An ultrasound nonlinear parametric imaging method, system, medium, and program product

Abstract

This invention belongs to the field of ultrasound imaging technology, specifically relating to an ultrasound nonlinear parameter imaging method, system, medium, and program product. The method includes: calculating a fundamental wave signal and harmonic signals based on ultrasound echo RF signals; extracting the amplitudes of the fundamental wave signal and the harmonic signals, and calculating the fundamental wave amplitude ratio and harmonic amplitude ratio; calculating an initial value of the nonlinear coefficient based on the ratio between the fundamental wave amplitude ratio and the harmonic amplitude ratio, and estimating the range of values ​​for the nonlinear coefficient using Newton's iteration method; and generating an ultrasound image based on the range of values ​​for the nonlinear coefficient. In the process of ultrasound nonlinear parameter imaging, a method for solving the nonlinear coefficient using Newton's iteration method is further disclosed, thereby expanding the dynamic range of the estimated nonlinear coefficient values, resulting in a clearer distinction between normal tissue and lesion tissue in the obtained ultrasound image, and higher sensitivity.

Description

Technical Field

[0001] This invention belongs to the field of ultrasound imaging technology, specifically relating to an ultrasound nonlinear parameter imaging method, system, medium, and program product. Background Technology

[0002] Ultrasound nonlinear parametric imaging is an emerging ultrasound imaging technique. Traditional B-mode grayscale imaging represents the spatial distribution of echo signal amplitude, while ultrasound nonlinear parametric imaging uses the fundamental frequency signal and second harmonic signal of ultrasound to represent the spatial distribution of tissue nonlinear parameters.

[0003] The existing patent, "A Method and System for Real-Time Quantitative Ultrasonic Imaging" (Publication No. CN 107970042A), fully proposes a method for obtaining nonlinear coefficients based on the principle of phase-reversed pulse (PI) and fundamental and second harmonic signals under low-pressure conditions, thereby realizing ultrasonic nonlinear parameter imaging.

[0004] In the methods described above, higher harmonic signals carry information about the nonlinear characteristics of tissues. Different types of tissues, such as normal tissues, benign lesions, and malignant tumors, exhibit different nonlinear responses to ultrasound. By analyzing the echo signals, we can better distinguish and characterize the properties of tissues, thereby providing more objective support for ultrasound-based diagnoses.

[0005] Ultrasound nonlinear parametric imaging shows great promise in the diagnosis of fatty liver and liver fibrosis. Fatty liver and liver fibrosis are two key pathological stages in the progression of chronic liver disease, and accurate assessment of the degree of hepatic steatosis and fibrosis is crucial for disease staging, prognosis, and treatment decisions.

[0006] In the diagnosis of fatty liver, nonlinear parametric ultrasound imaging can provide more sensitive and quantitative indicators than traditional B-mode ultrasound. Steroid degeneration alters the acoustic properties of liver tissue, leading to changes in nonlinear parameters. Studies have found that the nonlinear parameters of fatty liver are significantly higher than those of normal liver tissue and are positively correlated with liver fat content. Therefore, by measuring nonlinear parameters, the degree of hepatic steatosis can be quantitatively assessed, and even fat quantification can be achieved, which is invaluable for monitoring disease progression and evaluating treatment effectiveness.

[0007] The above method, when solving for the nonlinear coefficient, first uses the power series approximation function of the hyperbolic function to estimate the initial value of the unknown in the nonlinear coefficient function. Then, based on the initial value, Newton's iteration method is used to find the zero solution corresponding to the nonlinear coefficient function, thus obtaining the nonlinear coefficient. The value of the nonlinear coefficient calculated by this method is normally between 6 and 7. However, in the diagnosis of fatty liver, this nonlinear coefficient value is between 6 and 7.2, with a very small dynamic range. The lesions are displayed very small and unclearly in the ultrasound image. Summary of the Invention

[0008] In order to improve the dynamic range of the nonlinear coefficient, thereby making lesions in ultrasound images easier to distinguish and improving the sensitivity of lesion identification, this invention proposes an ultrasound nonlinear parameter imaging method, system, medium, and program product.

[0009] Based on the above concepts, the present invention proposes the following technical solutions:

[0010] An ultrasound nonlinear parameter imaging method includes the following steps:

[0011] The fundamental and harmonic signals are calculated based on the ultrasonic echo RF signals.

[0012] Extract the amplitude of the fundamental signal and the amplitude of the harmonic signal, and calculate the fundamental amplitude ratio and the harmonic amplitude ratio;

[0013] The initial value of the nonlinear coefficient is calculated based on the ratio between the fundamental amplitude ratio and the harmonic amplitude ratio. The range of values ​​for the nonlinear coefficient is estimated using the Newton-Raphson iteration method. An ultrasound image is generated based on the range of values ​​for the nonlinear coefficient.

[0014] Preferably, the initial value of the nonlinear coefficient is calculated using the following formula:

[0015] ;

[0016] Where Q is a parameter factor related to voltage, dielectric density, dielectric attenuation coefficient, probe operating frequency, and depth; r is the ratio between the fundamental amplitude and the harmonic amplitude.

[0017] Preferably, estimating the range of nonlinear coefficients using Newton's iteration method specifically includes the following steps:

[0018] Obtain the initial values ​​of the nonlinear coefficients;

[0019] Substitute the initial value of the nonlinear coefficient into the iterative formula to calculate the next approximate value;

[0020] Repeat the iteration until the accuracy requirement is met;

[0021] The iterative formula is as follows:

[0022] ;

[0023] These are nonlinear coefficients, and k represents the number of iterations. This represents an approximate value of the nonlinear coefficients obtained after k iterations. Indicates the value to be The function whose roots are to be found at a given time; Indicates the value to be The derivative of the function corresponding to the given time;

[0024] Preferably, the iteration is repeated until the convergence condition that meets the accuracy requirement is met: ;exist Estimate the nonlinear coefficients under known conditions The range.

[0025] Preferably, the steps further include: estimating the estimated values ​​of multiple nonlinear coefficients using the Newton-Raphson iteration method; and taking a weighted average of the estimated values ​​of the multiple nonlinear coefficients to obtain the optimized nonlinear coefficient values.

[0026] Preferably, the formula for calculating the optimized nonlinear coefficient value is as follows:

[0027] ;

[0028] in, The first nonlinear coefficient is estimated based on the fundamental signal. ， The second nonlinear coefficient is estimated based on the harmonic signal. ， It is the third nonlinear coefficient estimated by combining the fundamental signal and the harmonic signal; , , These are the corresponding weights.

[0029] Preferably, the method further includes: calculating a nonlinear parameter B / A using a nonlinear coefficient; calculating a histogram of the nonlinear parameter B / A distributed in a two-dimensional space based on the nonlinear parameter B / A; and calculating the proportion of data exceeding a specified threshold based on the histogram of the nonlinear parameter B / A, wherein the proportion of data is used to assist in identifying the location of lesions in a two-dimensional space.

[0030] Based on the same concept, an ultrasound nonlinear parametric imaging system is also proposed, including at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform an ultrasound nonlinear parametric imaging method as described above.

[0031] Based on the same concept, a medium is also proposed that stores instructions executable by a processor, which, when executed by the processor, cause the processor to perform any of the above-described ultrasonic nonlinear parametric imaging methods.

[0032] Based on the same concept, a program product for ultrasonic nonlinear parametric imaging is also proposed, which implements the ultrasonic nonlinear parametric imaging method described above when the program product is run on a computer.

[0033] Compared with the prior art, the beneficial effects of the present invention are as follows: In the process of ultrasound nonlinear parameter imaging, a method for solving the nonlinear coefficient using the Newton iteration method is further disclosed, thereby expanding the range of nonlinear coefficient values ​​and making the distinction between normal tissue and lesion tissue in the obtained ultrasound image more obvious and the sensitivity higher. Attached Figure Description

[0034] Figure 1 This is a flowchart of an ultrasound nonlinear parametric imaging method in Example 1;

[0035] Figure 2 This is a voltage operating range curve of the ultrasonic system in Example 1;

[0036] Figure 3 This is a data processing flowchart of the ultrasound nonlinear parametric imaging method in Example 3;

[0037] Figure 4 This is a graph showing the amplitude ratios at different depths in Example 5;

[0038] Figure 5 This is a correction curve of the amplitude ratio as a function of depth in Example 5;

[0039] Figure 6 This refers to nonlinear parameter imaging of the liver using the convex array probe in Example 5.

[0040] Figure 7 This is a histogram of the nonlinear parameters in Example 5. Detailed Implementation

[0041] The present invention will be further described in detail below with reference to experimental examples and specific embodiments. However, this should not be construed as limiting the scope of the above-mentioned subject matter of the present invention to the following embodiments; all technologies implemented based on the content of the present invention fall within the scope of the present invention.

[0042] Example 1

[0043] A nonlinear parametric ultrasound imaging method, flowchart as follows: Figure 1 As shown, it includes the following steps:

[0044] The fundamental and harmonic signals are calculated based on the ultrasonic echo RF signals.

[0045] Extract the amplitude of the fundamental signal and the amplitude of the harmonic signal, and calculate the fundamental amplitude ratio and the harmonic amplitude ratio;

[0046] The initial value of the nonlinear coefficient is calculated based on the ratio between the fundamental amplitude ratio and the harmonic amplitude ratio. The range of values ​​for the nonlinear coefficient is estimated using the Newton-Raphson iteration method. An ultrasound image is generated based on the range of values ​​for the nonlinear coefficient.

[0047] The fundamental signal includes the fundamental signal under the first voltage condition and the fundamental signal under the second voltage condition; the second harmonic signal includes the second harmonic signal under the first voltage condition and the second harmonic signal under the first voltage condition, wherein the first voltage is less than the second voltage.

[0048] Furthermore, the fundamental frequency signal and the second harmonic signal are calculated based on the ultrasonic echo RF signal, specifically including:

[0049] Using two sets of high and low voltages at the same scan line position, four sets of signals are continuously transmitted and received using PI pulse phase inversion technology. The four sets of signals are: low voltage forward pulse, low voltage reverse pulse, high voltage forward pulse, and high voltage reverse pulse.

[0050] Based on the principle of PI pulse phase reversal, the fundamental signal under low voltage (first voltage) conditions can be obtained through low-voltage forward pulses and low-voltage reverse pulses. and second harmonic signal The fundamental signal under high voltage (second voltage) conditions can be obtained through high-voltage forward pulses and high-voltage reverse pulses. and second harmonic signal The prerequisite is that both the high voltage (second voltage) and the low voltage (first voltage) operate within the linear range of the ultrasonic power supply system, in order to avoid the nonlinear characteristics of the system itself interfering with the tissue nonlinear characteristics carried by the echo signal.

[0051] Furthermore, since both the high voltage (second voltage) and low voltage (first voltage) operate within the linear range of the ultrasonic power supply system, it is necessary to determine the linear operating range of the ultrasonic system power supply. For example... Figure 2 As shown, the horizontal axis represents the normalized operating voltage of the ultrasound system, with the maximum voltage value being [value missing]. The minimum voltage value is 1. The scattered circles represent the energy of the echo signal corresponding to different voltages, the dashed curve represents the linear fitting result, and the solid curve represents the nonlinear fitting result. From Figure 2 As can be seen, the ultrasound system exhibits nonlinear characteristics under conditions of excessively low or high voltage. Therefore, it is necessary to reasonably select the range of high and low voltage values ​​to meet the requirements of ultrasound nonlinear parameter imaging.

[0052] Furthermore, the ultrasonic nonlinear parametric imaging method is based on the analytical solution obtained using a circular transducer under narrow-band conditions. The fundamental amplitude ratio is the amplitude ratio of the low voltage and high voltage of the fundamental signal; the harmonic amplitude ratio is the amplitude ratio of the low voltage and high voltage of the second harmonic signal.

[0053] The formula for calculating the fundamental amplitude ratio is:

[0054] (1);

[0055] The formula for calculating the harmonic amplitude ratio is:

[0056] (2);

[0057] in, It is the amplitude ratio of the low voltage (first voltage) and high voltage (second voltage) of the fundamental signal. It is the ratio of the low voltage to the high voltage amplitude of the harmonic signal. This represents the ratio of low voltage to high voltage within the linear segment of the input operating voltage. Represents the nonlinear coefficients. These are parameters related to voltage, dielectric density, dielectric attenuation coefficient, probe operating frequency, and depth. It can be known The calculation is performed using a medium (such as water).

[0058] The parameter factor Q, which is related to voltage, dielectric density, dielectric attenuation coefficient, probe operating frequency, and depth, is corrected during imaging. The formula used for this correction is:

[0059] (3);

[0060] in, Where C is the attenuation coefficient and C is the correction constant. Where k is the maximum voltage amplitude, k1 is the wavenumber of the fundamental frequency, and z is the depth of penetration. This is the initial reference depth along the probe axis. This represents the density of the medium in equilibrium.

[0061] Furthermore, the nonlinear coefficients in the formulas for calculating the fundamental amplitude ratio and the harmonic amplitude ratio are... The nonlinear coefficients are obtained by estimating the spatial distribution using Newton's iterative method. The calculation process includes:

[0062] Step 1. Solve for approximate values;

[0063] Given The value is obtained by using the formula for the amplitude ratio of the fundamental and harmonic signals, and then comparing the fundamental amplitude ratio with the harmonic amplitude ratio. for:

[0064] (4); Step 2. Use the approximate value r obtained in Step 1 as the initial value for iterative calculation, and use Newton's iteration method to find the function. The root;

[0065] (5);

[0066] (6);

[0067] (7);

[0068] in, It is a function whose roots are to be found. It is the derivative of the function whose roots are to be found. These are nonlinear coefficients, and k represents the number of iterations. This represents an approximate value of the nonlinear coefficients obtained after k iterations. Indicates the value to be The function whose roots are to be found at a given time; Indicates the value to be The derivative of the function corresponding to time, It represents the ratio of low voltage to high voltage within the linear segment of the input operating voltage.

[0069] Through a finite number of iterations, the variables The value will converge to ,exist Under known conditions, the nonlinear coefficients of the medium at different locations in space can be estimated. .

[0070] Example 2

[0071] To improve the robustness of the calculation, we can consider calculating multiple sets of estimates for nonlinear coefficients based on the formulas for calculating the fundamental amplitude ratio and the harmonic amplitude ratio, as well as the corresponding Newton-Raphson iteration method, and then taking a weighted average of these estimates.

[0072] (8);

[0073] in, It is the first nonlinear coefficient estimated based on the fundamental signal. It is the second nonlinear coefficient estimated based on the harmonic signal. It is the third nonlinear coefficient estimated by combining the fundamental signal and the harmonic signal; , , These are the corresponding weights. In the near field, the signal-to-noise ratio (SNR) of the fundamental frequency is higher than that of the harmonics. In the midfield, the sensitivity to harmonics is higher. In the far field, the signal-to-noise ratio (SNR) of the fundamental frequency is higher than that of the harmonic frequencies. Under all depth conditions, .

[0074] Furthermore, the low voltage of the fundamental frequency signal (First voltage) and high voltage The amplitude ratio of the second voltage is defined as the amplitude ratio of the first voltage, denoted by the letter r1.

[0075] (9);

[0076] The first Newton iteration function is constructed as follows:

[0077] (10);

[0078] (11);

[0079] Furthermore, the low voltage of harmonic signals (First voltage) and high voltage The amplitude ratio of the second voltage is defined as the amplitude ratio of the second voltage, denoted by the letter r2.

[0080] (12);

[0081] The second Newton iteration function is constructed as follows:

[0082] (13);

[0083] (14);

[0084] Furthermore, the voltage amplitude ratio of the fundamental signal and the voltage amplitude ratio of the harmonic signal are combined to construct the Newton iteration function, as follows:

[0085] The low-voltage and high-voltage amplitude ratios of the fundamental signal are obtained according to formulas (1) and (2) in Example 1, respectively. And the ratio of low voltage to high voltage amplitude of harmonic signals. The fundamental signal voltage amplitude ratio voltage amplitude ratio of harmonic signals The ratio is defined as the third voltage amplitude ratio, denoted by the letter r3.

[0086] (15);

[0087] The third Newton iteration function is constructed as follows:

[0088] (16);

[0089] (17);

[0090] Using approximate estimation As the initial value for Newton's iteration method, the first nonlinear coefficients estimated from the fundamental signal are then calculated according to the previous formulas (9)-(17). The second nonlinear coefficient obtained based on harmonic signal estimation And the third nonlinear coefficient estimated by combining the fundamental and harmonic signals. Therefore, a more reliable nonlinear coefficient value can be obtained by weighting and averaging these coefficients.

[0091] Example 3

[0092] A data processing flowchart for a specific ultrasound nonlinear parametric imaging method is shown below. Figure 3 As shown, the conversion of an RF signal to an amplitude signal involves demodulation, downsampling, and envelope calculation; the conversion of the amplitude ratio to an approximate solution involves smoothing; and the conversion of the numerical solution to imaging involves smoothing. Filtering the valid value range and color coding. When the calculated value... If the value is unreasonable, only the grayscale value at the corresponding position will be displayed; otherwise, it will be... The corresponding color codes are superimposed on the grayscale image, similar to Doppler flow imaging. It is a nonlinear parameter, defined as the coefficient of the quadratic term. coefficient of the first term ratio During the propagation of ultrasound waves within biological tissues, the sound pressure disturbances between A and B... With density perturbation The Taylor expansion of the function is obtained.

[0093] Example 4

[0094] Furthermore, the constant Q in the fundamental amplitude ratio calculation formula and harmonic amplitude ratio calculation formula in Example 1 is related to attenuation in actual situations. It is related to depth z, and appropriate corrections are needed during imaging. The correction formula is:

[0095] (18);

[0096] in, Where C is the attenuation coefficient and C is the correction constant. Where k is the maximum voltage amplitude, k1 is the wavenumber of the fundamental frequency, and z is the depth of penetration. This is the initial reference depth along the probe axis. This represents the density of the medium in equilibrium. Since the nonlinear parameters and attenuation coefficient of water are known, the constant C can be calculated through water tank experiments under multiple voltage conditions.

[0097] Example 5

[0098] The approximate values ​​obtained by comparing the fundamental amplitude ratio and harmonic amplitude ratio under different depth conditions using the shear wave phantom CIRS039 of the liver phantom are as follows. Corrections were made. The nonlinear constant of the 4 kPa phantom is close to that of a normal human liver. The amplitude ratio at different depths was measured under the same ultrasound parameters, such as... Figure 4 As shown, a correction curve that varies with depth will be obtained, such as... Figure 5 As shown in the figure. Based on this curve, the data is approximated and corrected so that the nonlinear coefficients estimated under phantom conditions are constant in the 2-D space.

[0099] Ultrasound nonlinear parametric imaging shows great promise in the diagnosis of fatty liver and liver fibrosis. Fatty liver and liver fibrosis are two key pathological stages in the progression of chronic liver disease, and accurate assessment of the degree of hepatic steatosis and fibrosis is crucial for disease staging, prognosis, and treatment decisions.

[0100] In the diagnosis of fatty liver, nonlinear parametric ultrasound imaging can provide more sensitive and quantitative indicators than traditional B-mode ultrasound. Steroid degeneration alters the acoustic properties of liver tissue, leading to changes in nonlinear parameters. Studies have found that the nonlinear parameters of fatty liver are significantly higher than those of normal liver tissue and are positively correlated with liver fat content. Therefore, by measuring nonlinear parameters, the degree of hepatic steatosis can be quantitatively assessed, and even fat quantification can be achieved, which is invaluable for monitoring disease progression and evaluating treatment effectiveness.

[0101] Compared to existing non-invasive liver fibrosis diagnostic techniques such as transient elastography (FibroScan), ultrasound nonlinear parametric imaging offers several unique advantages. First, it provides two-dimensional images of the liver, visually displaying the spatial distribution and heterogeneity of lesions. Second, it can simultaneously assess hepatic steatosis and fibrosis, comprehensively reflecting the complex pathological processes of liver disease. Furthermore, ultrasound examination is non-invasive, real-time, and low-cost, facilitating repeated examinations and dynamic monitoring, making it particularly suitable for primary healthcare institutions and large-scale screening.

[0102] With its unique technological advantages and promising clinical applications, nonlinear parametric ultrasound imaging is expected to become an important tool for the non-invasive diagnosis of fatty liver and liver fibrosis. It can not only improve the accuracy and reliability of diagnosis, but also enable early detection and quantitative assessment of the disease, providing strong support for precision diagnosis and treatment and improved prognosis of liver diseases.

[0103] Furthermore, nonlinear parametric ultrasound imaging shows great promise in the diagnosis of fatty liver and liver fibrosis. Fatty liver and liver fibrosis are two key pathological stages in the progression of chronic liver disease, and accurate assessment of the degree of hepatic steatosis and fibrosis is crucial for disease staging, prognosis, and treatment decisions.

[0104] In the diagnosis of fatty liver, nonlinear parametric ultrasound imaging can provide more sensitive and quantitative indicators than traditional B-mode ultrasound. Steroid degeneration alters the acoustic properties of liver tissue, leading to changes in nonlinear parameters. Studies have found that the nonlinear parameters of fatty liver are significantly higher than those of normal liver tissue and are positively correlated with liver fat content. Therefore, by measuring nonlinear parameters, the degree of hepatic steatosis can be quantitatively assessed, and even fat quantification can be achieved, which is invaluable for monitoring disease progression and evaluating treatment effectiveness. The nonlinear parametric imaging effect of the liver using a convex array probe is shown below. Figure 6 As shown.

[0105] Compared to existing non-invasive liver fibrosis diagnostic techniques such as transient elastography (FibroScan), ultrasound nonlinear parametric imaging offers several unique advantages. First, it provides two-dimensional images of the liver, visually displaying the spatial distribution and heterogeneity of lesions. Second, it can simultaneously assess hepatic steatosis and fibrosis, comprehensively reflecting the complex pathological processes of liver disease. Furthermore, ultrasound examination is non-invasive, real-time, and low-cost, facilitating repeated examinations and dynamic monitoring, making it particularly suitable for primary healthcare institutions and large-scale screening.

[0106] Ultrasound nonlinear parametric imaging, with its unique technical advantages and promising clinical applications, holds the potential to become an important tool for the non-invasive diagnosis of fatty liver and liver fibrosis. It can not only improve the accuracy and reliability of diagnosis but also enable early detection and quantitative assessment of diseases, providing strong support for precision diagnosis and treatment and improved prognosis of liver diseases. The histogram statistics of nonlinear parameters are shown below. Figure 7 As shown. Figure 7 In the histogram, the horizontal axis represents the BOA value, and the vertical axis represents the percentage of the BOA value in the overall histogram. The numbers 4, 6, 8, 10, and 12 on the horizontal axis are the scale values ​​for the BOA value. Figure 7 In the histogram, the BOA value, which accounts for the largest proportion, is 6.8, consistent with the characteristics of no fatty liver.

[0107] The original method could directly calculate the value of the nonlinear coefficient based on the amplitude ratio. The normal range of this nonlinear coefficient is between 6 and 7. However, in the diagnosis of fatty liver, the value of this nonlinear coefficient is between 6 and 7.2, with a very small dynamic range. Using the method of this invention, the dynamic range is 6 to 8, which increases the size of the lesion area, resulting in a more accurate result.

[0108] Finally, it should be noted that the embodiments described in the above description are merely preferred practices of the invention and should not be construed as limiting the scope of the invention. Equivalent substitutions for the technical solutions described in the foregoing embodiments do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the invention, and all such substitutions should be covered within the scope of the claims and specification of the invention.

Claims

1. An ultrasonic nonlinear parameter imaging method, characterized in that, Includes the following steps: The fundamental and harmonic signals are calculated based on the ultrasonic echo RF signals. Extract the amplitude of the fundamental signal and the amplitude of the harmonic signal, and calculate the fundamental amplitude ratio and the harmonic amplitude ratio; The initial value of the nonlinear coefficient is calculated based on the ratio between the fundamental amplitude ratio and the harmonic amplitude ratio. The range of values ​​for the nonlinear coefficient is estimated using Newton's iteration method. An ultrasound image is generated based on the range of values ​​for the nonlinear coefficient. The fundamental signal includes the fundamental signal under the first voltage condition and the fundamental signal under the second voltage condition; the second harmonic signal includes the second harmonic signal under the first voltage condition and the second harmonic signal under the second voltage condition, wherein the first voltage is less than the second voltage; The formula for calculating the fundamental amplitude ratio is: ； The formula for calculating the harmonic amplitude ratio is: ； in, It is the ratio of the amplitude of the first voltage and the second voltage of the fundamental signal. It is the ratio of the amplitude of the first voltage and the second voltage of the harmonic signal. This represents the ratio of the first voltage to the second voltage within the linear segment of the input operating voltage. This represents the nonlinear coefficient; Q is a parameter factor related to voltage, dielectric density, dielectric attenuation coefficient, probe operating frequency, and depth. Given The value is obtained by using the formulas for the fundamental amplitude ratio and the harmonic amplitude ratio, and then comparing the fundamental amplitude ratio with the harmonic amplitude ratio. for: ； The formula for calculating the initial value of the nonlinear coefficient is as follows: ； in, It is the initial value of the nonlinear coefficient.

2. The ultrasonic nonlinear parameter imaging method as described in claim 1, characterized in that, The method of estimating the range of nonlinear coefficients using Newton's iteration method specifically includes the following steps: Obtain the initial values ​​of the nonlinear coefficients; The initial value of the nonlinear coefficient is compared with the fundamental amplitude ratio and the harmonic amplitude ratio to obtain r, which is then substituted into the iterative formula to calculate the next approximate value; Repeat the iteration until the accuracy requirement is met; The iterative formula is as follows: ； These are nonlinear coefficients, and k represents the number of iterations. This represents an approximate value of the nonlinear coefficients obtained after k iterations. Indicates the value to be The function whose roots are to be found at a given time; Indicates the value to be The derivative of the function corresponding to the given time.

3. The ultrasonic nonlinear parameter imaging method as described in claim 2, characterized in that, Repeat the iterations until the convergence condition that meets the accuracy requirement is met: ;exist Estimate the nonlinear coefficients under known conditions The range.

4. The ultrasonic nonlinear parameter imaging method as described in claim 1, characterized in that, The steps also include: estimating the estimated values ​​of multiple nonlinear coefficients using the Newton-Raphson iteration method; and taking a weighted average of the estimated values ​​of the multiple nonlinear coefficients to obtain the optimized nonlinear coefficient values.

5. The ultrasonic nonlinear parameter imaging method as described in claim 4, characterized in that, The formula for calculating the optimized nonlinear coefficient value is as follows: ； in, These are optimized nonlinear coefficients. The first nonlinear coefficient is estimated based on the fundamental signal. ， The second nonlinear coefficient is estimated based on the harmonic signal. ， It is the third nonlinear coefficient estimated by combining the fundamental signal and the harmonic signal; , , These are the corresponding weights.

6. The ultrasonic nonlinear parametric imaging method according to any one of claims 1-5, characterized in that, It also includes calculating the nonlinear parameter B / A using nonlinear coefficients, and calculating a histogram of the nonlinear parameter B / A distributed in two-dimensional space based on the nonlinear parameter B / A; The percentage of data exceeding a specified threshold is calculated based on the histogram of the nonlinear parameter B / A. This percentage is used to assist in identifying the location of lesions in two-dimensional space.

7. An ultrasonic nonlinear parameter imaging system, characterized in that, It includes at least one processor and a memory communicatively connected to the at least one processor; the memory stores instructions executable by the at least one processor, which, when executed by the at least one processor, enable the at least one processor to perform an ultrasound nonlinear parametric imaging method according to any one of claims 1 to 6.

8. A storage medium, characterized in that, It stores instructions executable by a processor, which, when executed by the processor, cause the processor to perform an ultrasound nonlinear parametric imaging method as described in any one of claims 1 to 6.

9. A program product for ultrasonic nonlinear parametric imaging, characterized in that, The program product implements the ultrasonic nonlinear parameter imaging method as described in any one of claims 1 to 6 when the computer is running.

Images