Wall filtering method for color flow imaging

Abstract

The present application relates to a wall filtering method for color blood flow imaging, belonging to the technical field of image processing. The method comprises: using an in-phase component I and an orthogonal component Q signal to perform autocorrelation operation to obtain tissue motion information; performing phase rotation processing on an IQ signal when it is determined based on the tissue motion information that the current position meets the phase rotation condition; performing wall filtering processing on the IQ signal using an i-th level wall filter to obtain a filtered IQ signal; performing autocorrelation operation on the filtered IQ signal to obtain blood flow motion information; when it is determined based on the blood flow motion information that the wall filtering processing is to be continued, setting i=i+1 to perform the wall filtering processing again; performing reverse phase rotation processing on the filtered autocorrelation operation result when the wall filtering processing is not to be continued; the wall filtering efficiency and blood flow image performance in ultrasonic color Doppler blood flow imaging can be improved.

Description

[Technical field]

[0001] The present application relates to a wall filtering method for color blood flow imaging, belonging to the technical field of image processing. [Background technology]

[0002] The acquisition principle of color blood flow image includes: the beam emitted by the probe of the ultrasound device is reflected by the inspected part, generates frequency shift, and is then received by the probe, amplified, and the echo signal is orthogonally demodulated after beam synthesis. The demodulated signal is subjected to wall filtering, autocorrelation estimation, and color coding to obtain a color blood flow image.

[0003] Generally, echo signals from the human body include: blood flow echo signals from blood flow, and wall echo signals from slow-moving blood vessel walls and tissues. Among them, blood flow echo signals are required for color blood flow imaging, while wall echo signals are interference signals for imaging and are generally filtered using wall filters.

[0004] If the wall filter is too weak, clutter cannot be effectively filtered out and blood flow cannot be presented; if the wall filter is too strong, low-speed and low-energy blood flow may be filtered out, resulting in a decrease in blood flow sensitivity.

[0005] Existing wall filtering methods typically require the user to adjust the wall filtering level, confirm the most appropriate level through the image, and then save it as a preset.

[0006] However, the way in which users adjust the wall filter level is relatively subjective, and different wall filter levels need to be set in different application scenarios. It cannot adaptively change with different applications and tissue characteristics, and the adjustment efficiency is low. [Summary of the invention]

[0007] The present application provides a wall filtering method for color blood flow imaging, which can solve the problem that the existing wall filtering method cannot adaptively filter the wall. The present application provides the following technical solutions:

[0008] A wall filtering method for color blood flow imaging is provided, the method comprising:

[0009] Obtaining the in-phase component I signal and the orthogonal component Q signal of the current echo signal;

[0010] Perform autocorrelation operation using the in-phase component I signal and the orthogonal component Q signal to obtain tissue motion information;

[0011] Determining whether the current position meets the phase rotation condition based on the tissue movement information;

[0012] When it is determined that the phase rotation condition is met, performing phase rotation processing on the in-phase component I signal and the orthogonal component Q signal to obtain a rotated signal;

[0013] Performing wall filtering processing on the IQ signal using the i-th level wall filter to obtain a filtered IQ signal; the IQ signal is the in-phase component I signal and the orthogonal component Q signal, or the rotated signal; the wall filter includes m levels, the levels of the wall filter are arranged in order from low to high according to the cutoff frequency or the order, i is an integer from 1 to m in sequence; m is an integer greater than 1;

[0014] Performing autocorrelation operation on the filtered IQ signal to obtain blood flow motion information;

[0015] determining whether to continue wall filtering processing based on the blood flow motion information;

[0016] In the case where it is determined to continue the wall filtering process, i=i+1 is set, and the step of performing the wall filtering process on the IQ signal using the i-th level wall filter to obtain the filtered IQ signal is performed again;

[0017] When it is determined that the wall filtering process is not to be continued, a reverse phase rotation process is performed on the autocorrelation result of the filtered IQ signal to obtain final blood flow motion information.

[0018] Optionally, the tissue motion information includes energy, frequency shift and variance of tissue motion;

[0019] The determining whether the current position meets the phase rotation condition based on the tissue movement information includes:

[0020] When the energy, frequency shift and / or variance of the tissue movement meets the first threshold requirement, it is determined that the current position meets the phase rotation condition; when the energy, frequency shift and / or variance of the tissue movement does not meet the first threshold requirement, it is determined that the current position does not meet the phase rotation condition.

[0021] Optionally, the first threshold requirement includes: the energy is greater than a first energy threshold, and / or the frequency is less than a first frequency threshold, and / or the variance is less than a first variance threshold.

[0022] Optionally, when it is determined that the phase rotation condition is met, the in-phase component I signal and the orthogonal component Q signal are subjected to phase rotation processing to obtain a rotated signal, which is represented by the following formula:

[0023] φ=wT=2πf w T

[0024] I'(n)+jQ'(n)=(I(n)+jQ(n))*e -jwnT

[0025] =(I(n)+jQ(n))*(cos(nφ)-jsin(nφ))

[0026] =(I(n)*cos(nφ)+Q(n)*sin(nφ))+j(Q(n)*cos(nφ)-I(n)sin(nφ))

[0027] Wherein, I represents the in-phase component I signal, Q represents the quadrature component Q signal, I' represents the rotated in-phase component I signal, Q' represents the rotated quadrature component Q signal, and the rotated signal includes the rotated in-phase component I signal and the rotated quadrature component Q signal, f w represents the frequency shift in the tissue motion information, φ represents the tissue motion phase shift before wall filtering, n represents the nth pulse in the in-phase component I signal and the orthogonal component Q signal, and T represents the inverse of the pulse repetition frequency.

[0028] Optionally, the blood flow motion information includes energy, frequency shift and variance obtained by performing autocorrelation operation on the filtered IQ signal;

[0029] The determining whether to continue the wall filtering process based on the blood flow motion information comprises:

[0030] When the energy, frequency shift and / or variance in the autocorrelation result of the autocorrelation operation meets the second threshold requirement, it is determined to continue the wall filtering process; when the energy, frequency shift and / or variance in the autocorrelation result of the autocorrelation operation does not meet the second threshold requirement, it is determined not to continue the wall filtering process.

[0031] Optionally, the energy is greater than a second energy threshold, and / or the frequency shift is less than a second frequency shift threshold, and / or the variance is less than a second variance threshold.

[0032] Optionally, the filtered IQ signal is subjected to reverse phase rotation processing to obtain a component output signal, which is expressed by the following formula:

[0033] φ=wT=2πf w T

[0034] D'+jN'=(D+jN)*e jwT

[0035] =(D+jN)*(cos(φ)+jsin(φ))

[0036] =(D*cos(φ)-N*sin(φ))+j(N*cos(φ)+Dsin(φ))

[0037] Wherein, D and N represent the autocorrelation results in the tissue motion information; D' and N' represent the autocorrelation results after the reverse phase rotation processing; for the IQ signal subjected to the phase rotation processing, f w represents the frequency shift in the tissue motion information. For the IQ signal without phase rotation processing, f w is 0; φ represents the phase shift of tissue motion before wall filtering; T represents the inverse of the pulse repetition frequency.

[0038] Optionally, the autocorrelation operation is represented by the following formula:

[0039]

[0040]

[0041]

[0042]

[0043]

[0044] Where I is the in-phase component; Q is the quadrature component; PS is the number of times the same scanning line is scanned repeatedly; N and D are the autocorrelation results; T is the inverse of the pulse repetition frequency, is the average Doppler frequency shift; R(0) is the energy of the Doppler signal; σ 2 represents the variance of the Doppler signal.

[0045] The beneficial effects of the present application include at least: being able to adaptively select an appropriate wall filter level according to local tissue signal characteristics, improving the wall filter efficiency and blood flow image performance in ultrasonic color Doppler blood flow imaging, and more effectively suppressing low-frequency clutter signals and flash noise in blood flow imaging, while achieving a better blood flow image without too much subjective wall filter adjustment.

[0046] In addition, by determining whether the current position meets the phase rotation condition based on tissue motion information, performing phase rotation processing on the part that meets the phase rotation condition and then performing wall filtering processing, the movement of the tissue can be approximately zeroed, thereby improving the efficiency of wall filtering, effectively filtering out low-frequency tissue motion, and reducing flash artifacts.

[0047] In addition, by performing a reverse phase rotation operation on the autocorrelation result after the current wall filtering after determining to stop the wall filtering, the frequency shift or phase shift removed by the I / Q phase rotation operation can be compensated, thereby improving the accuracy of the component output signal.

[0048] The above description is only an overview of the technical solution of the present application. In order to more clearly understand the technical means of the present application and implement it according to the contents of the specification, the following is a detailed description of the preferred embodiments of the present application in conjunction with the accompanying drawings.

Brief Description of the Drawings

[0049] Figure 1 is a flow chart of a wall filtering method for color blood flow imaging provided by an embodiment of the present application;

[0050] Figure 2 is a flow chart of a color blood flow imaging process provided by an embodiment of the present application;

[0051] Figure 3 This is a block diagram of a wall filtering device for color blood flow imaging provided by an embodiment of the present application. [Specific implementation method]

[0052] The specific implementation methods of the present application are further described in detail below in conjunction with the accompanying drawings and examples. The following examples are used to illustrate the present application but are not intended to limit the scope of the present application.

[0053] First, several terms involved in this application are introduced.

[0054] Finite Impulse Response (FIR) filter: It can have a strict linear phase-frequency characteristic while ensuring arbitrary amplitude-frequency characteristics, and its unit sampling response is finite length.

[0055] After the echo signal used for blood flow imaging is processed by the FIR filter, there is a certain loss of effective data points, and the number of lost points is the filter order. When the filter order is low, the transition band is too wide and the filtering effect is not ideal; when the filter order is high (cannot exceed the data length), the data loss points are too many, which will increase the error of blood flow parameter estimation.

[0056] Infinite Impulse Response (IIR) digital filter: It uses a recursive structure, that is, a structure with a feedback loop. The IIR filter operation structure usually consists of basic operations such as delay, multiplication by coefficients, and addition.

[0057] The characteristic of IIR filter is that the transition band is narrow, and better filtering effect can be obtained at a lower order. However, the transient response of IIR filter is long. Therefore, when the data length of echo signal is short, the transient response will cause a large deviation in the estimation of blood flow parameters, which directly affects the performance of the filter. In addition to the type of IIR filter, the initialization method of the filter is more important to determine the transient response of the filter. There are currently three main initialization methods:

[0058] Zero-initialized IIR filters: that is, the initialized state vector is loaded into the register to zero.

[0059] Step-initialized IIR filters: that is, by assuming that the first complex sample value already exists when n is infinite to set the initial state vector.

[0060] Projection initialization: This is to minimize the transient response using the state-space method. First, the filter output is decomposed into transient and steady-state components, and then the amplitude of the projection of the transient response generated by the initial state is minimized.

[0061] After the ideal filter design is completed, its filter coefficients are input into the designed initialization program to obtain IIR filters under different initialization conditions. IIR filters are relatively easy to design, and the cutoff frequency can be continuously and dynamically changed. Among the three forms of IIR filters, the projection initialization method has the smallest transient response, does not lose data points, and has the best effect.

[0062] Regression filter: The basic principle is to treat the input signal as a polynomial function in the time domain. The low-frequency clutter component in the Doppler signal can be approximated by a polynomial of a given order (usually using least squares fitting), and then this part of the signal is subtracted from the Doppler signal to extract the blood flow signal. The essence of this filter is a high-pass filter.

[0063] The basic principles are as follows:

[0064] Assume that x(n) is the input signal, y(n) is the output signal, c(n) is the fitted low-frequency clutter signal, and a k is the polynomial coefficient, K is the maximum order of the filter, and N is the data length. The above process can be expressed as:

[0065]

[0066] y(n)=x(n)-c(n).

[0067] A typical regression filter includes a regression filter constructed with Legendre polynomials as basis vectors. Legendre polynomials can be obtained by transforming the polynomials {1,n 1 ,n 2 ,n 3 ,…n K} (K corresponds to the maximum order) is obtained by Gram-Schmidt orthogonalization. Assuming that the standard orthogonal basis of Legendre polynomials is {b0, b1, ... bK}, the filtering process is implemented in the following steps: first calculate the projection of the original signal along each basis vector, and then subtract the projection from the original signal to obtain the filtered signal.

[0068] The regression filter has the characteristics of smooth and monotonic frequency response, large stopband attenuation, narrow transition band, and no loss of data points, so it is very suitable as a wall filter for Doppler blood flow signals. From the basic principle and frequency response function of the regression filter, it can be seen that the cutoff frequency of the regression filter depends on the data length N and the maximum order K of the polynomial. In practical applications, in order to take into account both real-time performance and calculation accuracy, the data length is usually an integer between 8 and 24. When the data length is constant, the filter cutoff frequency depends entirely on the maximum order of the polynomial, and the polynomial order can only be integers such as 1, 2, 3, and 4 (if the order is too large, the cutoff frequency will be too high and the blood flow signal will be filtered out). Therefore, it is impossible to design a regression filter with an arbitrary cutoff frequency, which is a major defect of the regression filter.

[0069] The above-mentioned filters are only schematic. In actual implementation, there are wall filters used to filter Doppler blood flow signals, and there are also wall filtering methods based on eigenvalue decomposition or singular value decomposition. Such methods perform eigenvalue decomposition or singular value decomposition on the signal and then filter out the static components and low-frequency components therein. This application will not list them one by one here.

[0070] The conventional wall filtering method for ultrasonic color Doppler blood flow imaging comprises: obtaining an input signal; obtaining filter coefficient matrices of each order of a regression filter according to the data length of the input signal; linearly combining filter coefficient matrices of adjacent orders to construct a new filter coefficient matrix; and filtering the input signal according to the new filter coefficient matrix to obtain an output signal.

[0071] However, the above method is only applicable to regression filters and still cannot realize adaptive wall filtering of Doppler blood flow signals.

[0072] In addition, the conventional wall filtering method of ultrasonic color Doppler blood flow imaging further includes: acquiring a color echo signal, and obtaining color feature data of each sampling position according to the color echo signal; performing DSC digital scanning conversion according to the color feature data to obtain color image data of each pixel point in the image display area; determining that the data of the pixel points satisfying a preset threshold condition are blood flow data, and the preset threshold condition is determined according to the color image data; obtaining blood flow velocity data of the corresponding pixel point according to the blood flow data, and performing color coding imaging on the image according to the blood flow velocity data.

[0073] However, the above methods cannot adaptively implement wall filtering either.

[0074] Based on the problem that traditional wall filtering methods cannot achieve adaptive filtering, the present application provides a wall filtering method for color blood flow imaging, the main purpose of which is to achieve adaptive wall filtering according to tissue signal characteristics, thereby improving the sensitivity and expressiveness of blood flow imaging and reducing the adjustment burden of medical staff.

[0075] The following is a detailed introduction to the wall filtering method for color blood flow imaging provided by the present application. The present application takes the wall filtering method for color blood flow imaging provided by each embodiment as an example of being used in an electronic device. The electronic device is an ultrasound device, or a terminal or server connected to the ultrasound device for communication. The terminal can be a computer, or a tablet computer, etc. This embodiment does not limit the type of electronic device.

[0076] Figure 1 This is a flow chart of a wall filtering method for color blood flow imaging provided by an embodiment of the present application. The method includes at least the following steps:

[0077] Step 101: Acquire an in-phase component I signal and a quadrature component Q signal of a current echo signal.

[0078] After orthogonal demodulation is performed on the current echo signal, an in-phase component I signal and a quadrature component Q signal of the current echo signal are obtained.

[0079] The current echo signal is used for color blood flow imaging. The current echo signal is also called a Doppler blood flow signal or an ultrasonic echo signal, etc. This embodiment does not limit the name of the current echo signal.

[0080] Step 102: Perform autocorrelation operation using the in-phase component I signal and the orthogonal component Q signal to obtain tissue motion information.

[0081] The autocorrelation operation is expressed as follows:

[0082]

[0083]

[0084]

[0085]

[0086]

[0087] Wherein, I represents the in-phase component (including the in-phase component I signal in this step); Q represents the quadrature component (including the quadrature component Q signal in this step); PS is the number of times of repeated scanning of the same scan line; N and D represent the autocorrelation results; T is the reciprocal of the pulse repetition frequency, is the average Doppler shift; R(0) is the energy of the Doppler signal, and R(0) can be used for power Doppler imaging (PDI); σ 2 represents the variance of the Doppler signal.

[0088] Schematically, in order to ensure a certain frame rate and calculation accuracy, the value range of PS is usually between 8 and 24.

[0089] According to the above content, after inputting the in-phase component I signal and the quadrature component Q signal into the above autocorrelation operation formula, the obtained tissue motion information includes: the autocorrelation results N and D of tissue motion, the energy R(0), the frequency shift, and the variance.

[0090] Step 103, determine whether the current position meets the phase rotation condition based on the tissue motion information.

[0091] Schematically, determining whether the current position meets the phase rotation condition based on the tissue motion information includes: determining that the current position meets the phase rotation condition when the energy, frequency shift, and / or variance of the tissue motion meet the first threshold requirement. When the energy, frequency shift, and / or variance of the tissue motion do not meet the first threshold requirement, it is determined that the current position does not meet the phase rotation condition.

[0092] Since echo signals with generally large energy, small frequency shift, and small variance are initially determined to be tissue and need to be subjected to phase rotation processing to zero the frequency shift of tissue motion. Echo signals with small energy, large frequency shift, and large variance are initially determined to be blood flow and do not meet the phase rotation condition and do not need to be subjected to rotation processing to prevent the loss of normal blood flow signals. Based on this, the first threshold requirement includes that the energy is greater than the first energy threshold, and / or the frequency is less than the first frequency threshold, and / or the variance is less than the first variance threshold. Correspondingly, the judgment logic is expressed by the following formula:

[0093] If Rw > prev_r and / or fw < prev_f and / or δw < prev_δ, it is determined that the phase rotation condition is met, and step 104 is executed;

[0094] If Rw < prev_r and / or fw > prev_f and / or δw > prev_δ, it is determined that the phase rotation condition is not met, but it is tissue and step 105 is executed.

[0095] Wherein, Rw represents energy, fw represents frequency shift, δw represents variance; prev_r represents the first energy threshold, prev_f represents the first frequency shift threshold, and prev_δ represents the first variance threshold.

[0096] Step 104: When it is determined that the phase rotation condition is met, phase rotation processing is performed on the in-phase component I signal and the orthogonal component Q signal to obtain rotated signals.

[0097] In this embodiment, the purpose of performing phase rotation processing on echo signals that are not blood flow is to make the movement of tissues approximately return to zero, thereby improving the efficiency of wall filtering, effectively filtering out low-frequency tissue movement, and reducing color artifacts.

[0098] When it is determined that the phase rotation condition is met, the in-phase component I signal and the orthogonal component Q signal are subjected to phase rotation processing to obtain a rotated signal, which is expressed by the following formula:

[0099] φ=wT=2πf w T

[0100] I'(n)+jQ'(n)=(I(n)+jQ(n))*e -jwnT

[0101] =(I(n)+jQ(n))*(cos(nφ)-jsin(nφ))

[0102] =(I(n)*cos(nφ)+Q(n)*sin(nφ))+j(Q(n)*cos(nφ)-I(n)sin(nφ))

[0103] Wherein, I represents the in-phase component I signal, Q represents the quadrature component Q signal, I' represents the rotated in-phase component I signal, Q' represents the rotated quadrature component Q signal, and the rotated signal includes the rotated in-phase component I signal and the rotated quadrature component Q signal, f w represents the frequency shift in tissue motion information, φ represents the phase shift of tissue motion before wall filtering, n represents the nth pulse in the in-phase component I signal and the orthogonal component Q signal, and T represents the inverse of the pulse repetition frequency.

[0104] After the above phase rotation processing, it can be known that the original average frequency shift f in the echo signal is w After the phase rotation operation, the average frequency shift and the average phase shift are approximately zero.

[0105] Step 105, use the i-th level wall filter to perform wall filtering on the IQ signal to obtain a filtered IQ signal; the IQ signal is an in-phase component I signal and an orthogonal component Q signal, or a rotated signal; the wall filter includes m levels, and the levels of the wall filter are set in descending order according to the cutoff frequency or order, and i takes integers from 1 to m in sequence.

[0106] Here, m is an integer greater than 1.

[0107] When the current position does not meet the phase rotation condition, the IQ signal is an in-phase component I signal and an orthogonal component Q signal; when the current position is tissue, the IQ signal is or is a rotated signal.

[0108] The wall filters of each level in the m levels are composed of at least one different wall filter. The at least one different wall filter includes the following: FIR filter, IIR-free digital filter, regression filter, filter based on eigenvalue decomposition, filter based on singular value decomposition.

[0109] In this embodiment, as the wall filter level increases from low to high, the wall filter cutoff frequency or the wall filter order increases from low to high, and the wall filter belongs to a high-pass filter, that is, as the wall filter level increases from low to high, more and more low-frequency components are filtered out. In addition, the wall filter process is called from the lowest level and does not exceed the highest level.

[0110] Step 106, performing autocorrelation operation on the filtered IQ signal to obtain blood flow motion information.

[0111] The autocorrelation result of the autocorrelation operation of the IQ signal after wall filtering mainly reflects the blood flow motion information. The autocorrelation operation in this step refers to the autocorrelation operation process in step 102. At this time, I in the formula represents the in-phase component I signal after filtering; Q represents the orthogonal component Q signal after filtering.

[0112] According to the formula in step 102, the blood flow motion information includes energy, frequency shift and variance obtained by performing autocorrelation operation on the filtered IQ signal.

[0113] Step 107: Determine whether to continue wall filtering based on the blood flow motion information.

[0114] Illustratively, determining whether to continue the wall filtering process based on the blood flow motion information includes: determining to continue the wall filtering process when the energy, frequency shift and / or variance in the autocorrelation result of the autocorrelation operation meets the second threshold requirement.

[0115] Generally, signal points with relatively high energy, small frequency shift and small variance need to be subjected to a higher level of wall filtering to better filter out the low-frequency components of tissue movement; while signal points with relatively low energy, large frequency shift and large variance do not need to be subjected to a higher level of wall filtering to prevent normal blood flow signals from being filtered out and to increase the computational load. Based on this, the second threshold requirements include: the energy is greater than the second energy threshold, and / or the frequency shift is less than the second frequency shift threshold, and / or the variance is less than the second variance threshold. Correspondingly, the judgment logic is expressed by the following formula:

[0116] If R > post_r and / or f < post_f and / or δ < post_δ, it is determined to continue the wall filtering process and step 108 is executed;

[0117] If R < post_r and / or f > post_f and / or δ > post_δ, the wall filtering process is not continued and step 109 is executed.

[0118] Among them, the second threshold requirements are related to design parameters such as the stopband attenuation and cut-off frequency of the wall filter.

[0119] Step 108, in the case where it is determined to continue the wall filtering process, let i = i + 1, and the step of using the i-th level wall filter to perform wall filtering on the IQ signal again to obtain the filtered IQ signal is executed again, that is, step 105 is executed again.

[0120] Step 109, in the case where it is determined not to continue the wall filtering process, perform a reverse phase rotation process on the autocorrelation result of the filtered IQ signal to obtain the final blood flow motion information.

[0121] After the judgment in step 107, some signal points interrupt the wall filtering process; some signal points continue with the higher level wall filtering process and autocorrelation operation until the condition for exiting the wall filtering process is met or the wall filtering level reaches the highest. After exiting the wall filtering process, a reverse phase rotation (fw) operation needs to be performed on the current autocorrelation result after wall filtering, aiming to compensate for the frequency shift or phase shift removed by the previous I / Q phase rotation (-fw) operation. For signal points without phase rotation processing, fw is set to 0.

[0122] Specifically, performing a reverse phase rotation process on the autocorrelation result of the filtered IQ signal to obtain the final blood flow motion information is expressed by the following formula:

[0123] φ = wT = 2πf w T

[0124] D'+jN'=(D+jN)*e jwT

[0125] =(D+jN)*(cos(φ)+jsin(φ))

[0126] =(D*cos(φ)-N*sin(φ))+j(N*cos(φ)+Dsin(φ))

[0127] Where D and N represent the autocorrelation results in tissue motion information; D' and N' represent the autocorrelation results after reverse phase rotation processing; for the IQ signal after phase rotation processing, f w represents the frequency shift in tissue motion information. For the IQ signal without phase rotation processing, f w is 0; φ represents the phase shift of tissue motion before wall filtering; T represents the inverse of the pulse repetition frequency.

[0128] Optionally, refer to Figure 2 , after the reverse phase rotation process is performed, steps 21-28 may also be included:

[0129] Step 21, performing blood flow energy logarithmic compression on the blood flow motion information;

[0130] The purpose of logarithmic compression of blood flow energy is to compress a larger dynamic range into a display range of 0 to 255;

[0131] Step 22, performing N / D / R two-dimensional spatial smoothing on the compressed signal;

[0132] The purpose of N / D / R two-dimensional spatial smoothing is to improve the smoothness and expressiveness of blood flow;

[0133] Step 23, calculating and arbitrating the blood flow velocity, energy, and variance of the smoothed signal;

[0134] The purpose of blood flow velocity, energy, variance calculation and arbitration is to convert N / D / R into blood flow velocity, energy, variance and determine which signal points are blood flow;

[0135] Step 24, performing frame averaging on the arbitrated signal;

[0136] The purpose of frame averaging is to perform temporal smoothing between adjacent frames to improve the temporal continuity and expressiveness of blood flow;

[0137] Step 25, performing blood flow boundary processing on the frame averaged signal;

[0138] The purpose of blood flow boundary processing is to eliminate holes and boundary jagged edges;

[0139] Step 26, performing scan conversion on the boundary processed signal to convert the scan line space into the screen pixel space;

[0140] Step 27, performing B / C image mixing and arbitration on the transformed signal;

[0141] The purpose of B / C image blending and arbitration is to alpha blend the scan-converted B-mode and CF-mode images and determine which pixels are blood flow;

[0142] Step 28, displaying the mixed image.

[0143] Specifically, the mixed RGB image is sent to a display for final display.

[0144] It should be noted that the main technical solution of the present application is an adaptive wall filtering method in color Doppler blood flow imaging. Other color Doppler blood flow imaging processes are not the focus of the present application and will not be elaborated in depth here.

[0145] It is worth mentioning that in the process of blood flow velocity, energy, and variance calculation and arbitration, the threshold in the arbitration judgment is directly related to the design parameters such as the wall filter stopband attenuation and cutoff frequency involved in the adaptive wall filtering method of the present application, and is therefore also a relevant part of the adaptive wall filtering method of the present application.

[0146] In summary, the wall filtering method for color blood flow imaging provided in this embodiment can adaptively select a suitable wall filtering level according to the local tissue signal characteristics, improve the wall filtering efficiency and blood flow image performance in ultrasonic color Doppler blood flow imaging, and more effectively suppress low-frequency clutter signals and flash noise in blood flow imaging, and achieve a better blood flow image without too much subjective wall filtering adjustment.

[0147] In addition, by determining whether the current position meets the phase rotation condition based on tissue motion information, performing phase rotation processing on the part that meets the phase rotation condition and then performing wall filtering processing, the movement of the tissue can be approximately zeroed, thereby improving the efficiency of wall filtering, effectively filtering out low-frequency tissue motion, and reducing flash artifacts.

[0148] In addition, by performing a reverse phase rotation operation on the autocorrelation result after the current wall filtering after determining to stop the wall filtering, the frequency shift or phase shift removed by the I / Q phase rotation operation can be compensated, thereby improving the accuracy of the component output signal.

[0149] Figure 3 The block diagram of a wall filter device for color blood flow imaging provided by an embodiment of the present application includes at least the following modules: a signal acquisition module 310, a first autocorrelation module 320, a blood flow judgment module 330, a phase rotation module 340, a wall filter module 350, a second autocorrelation module 360, a filter judgment module 370 and a reverse rotation module 380.

[0150] The signal acquisition module 310 is used to acquire the in-phase component I signal and the orthogonal component Q signal of the current echo signal;

[0151] A first autocorrelation module 320 is used to perform autocorrelation operation using the in-phase component I signal and the orthogonal component Q signal to obtain tissue motion information;

[0152] A blood flow determination module 330, configured to determine whether the current position meets the phase rotation condition based on the tissue movement information;

[0153] The phase rotation module 340 is used to perform phase rotation processing on the in-phase component I signal and the orthogonal component Q signal to obtain rotated signals when it is determined that the phase rotation condition is met;

[0154] The wall filter module 350 is used to perform wall filtering processing on the IQ signal using the wall filter of the i-th level to obtain a filtered IQ signal; the IQ signal is the in-phase component I signal and the orthogonal component Q signal, or the rotated signal; the wall filter includes m levels, and the levels of the wall filter are arranged in descending order according to the cutoff frequency or the order, and i is an integer from 1 to m in sequence;

[0155] A second autocorrelation module 360 ​​is used to perform an autocorrelation operation on the filtered IQ signal to obtain blood flow motion information;

[0156] A filtering determination module 370, configured to determine whether to continue wall filtering processing based on the blood flow motion information;

[0157] The wall filtering module 350 is further configured to, when it is determined that the wall filtering process is to be continued, set i=i+1, and again perform the step of performing the wall filtering process on the IQ signal using the i-th level wall filter to obtain a filtered IQ signal;

[0158] The reverse rotation module 380 is used to perform reverse phase rotation processing on the autocorrelation result of the filtered IQ signal to obtain final blood flow motion information when it is determined that the wall filtering processing is not to be continued.

[0159] For relevant details, refer to the above method embodiment.

[0160] It should be noted that: the wall filter device for color blood flow imaging provided in the above embodiment only uses the division of the above functional modules as an example when performing wall filtering for color blood flow imaging. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the wall filter device for color blood flow imaging is divided into different functional modules to complete all or part of the functions described above. In addition, the wall filter device for color blood flow imaging provided in the above embodiment and the wall filtering method embodiment for color blood flow imaging belong to the same concept, and the specific implementation process is detailed in the method embodiment, which will not be repeated here.

[0161] Optionally, the present application also provides a computer-readable storage medium, in which a program is stored, and the program is loaded and executed by a processor to implement the wall filtering method for color blood flow imaging of the above method embodiment.

[0162] Optionally, the present application also provides a computer product, which includes a computer-readable storage medium, wherein the computer-readable storage medium stores a program, and the program is loaded and executed by a processor to implement the wall filtering method for color blood flow imaging of the above method embodiment.

[0163] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0164] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A wall filtering method for color blood flow imaging, characterized in that: The method comprises: Obtaining the in-phase component I signal and the orthogonal component Q signal of the current echo signal; Perform autocorrelation operation using the in-phase component I signal and the orthogonal component Q signal to obtain tissue motion information; Determining whether the current position meets the phase rotation condition based on the tissue movement information; When it is determined that the phase rotation condition is met, performing phase rotation processing on the in-phase component I signal and the orthogonal component Q signal to obtain a rotated signal; Performing wall filtering processing on the IQ signal using the i-th level wall filter to obtain a filtered IQ signal; the IQ signal is the in-phase component I signal and the orthogonal component Q signal, or the rotated signal; the wall filter includes m levels, the levels of the wall filter are arranged in order from low to high according to the cutoff frequency or the order, i is an integer from 1 to m in sequence; m is an integer greater than 1; Performing autocorrelation operation on the filtered IQ signal to obtain blood flow motion information; determining whether to continue wall filtering processing based on the blood flow motion information; In the case where it is determined to continue the wall filtering process, i=i+1 is set, and the step of performing the wall filtering process on the IQ signal using the i-th level wall filter to obtain the filtered IQ signal is performed again; When it is determined that the wall filtering process is not to be continued, a reverse phase rotation process is performed on the autocorrelation result of the filtered IQ signal to obtain final blood flow motion information.

2. The method according to claim 1, characterized in that The tissue motion information includes energy, frequency shift and variance of tissue motion; The determining whether the current position meets the phase rotation condition based on the tissue movement information includes: When the energy, frequency shift and / or variance of the tissue movement meets the first threshold requirement, it is determined that the current position meets the phase rotation condition; when the energy, frequency shift and / or variance of the tissue movement does not meet the first threshold requirement, it is determined that the current position does not meet the phase rotation condition.

3. The method according to claim 2, characterized in that The first threshold requirement includes: the energy is greater than a first energy threshold and / or the frequency shift is less than a first frequency shift threshold and / or the variance is less than a first variance threshold.

4. The method according to claim 1, characterized in that: When it is determined that the phase rotation condition is met, the in-phase component I signal and the quadrature component Q signal are subjected to phase rotation processing to obtain a rotated signal, which is represented by the following formula: φ=wT=2πf w T I'(n)+jQ'(n)=(I(n)+jQ(n))*e -jwnT =(I(n)+jQ(n))*(cos(nφ)-jsin(nφ)) =(I(n)*cos(nφ)+Q(n)*sin(nφ))+j(Q(n)*cos(nφ)-I(n)sin(nφ)) Wherein, I represents the in-phase component I signal, Q represents the quadrature component Q signal, I' represents the rotated in-phase component I signal, Q' represents the rotated quadrature component Q signal, and the rotated signal includes the rotated in-phase component I signal and the rotated quadrature component Q signal, f w represents the frequency shift in the tissue motion information, φ represents the tissue motion phase shift before wall filtering, n represents the nth pulse in the in-phase component I signal and the orthogonal component Q signal, and T represents the inverse of the pulse repetition frequency.

5. The method according to claim 1, characterized in that The blood flow motion information includes energy, frequency shift and variance obtained by performing autocorrelation operation on the filtered IQ signal; The determining whether to continue the wall filtering process based on the blood flow motion information comprises: When the energy, frequency shift and / or variance in the autocorrelation result of the autocorrelation operation meets the second threshold requirement, it is determined to continue the wall filtering process; when the energy, frequency shift and / or variance in the autocorrelation result of the autocorrelation operation does not meet the second threshold requirement, it is determined not to continue the wall filtering process.

6. The method according to claim 5, characterized in that The second threshold requirement includes: The energy is greater than a second energy threshold and / or the frequency shift is less than a second frequency shift threshold and / or the variance is less than a second variance threshold.

7. The method according to claim 1, characterized in that The autocorrelation result of the filtered IQ signal is subjected to reverse phase rotation processing to obtain final blood flow motion information, which is expressed by the following formula: φ=wT=2πf w T D'+jN'=(D+jN)*e jwT =(D+jN)*(cos(φ)+jsin(φ)) =(D*cos(φ)-N*sin(φ))+j(N*cos(φ)+Dsin(φ)) Wherein, D and N represent the autocorrelation results in the tissue motion information; D' and N' represent the autocorrelation results after the reverse phase rotation processing; for the IQ signal subjected to the phase rotation processing, f w represents the frequency shift in the tissue motion information. For the IQ signal without phase rotation processing, f w is 0; φ represents the phase shift of tissue motion before wall filtering; T represents the inverse of the pulse repetition frequency.

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