A method, device and storage medium for filtering blood flow

Abstract

The present application relates to the technical field of ultrasonic medical detection, and discloses a method, device and storage medium for filtering blood flow. The method comprises: performing segmentation processing on an original signal based on the movement speed of tissue to obtain a plurality of original segmentation signals, wherein the tissue corresponding to the tissue clutter signal in the original segmentation signal moves at a uniform speed, obtaining a target signal after filtering out the tissue clutter signal in each original segmentation signal, performing segmentation processing on the target signal based on a preset sliding window to obtain a plurality of target segmentation signals, comparing the parameters of the target segmentation signal with the corresponding parameter threshold, selecting the target segmentation signal based on the comparison result, and composing the selected target segmentation signal into a target blood flow signal. The above segmentation processing can effectively filter out the tissue clutter signal and the noise signal, thereby retaining the effective blood flow signal, and effectively improving the accuracy of detecting the blood flow signal.

Description

Technical Field

[0001] The present application relates to the technical field of ultrasonic medical detection, and provides a method, device and storage medium for filtering blood flow. Background Art

[0002] At present, the commonly used mode in ultrasound systems is C mode, which is color blood flow imaging, that is, using the Doppler effect to image radial blood flow, which is convenient for diagnosing the radial blood flow velocity in relevant parts of the patient. Because there is not only blood flow but also tissue in human tissue, when ultrasound passes through these tissues, tissue clutter signals will be generated, and the intensity of tissue clutter signals is usually 40 to 100 dB higher than that of blood flow signals, so blood flow signals are easily affected by tissue clutter.

[0003] In the existing processing mode, in order to filter out the above-mentioned tissue clutter signals, the clutter is usually uniformly filtered out point by point in the blood flow signal, that is, the tissue clutter signal is processed as a signal of uniform motion. However, relative to the blood flow, the tissue usually moves at a non-uniform speed. Therefore, in the existing processing method, the tissue clutter signal is likely to exceed the suppression capability range of the wall filter, that is, the tissue clutter signal is treated as a blood flow signal during the filtering process, resulting in inaccurate blood flow detection. Summary of the invention

[0004] The embodiments of the present application provide a method, device and storage medium for filtering blood flow, so as to improve the accuracy of detecting blood flow signals.

[0005] The specific technical solutions provided by this application are as follows:

[0006] In a first aspect, an embodiment of the present application provides a method for filtering blood flow, comprising:

[0007] The original signal is segmented based on the movement speed of the tissue to obtain a plurality of original segmented signals, wherein the tissue corresponding to the tissue clutter signal in the original segmented signal is in uniform movement;

[0008] The target signal is obtained after filtering out tissue clutter signals in each original segmented signal;

[0009] Segmenting the target signal based on a preset sliding window to obtain a plurality of target segmented signals;

[0010] The parameters of the target segmented signal are compared with the corresponding parameter thresholds, and the target segmented signal is selected based on the comparison result, and the selected target segmented signal is combined into a target blood flow signal.

[0011] In some possible embodiments, the original signal is segmented based on the movement speed of the tissue to obtain multiple original segmented signals, including:

[0012] determining the blood flow velocity of the blood flow signal and the tissue velocity of the tissue clutter signal in the original signal;

[0013] The signal segment in which the difference between the tissue velocity and the blood flow velocity in the original signal is less than the preset difference is taken as the original segmented signal.

[0014] In some possible embodiments, obtaining the target signal after filtering out tissue clutter signals in each original segmented signal includes:

[0015] Convert each original segmented signal into the frequency domain to obtain a plurality of frequency domain segmented signals;

[0016] The spectrum of each frequency domain segmented signal is shifted according to the distance corresponding to the phase in the frequency domain, and the tissue clutter signal is filtered out; after the filtered frequency domain segmented signals are converted to the time domain, the target signal is composed according to the continuity of the time domain.

[0017] In some possible embodiments, segmenting the target signal based on a preset sliding window to obtain a plurality of target segmented signals includes:

[0018] The target signal is intercepted in sequence using a preset sliding window to obtain multiple starting points and ending points;

[0019] The target signals between the start point and the end point belonging to the same sliding window are divided into the same target segmented signal.

[0020] In some possible embodiments, selecting a target segment signal based on the comparison result includes:

[0021] From all target segmented signals, select a target segmented signal whose comparison result satisfies part or all of the following conditions;

[0022] The speed parameter value is less than the speed threshold;

[0023] The speed variance is greater than the variance threshold;

[0024] The energy parameter value is less than the energy threshold.

[0025] In some possible embodiments, the selected target segmented signals are combined into a target blood flow signal, including:

[0026] Extracting time domain labels from the selected target segmented signal;

[0027] The target segmented signals are combined into target blood flow signals according to the order of time domain labels.

[0028] In some possible embodiments, tissue clutter signals are filtered out by a wall filter.

[0029] In a second aspect, an embodiment of the present application provides a device for filtering blood flow, comprising:

[0030] A segmentation unit is used to perform segmentation processing on the original signal based on the movement speed of the tissue to obtain a plurality of original segmented signals, wherein the tissue corresponding to the tissue clutter signal in the original segmented signal is in uniform movement;

[0031] A filtering unit, used for filtering out tissue clutter signals in each original segmented signal to obtain a target signal;

[0032] A re-segmentation unit, used for segmenting the target signal based on a preset sliding window to obtain a plurality of target segmented signals;

[0033] The comparison unit is used to compare the parameters of the target segmented signal with the corresponding parameter threshold, and select the target segmented signal based on the comparison result, and compose the selected target segmented signal into a target blood flow signal.

[0034] In a third aspect, an embodiment of the present application provides an ultrasonic device, comprising:

[0035] a probe configured to transmit a wide beam and receive a raw signal;

[0036] a display unit configured to display an ultrasound image corresponding to a target blood flow signal;

[0037] The processor is connected to the probe and the display unit respectively, and is configured to execute:

[0038] The original signal is segmented based on the movement speed of the tissue to obtain a plurality of original segmented signals, wherein the tissue corresponding to the tissue clutter signal in the original segmented signal is in uniform movement;

[0039] The target signal is obtained after filtering out tissue clutter signals in each original segmented signal;

[0040] Segmenting the target signal based on a preset sliding window to obtain a plurality of target segmented signals;

[0041] The parameters of the target segmented signal are compared with the corresponding parameter thresholds, and the target segmented signal is selected based on the comparison result, and the selected target segmented signal is combined into a target blood flow signal.

[0042] In some possible embodiments, when the processor performs segmentation processing on the original signal based on the movement speed of the tissue to obtain a plurality of original segmented signals, the processor is configured as follows:

[0043] determining the blood flow velocity of the blood flow signal and the tissue velocity of the tissue clutter signal in the original signal;

[0044] The signal segment in which the difference between the tissue velocity and the blood flow velocity in the original signal is less than the preset difference is taken as the original segmented signal.

[0045] In some possible embodiments, when the processor obtains the target signal after filtering out tissue clutter signals in each original segmented signal, the processor is configured to:

[0046] Convert each original segmented signal into the frequency domain to obtain a plurality of frequency domain segmented signals;

[0047] The spectrum of each frequency domain segmented signal is shifted according to the distance corresponding to the phase in the frequency domain, and the tissue clutter signal is filtered out; after the filtered frequency domain segmented signals are converted to the time domain, the target signal is composed according to the continuity of the time domain.

[0048] In some possible embodiments, when the processor performs segmentation processing on the target signal based on a preset sliding window to obtain multiple target segmented signals, the processor is configured to:

[0049] The target signal is intercepted in sequence using a preset sliding window to obtain multiple starting points and ending points;

[0050] The target signals between the start point and the end point belonging to the same sliding window are divided into the same target segmented signal.

[0051] In some possible embodiments, when the processor selects the target segment signal based on the comparison result, the processor is configured to:

[0052] From all target segmented signals, select a target segmented signal whose comparison result satisfies part or all of the following conditions;

[0053] The speed parameter value is less than the speed threshold;

[0054] The speed variance is greater than the variance threshold;

[0055] The energy parameter value is less than the energy threshold.

[0056] In some possible embodiments, when the processor executes the process of combining the selected target segmented signals into a target blood flow signal, the processor is configured to:

[0057] Extracting time domain labels from the selected target segmented signal;

[0058] The target segmented signals are combined into target blood flow signals according to the order of time domain labels.

[0059] In some possible embodiments, tissue clutter signals are filtered out by a wall filter.

[0060] In a fourth aspect, a computer-readable storage medium is provided. When instructions in the storage medium are executed by a processor, the processor is enabled to execute any method described in the first aspect.

[0061] The beneficial effects of this application are as follows:

[0062] In summary, in an embodiment of the present application, a method, device and storage medium for filtering blood flow are provided, the method comprising: performing segmentation processing on an original signal based on the movement speed of the tissue to obtain a plurality of original segmented signals, wherein the tissue corresponding to the tissue clutter signal in the original segmented signal moves at a uniform speed, filtering out the tissue clutter signal in each original segmented signal to obtain a target signal, performing segmentation processing on the target signal based on a preset sliding window to obtain a plurality of target segmented signals, comparing a parameter of the target segmented signal with a corresponding parameter threshold, and selecting a target segmented signal based on the comparison result, and forming a target blood flow signal with the selected target segmented signal, and the above-mentioned segmentation processing can effectively filter out tissue clutter signals and noise signals, thereby retaining effective blood flow signals, thereby improving the detection efficiency and accuracy of blood flow signals.

[0063] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or be understood by practicing the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description, claims, and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0064] The drawings described herein are used to provide a further understanding of the present application and constitute a part of the present application. The illustrative embodiments of the present application and their descriptions are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0065] Figure 1 It is a structural schematic diagram of an ultrasonic device;

[0066] Figure 2 A schematic diagram of an application principle according to an embodiment of the present application;

[0067] Figure 3 This is a schematic diagram of the process of filtering blood flow in an embodiment of the present application;

[0068] Figure 4 A schematic diagram of the composition of an original signal;

[0069] Figure 5 This is a schematic diagram of using an ultrasonic device to collect original signals in an embodiment of the present application;

[0070] Figure 6 A schematic diagram of a process for determining an original segmented signal in an embodiment of the present application;

[0071] Figure 7 This is another schematic diagram of a process of filtering blood flow in an embodiment of the present application;

[0072] Figure 8 A schematic diagram of a process for determining a target signal in an embodiment of the present application;

[0073] Fig. 9 A schematic diagram of a process for determining a target segment signal in an embodiment of the present application;

[0074] Fig.10 This is a schematic diagram of the third process of filtering blood flow in the embodiment of the present application;

[0075] Fig.11 This is a schematic diagram of a process for determining a target blood flow signal in an embodiment of the present application;

[0076] Fig.12 This is a schematic diagram of the fourth process of filtering blood flow in the embodiment of the present application;

[0077] Fig.13 This is a schematic diagram of a process for determining a target blood flow signal according to a time domain label in an embodiment of the present application;

[0078] Fig.14 This is a schematic diagram of a process for determining a target blood flow signal according to a target segmented signal in an embodiment of the present application;

[0079] Fig.15 This is a schematic diagram of filtering out tissue clutter signals according to frequency in an embodiment of the present application;

[0080] Fig.16 This is a schematic diagram of filtering out tissue clutter signals according to frequency based on frequency shift in an embodiment of the present application;

[0081] Fig.17 This is a schematic diagram of filtering out tissue clutter signals to obtain blood flow signals in an application scenario in an embodiment of the present application;

[0082] Fig.18 This is a schematic diagram of obtaining a target blood flow signal after filtering out noise in an application scenario in an embodiment of the present application;

[0083] Fig.19 Schematic diagram of the logical architecture of the device for filtering blood flow in an embodiment of the present application;

[0084] Fig. 20 Schematic diagram of the physical structure of the ultrasound device in the embodiment of the present application. DETAILED DESCRIPTION

[0085] In order to make the purpose, technical solution and advantages of the embodiments of the present application clearer, the technical solution of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the technical solution of the present application, rather than all of the embodiments. Based on the embodiments recorded in the application documents, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the technical solution of the present application.

[0086] The terms "first", "second", etc. in the specification and claims of the present invention and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the numbers used in this way can be interchanged where appropriate, so that the embodiments of the present invention described herein can be implemented using sequences other than those illustrated or described herein.

[0087] See also Figure 1 As shown, in the embodiment of the present application, the system includes at least one probe, a display unit and a processor. During use, the probe is used to collect original signals. It should be noted that the original signals here include blood flow signals and tissue clutter signals, and the probe sends the collected original signals to the processor, which filters out tissue clutter signals and the like in the original signals, and forms the final signal obtained after processing into an ultrasound display signal for display by the display unit.

[0088] Figure 1 FIG. 1 is a schematic diagram showing the structure of an ultrasound device 100 provided in an embodiment of the present application. The embodiment is described in detail below using the ultrasound device 100 as an example. It should be understood that: Figure 1 The ultrasound device 100 shown is only one example, and the ultrasound device 100 may have more Figure 1 The more or less components shown in the figure can be combined with two or more components, or can have different component configurations. The various components shown in the figure can be implemented in hardware, software, or a combination of hardware and software including one or more signal processing and / or application specific integrated circuits.

[0089] Figure 1 Schematically shows a hardware configuration block diagram of the ultrasound apparatus 100 according to an exemplary embodiment.

[0090] like Figure 1 As shown, the ultrasound device 100 may include, for example: a processor 110, a memory 120, a display unit 130 and a probe 140; wherein,

[0091] The probe 140 is configured to transmit a wide beam and receive echo signals fed back by each sampling gate;

[0092] A display unit 130 configured to display an ultrasound image;

[0093] The memory 120 is configured to store data required for ultrasound images, which may include software programs, application interface data, etc.;

[0094] The processor 110 is connected to the probe 140 and the display unit 130 respectively, and is configured to:

[0095] dividing a pre-selected blood flow imaging region into a plurality of sub-regions;

[0096] Transmitting ultrasonic waves to each of the sub-areas in sequence to obtain respective receiving lines of each of the sub-areas;

[0097] Execute for each receiving line respectively, divide the receiving line into multiple parts according to the position of the sub-sampling gate corresponding to the receiving line, and obtain the sub-receiving lines corresponding to each sub-sampling gate;

[0098] A four-dimensional model of the blood flow imaging area is constructed based on the receiving lines of each sub-area and the sub-receiving lines corresponding to each sub-sampling gate. The four-dimensional model is used to describe the correlation between time, blood flow velocity, imaging depth and receiving lines.

[0099] In some possible embodiments, when the processor executes dividing the pre-selected blood flow imaging area into a plurality of sub-areas, the processor is configured to:

[0100] Based on the number of receiving lines obtained after a single ultrasonic wave is emitted and the total number of receiving lines in the blood flow imaging area, the blood flow imaging area is divided to obtain the multiple sub-areas; wherein the number of the multiple sub-areas is inversely proportional to the total number of receiving lines, and is inversely proportional to the number of receiving lines obtained after a single ultrasonic wave is emitted.

[0101] In some possible embodiments, the processor is further configured to:

[0102] A time-sharing detection scanning method is adopted for different sub-areas, ultrasonic waves are sequentially sent to each sub-area in one scanning cycle, and the scanning cycles are repeated n times to obtain respective receiving lines of each sub-area in different scanning cycles; wherein n is a positive integer;

[0103] For the same sub-region, an interpolation processing method is used to obtain the receiving lines between two adjacent scanning cycles.

[0104] In some possible embodiments, adjacent sub-sampling gates in the plurality of sub-sampling gates of each receiving line have overlapping regions.

[0105] In some possible embodiments, for each receiving line, the number of sub-sampling gates of the receiving line is proportional to the total number of points in the sampling gates of the receiving line, inversely proportional to the number of points set in a single sub-sampling gate, and inversely proportional to the size of the overlapping area between the sub-sampling gates.

[0106] In some possible embodiments, the processor is further configured to:

[0107] receiving a viewing instruction for a four-dimensional model of a blood flow imaging region, wherein the four-dimensional model is used to describe: a correlation relationship among time, blood flow velocity, imaging depth, and receiving lines;

[0108] In response to the viewing instruction, imaging information of the viewing instruction object in the four-dimensional model is displayed.

[0109] In some possible embodiments, the viewing instruction is used to obtain data corresponding to the viewing instruction from the four-dimensional model to construct any one of the four dimensions of time, blood flow velocity, imaging depth, and receiving line representation in the four-dimensional model;

[0110] When the processor responds to the viewing instruction and displays imaging information of the viewing instruction object in the four-dimensional model, the processor is configured to:

[0111] From the four-dimensional model, information of other three dimensions corresponding to the selected dimension is obtained, and a three-dimensional model is constructed and displayed.

[0112] In some possible embodiments, the processor is further configured to:

[0113] Constructing the four-dimensional model includes:

[0114] Dividing the blood flow imaging area into a plurality of sub-areas;

[0115] Transmitting ultrasonic waves to each of the sub-areas in sequence to obtain respective receiving lines of each of the sub-areas;

[0116] Execute for each receiving line respectively, divide the receiving line into multiple parts according to the position of the sub-sampling gate corresponding to the receiving line, and obtain the sub-receiving lines corresponding to each sub-sampling gate;

[0117] A four-dimensional model of the blood flow imaging area is constructed based on the receiving lines of each sub-area and the sub-receiving lines corresponding to each sub-sampling gate.

[0118] In some possible embodiments, when the processor executes dividing the pre-selected blood flow imaging area into a plurality of sub-areas, the processor is configured to:

[0119] Based on the number of receiving lines obtained after a single ultrasonic wave is emitted and the total number of receiving lines in the blood flow imaging area, the blood flow imaging area is divided to obtain the multiple sub-areas; wherein the number of the multiple sub-areas is inversely proportional to the total number of receiving lines, and is inversely proportional to the number of receiving lines obtained after a single ultrasonic wave is emitted.

[0120] In some possible embodiments, the processor 110 is configured to receive a viewing instruction for a four-dimensional model of a blood flow imaging region, wherein the four-dimensional model is used to describe: a correlation relationship between time, blood flow velocity, imaging depth, and receiving lines;

[0121] In response to the viewing instruction, imaging information of the viewing instruction object in the four-dimensional model is displayed.

[0122] In some possible embodiments, the viewing instruction is used to obtain data corresponding to the viewing instruction from the four-dimensional model to construct any one of the four dimensions of time, blood flow velocity, imaging depth, and receiving line representation in the four-dimensional model;

[0123] When the processor 110 responds to the viewing instruction and displays the imaging information of the viewing instruction object in the four-dimensional model, the processor 110 is configured to:

[0124] From the four-dimensional model, information of other three dimensions corresponding to the selected dimension is obtained, and a three-dimensional model is constructed and displayed.

[0125] In some possible embodiments, the device is further configured to:

[0126] Constructing the four-dimensional model includes:

[0127] Dividing the blood flow imaging area into a plurality of sub-areas;

[0128] Transmitting ultrasonic waves to each of the sub-areas in sequence to obtain respective receiving lines of each of the sub-areas;

[0129] Execute for each receiving line respectively, divide the receiving line into multiple parts according to the position of the sub-sampling gate corresponding to the receiving line, and obtain the sub-receiving lines corresponding to each sub-sampling gate;

[0130] A four-dimensional model of the blood flow imaging area is constructed based on the receiving lines of each sub-area and the sub-receiving lines corresponding to each sub-sampling gate.

[0131] Figure 2 Schematic diagram of the application principle according to an embodiment of the present application. Figure 1The partial modules or functional components of the ultrasonic device shown are implemented, and only the main components will be described below, while other components, such as memory, controller, control circuit, etc., will not be described here in detail.

[0132] like Figure 2 As shown, the application environment may include a user interface 210 provided via an input and output unit to be operated by a user, a display unit 220 for displaying the user interface, and a processor 230.

[0133] The display unit 220 may include a display panel 221 and a backlight assembly 222. The display panel 221 is configured to display an ultrasound image, the backlight assembly 222 is located behind the display panel 221, and the backlight assembly 222 may include a plurality of backlight partitions (not shown in the figure), each of which may emit light to illuminate the display panel 221.

[0134] The processor 230 may be configured to control the brightness of the backlight source of each backlight partition in the backlight assembly 222 , and control the probe to transmit a wide beam and receive an echo signal.

[0135] The processor 230 may include a focusing processing unit 231, a beamforming unit 232, and a spectrum generating unit 233. The focusing processing unit 231 may be configured to perform focusing processing on each sampling gate one by one, and the focusing processing includes: taking the sampling gate as the focusing position of the wide beam, transmitting the wide beam to the target detection area according to the transmission coefficient of the sampling gate; and receiving the echo signal fed back by each sampling gate. The beamforming unit 232 is configured to perform beamforming on the echo signals fed back by the same sampling gate after completing a round of focusing processing for all sampling gates in the target detection area, and obtain scanning information. The spectrum generating unit 233 is configured to perform Doppler imaging based on the scanning information of each sampling gate.

[0136] In view of the prior art, the patient's signal collected by the probe includes the patient's blood flow signal of the collected part, and will inevitably carry the tissue clutter signal of the above-mentioned collected part. Research and practice have shown that the blood flow signal in the human body moves at a uniform speed, but the tissue moves in real time with the human body's active conditions (for example, crying, laughing, anger, etc.), and the corresponding tissue clutter signal is non-uniform motion. In order to eliminate the influence of tissue clutter signals on blood flow signals, at present, the tissue clutter signals in the blood flow signals are usually uniformly filtered out, that is, the tissue clutter signals are treated as a uniformly moving signal, which will result in the inability to completely filter out the tissue clutter signals, thereby affecting the accuracy of the blood flow signals.

[0137] The preferred implementation modes of the present application are described in detail below with reference to the accompanying drawings.

[0138] In the embodiment of the present application, the method for filtering the blood flow is mainly implemented on the ultrasound device side, which is described in detail below.

[0139] See also Figure 3 As shown, in the embodiment of the present application, the specific process of filtering the blood flow is as follows:

[0140] Step 301: segmenting the original signal based on the movement speed of the tissue to obtain a plurality of original segmented signals, wherein the tissue corresponding to the tissue clutter signal in the original segmented signal is in uniform movement.

[0141] First of all, it should be noted that Figure 4 As shown, the blood flow signal and tissue clutter signal in a certain collection site in the human body are generated in the center of the collection site, that is, the blood flow signal and tissue clutter signal are wrapped by the biological tissue of the above collection site. In addition, the blood flow signal and the tissue clutter signal correspond to each other in the time domain, but the difference is that the blood flow signal moves at a uniform speed throughout the human body and is not affected by the collection site, while the tissue clutter signal moves at a non-uniform speed, that is, the tissue clutter signal is affected by the activity of the collection site. In the specific implementation process, the blood flow signal and the tissue clutter signal are collectively referred to as the original signal, that is, the original signal is what the ultrasonic device collects.

[0142] The following is a detailed introduction on how the original signal is collected. Figure 5 As shown, the ultrasound device includes a probe, a processor and a display unit, wherein the probe is a collection device. Usually, during the implementation process, a chemical agent is applied to the probe in advance, and then the probe contacts the patient's collected part, i.e., a certain tissue, so as to collect the original signal corresponding to the tissue. The original signal here includes both blood flow signals and tissue clutter signals. Considering that the above-mentioned collection process will last for a period of time, and the speed of the tissue clutter signal will vary with the activity of the collected part.

[0143] Based on the above situation, in the embodiment of the present application, the original signal is processed by segmented processing. The basis of segmentation is the movement speed of the tissue, that is, considering that the tissue corresponding to the tissue clutter signal moves at a uniform speed in a small time period, the corresponding collected tissue clutter signal is also at a uniform speed. The process of segmented processing of the original signal is specifically introduced below.

[0144] The original signal is segmented based on the movement speed of the tissue to obtain multiple original segmented signals, see Figure 6 As shown, including:

[0145] Step 3011: Determine the blood flow velocity of the blood flow signal and the tissue velocity of the tissue clutter signal in the original signal.

[0146] Since the blood flow signal and the tissue clutter signal correspond in the time domain, that is, the original signal collected at the same time includes both the blood flow signal and the tissue clutter signal, and the blood flow velocity of the blood flow signal in the original signal is constant, therefore, in the segmentation process, the blood flow velocity of the blood flow signal in the original signal and the tissue velocity of the tissue clutter signal are first determined respectively, and the tissue velocity of the tissue clutter signal is measured with the blood flow velocity of the blood flow signal as a reference.

[0147] Specifically, the blood flow velocity of the blood flow signal can be determined in the following manner: obtain the sum of the paths through which the blood flow signal flows, and call the sum of the paths through which the blood flow signal flows as the blood flow length; and obtain the end time of occurrence corresponding to the blood flow signal and the start time of occurrence corresponding to the blood flow signal, and calculate the time difference between the end time of occurrence and the start time of occurrence, and use the blood flow length and duration of the blood flow signal as the quotient to obtain the blood flow velocity of the blood flow signal.

[0148] Accordingly, the tissue velocity of the tissue clutter signal can be determined in the following manner: pre-dividing the original signal into a plurality of preset original segmented signals of equal length, where the equal length here refers to the path length of the tissue clutter signal in each preset original segmented signal, and obtaining the time length corresponding to each preset original segmented signal, which is called the preset segment duration, and determining the tissue velocity of the tissue clutter signal in the preset original segmented signal based on the tissue length and the preset segment duration of the tissue clutter signal in the same preset original segmented signal. Through the above processing, the tissue velocities of a plurality of tissue clutter signals with different velocity values ​​can be obtained.

[0149] Step 3012: The signal segment in which the difference between the tissue velocity and the blood flow velocity in the original signal is less than the preset difference is taken as the original segmented signal.

[0150] After obtaining the blood flow velocity of a blood flow signal and the tissue velocities of multiple tissue clutter signals with different velocity values, the following operations are performed for the tissue velocity of each tissue clutter signal: the difference between the tissue velocity and the blood flow velocity is calculated, and the difference is further compared to see whether it is less than a preset difference, and a preset original segmented signal in the original signal whose difference is less than the preset difference is used as the original segmented signal, where the preset difference is determined based on historical experience data.

[0151] It should be noted that if the above difference is not less than the preset difference, the preset original segmented signal will be further divided, so that the path length of the preset original segmented signal obtained is shorter, and correspondingly, the preset segment duration corresponding to the preset original segmented signal is shorter, and the tissue velocity of the tissue clutter signal determined based on the path length and the preset segment duration is more accurate. On this basis, continue to compare whether the difference between the tissue velocity and the blood flow velocity is less than the preset difference.

[0152] The above processing is repeated until the differences between all tissue velocities and blood flow velocities in the collected original signals are less than the preset difference, thereby obtaining multiple original segmented signals, and the tissue clutter signal in each original segmented signal moves at a uniform speed.

[0153] See also Figure 7 As shown, in the embodiment of the present application, after executing step 3011: determining the blood flow velocity of the blood flow signal and the tissue velocity of the tissue clutter signal in the original signal and step 3012: taking the signal segment in which the difference between the tissue velocity and the blood flow velocity in the original signal is less than the preset difference as the original segmented signal, the following step 302 can be continued.

[0154] Step 302: Filter out tissue clutter signals in each original segmented signal to obtain a target signal.

[0155] After obtaining each original segmented signal, the tissue clutter signal with the same tissue velocity included in each segmented original signal can be filtered out. Further, each original segmented signal after filtering out the tissue clutter signal is combined to obtain the target signal.

[0156] The following is a detailed introduction to filtering out the tissue clutter signals in each original segmented signal to obtain the target signal. Figure 8 As shown, including:

[0157] Step 3021: Convert each original segmented signal into the frequency domain to obtain a plurality of frequency domain segmented signals.

[0158] Since the original signal is a continuous signal in the time domain, correspondingly, each original segmented signal obtained after segmentation processing of the original signal is also a continuous signal in the time domain, and the phases of each original segmented signal in the time domain are consistent.

[0159] In order to facilitate the processing of each original segmented signal, in the embodiment of the present application, each original segmented signal is converted to the frequency domain to obtain multiple frequency domain segmented signals. It should be supplemented that the specific method of converting the above-mentioned original segmented signals from the time domain to the frequency domain is not specifically limited, for example, Fourier transform, fast Fourier transform, etc. can be used.

[0160] Step 3022: Spectrum shifting is performed on each frequency domain segment signal according to the distance corresponding to the phase in the frequency domain, and tissue clutter signals are filtered out.

[0161] During the implementation process, after converting each original segmented signal from the time domain to the frequency domain, the blood flow signal and the tissue clutter signal may overlap in the frequency domain, or the blood flow signal and the tissue clutter signal may be close to each other in the frequency domain. Obviously, in the above-mentioned cases, the tissue clutter signal cannot be extracted from the original segmented signal, and it is even more impossible to effectively filter it out.

[0162] Based on this, it is necessary to perform spectrum shifting of the corresponding distance of each frequency domain segmented signal in the frequency domain according to the distance corresponding to the above phase in the frequency domain. It should be noted that in the above spectrum shifting process, only the distance between the blood flow signal and the tissue clutter signal in the frequency domain is changed. In this way, the boundary between the blood flow signal and the tissue clutter signal after the spectrum shift is clear, and the tissue clutter signal can be filtered out by using a filter.

[0163] It should be further explained that, in the embodiment of the present application, the above-mentioned tissue clutter signal is filtered out by a wall filter. Specifically, the filtering frequency of the wall filter is set between the frequencies corresponding to the blood flow signal and the tissue clutter signal, that is, only the frequency corresponding to the blood flow signal is retained by the filtering frequency of the wall filter. It should be supplemented that, since the tissue clutter signal in each original segmented signal moves at a uniform speed, the frequency corresponding to it in the frequency domain is also the same frequency value, and therefore, the wall filter can be used for uniform filtering.

[0164] Common wall filters include FIR filters, IIR filters and regression filters. The following is a brief introduction to FIR filters, IIR filters and regression filters:

[0165] FIR (Finite Impulse Response, FIR) filter, also known as non-recursive filter, is a finite-length unit impulse response filter that can have strict linear phase-frequency characteristics while ensuring arbitrary amplitude-frequency characteristics. At the same time, its unit sampling response is finite length. Therefore, FIR filter is a stable system and is widely used in communications, image processing, pattern recognition and other fields.

[0166] IIR (Infinite Impulse Response, IIR) filter is a digital filter. IIR filter adopts recursive structure (that is, the structure has feedback loop). The operation structure of IIR filter usually consists of basic operations such as delay, product and addition. Common IIR filters include direct type, quasi type, cascade type and parallel type. And each structure has feedback loop.

[0167] Common regression filters include Kalman filters, Bayesian filters, and Hamiltonian filters. They take up little memory, run fast, and can estimate the state of a dynamic system from the combined information of many uncertain situations during the filtering process. They are powerful and versatile filtering tools.

[0168] In the embodiment of the present application, one of the above-mentioned FIR filter, IIR filter, regression filter, etc. can be selected to filter out tissue clutter signals in each frequency domain segmented signal.

[0169] Step 3023: After converting the filtered frequency domain segmented signals into the time domain, the target signal is formed according to the continuity of the time domain.

[0170] During the implementation process, in order to obtain a complete blood flow signal in the time domain, after filtering out the tissue clutter signal in each frequency domain segmented signal in the frequency domain, each filtered frequency domain segmented signal is converted from the frequency domain to the time domain, and the time domain continuity between each signal converted to the time domain is determined. According to the above-mentioned time domain continuity, the signals converted to the time domain corresponding to each frequency domain segmented signal are combined to obtain the target signal.

[0171] Step 303: Segment the target signal based on a preset sliding window to obtain a plurality of target segment signals.

[0172] Since the original signal collected by the ultrasonic device will inevitably contain noise signals, and the presence of noise signals will also affect the accuracy of the blood flow signal, therefore, after filtering out the tissue clutter signal, the noise signal also needs to be filtered out.

[0173] Correspondingly, the noise signal also changes in the time-domain continuous target signal, that is, the noise signal is large and small. In this case, if a unified method is used to filter the noise signal, the noise signal will not be completely filtered or over-filtered. Based on this, in the embodiment of the present application, the target signal obtained above is segmented again to filter out the noise signal in each target segmented signal one by one.

[0174] The following describes how to segment the target signal based on a preset sliding window to obtain multiple target segment signals. Fig. 9 As shown, specifically including:

[0175] Step 3031: Use a preset sliding window to intercept the target signal in sequence to obtain multiple starting points and ending points.

[0176] The sliding window is essentially a flow control technology, which allows the sender to transmit additional packets before receiving any response. The receiver tells the sender how many packets can be sent at a certain time, which is called the window size. The window size of the sliding window in the embodiment of the present application is usually a known fixed value. Starting from the starting point of the target signal, the sliding window is used to intercept to obtain the first starting point and the first ending point. The sliding window is used to intercept with the first ending point as the new starting point to obtain the second starting point and the second ending point, and so on, until the last time the sliding window is used to intercept the target signal (including the end point of the target signal), and multiple starting points and ending points are obtained through the above interception process.

[0177] Step 3032: Divide the target signal between the start point and the end point of the same sliding window into the same target segmented signal.

[0178] In the embodiment of the present application, in each interception process, that is, after the target signal is divided by the same sliding window to obtain the corresponding starting point and ending point, the target signal between the starting point and the ending point is divided into one target signal.

[0179] It should be noted that the above steps are repeated in each interception process to divide the target signal into a plurality of target segmented signals, where the number of target segmented signals is equal to the number of interceptions performed using the sliding window.

[0180] See also Fig.10 As shown, after executing step 301 to segment the original signal based on the movement speed of the tissue to obtain multiple original segmented signals and step 302 to filter out the tissue clutter signals in each original segmented signal to obtain the target signal, step 3031 can be directly executed to intercept the target signal in sequence using a preset sliding window to obtain multiple starting points and ending points and step 3032 can be executed to divide the target signal between the starting point and the ending point belonging to the same sliding window into the same target segmented signal. After executing steps 3031 and 3032, continue to execute the following step 304.

[0181] Step 304: See Fig.11 and Fig.12 As shown, step 304 specifically includes the following steps:

[0182] Step 3041: Compare the parameters of the target segmented signal with the corresponding parameter thresholds.

[0183] Since the collected blood flow signal is in real-time motion, its speed, energy and other parameters during motion can reflect the effectiveness of the corresponding target segmented signal from the side. Therefore, in the implementation process, for each of the above target segmented signals, the parameters of the target segmented signal are extracted, and the extracted parameters are compared with the corresponding parameter thresholds, where the corresponding parameter thresholds are the maximum values ​​of the corresponding speed and energy under the maximum amount of noise that the target segmented signal can contain.

[0184] Step 3042: Select a target segment signal based on the comparison result.

[0185] Since the target signal is divided into several target segmented signals, during the implementation process, if a target segmented signal does not meet the corresponding parameter threshold, it means that the target segmented signal contains too many noise signals. In this case, the target segmented signal is deleted. That is, during the implementation process, only the target segmented signal that meets the parameter threshold is retained, and a new target signal, i.e., a target blood flow signal, is formed by all target segmented signals that meet the parameter threshold, thereby eliminating the influence of noise on blood flow.

[0186] In the process of selecting the target segmented signal based on the comparison result, a target segmented signal whose comparison result satisfies part or all of the following conditions is selected from all the target segmented signals:

[0187] The speed parameter value is less than the speed threshold;

[0188] The speed variance is greater than the variance threshold;

[0189] The energy parameter value is less than the energy threshold.

[0190] During implementation, the number of selection conditions for the target segmented signal can be flexibly set according to actual scenarios.

[0191] Preferably, the target segmented signal is selected as a part of the target blood flow signal when the velocity parameter value of the target segmented signal is less than the velocity threshold, the velocity variance is greater than the variance threshold, and the energy parameter value is less than the energy threshold are all satisfied.

[0192] It should be supplemented that the speed threshold, variance threshold and energy threshold here can be determined based on empirical data or historical data of the target signal.

[0193] Step 3043: Combining the selected target segmented signals into a target blood flow signal.

[0194] In the embodiment of the present application, considering that after the target signal is divided into multiple target segmented signals by a preset sliding window, each target segmented signal is a discrete signal, especially after a target segmented signal that does not meet the parameter threshold is eliminated, the selected target segmented signals can be combined into a target blood flow signal by referring to the following steps.

[0195] Specifically, the selected target segmented signals are combined into target blood flow signals, see Fig.13 As shown, including:

[0196] Step 30431: Extract time domain labels from the selected target segmented signal.

[0197] Considering the continuity of blood flow in the time domain, each target segment signal will also carry time domain information. Therefore, during the implementation process, a time domain label is extracted from each target segment signal selected above. The time domain label here represents the time information of the corresponding target segment signal.

[0198] Step 30432: compose the target blood flow signal from the target segmented signals according to the order of the time domain labels.

[0199] After obtaining the time domain labels of the target segmented signals, the corresponding target segmented signals are sorted according to the sequence of time information represented by the time domain labels, so that the sorted target segmented signals can constitute the target blood flow signal.

[0200] See also Fig.14 As shown, after executing step 3041 to compare the parameters of the target segmented signal with the corresponding parameter threshold and step 3042 to select the target segmented signal based on the comparison result, step 30431 is executed to extract the time domain label from the selected target segmented signal and step 30432 is executed to form a target blood flow signal according to the sequence of the time domain labels.

[0201] The following is a commonly used method for filtering out tissue clutter signals - DownMix method, see Fig.15 and Fig.16 As shown in Figure 2, after converting an original signal including blood flow signal and tissue clutter signal into frequency domain, Fig.15 It can be seen that the blood flow signal (i.e. Fig.15 blood flow in the Fig.15 The distance between the tissues in the gyrosphere is relatively close, and it is difficult to filter out the tissue clutter signal directly using the wall filter. After DownMix frequency shifting of the tissue clutter signal using the DownMix method, Fig.16 It can be seen that the blood flow signal (i.e. Fig.16 blood flow in the Fig.16 The distance between the tissues in the image is far, and the use of wall filters can easily filter out tissue clutter signals.

[0202] The following introduces a specific method for filtering out tissue clutter signals and noise signals in the original signal based on the DownMix method.

[0203] First, for filtering out tissue clutter in the original signal, refer to Fig.17As shown, the signal sequence IQ(t) before filtering represents the original signal. After the signal sequence IQ(t) before filtering is segmented based on the movement speed of the tissue, IQ subsequence 1, IQ subsequence 2...IQ subsequence N are obtained. The movement speed of the tissue corresponding to the tissue clutter signal in each subsequence is the same, that is, the corresponding tissue movement is uniform, and the movement speed of the tissue corresponding to the tissue clutter signal in different subsequences is different. In order to use the DownMix method to perform frequency shifting, that is, to determine the distance of each frequency shift, it is necessary to determine the phase of each subsequence. After determining the phase value, the DownMix method is used to perform signal frequency shifting on each subsequence in the frequency domain. After that, the frequency-shifted signal is filtered using a wall filter, that is, the tissue clutter signal is filtered out, and the signal is correspondingly inversely shifted to obtain the filtered signal sequence IQ′(t) based on the signal in each time domain.

[0204] Secondly, for filtering out noise signals in the original signal (or the original signal after filtering out tissue clutter signals), refer to Fig.18 As shown, the signal sequence IQ(t) before filtering, which represents the original signal, is segmented and processed. The segmentation processing here is realized by a preset sliding window. After segmentation, IQ′ subsequence 1, IQ′ subsequence 2, ..., IQ′ subsequence N are obtained. Then, each subsequence is autocorrelated and calculated to obtain a speed parameter V, a speed variance T, and an energy parameter P. That is, speed parameter V1, speed variance T1, and energy parameter P1 are calculated for IQ′ subsequence 1, and speed parameter V2, speed variance T2, and energy parameter P1 are calculated for IQ′ subsequence 2. and energy parameter P2, ... IQ' subsequence N, the speed parameter VN, speed variance TN and energy parameter PN are calculated, and the accuracy of each subsequence is judged: compare whether the corresponding speed parameter value is less than the speed threshold, whether the speed variance is greater than the variance threshold, and whether the energy parameter value is less than the energy threshold. On this basis, remove the subsequences that do not meet the above accuracy judgment conditions, perform autocorrelation calculation on the remaining subsequences and take the average to obtain the speed parameter V, speed variance T and energy parameter P that are not interfered by the noise signal. And, the subsequences that are still retained are composed of the target blood flow signal according to the order of the time domain labels.

[0205] Based on the same inventive concept, refer to Fig.19 As shown, an embodiment of the present application provides a device for filtering blood flow, comprising:

[0206] The segmentation unit 1901 is used to perform segmentation processing on the original signal based on the movement speed of the tissue to obtain a plurality of original segmented signals, wherein the tissue corresponding to the tissue clutter signal in the original segmented signal is in uniform movement;

[0207] A filtering unit 1902 is used to filter out tissue clutter signals in each original segmented signal to obtain a target signal;

[0208] A re-segmentation unit 1903 is used to perform segmentation processing on the target signal based on a preset sliding window to obtain multiple target segmented signals;

[0209] The comparison unit 1904 is used to compare the parameters of the target segmented signal with the corresponding parameter threshold, and select the target segmented signal based on the comparison result, and form the selected target segmented signal into a target blood flow signal.

[0210] Based on the same inventive concept, refer to Fig. 20 As shown, an embodiment of the present application provides an ultrasonic device, comprising:

[0211] The probe 2001 is configured to transmit a wide beam and receive a raw signal;

[0212] The processor 2002 is connected to the probe and the display unit respectively, and is configured to execute:

[0213] The original signal is segmented based on the movement speed of the tissue to obtain a plurality of original segmented signals, wherein the tissue corresponding to the tissue clutter signal in the original segmented signal is in uniform movement;

[0214] The target signal is obtained after filtering out tissue clutter signals in each original segmented signal;

[0215] Segmenting the target signal based on a preset sliding window to obtain a plurality of target segmented signals;

[0216] Comparing the parameters of the target segmented signal with the corresponding parameter thresholds, selecting the target segmented signal based on the comparison result, and forming the selected target segmented signal into a target blood flow signal;

[0217] The display unit 2003 is configured to display the ultrasound image corresponding to the target blood flow signal.

[0218] In some possible embodiments, the processor 2002 performs segmentation processing on the original signal based on the movement speed of the tissue to obtain a plurality of original segmented signals, which are configured as follows:

[0219] determining the blood flow velocity of the blood flow signal and the tissue velocity of the tissue clutter signal in the original signal;

[0220] The signal segment in which the difference between the tissue velocity and the blood flow velocity in the original signal is less than the preset difference is taken as the original segmented signal.

[0221] In some possible embodiments, the processor 2002 obtains the target signal after filtering out tissue clutter signals in each original segmented signal, and is configured as follows:

[0222] Convert each original segmented signal into the frequency domain to obtain a plurality of frequency domain segmented signals;

[0223] The spectrum of each frequency domain segmented signal is shifted according to the distance corresponding to the phase in the frequency domain, and the tissue clutter signal is filtered out; after the filtered frequency domain segmented signals are converted to the time domain, the target signal is composed according to the continuity of the time domain.

[0224] In some possible embodiments, the processor 2002 performs segmentation processing on the target signal based on a preset sliding window to obtain a plurality of target segmented signals, and is configured as follows:

[0225] The target signal is intercepted in sequence using a preset sliding window to obtain multiple starting points and ending points;

[0226] The target signals between the start point and the end point belonging to the same sliding window are divided into the same target segmented signal.

[0227] In some possible embodiments, the processor 2002 is configured to select a target segment signal based on the comparison result as follows:

[0228] From all target segmented signals, select a target segmented signal whose comparison result satisfies some or all of the following conditions: the speed parameter value is less than the speed threshold;

[0229] The speed variance is greater than the variance threshold;

[0230] The energy parameter value is less than the energy threshold.

[0231] In some possible embodiments, the processor 2002 executes the process of composing the selected target segmented signals into a target blood flow signal, and is configured to:

[0232] Extracting time domain labels from the selected target segmented signal;

[0233] The target segmented signals are combined into target blood flow signals according to the order of time domain labels.

[0234] In some possible embodiments, tissue clutter signals are filtered out by a wall filter.

[0235] Based on the same inventive concept, an embodiment of the present application provides a computer-readable storage medium. When instructions in the storage medium are executed by a processor, the processor is enabled to execute the method described in any one of the first aspects above.

[0236] In summary, in an embodiment of the present application, a method, device and storage medium for filtering blood flow are provided, the method comprising: performing segmentation processing on an original signal based on the movement speed of the tissue to obtain a plurality of original segmented signals, wherein the tissue corresponding to the tissue clutter signal in the original segmented signal moves at a uniform speed, filtering out the tissue clutter signal in each original segmented signal to obtain a target signal, performing segmentation processing on the target signal based on a preset sliding window to obtain a plurality of target segmented signals, comparing a parameter of the target segmented signal with a corresponding parameter threshold, and selecting a target segmented signal based on the comparison result, and forming a target blood flow signal with the selected target segmented signal, and the above-mentioned segmentation processing can effectively filter out tissue clutter signals and noise signals, thereby retaining effective blood flow signals, thereby improving the detection efficiency of blood flow signals.

[0237] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program product systems. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product system implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0238] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program product systems according to the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0239] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0240] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0241] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for filtering blood flow, It is characterized in that include: Determine the blood flow velocity of the blood flow signal and the tissue velocity of the tissue clutter signal in the original signal, and use the signal segment in which the difference between the tissue velocity and the blood flow velocity in the original signal is less than a preset difference as the original segmented signal to obtain a plurality of the original segmented signals, wherein the tissue corresponding to the tissue clutter signal in the original segmented signal moves at a uniform speed; and obtain the target signal after filtering out the tissue clutter signal in each original segmented signal; Segmenting the target signal based on a preset sliding window to obtain a plurality of target segmented signals; The parameters of the target segmented signal are compared with corresponding parameter thresholds, and the target segmented signal is selected based on the comparison result, and the selected target segmented signal is composed of a target blood flow signal.

2. The method according to claim 1, It is characterized in that The step of filtering out the tissue clutter signals in each original segmented signal to obtain a target signal comprises: Convert each original segmented signal into the frequency domain to obtain a plurality of frequency domain segmented signals; Spectrum shifting of each of the frequency domain segmented signals according to the distance corresponding to the phase in the frequency domain, and filtering out tissue clutter signals; After converting the filtered frequency domain segmented signals into the time domain, the target signal is composed according to the continuity of the time domain.

3. The method according to claim 1, It is characterized in that The target signal is segmented based on a preset sliding window to obtain a plurality of target segmented signals, including: Adopting a preset sliding window to intercept the target signal in sequence to obtain a plurality of starting points and ending points; The target signal between the start point and the end point belonging to the same sliding window is divided into the same target segmented signal.

4. The method according to claim 1, It is characterized in that The selecting the target segment signal based on the comparison result comprises: From all the target segmented signals, select the target segmented signal whose comparison result satisfies part or all of the following conditions; The speed parameter value is less than the speed threshold; The speed variance is greater than the variance threshold; The energy parameter value is less than the energy threshold.

5. The method according to claim 1, It is characterized in that The step of forming a target blood flow signal from the selected target segmented signal comprises: Extracting a time domain label from the selected target segment signal; The target segmented signals are combined into a target blood flow signal according to the sequence of the time domain labels.

6. The method according to claim 1, It is characterized in that The tissue clutter signal is filtered out by the wall filter.

7. A device for filtering blood flow, It is characterized in that include: a segmentation unit, for determining the blood flow velocity of the blood flow signal and the tissue velocity of the tissue clutter signal in the original signal, and taking a signal segment in which the difference between the tissue velocity and the blood flow velocity in the original signal is less than a preset difference as an original segmentation signal, so as to obtain a plurality of original segmentation signals, wherein the tissue corresponding to the tissue clutter signal in the original segmentation signal moves at a uniform speed; A filtering unit, used for filtering the tissue clutter signal in each original segmented signal to obtain a target signal; a re-segmentation unit, configured to perform segmentation processing on the target signal based on a preset sliding window to obtain a plurality of target segmented signals; A comparison unit is used to compare the parameters of the target segmented signal with the corresponding parameter threshold, and select the target segmented signal based on the comparison result, and form the selected target segmented signal into a target blood flow signal.

8. An ultrasonic device, It is characterized in that include: a probe configured to transmit a wide beam and receive a raw signal; a display unit configured to display an ultrasound image corresponding to a target blood flow signal; A processor is connected to the probe and the display unit respectively, and is configured to execute the method according to any one of claims 1 to 6.

9. A computer-readable storage medium, It is characterized in that When the instructions in the storage medium are executed by a processor, the processor is enabled to execute the method according to any one of claims 1 to 6.

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