A method and device for detecting low-speed blood flow Doppler signals through a wall filter

Abstract

The present invention discloses a method and a device for detecting low-speed blood flow Doppler signals through a wall filter. The device includes two wall filters, two autocorrelation processing modules, two blood flow detection modules, a low-speed blood flow detection module, and a scan conversion module. The two wall filters adopt different numbers of filtering stages, and the two wall filters perform wall filtering on the detection signals of the Doppler ultrasound device. The method and device of the present invention can automatically adjust the number of wall filter stages according to the proportion of newly added effective blood flow points, improving the accuracy of detecting low-speed blood flow, such as venous blood flow. It is particularly meaningful when the operator of the Doppler ultrasound device lacks experience, and can help the operator obtain accurate blood flow images and effectively avoid misdiagnosis.

Description

Technical Field

[0001] The present invention relates to the field of ultrasonic medical detection, and particularly relates to a method and device for detecting low-speed blood flow Doppler signals through a wall filter. Background Art

[0002] Ultrasonic examination is one of the most commonly used methods in medical imaging examinations. It has the advantages of convenience, non-invasiveness, high cost performance, etc., and has always been relied on by clinicians. The maturity of the blood flow detection technology using the Doppler principle further improves the application scope and importance of ultrasonic diagnosis. Color Doppler ultrasound generally uses autocorrelation technology to process Doppler signals, and the obtained blood flow signals are superimposed on the two-dimensional image in real time after color coding, that is, a color Doppler ultrasound blood flow image is formed. Therefore, it not only has the advantages of two-dimensional ultrasonic structure images, but also provides rich information on hemodynamics, so it has received extensive attention and welcome in medical clinical diagnosis.

[0003] Color Doppler ultrasound imaging uses the Doppler principle to detect the human blood flow within the color sampling frame, calculate its magnitude and direction, and then superimpose it with the black-and-white image. After undergoing scan conversion, the blood flow image is output on the display screen. First, the echo signal is quadrature demodulated to form ultrasonic blood flow data (IQ_Data), and then it is sent to the B-mode processing module to obtain a grayscale image and to the C-mode processing module to obtain a color image. In the C-mode module, the signal is first sent to a wall filter to filter out non-blood flow signals. When performing Doppler ultrasound examination, the signals received by the probe not only come from red blood cells but also contain reflected Doppler signals generated by the movement of blood vessel walls and surrounding tissues. The characteristics of this kind of signal are low frequency but higher echo intensity than blood flow signals, which will interfere with blood flow detection. The function of the wall filter is to filter out these reflected Doppler signals and only allow blood flow signals with low echo intensity but high frequency to enter the signal processor. According to the degree of signal filtering, the instrument is equipped with different levels of filtering. Higher-level filtering will not only filter out non-blood flow signals but also filter out low-speed blood flow signals. The selection of filtering should depend on the examination object. In the abdominal area, unless it is to detect large blood vessels with high blood flow velocity such as the abdominal aorta, the filtering is generally adjusted to a relatively small level, especially when detecting venous blood flow. The smaller the filtering level, the more fully the low-speed blood flow is displayed. After the wall filtering is completed, autocorrelation processing will be performed. Autocorrelation processing is to estimate the average blood flow velocity (v) and blood flow energy (P) using the data after wall filtering at each spatial position of the ultrasonic scan. After obtaining these two key ultrasonic blood flow-related parameters, the data will be sent to the blood flow detection module, which will use the blood flow-related parameters and the envelope data of the B-mode image to distinguish whether the position is blood flow or tissue. The reference information provided by the black-and-white image in the B-mode is brightness (E). This module will determine which points are blood flow points, which points are tissue points or blood vessel walls. The velocity signal after blood flow detection will undergo scan conversion processing to convert the ultrasonic blood flow signal in the rectangular coordinate system into a signal in the polar coordinate system for display. The final display module will display the color and black-and-white signals overlapped in one frame of the image. If it is blood flow, the color signal will be displayed according to the blood flow velocity, and if it is tissue, the black-and-white signal will be displayed according to the B-mode brightness. Thus, the final two-dimensional color ultrasound image is obtained.

[0004] Most of the existing ultrasound devices are not intelligent. The default parameters pre-set by manufacturers are only suitable for some normal situations. Most of the time, we still need to adjust the parameters to obtain better color images and spectral patterns, and avoid misdiagnosis caused by false images due to improper parameter settings. If the operator lacks experience, such inaccurate images are very likely to occur. Among all the C-mode data processing steps, the wall filter is the first and very crucial step. The adjustment of the wall filter level is closely related to the operator's experience. If we are measuring low-speed blood flow, such as venous blood flow, a lower wall filter level should be set to avoid the loss of blood flow signals and information, which may lead to incorrect diagnoses, such as misdiagnosing a thrombus.

[0005] Now, with the popularization of portable medical devices, Doppler ultrasound devices are also being used in many primary care clinics or emergency situations. In many cases, the users are no longer experienced ultrasound doctors, but ordinary medical workers or even non-medical personnel. Considering their lack of experience, inaccurate results may occur during ultrasound diagnosis. Therefore, if an ultrasound device can automatically adjust the wall filter to improve the detection accuracy of low-speed blood flow, it will have great clinical significance. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides a method and device for detecting low-speed blood flow Doppler signals through a wall filter, which can automatically adjust the wall filter according to image data, thereby improving the detection of low-speed blood flow and solving the problems of missing blood flow information or tissue signals being misinterpreted as blood flow in current low-speed blood flow detection.

[0007] To achieve the above object, the technical solution of the present invention is as follows: A device for detecting low-speed blood flow Doppler signals through a wall filter includes two wall filters, two autocorrelation processing modules, two blood flow detection modules, a low-speed blood flow detection module, and a scan conversion module; the two wall filters use different filter levels, and the two wall filters perform wall filtering on the detection signals of the Doppler ultrasound device; each wall filter is electrically connected to an autocorrelation processing module and a blood flow detection module in sequence; the blood flow detection module connected to the wall filter with a high filter level is connected to the scan conversion module; the blood flow detection module connected to the wall filter with a low filter level is connected to the low-speed blood flow detection module; the low-speed blood flow detection module is connected to the scan conversion module; the two blood flow detection modules are connected to each other.

[0008] As a preferred solution of the above device, it further includes a display module; the display module is connected to the scan conversion module and is used to display the information processed by the scan conversion module through an image.

[0009] A method for detecting low - velocity blood flow Doppler signals through a wall filter, using the aforementioned device for detecting low - velocity blood flow Doppler signals through a wall filter, includes the following steps:

[0010] Step 1, through one of the wall filters, according to different measurement targets, set the initial stage number of the wall filter, perform wall filtering on the detection signals of the Doppler ultrasound device; after the signals after the wall filter are processed by one of the autocorrelation processing modules, speed and power information are obtained, and a blood flow detection module combines the brightness information E of B - mode ultrasound to determine which points are blood flow points, and transmits the relevant blood flow information to another blood flow detection module;

[0011] Step 2, through another wall filter, set a smaller filtering stage number for wall filtering. The signals after wall filtering will be transmitted to another autocorrelation processing module together with the signals before wall filtering to obtain speed and power information and the energy ratio of the signals after and before wall filtering; then the information is transmitted to another blood flow detection module, and combined with the brightness information E of B - mode ultrasound, to determine which points are blood flow points;

[0012] Step 3, compare the blood flow points given in Step 1 with the blood flow points given in Step 2, select the set of newly added blood flow points in Step 2, and transmit them together with other information to the low - velocity blood flow detection module; after being checked by the low - velocity blood flow detection module, newly added effective blood flow points are identified.

[0013] As a preferred solution of the above - mentioned method, it further includes Step 4. Compare the number of newly added effective blood flow points in Step 3 with the number of blood flow points of high - frequency filtering in Step 1 to determine whether it is necessary to downgrade the wall filter; if the ratio C obtained by dividing the number of newly added effective blood flow points in Step 3 by the number of blood flow points of high - frequency filtering in Step 1 is lower than a certain threshold C0, it is considered that the effect of improvement and adjustment has been achieved, and there is no need to continue adjusting the wall filter stage number; only need to merge and convert the display with the information of the blood flow detection module in Step 1; if C still exceeds the threshold C0, it is determined that the wall filter stage number is not low enough and needs to be further reduced; save the obtained blood flow information, then update the stage number of the wall filter in Step 1 to the stage number of the wall filter in Step 2, change the stage number of the wall filter in Step 2 to a lower stage number, and repeat the process again until the proportion of newly added effective blood flow points is lower than the threshold.

[0014] As a preferred solution of the above - mentioned method, in Step 3, when the low - velocity blood flow detection module checks for newly added effective blood flow points, a new brightness threshold is set to eliminate tissue points; for the brightness threshold E0 used to detect blood flow in Step 1 and Step 2, use all the blood flow points given in Step 2 as the base number to calculate the average brightness value and standard deviation; the calculation formula is:

[0015]

[0016] Then, compare point by point. Now, the new threshold E new = E A -σ is a value lower than the original E0. If E < E new , it is considered an effective blood flow point after this round of investigation; otherwise, it is determined as a non-blood flow point.

[0017] As a preferred solution of the above method, in step 3, the low-speed blood flow detection module is used to investigate newly added effective blood flow points, and the energy ratio is used to continue the investigation; the energy ratio R of the signals after and before wall filtering in step 2 = P2 / P 2original , where P2 is the autocorrelation processing of the signal after wall filtering in step 2, and P 2original is the autocorrelation processing of the signal before wall filtering in step 2. A threshold R0 is preset. If R > R0 is satisfied, then it is considered an effective blood flow point; otherwise, it is determined as a non-blood flow point.

[0018] As a preferred solution of the above method, in step 3, the low-speed blood flow detection module is used to investigate newly added effective blood flow points, and isolated points need to be excluded; a 5x5 = 25-point area is used as the determination unit, the middle point is the target point, and there are 24 adjacent points around it, including 4 nearest neighbor points; the area has 5 rows and 5 columns, including this row and this column, 2 nearest neighbor rows and columns, and 2 next-nearest neighbor rows and columns: non-isolated points need to satisfy the following conditions simultaneously:

[0019] a. There are at least two blood flow points in at least two of the 4 nearest neighbor positions;

[0020] b. There is at least 1 column with blood flow points in at least two of the 2 nearest neighbor columns and the continuous columns are greater than or equal to 3;

[0021] c. There is at least 1 row with blood flow points in at least two of the 2 nearest neighbor rows and the continuous rows are greater than or equal to 3;

[0022] d. There are at least 9 adjacent blood flow points among the 24 area positions.

[0023] Through the above technical solutions, the beneficial effects of the technical solutions of the present invention are as follows: The method and device of the present invention can automatically adjust the number of wall filter stages according to the proportion of newly added effective blood flow points, improve the accuracy of detecting low-speed blood flow, such as venous blood flow, which is especially meaningful when the operator of the Doppler ultrasound device lacks experience, and can help the operator obtain accurate blood flow images and effectively avoid misdiagnosis. Description of the Drawings

[0024] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following will briefly introduce the accompanying drawings required for the description of the embodiments or the prior art. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For those of ordinary skill in the art, without creative efforts, other accompanying drawings can be obtained based on these drawings.

[0025] Figure 1 It is a flowchart of the blood flow detection method of Doppler ultrasound described in the embodiments of the present invention.

[0026] Figure 2 It is the ultrasonic power spectrum diagram of wall filters with different orders described in the embodiments of the present invention.

[0027] Figure 3 It is a schematic diagram of the change in the blood flow point set caused by wall filter 2 relative to wall filter 1 in the embodiments of the present invention.

[0028] Figure 4 It is the overall block diagram of the process described in the embodiments of the present invention.

[0029] Figure 5 It is the algorithm flowchart for adjusting the brightness threshold to detect non-blood flow points described in the embodiments of the present invention.

[0030] Figure 6 It is the algorithm flowchart for detecting non-blood flow points based on the energy ratio described in the embodiments of the present invention.

[0031] Figure 7 It is an example diagram for detecting isolated points in the defined area described in the embodiments of the present invention.

[0032] Figure 8 It is the algorithm flowchart for detecting isolated points described in the embodiments of the present invention. Detailed implementation manners

[0033] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, rather than all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present invention.

[0034] Embodiment

[0035] The present invention provides a method and device that can automatically adjust the wall filter according to image data, thereby improving the detection of low-speed blood flow, solving the problems of missing blood flow information or mistaking tissue signals for blood flow in current low-speed blood flow detection, and reducing the misdiagnosis rate.

[0036] Combined withFigure 1 In the C mode, wall filtering, autocorrelation, and blood flow detection are all completed by the FPGA processing module. The finally detected qualified results will be output to the DSC scan conversion module and displayed.

[0037] Different from the conventional C mode processing method, the present invention sets two wall filters. The first is the conventional wall filter 1, and according to different measurement targets, the initial stage is set to medium or slightly smaller than medium. The signal after wall filtering is processed by the autocorrelation processing module 1 to obtain velocity and power information, and the blood flow detection module 1 combines the brightness information E of the B-ultrasound to determine which points are blood flow points and transmits the relevant blood flow information to the blood flow detection module 2.

[0038] At the same time, the present invention also sets a wall filter 2, and this wall filter is set to a smaller filtering stage, that is to say, more signals will be retained. The signal after wall filtering 2 and the signal before wall filtering 2 will be transmitted to the autocorrelation processing module 2 together. In addition to the usual velocity and power information, there is an additional piece of data here, which is the energy ratio of the signal after and before wall filtering, defined as R = P2 / P 2original , where P2 comes from the autocorrelation processing of the signal after wall filtering 2, and P 2original comes from the autocorrelation processing of the signal before wall filtering 2. Because wall filtering will filter out most of the tissue signals, the energy ratio of the tissue signals will be significantly lower than that of the blood flow signals.

[0039] The above information is transmitted to the blood flow detection module 2, and combined with the brightness information E of the B-ultrasound, it is determined which points are blood flow points. At the same time, it is compared with the blood flow points given by the blood flow detection module 1, and the newly added set of blood flow points is selected, and together with the previously calculated velocity, power, energy ratio, B-ultrasound information, etc., it is transmitted to the low-speed blood flow detection module. After the low-speed blood flow detection module checks, some points will be excluded. The blood flow points that are still retained are considered to be the effective blood flow points after reducing the wall filtering stage. Here, a criterion is made again. If the ratio of this part of the newly added effective blood flow points to the high-frequency filtered blood flow points given by the blood flow detection module 1 is lower than a certain threshold C < C0, we consider that the improvement adjustment effect has been achieved and there is no need to continue adjusting the wall filtering stage. It only needs to be combined with the blood flow detection module 1 for conversion and display. If C still exceeds the threshold C0, it is considered that the wall filtering stage is not low enough and needs to be further reduced. Then the obtained blood flow information is saved, and then the stage of the wall filter 1 is adjusted to the stage of the wall filter 2, and the wall filter 2 is one stage lower than the original. The above operations are repeated until the ratio of the newly added effective blood flow points is lower than the threshold. The ultrasonic power spectrograms of wall filters with different stages are shown in Figure 2 . The specific invention flow chart is shown in Figure 4 .

[0040] The following is a specific analysis. Figure 3 It is a schematic diagram of the change in the blood flow point set caused by wall filter 2 relative to wall filter 1. Figure 3 Only high-speed blood flow comes from blood flow detection module 1, that is, the effect of wall filter 1. Reducing the number of wall filter stages brings new low-speed blood flow information (new blood flow information near the original blood flow and new blood flow information separated separately), but at the same time it also brings tissue information (close to the blood vessel wall and separated separately), and there is also some noise interference. Among them, the noise interference is usually relatively isolated, resulting in fewer and less concentrated blood flow points. Since the brightness threshold E0 used to detect blood flow is the same for both blood flow detection modules 1 and 2, the overly bright tissue has already been excluded. The remaining blood flow and tissue are relatively close in brightness, but because the echo of the tissue is relatively strong, relatively speaking, the brighter ones are basically tissues, and the darker ones are blood flow. That is to say, the original standard threshold E0 is relatively loose and there is room for further adjustment. Although the speed, power, and brightness of blood flow and tissue among the newly added points are relatively similar, when wall filtering 2 was performed before, most of the energy of the tissue was filtered out, making it close to the energy of the blood flow. In this way, further screening can be carried out through the energy ratio before and after wall filter 2, so as to obtain the truly effective blood flow points to the greatest extent. The above is the principle of the specific implementation of the screening.

[0041] Now combine Figures 4 to 8 to explain the specific steps.

[0042] First, set a new brightness threshold to exclude more tissue points. Use all the blood flow points given by blood flow detection module 2 as the basis to calculate the average brightness value and standard deviation. The calculation formula is:

[0043]

[0044] Then compare point by point. Now the new threshold E new = E A -σ is a value lower than the original E0. If E < E new , it is considered an effective blood flow point after this round of screening, otherwise, it is determined as a non-blood flow point. See the algorithm flowchart in Figure 5 as shown.

[0045] Next, for the blood flow points remaining after the previous round of screening, retrieve the previously saved energy ratio for a new round of screening. As mentioned before, R = P2 / P 2original . Here, a threshold R0 will be preset. If R > R0, it is considered an effective blood flow point after this round of screening, otherwise, it is determined as a non-blood flow point. See the algorithm flowchart in Figure 6 as shown.

[0046] After these two rounds of investigation, the organizational points have basically been investigated. If the remaining points exist in isolation, they are basically noise interferences. Even if there are individual blood flow and organizational points, their isolated existence has little significance for the judgment of the examination. Therefore, the isolated points will be eliminated by the algorithm below. The algorithm flowchart is as Figure 8 shown.

[0047] Here, the definition of an isolated point is that a 5x5 = 25-point area is used as the judgment unit. As Figure 7 shown, the large point in the middle is the target point, and there are 24 adjacent points around it, including 4 nearest neighbor points. The area has 5 rows and 5 columns, including the current row and column, 2 nearest neighbor rows and columns, and 2 next-nearest neighbor rows and columns.

[0048] Non-isolated points need to meet the following conditions simultaneously:

[0049] a. There are at least two blood flow points in at least two of the 4 nearest neighbor positions;

[0050] b. There is at least 1 column with blood flow points in at least two of the 2 nearest neighbor columns and the continuous columns are greater than or equal to 3;

[0051] c. There is at least 1 row with blood flow points in at least two of the 2 nearest neighbor rows and the continuous rows are greater than or equal to 3;

[0052] d. There are at least 9 adjacent blood flow points among the 24 area positions.

[0053] Figure 7 In , Ⅰ, Ⅱ, and Ⅲ are examples of non-isolated points that meet the conditions, while Ⅳ, Ⅴ, and Ⅵ are examples of isolated points that do not meet the conditions. Among them, Ⅳ does not meet condition a, Ⅴ does not meet condition b, and Ⅵ does not meet condition d.

[0054] Figure 8 is the algorithm flowchart for this round of investigation. After this round of investigation, the isolated noise interferences are eliminated.

[0055] After the three rounds of investigation, compared with the wall filter 1, the newly added blood flow points brought by the wall filter 2 are carefully inspected to ensure that the remaining effective information points are basically real effective blood flow points. The newly added effective blood flow points obtained after the investigation are merged with the high-frequency blood flow points obtained by the blood flow detection module 1 and saved as the confirmed effective blood flow points.

[0056] If the number of these newly added effective blood flow points is too small and the proportion is lower than the threshold, it is considered that continuing to reduce the filter level has little significance. If the proportion of the newly added effective blood flow points is very high, the wall filter 2 will be reduced by one level, and the wall filter 1 will take the level of the wall filter 2 and recalculate to obtain more low-speed effective blood flow points and merge them with the previous effective blood flow points until the automatic adjustment is completed and the improved low-speed blood flow detection image is output.

[0057] The method and device proposed by the present invention can automatically adjust the wall filter order according to the proportion of newly added effective blood flow points, improving the accuracy of detecting low-speed blood flow, such as venous blood flow. This is particularly meaningful when the operator of a Doppler ultrasound device lacks experience, and it can help the operator obtain accurate blood flow images and effectively avoid misdiagnosis.

[0058] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present invention. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to these embodiments shown herein, but rather should be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. An apparatus for detecting Doppler signals of low - velocity blood flow through a wall filter, characterized in that, It includes two wall filters, two autocorrelation processing modules, two blood flow detection modules, a low-speed blood flow detection module and a scan conversion module; the two wall filters adopt different filtering levels, and the two wall filters perform wall filtering on the detection signals of the Doppler ultrasound device; each of the wall filters is electrically connected to one of the autocorrelation processing modules and one of the blood flow detection modules in sequence. Among them, the autocorrelation processing module electrically connected to the wall filter with a high filtering level is used to receive the signal after the wall filter with a high filtering level and process it to obtain rate and power information. The autocorrelation processing module electrically connected to the wall filter with a low filtering level is used to receive the signal after and before the filter with a low filtering level and process it to obtain speed and power information and the energy ratio of the signals before and after the wall filtering with a low filtering level; the blood flow detection module connected to the wall filter with a high filtering level is used to receive the rate and power information from the autocorrelation processing module electrically connected to the wall filter with a high filtering level and the brightness information E of the B-ultrasound to determine which points are blood flow points and transfer the relevant blood flow information to the blood flow detection module connected to the wall filter with a low filtering level. The blood flow detection module connected to the wall filter with a high filtering level is connected to the scan conversion module; the blood flow detection module connected to the wall filter with a low filtering level is connected to the low-speed blood flow detection module. The blood flow detection module connected to the wall filter with a low filtering level is used to receive the rate and power information, energy ratio and the brightness information E of the B-ultrasound from the autocorrelation processing module electrically connected to the wall filter with a low filtering level to determine which points are blood flow points, compare them with the blood flow points from the blood flow detection module connected to the wall filter with a high filtering level, select the newly added blood flow point set, and transfer it together with other information to the low-speed blood flow detection module; the low-speed blood flow detection module is connected to the scan conversion module. The low-speed blood flow detection module is used to check for newly added effective blood flow points, compare the number of newly added effective blood flow points with the number of blood flow points of the blood flow detection module connected to the wall filter with a high filtering level to determine whether it is necessary to downgrade the wall filter.

2. The device for detecting low-speed blood flow Doppler signals through a wall filter according to claim 1, wherein It further includes a display module; the display module is connected to the scan conversion module and is used to display the information processed by the scan conversion module through an image.

3. A method for detecting low-speed blood flow Doppler signals through a wall filter, characterized in that, Using the device for detecting low-speed blood flow Doppler signals through a wall filter according to claim 1 or 2, it includes the following steps: Step 1, through one of the wall filters, according to different measurement targets, set the initial level of the wall filter, and perform wall filtering on the detection signals of the Doppler ultrasound device; after the signal after the wall filter is processed by one of the autocorrelation processing modules, speed and power information is obtained, and a blood flow detection module combines the brightness information E of the B-ultrasound to determine which points are blood flow points and transfers the relevant blood flow information to the other blood flow detection module; Step 2: Set a smaller filtering level through another wall filter to perform wall filtering. The signal after wall filtering will be transmitted to another self-correlation processing module together with the signal before wall filtering to obtain velocity and power information and the energy ratio of the signals before and after wall filtering. Then, the information will be transmitted to another blood flow detection module, which combines the brightness information E of B-ultrasound to determine which points are blood flow points. Step 3: Compare the blood flow points given in Step 1 with those given in Step 2, select the newly added blood flow point set in Step 2, and transmit it together with other information to the low-speed blood flow detection module. After being checked by the low-speed blood flow detection module, newly added effective blood flow points are identified. Step 4: Compare the number of newly added effective blood flow points in Step 3 with the number of blood flow points after high-frequency filtering in Step 1 to determine whether the wall filter needs to be downgraded.

4. The method for detecting low-speed blood flow Doppler signals through a wall filter according to claim 3, wherein If the ratio C obtained by dividing the number of newly added effective blood flow points in Step 3 by the number of blood flow points after high-frequency filtering in Step 1 is lower than a certain threshold C0, it is considered that the improvement adjustment effect has been achieved and there is no need to continue adjusting the wall filtering level. It only needs to be merged with the information of the blood flow detection module in Step 1 for conversion and display. If C still exceeds the threshold C0, it is determined that the wall filter level is not low enough and needs to be further reduced. Save the obtained blood flow information, then update the level of the wall filter in Step 1 to the level of the wall filter in Step 2, change the level of the wall filter in Step 2 to a lower level, and repeat the process again until the ratio of newly added effective blood flow points is lower than the threshold.

5. The method for detecting a low-speed blood flow Doppler signal through a wall filter according to claim 3, characterized in that, In Step 3, the low-speed blood flow detection module checks the newly added effective blood flow points and uses a newly set brightness threshold to exclude tissue points. The brightness threshold E0 used to detect blood flow in Steps 1 and 2 is used to calculate the average brightness value and standard deviation based on all the blood flow points given in Step 2. The calculation formula is: Then compare point by point. Now the new threshold E new = E A -σ is a value lower than the original E0. If E < E new , it is considered a valid blood flow point after this round of investigation; otherwise, it is determined as a non-blood flow point.

6. The method for detecting a low-speed blood flow Doppler signal through a wall filter according to claim 5, wherein In step 3, the newly added effective blood flow points are checked by the low-speed blood flow detection module, and the energy ratio is used for further checking; the energy ratio R of the signals after and before wall filtering in step 2 is R = P2 / P 2original , where P2 is the autocorrelation processing of the signal after wall filtering in step 2, and P 2original is the autocorrelation processing of the signal before wall filtering in step 2. A threshold R0 is preset. If R > R0 is satisfied, then it is considered an effective blood flow point; otherwise, it is determined as a non-blood flow point.

7. The method for detecting a low-speed blood flow Doppler signal through a wall filter according to claim 6, characterized in that, In Step 3, the low-speed blood flow detection module checks the newly added effective blood flow points and needs to exclude isolated points. A 5x5 = 25-point area is used as the determination unit, with the middle point as the target point and 24 adjacent points around it, including 4 nearest neighbor points. The area has 5 rows and 5 columns, including the current row and column, 2 nearest neighbor rows and columns, and 2 next-nearest neighbor rows and columns. Non-isolated points need to meet the following conditions simultaneously: a. There are at least two blood flow points in at least two of the 4 nearest neighbor positions. b. There is at least 1 column with blood flow points and the continuous columns are greater than or equal to 3 in at least one of the two nearest neighbor columns. c. There is at least 1 row with blood flow points and the continuous rows are greater than or equal to 3 in at least one of the two nearest neighbor rows. d. There are at least 9 adjacent blood flow points among the 24 area positions.

Images