshold (VarThrdPer) is a critical value for variance percentage determined in advance through statistical analysis based on the blood flow characteristics of different diagnostic sites. This threshold is related to the diagnostic site, and its setting principle is to ensure that when the percentage threshold is met, the corresponding sampling point has a high probability of being a real blood flow signal rather than a noise signal.

[0035] The comparison result refers to the judgment state obtained by comparing the variance ratio of the current frame blood flow image with the preset variance ratio threshold, which directly reflects the judgment result of the current blood flow state.

[0036] Gain adjustment strategy refers to the gain optimization method selected based on the comparison results (i.e., the judgment result of the current blood flow state).

[0037] Specifically, after calculating the variance percentage representing the blood flow status of the entire image frame, the calculated variance percentage is compared with another preset reference standard, namely the preset variance percentage threshold. This threshold is pre-calibrated based on a large amount of historical data and the characteristics of different diagnostic sites; it represents an ideal or critical state ratio. By comparing whether the variance percentage is higher or lower than this threshold, a qualitative judgment can be made on the current state of the blood flow image. For example, it can be determined whether the blood flow signal is weak and needs enhancement, or whether the blood flow signal is sufficient or even excessive and requires fine analysis. The result of this qualitative judgment directly determines which gain adjustment scheme will be adopted subsequently.

[0038] Step S105: Based on the gain adjustment strategy, determine the target gain parameter value corresponding to the blood flow image of the current frame.

[0039] The target gain parameter value refers to the control parameter value that, after calculation using a gain adjustment strategy, is ultimately determined and needs to be applied to the blood flow image to optimize its display effect. This parameter value characterizes the magnitude or target value of the gain adjustment and is the final quantized output of the gain optimization, which will be applied to the blood flow image processing of subsequent frames. For example, this parameter value can be specifically represented as the number of gain levels (UserGain), gain factor, energy threshold, etc. Specifically, after determining which gain adjustment strategy to use, a specific control parameter value for optimizing the image is calculated according to the logic of that strategy. The core of this process is to map the statistical characteristics of the current frame's blood flow image into a specific gain adjustment amount. By analyzing the data information related to blood flow intensity and distribution in the current frame image, and based on the rules of the selected strategy, this information is comprehensively evaluated and calculated to obtain a quantized output value that can optimize the image display effect. This value represents the gain target that this automatic optimization should achieve.

[0040] Step S106: Perform image processing on the next frame of blood flow image according to the target gain parameter value to obtain the target blood flow image.

[0041] The next frame of blood flow image refers to the blood flow image that the electronic device will acquire and generate after the current frame. The optimized parameters (target gain parameter value) calculated in the current frame will be applied to the processing flow of this frame. The target blood flow image refers to the final blood flow image obtained after applying the target gain parameter value to the next frame, resulting in improved gain and display quality. Specifically, when the electronic device begins acquiring and constructing the next frame of blood flow image, the newly calculated target gain parameter value is used as a key control input in the image processing pipeline of this frame. The image processing engine within the electronic device refers to this new parameter value and performs corresponding signal amplification, attenuation, or other dynamic range adjustments and mappings on the raw data of the next frame of blood flow image. After this optimized parameter processing, the final generated and displayed next frame image is the target blood flow image. The gain level of this image has been automatically adjusted to a more ideal state, improving the fullness of blood flow display, noise suppression, and presentation of key information.

[0042] The blood flow image processing method provided in this application obtains the target variance data of effective sampling points in the current frame blood flow image and compares each target variance data with a preset variance threshold to determine the target data points, thus achieving objective screening of sampling points; calculates the variance ratio based on the total number of points and the target number of points, and converts the screening result into an overall quantitative index; determines the gain adjustment strategy by comparing the variance ratio with a preset variance ratio threshold, so that the direction of gain adjustment has a clear basis; determines the target gain parameter value based on the strategy, and processes the next frame blood flow image according to the parameter value to obtain the target blood flow image, thus realizing automatic inter-frame adjustment of gain, enabling the image processing result to adaptively optimize according to the blood flow state of the current frame.

[0043] This embodiment provides a method for processing blood flow images, which can be used in electronic devices, such as ultrasound imaging equipment. Figure 2 This is a flowchart of a blood flow image processing method according to an embodiment of this application, such as... Figure 2 As shown, the process includes the following steps: Step S201: Obtain the target variance data of the effective sampling points in the current frame blood flow image.

[0044] Specifically, step S201 includes: Step S2011: Obtain the raw variance data of all sampling points in the current frame blood flow image.

[0045] Raw variance data refers to the variance component information directly obtained by the electronic device from all sampling points when acquiring the current frame of blood flow image, without any subsequent processing. Specifically, after acquiring a frame of blood flow image, the electronic device's internal signal processing system performs Doppler decoding on the received echo signal. During this process, the electronic device calculates a set of multidimensional data describing the blood flow characteristics at each spatial sampling location in the image, including a component characterizing the dispersion or stability of the blood flow signal velocity at that location. This initial stability characterization data, obtained directly from the decoding process without any subsequent modification or transformation, is the raw variance data. The electronic device can acquire the raw variance data for all sampling points in the entire image by directly reading this specific data component from each sampling point in the memory or cache region corresponding to that frame of image.

[0046] Step S2012: Quantize the original variance data to obtain quantized variance data.

[0047] Quantized variance data refers to variance data obtained after standardizing or discretizing the numerical range of the original variance data. Specifically, after obtaining the original variance data, a standardization operation on the numerical range is performed. Since the values ​​of the original data may fall on various numerical scales or precisions, to facilitate subsequent standardized processing and threshold comparison, each original variance value is mapped to a standardized, discrete, or continuous numerical interval according to a preset numerical transformation rule. This mapping process is called quantization; it transforms the original data from its original numerical space to a normalized feature space, and the resulting new numerical sequence is the quantized variance data. Its purpose is to eliminate data scale differences, enabling electronic devices to use a unified standard to measure and compare the signal stability characteristics of all sampling points.

[0048] In some alternative implementations, the variance component can be quantized to a numerical range of [0,1], the energy component to a numerical range of [0,96], and the component data can be stored as floating-point numbers.

[0049] In some alternative implementations, the quantization process described above is a preferred preprocessing method, but it is not a mandatory operation. In alternative implementations, the quantization step can be skipped, and the collected raw variance data and raw energy data can be used directly for subsequent statistical analysis and fitting calculations.

[0050] Step S2013: Remove invalid data from the quantized variance data to obtain the target variance data of the valid sampling points.

[0051] Invalid data refers to data points in quantized variance data that are identified as representing background regions, no-signal regions, or not carrying valid blood flow information. These data typically correspond to points with extremely low or zero energy. Specifically, after quantization, not all points in the resulting dataset are meaningful for judging blood flow status; a large number of points inevitably correspond to background or no-signal regions. These worthless data points are identified based on a pre-defined invalidity criterion, which is usually related to the detectability of signal strength. By verifying each point in the quantized variance data against this criterion, points judged as background or no-signal are marked as invalid data and removed from the dataset. After removing these points, only the valid sampling points considered to carry potential blood flow or noise information are retained. The quantized variance data corresponding to these points is the target variance data used in subsequent analysis.

[0052] The blood flow image processing method provided in this application first obtains the original variance data of all sampling points of the current frame blood flow image, then performs quantization processing, and finally removes invalid data from the quantized variance data to obtain the target variance data. This process ensures the integrity of the original data, unifies the data format and range through quantization, and removes invalid data to eliminate interference, thus obtaining standardized effective variance data. This provides a clean and reliable input foundation for subsequent calculation and judgment processes based on variance data.

[0053] Step S202: Compare each target variance data with a preset variance threshold to determine the target data points among the valid sampling points.

[0054] Specifically, step S202 includes: identifying the sampling points in the effective sampling points whose target variance data does not exceed the preset variance threshold as target data points.

[0055] After obtaining the target variance data of the valid sampling points, these points need to be further classified to distinguish which points are more likely to represent real blood flow signals and which points are more likely to be noise. This classification is based on a pre-set boundary standard, namely a preset variance threshold. Each valid sampling point is iterated through, its corresponding target variance value is extracted, and compared with the preset variance threshold. Based on the result of this comparison, sampling points whose numerical characteristics conform to the blood flow signal pattern, i.e., whose target variance data falls within a specific range defined by the threshold, are selected and uniformly marked as target data points. These points are considered to be valid signals with a high probability of corresponding to the real blood flow region.

[0056] The blood flow image processing method provided in this application embodiment clarifies the direct correspondence between target data points and preset variance thresholds, provides a basis for defining target data points, precisely limits the sample set for subsequent statistical calculations, and ensures that all subsequent processing based on target data points strictly revolves around sampling points that meet the variance condition.

[0057] Step S203: Based on the total number of valid sampling points and the target number of target data points, determine the variance percentage corresponding to the blood flow image in the current frame. For details, please refer to [link to relevant documentation]. Figure 1 Step S103 of the illustrated embodiment will not be described again here.

[0058] Step S204: Compare the variance percentage with the preset variance percentage threshold, and determine the gain adjustment strategy corresponding to the blood flow image of the current frame based on the comparison result.

[0059] Specifically, step S204 includes: Step S2041: If the variance percentage exceeds the variance percentage threshold, the blood flow state of the current frame blood flow image is determined to be the first state, and the first gain adjustment strategy corresponding to the first state is determined.

[0060] The first state refers to the working state where the blood flow image of the current frame is determined to have a weak blood flow signal and insufficient gain. In this state, the blood flow in the image is not fully displayed and needs to be enhanced. The first gain adjustment strategy refers to the gain optimization scheme triggered by the first state. The logic of this strategy is to directly and rapidly increase the current gain by a large amount so that the weak blood flow signal can be effectively enhanced and displayed. Specifically, after calculating the variance ratio of the current frame, it is compared with a preset variance ratio threshold. If the calculated variance ratio exceeds the threshold, this statistically indicates that the proportion of areas in the current image that match the characteristics of blood flow signals is too high. However, this usually does not mean that the blood flow is full and ideal, but rather reveals another situation—due to the overall gain setting being too low, only a very small number of blood flow points with strong signals are successfully detected and displayed, while a large number of weak blood flow signals that should be visible are lost because they are below the display threshold. Therefore, this overall image performance is characterized as the first state, that is, the working state of weak blood flow signal and insufficient gain. Based on this state determination, the corresponding processing scheme is automatically triggered and adopted, namely the first gain adjustment strategy. The core objective of this strategy is to quickly and directly increase the gain in order to enhance the display of blood flow signals.

[0061] Step S2042: If the variance percentage does not exceed the variance percentage threshold, the blood flow state of the current frame blood flow image is determined to be the second state, and the second gain adjustment strategy corresponding to the second state is determined.

[0062] The second state refers to the working state where the current frame of blood flow image is determined not to have weak blood flow. In this state, the blood flow signal may be sufficient, or even close to oversaturation or have introduced noise, thus requiring more precise analysis. The second gain adjustment strategy refers to the gain optimization scheme triggered by the second state. This strategy does not use a direct, large-scale adjustment, but rather models and analyzes the inherent characteristics of the current blood flow data to accurately calculate the optimal gain value to achieve fine-grained optimization. Specifically, if the calculated variance percentage does not exceed the preset variance percentage threshold, it indicates that the proportion of regions in the current image that conform to the characteristics of blood flow signals is at a relatively reasonable or high level. This usually means that sufficient blood flow signals have been successfully detected and displayed, and the image does not show obvious signal weakness or defects. Therefore, the blood flow state of the current image is characterized as the second state, i.e., the state where the blood flow signal is not weak. For this state, gain adjustment needs to be more cautious and precise to avoid over-enhancing, which would amplify noise as well. Therefore, the electronic device will automatically select the second gain adjustment strategy. This strategy does not use a simple fixed amplitude increase or decrease, but rather analyzes and models the inherent patterns of the current data to accurately calculate the optimal gain control amount.

[0063] The blood flow image processing method provided in this application divides the comparison results of variance ratio and preset threshold into two cases, which correspond to two blood flow states and their respective exclusive gain adjustment strategies. The execution process is simple and efficient, and can quickly complete the classification of blood flow states and match the corresponding gain adjustment path.

[0064] In some alternative implementations, as an alternative or supplement to the state determination logic, the determination of blood flow state can also introduce more determination conditions to improve accuracy. For example, independent or joint determination of blood flow velocity components and energy components can be added, or the state determination can be made after comprehensive processing of the variance ratio of multiple consecutive blood flow images. This can further reduce the interference of random noise in a single frame image on state classification.

[0065] In some optional implementations, the preset variance threshold and the preset variance percentage threshold are predetermined based on the analysis results of blood flow velocity data, energy data, and variance data in historical blood flow images of different diagnostic sites.

[0066] Historical blood flow images refer to a large number of representative blood flow image samples collected in advance for different diagnostic sites during the development or calibration phase. These images and the data they contain are the subject of statistical analysis to determine the thresholds and parameters to be used in advance.

[0067] Blood flow velocity data refers to component data extracted from historical blood flow images that reflect the speed and direction of blood flow in different locations. It is a direct manifestation of the Doppler effect and is used to comprehensively assess the dynamic characteristics of real blood flow.

[0068] Energy data refers to component data extracted from historical blood flow images that reflects the intensity of blood flow signals at different locations. It represents the number of moving scatterers at the sampling point and is an important basis for determining the presence and amount of blood flow.

[0069] Variance data refers to component data extracted from historical blood flow images that reflects the degree of dispersion or turbulence of blood flow signal velocity in different locations. It is a key feature for distinguishing laminar blood flow signals from cluttered noise signals.

[0070] The analysis results refer to the conclusions drawn during the development phase from a comprehensive statistical study of multidimensional data (velocity, energy, variance) from a large number of historical blood flow images. These conclusions typically include critical parameter values ​​that can effectively distinguish blood flow from noise and determine whether blood flow is full or not, namely, the preset variance threshold and the preset variance percentage threshold.

[0071] Specifically, the determination of the preset variance threshold and the preset variance proportion threshold is an offline calibration process that occurs before real-time application. Developers collect a large number of representative historical blood flow images as statistical analysis samples for different diagnostic sites. In each image, multi-dimensional blood flow information is extracted from the sampling points, including velocity data reflecting movement speed, energy data reflecting signal strength, and variance data reflecting signal stability. Through comprehensive analysis and probability distribution estimation of this massive amount of data, the signal feature boundary that best distinguishes real blood flow signals from random noise, and the statistical proportion boundary that distinguishes whether the blood flow filling level of the image meets the standard, can be found. After verification and calibration, the values ​​of these two empirical boundaries are solidified as the preset variance threshold and the preset variance proportion threshold, pre-set within the system for direct use during real-time optimization.

[0072] In the above implementation, the preset variance threshold and the preset variance percentage threshold are predetermined based on the analysis results of blood flow velocity, energy and variance data in historical blood flow images of different diagnostic sites. This not only allows the threshold to be adapted to the blood flow characteristics of different sites by distinguishing different diagnostic sites, but also makes the setting of the threshold more scientific and reasonable by relying on the statistical analysis of multi-dimensional historical data, and provides a reliable basis for the subsequent threshold-based judgment process.

[0073] Step S205: Based on the gain adjustment strategy, determine the target gain parameter value corresponding to the blood flow image of the current frame.

[0074] In some optional implementations, the target gain parameter value corresponding to the current frame blood flow image is determined based on the second gain adjustment strategy, including: Step a1: Obtain the target energy data of the effective sampling points in the current frame blood flow image, as well as the mapping relationship between the gain parameter value and the energy value.

[0075] Target energy data refers to the energy component information extracted from the effective sampling points of the current frame blood flow image when executing the second gain adjustment strategy. This data represents the intensity of the corresponding blood flow signal. The mapping relationship refers to the pre-defined conversion rules between gain control parameter values ​​and image energy values. Specifically, when executing a refined gain adjustment strategy, two types of information need to be acquired simultaneously: first, feature data directly related to signal intensity in the current image; and second, internally preset control parameter conversion rules. For the former, the component information corresponding to each point, used to characterize the strength of the blood flow signal at that point, is extracted from the previously selected effective sampling points; these values ​​are the target energy data. For the latter, an internally preset correspondence table or function is retrieved. This correspondence defines the quantization conversion rules between device gain control parameters (e.g., user-adjustable levels) and the final image display energy or signal intensity. Both types of information are prepared together, providing a data foundation and conversion basis for subsequently converting theoretical calculation values ​​into actual control quantities.

[0076] Step a2: Fit the target variance data and target energy data to obtain the fitting relationship corresponding to the blood flow image of the current frame.

[0077] A fitting relationship refers to a functional expression that describes the intrinsic correlation between energy and variance data in the current frame of a blood flow image. This relationship model reveals how signal intensity changes with its stability. Specifically, after acquiring the target variance and target energy data from the same batch of valid sampling points, the intrinsic correlation between these two sets of data is explored. Treating this set of two-dimensional data points from the current frame as a whole, a pre-selected mathematical model is used to find a mapping curve that best describes how changes in variance cause changes in energy intensity. This process of finding the best-matching model is called fitting. By adjusting the internal parameters of the selected model, this curve is made to closely approximate the distribution trend of all actual data points. The final determined functional expression is the fitting relationship unique to the current frame of the blood flow image, describing the law of energy variation with variance.

[0078] In some alternative implementations, the preferred mathematical model in the above fitting process is a power function model. However, in alternative implementations, exponential fitting or other nonlinear fitting methods can also be used to establish the mapping relationship between energy data and variance data. Different fitting methods will have different fitting accuracy and computational complexity, and can be flexibly selected according to the computing power and accuracy requirements of the equipment.

[0079] In some alternative implementations, step a2 above includes: Step a21: Perform logarithmic processing on the target variance data and target energy data to obtain logarithmic variance data and logarithmic energy data.

[0080] Logarithmic variance data refers to the data obtained by performing a logarithmic operation on the target variance data. Logarithmic energy data refers to the data obtained by performing a logarithmic operation on the target energy data. Specifically, to find the relationship between variance and energy more efficiently and concisely, it is often necessary to transform the original data. A common transformation method is to replace the original values ​​with their corresponding logarithmic values. This involves iterating through all the target variance and target energy data, performing a logarithmic operation on each value. After this transformation, the relationship, which might have appeared as a complex curve in a conventional coordinate system, will be stretched or compressed in the new logarithmic coordinate system, transforming into a simpler, more easily identifiable, and processable distribution. The resulting new numerical sequence corresponds to the logarithmic variance and logarithmic energy data.

[0081] For example, electronic devices can use a power function model, P = a * T^b, to describe the relationship between energy data and variance data, where P represents the energy component, T represents the variance component, and a and b are the model parameters to be fitted. To find the relationship between variance and energy more efficiently and concisely, this power function model is transformed by taking the logarithm of both sides of the equation, thus converting the power function relationship into a linear relationship: ln(P) = ln(a) + b * ln(T). Based on this transformation, all target variance and target energy data are iterated through, and the logarithm of each value is taken. After this transformation, the relationship, which was originally a complex curve in the conventional coordinate system, is stretched or compressed in the new logarithmic coordinate system, transforming into a simpler, more easily identifiable, and processable linear distribution. The resulting new numerical sequence corresponds to the logarithmic variance data and the logarithmic energy data.

[0082] Step a22: Perform linear fitting on the log-variance data and log-energy data to obtain the slope parameter and intercept parameter.

[0083] The slope parameter refers to the coefficient of the independent variable (logarithmic variance) in the linear function expression obtained after linear fitting of logarithmic variance data and logarithmic energy data in the logarithmic domain. It quantifies the rate at which energy changes with variance in logarithmic coordinates.

[0084] The intercept parameter refers to the function value in the linear function expression obtained after linear fitting of logarithmic variance data and logarithmic energy data in the logarithmic domain, when the independent variable (logarithmic variance) is zero. It, together with the slope parameter, defines the position of the fitted line.

[0085] Specifically, when the original data is transformed into logarithmic data through a logarithmic transformation, if the distribution of these new data in the coordinate space exhibits an approximately linear trend, a linear model can be used for fitting. All data point pairs are plotted in a logarithmic coordinate system, and then, based on a criterion of minimizing error, a best-fitting straight line that represents the overall trend of these points is found. The geometric properties of this straight line are defined by two core parameters: a slope parameter describing the inclination of the line, and an intercept parameter describing the offset of the line on the coordinate axes. The specific values ​​of these two parameters are calculated.

[0086] For example, the linear fitting can be achieved using the least squares method. The slope parameter b obtained by solving is the exponential parameter in the power function model, and the intercept parameter ln(a) corresponds to the natural logarithm of the coefficient a in the power function model.

[0087] Step a23: Based on the slope parameter and intercept parameter, construct the fitting relationship between the target variance data and the target energy data.

[0088] Having obtained the slope and intercept parameters describing the fitted line in the logarithmic domain, we possess all the elements describing the relationship between variance and energy in the transformation space. The next step for the electronic device is to restore this simplified relationship back to the original data space. Using the inverse transformation rule of logarithmic operations, the obtained logarithmic domain linear equation is reconstructed into a functional expression relating the original variance and the original energy. This final expression is the formal fitting relationship between the target variance data and the target energy data used in subsequent calculations; it fully describes how the energy intensity of the blood flow signal changes regularly with its stability characteristics in the current frame.

[0089] For example, the fitting relationship is P = a * T^b, where a = exp(intercept parameter) and b = slope parameter. This fitting relationship can be visually represented by a variance energy distribution plot, such as... Figure 3As shown in the figure, the horizontal axis represents the variance component T, with a value range of [0,1]; the vertical axis represents the energy component P, with a value range of [0,96]. The discrete points in the figure are the original energy-variance data points extracted from the effective sampling points of the current frame, and the continuous curve is the fitting curve of P=a*T^b obtained by fitting a power function model. The figure marks the position of the preset variance threshold VarThrd and the corresponding reference energy value P0 on the fitting curve. This figure clearly shows the nonlinear correlation characteristics between the energy component and the variance component in the blood flow signal, intuitively reflects the fitting effect of the fitting curve on the actual data distribution, and the core logic of deriving the reference energy value through the preset variance threshold.

[0090] In the above implementation, by first performing logarithmic processing on the target variance data and target energy data, then performing linear fitting on the processed logarithmic data to obtain slope and intercept parameters, and finally constructing a fitting relationship based on these two parameters, the original nonlinear fitting problem is transformed into a linear fitting problem that is simpler to calculate and more stable. This significantly reduces the complexity of fitting calculations and improves the efficiency of fitting operations. At the same time, the fitting relationship constructed through explicit slope and intercept parameters is clearly quantified and traceable, enabling the correspondence between target variance data and target energy data to be obtained quickly and reliably.

[0091] Step a3: Based on the preset variance threshold and fitting relationship, determine the reference energy value corresponding to the blood flow image of the current frame.

[0092] The reference energy value refers to the theoretical energy value calculated by substituting a preset variance threshold into the fitted relation. This value represents the theoretical signal strength that should exist when the blood flow signal characteristics just reach the critical stable state that distinguishes blood flow from noise, and serves as an intermediate reference for determining the target gain. Specifically, once the fitted relation describing the energy variation with variance in the current image is obtained, this relational model can be used for theoretical deduction and estimation. The key boundary value used to distinguish blood flow from noise in blood flow state judgment, i.e., the preset variance threshold, is used as an input variable and substituted into the fitted relation expression that has just been constructed. After function calculation, a corresponding energy output value can be obtained. This calculated energy value represents the theoretically observable signal strength level when the signal stability just reaches the critical boundary between blood flow and noise under the blood flow characteristic law of the current frame. This value is the reference energy value, which is the intermediate basis for determining the final gain adjustment target.

[0093] Step a4: Based on the mapping relationship, determine the target gain parameter value corresponding to the reference energy value.

[0094] The reference energy value calculated through the fitting relationship is a theoretical signal strength value, but the electronic device ultimately requires a specific gain control parameter to perform the adjustment. At this point, the previously prepared mapping relationship between the gain parameter value and the energy value is retrieved. This mapping relationship defines the energy display effect corresponding to each gain control parameter level. Using the previously obtained reference energy value as a query condition, a search or match is performed within this mapping relationship to find the closest and most corresponding control parameter level. This found parameter level is the final quantized target value that needs to be output and applied to directly control the device gain.

[0095] In the above implementation, for the second gain adjustment strategy, a specific fitting relationship is obtained by combining the variance data and energy data of the effective sampling points of the current frame. Then, the corresponding reference energy value is derived based on the preset variance threshold. Finally, the target gain parameter value is determined based on the mapping relationship between the gain parameter value and the energy value. The whole process is dynamically calculated based on the actual data of the current frame rather than using a fixed adjustment value. It has a high degree of quantification and can provide accurate and traceable parameter basis for gain adjustment in the second state, ensuring the pertinence and accuracy of gain adjustment.

[0096] In some optional implementations, when the target gain parameter value is expressed in the form of gain level, the above mapping relationship is that each gain level corresponds to an energy threshold. The essence of gain adjustment is to change the enhancement or attenuation level of energy components in the blood flow image. After obtaining the reference energy value P0, the level closest to P0 can be determined in the energy range corresponding to all levels, which is used as the final target gain level UserGain.

[0097] In some optional implementations, based on a first gain adjustment strategy, the target gain parameter value corresponding to the current frame blood flow image is determined, including: Step b1: Obtain the initial gain parameter value corresponding to the blood flow image of the current frame.

[0098] The initial gain parameter value refers to the original value of the gain control parameter currently used by the electronic device and applied to the blood flow image of that frame before the execution of the first gain adjustment strategy. Specifically, when it is determined that a direct gain adjustment strategy (i.e., the first gain adjustment strategy) needs to be executed, the starting point of the adjustment must first be determined. This starting point is the actual setting value of the gain control parameter of the electronic device at the current moment, applied to the current blood flow image frame. The electronic device will directly query the registers or variables inside the electronic device used to store the current operating parameters and read the current value of the specific parameter item used to control the blood flow image gain. This read-out current valid value is the initial gain parameter value, which reflects the gain operating level of the electronic device before automatic optimization is triggered.

[0099] Step b2: Determine the target gain parameter value based on the fusion result of the initial gain parameter value and the preset gain step value.

[0100] The preset gain step value refers to a pre-set, fixed amplitude value for a single direct gain adjustment. When the first gain adjustment strategy is triggered, this fixed change is directly added to or subtracted from the initial gain parameter value to quickly enhance the signal. This preset gain step value is determined in advance based on statistical analysis of a large amount of blood flow data from different diagnostic sites, combined with default blood flow parameter settings. Specifically, after obtaining the starting value for adjustment (i.e., the initial gain parameter value), the adjustment amplitude also needs to be known. This amplitude is a pre-set, empirically verified fixed change value, i.e., the preset gain step value. This step value represents the standard amplitude that should be added for each automatic adjustment when the signal is weak. The initial gain parameter value and the preset gain step value are combined and calculated according to the goal of enhancing the signal, usually by adding the two together to obtain a new gain control parameter magnitude. The result of this fusion calculation is the final target gain parameter value, which directly represents the new gain level that the device should be set to after one-click optimization.

[0101] In the above implementation, for the first gain adjustment strategy, the target gain parameter value is determined by obtaining the initial gain parameter value corresponding to the blood flow image of the current frame and then fusing it with the preset gain step value. The execution speed is fast and the target gain parameter value under this strategy can be determined efficiently.

[0102] Step S206: Perform image processing on the next frame of the blood flow image according to the target gain parameter value to obtain the target blood flow image. For details, please refer to [link to relevant documentation]. Figure 1 Step S106 of the illustrated embodiment will not be described again here.

[0103] It should be added that the above embodiments all use the current single-frame blood flow image as the analysis object to calculate the target gain parameter value. However, this application also supports extension to multi-frame mode, that is, inputting multiple consecutive frames of blood flow images, repeating the complete process of the above technical solution for each frame, and combining the analysis results of multiple frames to determine the final target gain parameter value, thereby obtaining a more stable and accurate gain optimization effect.

[0104] This embodiment also provides a blood flow image processing apparatus for implementing the above embodiments and preferred embodiments; details already described will not be repeated. As used below, the term "module" can refer to a combination of software and / or hardware that performs a predetermined function. Although the apparatus described in the following embodiments is preferably implemented in software, hardware implementation, or a combination of software and hardware, is also possible and contemplated.

[0105] This embodiment provides a blood flow image processing device, such as... Figure 4 As shown, it includes: The acquisition module 401 is used to acquire the target variance data of the effective sampling points in the current frame blood flow image; The first comparison module 402 is used to compare each target variance data with a preset variance threshold to determine the target data point among multiple sampling points. The first determining module 403 is used to determine the variance percentage of the blood flow image in the current frame based on the total number of multiple sampling points and the target number of target data points. The second comparison module 404 is used to compare the variance proportion with a preset variance proportion threshold, and determine the gain adjustment strategy corresponding to the blood flow image of the current frame based on the comparison result. The second determining module 405 is used to determine the target gain parameter value corresponding to the blood flow image of the current frame based on the gain adjustment strategy; The processing module 406 is used to perform image processing on the next frame of blood flow image according to the target gain parameter value to obtain the target blood flow image.

[0106] In some alternative implementations, the second comparison module 404 includes: The first determination submodule is used to determine the blood flow state of the current frame blood flow image as the first state if the variance ratio exceeds the variance ratio threshold, and to determine the first gain adjustment strategy corresponding to the first state. The second determination submodule is used to determine the blood flow state of the current frame blood flow image as the second state if the variance ratio does not exceed the variance ratio threshold, and to determine the second gain adjustment strategy corresponding to the second state.

[0107] In some alternative implementations, the second determining module 405 includes: The first acquisition submodule is used to acquire the target energy data of the effective sampling points in the current frame blood flow image, as well as the mapping relationship between the gain parameter value and the energy value; The fitting submodule is used to fit the target variance data and target energy data to obtain the fitting relationship corresponding to the blood flow image of the current frame. The third determination submodule is used to determine the reference energy value corresponding to the blood flow image of the current frame based on the preset variance threshold and fitting relationship; The fourth determination submodule is used to determine the target gain parameter value corresponding to the reference energy value based on the mapping relationship.

[0108] In some alternative implementations, the fitting submodule includes: The processing unit is used to perform logarithmic processing on the target variance data and target energy data to obtain logarithmic variance data and logarithmic energy data. The fitting unit is used to perform linear fitting on log-variance data and log-energy data to obtain slope parameters and intercept parameters. The building unit is used to construct a fitting relationship between the target variance data and the target energy data based on the slope parameter and the intercept parameter.

[0109] In some optional implementations, the second determining module 405 further includes: The second acquisition submodule is used to acquire the initial gain parameter value corresponding to the blood flow image of the current frame; The fifth determination submodule is used to determine the target gain parameter value based on the fusion result of the initial gain parameter value and the preset gain step value.

[0110] In some optional implementations, the preset variance threshold and the preset variance percentage threshold are predetermined based on the analysis results of blood flow velocity data, energy data, and variance data in historical blood flow images of different diagnostic sites.

[0111] In some optional implementations, the acquisition module 401 includes: The third acquisition submodule is used to acquire the raw variance data of all sampling points in the current frame blood flow image; The processing submodule is used to quantize the raw variance data to obtain quantized variance data; The elimination submodule is used to remove invalid data from the quantized variance data to obtain the target variance data of the valid sampling points.

[0112] In some alternative implementations, the first comparison module 402 includes: The sixth determination submodule is used to determine the sampling points in the effective sampling points whose target variance data does not exceed the preset variance threshold as target data points.

[0113] The blood flow image processing apparatus provided in this application embodiment can execute the blood flow image processing method provided in any embodiment of this application, and has the corresponding functional modules and beneficial effects for executing the method. Further functional descriptions of the above modules and units are the same as those in the corresponding embodiments described above, and will not be repeated here.

[0114] Figure 5 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application.

[0115] The following is a detailed reference. Figure 5The diagram illustrates a structural schematic suitable for implementing the electronic device described in the embodiments of this application. The electronic device may include a processor (e.g., a central processing unit, graphics processor, etc.) 501, which can perform various appropriate actions and processes according to a program stored in read-only memory (ROM) 502 or a program loaded from memory 508 into random access memory (RAM) 503. The RAM 503 also stores various programs and data required for the operation of the electronic device. The processor 501, ROM 502, and RAM 503 are interconnected via a bus 504. An input / output (I / O) interface 505 is also connected to the bus 504.

[0116] Typically, the following devices can be connected to I / O interface 505: input devices 506 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 507 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; memory devices 508 including, for example, magnetic tapes, hard disks, etc.; and communication devices 509. Communication device 509 allows electronic devices to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 5 Electronic devices with various devices are shown, but it should be understood that it is not required to implement or have all of the devices shown, and more or fewer devices may be implemented or have instead.

[0117] Specifically, according to embodiments of this application, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, embodiments of this application include a computer program product comprising a computer program carried on a non-transitory computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via a communication device 509, or installed from a memory 508, or installed from a ROM 502. When the computer program is executed by the processor 501, it performs the functions defined in the blood flow image processing method of embodiments of this application.

[0118] Figure 5 The electronic device shown is merely an example and should not impose any limitation on the functionality and scope of use of the embodiments of this application.

[0119] This application also provides a computer-readable storage medium. The methods described in this application can be implemented in hardware or firmware, or implemented as recordable on a storage medium, or implemented as computer code downloaded via a network and originally stored on a remote storage medium or a non-transitory machine-readable storage medium and subsequently stored on a local storage medium. Thus, the methods described herein can be processed by software stored on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. The storage medium can be a magnetic disk, optical disk, read-only memory, random access memory, flash memory, hard disk, or solid-state drive, etc.; further, the storage medium can also include combinations of the above types of memory. It is understood that computers, processors, microprocessor controllers, or programmable hardware include storage components capable of storing or receiving software or computer code. When the software or computer code is accessed and executed by the computer, processor, or hardware, the blood flow image processing method shown in the above embodiments is implemented.

[0120] A portion of this application can be applied as a computer program product, such as computer program instructions, which, when executed by a computer, can invoke or provide the methods and / or technical solutions according to this application through the operation of the computer. Those skilled in the art will understand that the forms in which computer program instructions exist in a computer-readable medium include, but are not limited to, source files, executable files, installation package files, etc. Correspondingly, the ways in which computer program instructions are executed by a computer include, but are not limited to: the computer directly executing the instructions, or the computer compiling the instructions and then executing the corresponding compiled program, or the computer reading and executing the instructions, or the computer reading and installing the instructions and then executing the corresponding installed program. Here, the computer-readable medium can be any available computer-readable storage medium or communication medium accessible to a computer.

[0121] Although embodiments of this application have been described in conjunction with the accompanying drawings, those skilled in the art can make various modifications and variations without departing from the spirit and scope of this application, and all such modifications and variations fall within the scope defined by the appended claims.

Claims

1. A method for processing blood flow images, characterized in that, The method includes: Obtain the target variance data of valid sampling points in the current frame of the blood flow image; Each of the target variance data is compared with a preset variance threshold to determine the target data points among the valid sampling points; Based on the total number of effective sampling points and the target number of target data points, the variance percentage corresponding to the current frame blood flow image is determined; The variance percentage is compared with a preset variance percentage threshold, and the gain adjustment strategy corresponding to the blood flow image of the current frame is determined based on the comparison result. Based on the gain adjustment strategy, the target gain parameter value corresponding to the current frame blood flow image is determined; The next frame of blood flow image is processed according to the target gain parameter value to obtain the target blood flow image.

2. The method according to claim 1, characterized in that, The step of comparing the variance proportion with a preset variance proportion threshold and determining the gain adjustment strategy corresponding to the current frame blood flow image based on the comparison result includes: If the variance percentage exceeds the variance percentage threshold, the blood flow state of the current frame blood flow image is determined to be the first state, and the first gain adjustment strategy corresponding to the first state is determined. If the variance percentage does not exceed the variance percentage threshold, the blood flow state of the current frame blood flow image is determined to be the second state, and the second gain adjustment strategy corresponding to the second state is determined.

3. The method according to claim 2, characterized in that, Based on the second gain adjustment strategy, the target gain parameter value corresponding to the current frame blood flow image is determined, including: Obtain the target energy data of the effective sampling points in the current frame blood flow image, as well as the mapping relationship between the gain parameter value and the energy value; By fitting the target variance data and the target energy data, the fitting relationship corresponding to the current frame blood flow image is obtained; Based on the preset variance threshold and the fitting relationship, the reference energy value corresponding to the current frame blood flow image is determined; Based on the mapping relationship, the target gain parameter value corresponding to the reference energy value is determined.

4. The method according to claim 3, characterized in that, The process of fitting the target variance data and the target energy data to obtain the fitting relationship corresponding to the current frame blood flow image includes: Logarithmic processing is performed on the target variance data and the target energy data to obtain logarithmic variance data and logarithmic energy data; Linear fitting is performed on the logarithmic variance data and the logarithmic energy data to obtain the slope parameter and intercept parameter; Based on the slope parameter and the intercept parameter, a fitting relationship is constructed between the target variance data and the target energy data.

5. The method according to claim 2, characterized in that, Based on the first gain adjustment strategy, the target gain parameter value corresponding to the current frame blood flow image is determined, including: Obtain the initial gain parameter value corresponding to the current frame blood flow image; The target gain parameter value is determined based on the fusion result of the initial gain parameter value and the preset gain step value.

6. The method according to claim 1, characterized in that, The preset variance threshold and the preset variance percentage threshold are predetermined based on the analysis results of blood flow velocity data, energy data, and variance data in historical blood flow images of different diagnostic sites.

7. The method according to claim 1, characterized in that, The step of obtaining the target variance data of effective sampling points in the current frame blood flow image includes: Obtain the raw variance data of all sampling points in the current frame blood flow image; The original variance data is quantized to obtain quantized variance data; Invalid data in the quantized variance data are removed to obtain the target variance data of the valid sampling points.

8. The method according to claim 1, characterized in that, The step of comparing each of the target variance data with a preset variance threshold to determine the target data points among the valid sampling points includes: The sampling points in the effective sampling points whose target variance data does not exceed the preset variance threshold are determined as the target data points.

9. A blood flow image processing apparatus, characterized in that, The device includes: The acquisition module is used to acquire the target variance data of the effective sampling points in the current frame of the blood flow image; The first comparison module is used to compare each of the target variance data with a preset variance threshold to determine the target data point among the multiple sampling points. The first determining module is used to determine the variance percentage of the current frame blood flow image based on the total number of sampling points and the target number of target data points. The second comparison module is used to compare the variance ratio with a preset variance ratio threshold, and determine the gain adjustment strategy corresponding to the blood flow image of the current frame based on the comparison result. The second determining module is used to determine the target gain parameter value corresponding to the current frame blood flow image based on the gain adjustment strategy. The processing module is used to perform image processing on the next frame of blood flow image according to the target gain parameter value to obtain the target blood flow image.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions for causing the computer to perform the blood flow image processing method according to any one of claims 1 to 8.

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