Ultrasonic harmonic image generation method and device, electronic equipment and medium

Abstract

The embodiment of the invention discloses an ultrasonic harmonic image generation method and device, electronic equipment and a medium. A specific embodiment of the method comprises the following steps: controlling an ultrasonic transmitting probe to transmit a low-frequency ultra-harmonic signal; controlling an ultrasonic receiving probe to receive the echo signal set to be detected; carrying out compressed sampling processing on the echo signal set to be detected and then carrying out signal reconstruction to obtain a reconstructed echo signal set; carrying out delay superposition processing on the reconstructed echo signal set to obtain a superposed echo signal; performing signal filtering decomposition processing on the superposed echo signals to obtain a super-harmonic signal set; performing envelope detection on the super-harmonic signal set to obtain an echo amplitude information set; and generating a target area ultrasonic harmonic image set, and sending the target area ultrasonic harmonic image set to a target terminal for display. According to the embodiment, by transmitting the low-frequency signal and receiving the ultra-harmonic signal, the attenuation of the received signal can be reduced, the signal penetration depth can be improved, the purity of the received signal can be improved, and the quality and the resolution of the generated ultrasonic harmonic image can be improved.

Description

Technical Field

[0001] Embodiments of the present disclosure relate to the field of computer technology, and in particular to an ultrasonic harmonic image generation method, device, electronic device, and medium. Background Art

[0002] At present, with the rapid development of random science and technology, dual-frequency harmonic imaging technology based on ultrasound imaging is more widely used for lesion development detection. Its main principle is to use low-frequency ultrasound to incident into the target area, which will produce high-order harmonics on the receiving nonlinear effect, and use the high-order harmonic signals carrying the target area to perform high-contrast, high-definition regional imaging. For the generation of ultrasonic harmonic images, the method usually adopted is: through a dual-frequency transducer with high-frequency transmission and high-frequency reception, a high-frequency second harmonic signal set is received. Then, an ultrasonic harmonic image is generated based on the high-frequency second harmonic signal set.

[0003] However, it is found in practice that when the above method is used to generate ultrasonic harmonic images, the following technical problems often occur: Since high-frequency ultrasonic signals attenuate more than low-frequency signals during propagation, they are more easily absorbed and scattered, and have a lower penetration depth, so that the received signals contain fewer regional signals and some areas cannot be reached, resulting in low resolution and clarity of the generated ultrasonic harmonic images, and the received echo signals include a large amount of noise signals, wasting storage resources and transmission resources.

[0004] The above information disclosed in this Background section is only for enhancement of understanding of the background of the present disclosure concept and therefore it may contain information that does not form the prior art that is already known in this country to a person of ordinary skill in the art. Summary of the invention

[0005] The content of this disclosure is used to introduce concepts in a brief form, which will be described in detail in the detailed implementation section below. The content of this disclosure is not intended to identify the key features or essential features of the technical solution claimed for protection, nor is it intended to limit the scope of the technical solution claimed for protection.

[0006] Some embodiments of the present disclosure propose an ultrasonic harmonic image generation method, device, electronic device, and medium to solve one or more of the technical problems mentioned in the above background technology section.

[0007] In a first aspect, some embodiments of the present disclosure provide an ultrasonic harmonic image generation method, comprising: controlling an ultrasonic transmitting probe to transmit a low-frequency ultrasonic signal to a target area to be detected of a target patient; controlling an ultrasonic receiving probe to receive a set of echo signals to be detected that reflect the low-frequency ultrasonic signal from the target area to be detected, wherein the ultrasonic transmitting probe and the ultrasonic receiving probe are probes synchronized in time and phase; performing compression sampling processing on the set of echo signals to be detected to obtain a sampled set of echo signals to be detected; performing signal reconstruction processing on the sampled set of echo signals to be detected to obtain a reconstructed echo signal set; performing delay and superposition processing on the reconstructed echo signal set to obtain a superimposed echo signal; performing signal filtering and decomposition processing on the superimposed echo signal to obtain a filtered echo signal set as a superharmonic signal set; performing envelope detection processing on the superharmonic signal set to obtain an echo amplitude information set; generating a target area ultrasonic harmonic image set according to the echo amplitude information set and the superharmonic signal set, and sending the target area ultrasonic harmonic image set to a target terminal to display the target area ultrasonic harmonic image set.

[0008] In a second aspect, some embodiments of the present disclosure provide an ultrasonic harmonic image generating device, comprising: a first control unit, configured to control an ultrasonic transmitting probe to transmit a low-frequency ultrasonic signal to a target area to be detected of a target patient; a second control unit, configured to control an ultrasonic receiving probe to receive a set of echo signals to be detected that reflect the low-frequency ultrasonic signal from the target area to be detected, wherein the ultrasonic transmitting probe and the ultrasonic receiving probe are probes synchronized in time and phase; a compressed sampling unit, configured to perform compressed sampling processing on the set of echo signals to be detected to obtain a sampled set of echo signals to be detected; and a signal reconstruction unit, configured to perform signal reconstruction processing on the sampled set of echo signals to be detected. The invention relates to a method for performing delay and superposition processing on the reconstructed echo signal set to obtain a reconstructed echo signal set; a delay and superposition unit is configured to perform delay and superposition processing on the reconstructed echo signal set to obtain a superposed echo signal; a signal filtering and decomposition unit is configured to perform signal filtering and decomposition processing on the superposed echo signal to obtain a filtered echo signal set as a superharmonic signal set; an envelope detection unit is configured to perform envelope detection processing on the superharmonic signal set to obtain an echo amplitude information set; and a generation unit is configured to generate an ultrasonic harmonic image set of a target area according to the echo amplitude information set and the superharmonic signal set, and send the ultrasonic harmonic image set of the target area to a target terminal to display the ultrasonic harmonic image set of the target area.

[0009] In a third aspect, some embodiments of the present disclosure provide an electronic device comprising: one or more processors; a storage device on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation manner in the first aspect.

[0010] In a fourth aspect, some embodiments of the present disclosure provide a computer-readable medium having a computer program stored thereon, wherein when the computer program is executed by a processor, the method described in any implementation manner in the first aspect is implemented.

[0011] The above-mentioned embodiments of the present disclosure have the following beneficial effects: the ultrasonic harmonic image generation method of some embodiments of the present disclosure can reduce the attenuation and signal penetration depth of the received signal by transmitting a low-frequency signal and receiving a superharmonic signal, improve the purity of the received signal, and the quality and resolution of the generated ultrasonic harmonic image, and reduce the waste of storage resources and transmission resources. Specifically, the resolution and clarity of the generated ultrasonic harmonic image are low, and the received echo signal includes a large number of noise signals, and the storage resources and transmission resources are wasted because: the high-frequency ultrasonic signal is more attenuated than the low-frequency signal during the propagation process, is more easily absorbed and scattered, and has a low penetration depth, so that the received signal contains fewer regional signals and some areas cannot be reached, resulting in the resolution and clarity of the generated ultrasonic harmonic image is low, and the received echo signal includes a large number of noise signals, which wastes storage resources and transmission resources. Based on this, the ultrasonic harmonic image generation method of some embodiments of the present disclosure can first control the ultrasonic transmitting probe to transmit a low-frequency ultrasonic signal to the target area to be detected of the target patient. Here, it is convenient for the subsequent target area to be detected to reflect the ultrasonic echo signal back. Secondly, the ultrasonic receiving probe is controlled to receive the echo signal set to be detected that reflects the low-frequency ultrasonic signal in the target area to be detected, wherein the ultrasonic transmitting probe and the ultrasonic receiving probe are probes synchronized in time and phase. Here, the ultrasonic transmitting probe and the ultrasonic receiving probe synchronized in time and phase can ensure that the received signal corresponds to the transmitted signal and can accurately reflect the characteristics of the tissue in the target area to be detected. Again, the echo signal set to be detected is compressed and sampled to obtain the echo signal set to be detected after sampling. Here, the main features of the echo signal can be maintained while reducing the amount of data of the received echo signal, and the focus sampling efficiency can be improved to increase the frame rate and reduce the waste of storage resources. The echo signal set to be detected after sampling is subjected to signal reconstruction processing to obtain the reconstructed echo signal set. Here, high-quality signal reconstruction can be achieved through a small amount of echo signals, the reconstruction efficiency can be improved, and high resolution can be provided for the subsequently generated ultrasonic harmonic image of the target area. Subsequently, the reconstructed echo signal set is subjected to delayed superposition processing to obtain the superimposed echo signal. Here, the signal attenuation of the received superharmonic signal set can be reduced, the detailed information of the target area to be detected included in the received superharmonic signal set can be expanded, and the signal strength and imaging resolution can be enhanced so as to facilitate subsequent modal decomposition. Afterwards, the above-mentioned superimposed echo signal is subjected to signal filtering and decomposition processing to obtain a filtered echo signal set as a superharmonic signal set. Here, the noise of the superharmonic signal can be removed and the purity of the superharmonic signal set can be improved so as to improve the quality of subsequent harmonic imaging. Then, the above-mentioned superharmonic signal set is subjected to envelope detection processing to obtain an echo amplitude information set.Here, we can fully understand the change of the signal amplitude of the echo signal after filtering over time, as well as the characteristics such as the boundary and structure of the target area to be detected. Finally, according to the above echo amplitude information set and the above superharmonic signal set, the target area ultrasonic harmonic image set is generated, and the above target area ultrasonic harmonic image set is sent to the target terminal to display the above target area ultrasonic harmonic image set. Here, the quality of the target area ultrasonic harmonic image can be improved and the transmission resources can be reduced, and accurate amplitude can be provided for medical staff. It can be obtained that this ultrasonic harmonic image generation method can reduce the attenuation and signal penetration depth of the received signal by transmitting low-frequency signals and receiving superharmonic signals, improve the purity of the received signal, and the quality and resolution of the generated ultrasonic harmonic image, and reduce the waste of storage resources and transmission resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The above and other features, advantages and aspects of the embodiments of the present disclosure will become more apparent with reference to the following detailed description in conjunction with the accompanying drawings. Throughout the accompanying drawings, the same or similar reference numerals represent the same or similar elements. It should be understood that the drawings are schematic and that components and elements are not necessarily drawn to scale.

[0013] Figure 1 is a flow chart of some embodiments of the ultrasound harmonic image generation method according to the present disclosure;

[0014] Figure 2 is a schematic structural diagram of some embodiments of an ultrasonic harmonic image generating device according to the present disclosure;

[0015] Figure 3 is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure;

[0016] Figure 4 is a schematic internal test scene diagram of an ultrasound harmonic generation system for generating an ultrasound harmonic image of a target area in some embodiments of the ultrasound harmonic image generation method according to the present disclosure;

[0017] Figure 5 are time domain diagrams and frequency domain diagrams of superimposed echo signals of schematic internal tests in some embodiments of the ultrasound harmonic image generation method disclosed herein;

[0018] Figure 6 It is a screenshot of an interface of a schematic internal test for generating an ultrasonic harmonic image of a target area in some embodiments of the ultrasonic harmonic image generating method according to the present disclosure. DETAILED DESCRIPTION

[0019] Embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although certain embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be construed as being limited to the embodiments set forth herein. On the contrary, these embodiments are provided to provide a more thorough and complete understanding of the present disclosure. It should be understood that the drawings and embodiments of the present disclosure are only for exemplary purposes and are not intended to limit the scope of protection of the present disclosure.

[0020] It should also be noted that, for ease of description, only the parts related to the invention are shown in the drawings. In the absence of conflict, the embodiments and features in the embodiments of the present disclosure can be combined with each other.

[0021] It should be noted that the concepts such as "first" and "second" mentioned in the present disclosure are only used to distinguish different devices, modules or units, and are not used to limit the order or interdependence of the functions performed by these devices, modules or units.

[0022] It should be noted that the modifications of "one" and "plurality" mentioned in the present disclosure are illustrative rather than restrictive, and those skilled in the art should understand that unless otherwise clearly indicated in the context, it should be understood as "one or more".

[0023] The names of the messages or information exchanged between multiple devices in the embodiments of the present disclosure are only used for illustrative purposes and are not used to limit the scope of these messages or information.

[0024] The present disclosure will be described in detail below with reference to the accompanying drawings and in conjunction with embodiments.

[0025] Figure 1 The flowchart 100 of some embodiments of the ultrasound harmonic image generation method according to the present disclosure is shown. The ultrasound harmonic image generation method comprises the following steps:

[0026] Step 101 : Control the ultrasound transmitting probe to transmit a low-frequency ultrasound signal to a target area to be detected of a target patient.

[0027] In some embodiments, the execution subject (e.g., electronic device) of the ultrasonic harmonic image generation method can control the ultrasonic transmitting probe to transmit low-frequency ultrasonic signals to the target area to be detected of the target patient through a wired connection or a wireless connection. The ultrasonic transmitting probe can be a linear array probe including 64 array elements for transmitting low-frequency ultrasonic waves. The low-frequency ultrasonic waves can be 5 MHz (megahertz). The ultrasonic transmitting probe can be a linear array probe including 64 array elements for transmitting low-frequency ultrasonic waves. Figure 2As shown. The target patient may be a patient scanned by an ultrasonic transmitting probe. The target area to be detected may be a body area of ​​the target patient waiting to be incident with the ultrasonic wave emitted by the ultrasonic transmitting probe. For example, the target area to be detected may be the skin on an arm. The low-frequency ultrasonic signal may be an ultrasonic wave below an ultrasonic frequency threshold generated by the ultrasonic transmitting probe through the inverse piezoelectric effect of a high-voltage source to stimulate the piezoelectric crystal in the ultrasonic transmitting probe. The ultrasonic frequency threshold may be a preset threshold. Figure 4 A schematic diagram showing a scenario in which the ultrasound harmonic image generation method is applied to an ultrasound harmonic imaging system. Figure 4 Among the two rightmost figures, the upper figure may be a screenshot of the interface before shear wave excitation, and the lower figure may be a screenshot of the interface after shear wave excitation.

[0028] Step 102 : Control the ultrasonic receiving probe to receive a set of echo signals to be detected that reflect low-frequency ultrasonic signals from the target area to be detected.

[0029] In some embodiments, the execution subject may control the ultrasonic receiving probe to receive the echo signal set to be detected that reflects the low-frequency ultrasonic signal from the target area to be detected, wherein the ultrasonic transmitting probe and the ultrasonic receiving probe are probes synchronized in time and phase. The ultrasonic receiving probe may be a linear probe that receives ultrasonic waves reflected from the target area to be detected. The ultrasonic receiving probe and the ultrasonic transmitting probe may be the same probe or may not be the same probe. The echo signal to be detected in the echo signal set to be detected may be an electrical signal converted by an analog-to-digital converter by reflecting the low-frequency ultrasonic signal from the tissue in the target detection area. The feedback signal set to be detected may include but is not limited to at least one of the following: a low-frequency ultrasonic signal, a second harmonic signal, and a high-order harmonic signal. The high-order harmonic signal may be an ultrasonic signal whose frequency is greater than or equal to the second frequency of the ultrasonic signal. The time and phase synchronization may be achieved by configuring the synchronization of the ultrasonic transmitting probe and the ultrasonic receiving probe through a high-precision pulse transmitter and the delay time of each focal point of the low-frequency ultrasonic signal in the target area to be detected.

[0030] Step 103: perform compression sampling processing on the echo signal set to be detected to obtain a sampled echo signal set to be detected.

[0031] In some embodiments, the execution subject may perform compressive sampling processing on the echo signal set to be detected to obtain a sampled echo signal set to be detected. The sampled echo signal to be detected in the sampled echo signal set may be an echo signal located in a low-dimensional space and containing the main information of the echo signal to be detected. The main information may be information that the signal reconstructed by pushing the sampled echo information to be detected is the echo signal to be detected. The main information may include but is not limited to at least one of the following: tissue structure and tissue lesion information of the target area to be detected. In practice, the execution subject may use a compressed sensing algorithm to perform compressive sampling processing on the echo signal set to be detected to obtain a sampled echo signal set to be detected.

[0032] Step 104 , performing signal reconstruction processing on the sampled to-be-detected echo signal set to obtain a reconstructed echo signal set.

[0033] In some embodiments, the execution subject may perform signal reconstruction processing on the sampled echo signal set to be detected to obtain a reconstructed echo signal set. The signal reconstruction processing may be signal reconstruction performed using a greedy algorithm.

[0034] Step 105: Delay and stack processing is performed on the reconstructed echo signal set to obtain a stacked echo signal.

[0035] In some embodiments, the execution subject may perform delay superposition processing on the reconstructed echo signal set to obtain a superimposed echo signal. The superimposed echo signal may be a signal obtained by superimposing multiple echo signals to be detected at a focal point in the target area to be detected. The focal point may be the focus of a focused echo signal that can improve image resolution and clarity by statistically analyzing a large number of ultrasonic images of the target area to be detected. In practice, the execution subject may first perform preprocessing on the reconstructed echo signal set to obtain a preprocessed echo signal set. The preprocessing may include but is not limited to at least one of the following: signal filtering processing based on a Butterworth filter and signal amplification and enhancement processing. Then, the array element distance set of each array element included in the ultrasonic receiving probe and the above-mentioned focal point is determined. Afterwards, the ratio of each array element distance in the array element distance set to the ultrasonic signal propagation rate is determined as the signal delay time to obtain a signal delay time set. Finally, the reconstructed echo signal set is time-delayed and superimposed by the signal delay time set to obtain a superimposed echo signal. Figure 5 A schematic internal test signal diagram showing the generated post-superposition echo signal. Figure 5 The first figure shows the time-frequency diagram of the echo signal after superposition, with the horizontal axis representing the position and the vertical axis representing the signal amount. Figure 5The second figure in the figure shows the frequency domain diagram of the echo signal after superposition, with the horizontal axis being frequency and the vertical axis being frequency energy.

[0036] Step 106, performing signal filtering and decomposition processing on the superimposed echo signals to obtain filtered echo signals as a superharmonic signal set.

[0037] In some embodiments, the execution subject may perform signal filtering and decomposition processing on the superimposed echo signal to obtain a filtered echo signal. The filtered echo signal may be a nonlinear echo signal obtained by removing the fundamental signal from the superimposed echo signal and extracting the second harmonic or third harmonic. The signal filtering and decomposition processing may be a decomposition processing performed using an empirical mode decomposition algorithm.

[0038] In some optional implementations of some embodiments, the above-mentioned signal filtering and decomposition processing is performed on the above-mentioned superimposed echo signal to obtain a filtered echo signal set as a superharmonic signal set, which may include the following steps:

[0039] The first step is to determine the number of filtered signals and the signal bandwidth control factor of the filtered echo signal set corresponding to the superimposed echo signal as the target number of filtered signals and the target signal bandwidth control factor. The target number of filtered signals can characterize the number of different signal frequencies included in the superharmonic signal set. The target signal bandwidth control factor can characterize the factor that controls the bandwidth of the signal obtained by decomposing the superimposed echo signal to avoid the modal aliasing phenomenon. The bandwidth can be the distribution range of the frequency at which the decomposed signal is located. The determination of the target number of filtered signals can be the number of filtered signals determined by the number of peaks of the spectrum obtained by fast Fourier transforming the superimposed echo signal. The target signal bandwidth control factor can be determined based on prior knowledge.

[0040] In the second step, according to the target number of filtered signals and the target signal bandwidth control factor, the above-mentioned superimposed echo signal is subjected to modal decomposition processing to obtain a decomposed intrinsic mode signal set. The decomposed intrinsic mode signals in the above-mentioned decomposed intrinsic mode signal set may be signals of different signal frequencies decomposed from the above-mentioned superimposed echo signal. The above-mentioned decomposed intrinsic mode signal set may include but is not limited to at least one of the following: a fundamental signal, a second harmonic signal, a third harmonic signal, and a noise signal.

[0041] As an example, the execution subject may utilize an adaptive variational modal decomposition algorithm to perform modal decomposition processing on the superimposed echo signal according to the target filter signal quantity and the target signal bandwidth control factor to obtain a decomposed intrinsic modal signal set.

[0042] The third step is to determine the signal cloud similarity value of each decomposed intrinsic mode signal in the decomposed intrinsic mode signal set and the superimposed echo signal to obtain a signal cloud similarity value set. The signal cloud similarity value in the signal cloud similarity value set can be a value proposed by the cloud model theory to measure the similarity of any two decomposed intrinsic mode signals.

[0043] The fourth step is to select at least one decomposed intrinsic mode signal whose corresponding signal cloud similarity value is greater than or equal to a preset cloud similarity threshold from the above decomposed intrinsic mode signal set to obtain a target decomposed intrinsic mode signal set. The above preset cloud similarity threshold can be a minimum value pre-determined by a technician based on statistical analysis of a large number of superimposed echo signals and decomposed intrinsic mode signals.

[0044] The fifth step is to perform singular spectrum filtering on the above-mentioned target decomposition intrinsic mode signal set to obtain the filtered intrinsic mode signal set. Among them, the filtered intrinsic mode signal in the above-mentioned filtered intrinsic mode signal set can be a signal that removes the signal noise caused by low-frequency oscillation in the above-mentioned target decomposition intrinsic mode signal and improves the signal-to-noise ratio of the filtered signal. In practice, the above-mentioned execution subject can use the singular spectrum analysis algorithm to perform trajectory decomposition, grouping and reconstruction on the above-mentioned target decomposition intrinsic mode information set to obtain the filtered intrinsic mode signal set.

[0045] The sixth step is to perform signal extraction on the filtered intrinsic mode signal set to obtain a target intrinsic mode signal set as a super harmonic signal set. The signal extraction may be extraction of a second harmonic signal or a higher harmonic signal from the filtered intrinsic mode signal set.

[0046] In some optional implementations of some embodiments, the above-mentioned determination of the number of filtered signals and the signal bandwidth control factor of the filtered echo signal set corresponding to the above-mentioned superimposed echo signal as the target number of filtered signals and the target signal bandwidth control factor may include the following steps:

[0047] The first step is to perform chaos initialization coding processing on the above-mentioned superimposed echo signal to obtain an initial signal coding population. The initial signal coding individuals in the above-mentioned initial signal coding population include: individual value range. The initial signal coding population also includes the first preset execution threshold of the entire population. The initial signal coding individuals in the above-mentioned initial signal coding population can be individuals consisting of the number of filtered signals and the signal bandwidth control factor. The above-mentioned individual value range can be the value range of the above-mentioned number of filtered signals and the value range of the signal bandwidth control factor. For example, the value range of the above-mentioned number of filtered signals can be [2, 5], and the value range of the above-mentioned signal bandwidth control factor can be [1000, 2000]. The above-mentioned initial signal coding individuals can be individuals represented in vector form consisting of the number of filtered signals and the signal bandwidth control factor. The above-mentioned first preset execution threshold can be the maximum number of executions that can be performed in the determination step. In practice, the above-mentioned execution subject can use the Tent chaotic mapping algorithm to perform chaos initialization processing on the above-mentioned superimposed echo signal to obtain the initial signal coding population.

[0048] The second step is to generate a filter fitness function for the above-mentioned initial signal coding population. The above-mentioned filter fitness function can be a function of minimizing each signal after decomposing the superimposed echo signal. The above-mentioned filter fitness function can be obtained by the following steps: First, perform Hilbert transform on each decomposed signal to obtain a transformed decomposed signal set, i.e., an envelope signal set. Secondly, determine the ratio of each envelope signal in the envelope signal set to the cumulative sum of the envelope signal set to obtain an envelope signal probability value set. Then, determine the cumulative sum of the product of each envelope signal probability value in the envelope signal probability value set and the logarithmic function of the envelope signal probability value with a base of 10 as the filter fitness function.

[0049] The third step is to input the above-mentioned initial signal coding population into the above-mentioned filtering fitness function to obtain an initial fitness value set. Among them, the initial fitness value in the above-mentioned initial fitness value set can characterize the sparsity and uncertainty of the signal after decomposition and filtering according to the number of filtered signals corresponding to the initial signal coding individuals and the signal bandwidth control factor. The smaller the above-mentioned initial fitness value, the better the effect of decomposition and filtering of the above-mentioned superimposed echo signal.

[0050] In the fourth step, based on the initial signal coding population, the following determination steps are performed:

[0051] Sub-step 1, determining the number of times the determining step of the above determining step has been executed.

[0052] Sub-step 2, based on the initial fitness value set and the number of times the determination step has been executed, each initial signal coding individual in the initial signal coding population is updated to generate an updated signal coding individual, thereby obtaining an updated signal coding population. The updated signal coding individual may be an updated individual obtained by updating using an update formula in a heuristic algorithm.

[0053] As an example, the execution subject may use a heuristic algorithm to update each initial signal coding individual in the initial signal coding population according to the accident fitness value set to generate an updated signal coding individual and obtain an updated signal coding population. The heuristic algorithm may be a single heuristic algorithm or a combination of multiple existing heuristic algorithms. The heuristic algorithm may include but is not limited to at least one of the following: a mantis search algorithm, a gray wolf optimization algorithm, and a whale optimization algorithm.

[0054] Sub-step 3, performing individual mutation processing on each updated signal coding individual in the updated signal coding population to generate a mutated signal coding individual and obtain a mutated signal coding population. The mutated signal coding individual can be a mutated individual obtained by mutating the updated signal coding individual using the Cauchy mutation strategy. It should be noted that in the late stage of the cyclic iteration of the updated signal coding population, each updated signal individual in the updated signal population is no longer updated at the same position. Therefore, the position perturbation of the updated signal coding population in the late stage of the iteration by Cauchy mutation can prevent the updated signal coding population from converging to the local optimal solution rather than the global optimal solution too early to a certain extent, thereby improving the accuracy of the obtained number of filtered signals and the signal bandwidth control factor.

[0055] Sub-step 4, determining the reverse learning coding individual of each mutated signal individual in the mutated signal coding population, and obtaining the reverse learning coding population. The reverse learning coding individual may be a coding individual obtained by using a reverse learning strategy. The reverse learning coding individual may be a coding individual obtained by adding the maximum value in the individual value range of the corresponding initial signal coding individual to the minimum value minus the value of the current step corresponding to the corresponding mutated individual.

[0056] Sub-step 5, inputting the mutated signal coding population and the reverse learning coding population into the above-mentioned filtering fitness function to obtain an updated fitness value set and a reverse learning fitness value set.

[0057] Sub-step 6, according to the updated fitness value set, the reverse learning fitness value set and the initial fitness value, the mutated signal coding population, the reverse learning coding population and the initial signal coding population are screened to obtain the target coding individual. The target coding individual may be the individual with the largest corresponding fitness value among the mutated signal coding population, the reverse learning coding population and the initial signal coding population.

[0058] As an example, the execution subject may first select the fitness value with the largest value from each updated fitness value in the updated fitness value set, the corresponding reverse learning fitness value in the reverse learning fitness value set, and the initial fitness value corresponding to the initial fitness value, to obtain a target fitness value set. Then, the fitness value with the largest value is selected from the target fitness value set to obtain a selected target fitness value. Finally, the mutated signal coding individual or the reverse learning coding individual or the initial signal coding individual corresponding to the selected target fitness value is determined as the target coding individual.

[0059] Sub-step 7, in response to determining that the determining step has been executed a number of times greater than or equal to a first preset execution threshold, determining the number of filtered signals and the signal bandwidth control factor corresponding to the target coding individual as the target number of filtered signals and the target signal bandwidth control factor. The first preset execution threshold may be a preset maximum number of times the determining step is executed.

[0060] In step 5, in response to determining that the number of times the determination step has been executed is less than the first preset execution threshold, the mutated signal coding population, the reverse learning coding population and the initial signal coding population are screened to obtain the screened coding population as the initial signal coding population, and the sum of the number of times the determination step has been executed and the preset value is determined as the number of times the determination step has been executed, so as to perform the determination step again. The preset value may be a pre-set value. For example, the preset value may be 1.

[0061] In some optional implementations of some embodiments, the above-mentioned updating process is performed on each initial signal coding individual in the initial signal coding population according to the initial fitness value set and the number of times the determination step has been executed to generate an updated signal coding individual to obtain the updated signal coding population, which may include the following steps:

[0062] In the first step, for each initial signal coding individual in the above initial signal coding population, perform the following update steps:

[0063] Sub-step 1, determining the average individual of the initial signal coding of the above initial signal coding population.

[0064] Sub-step 2, according to the number of executions of the above-mentioned update step and the above-mentioned second preset execution threshold, determine the individual space search control factor of the above-mentioned initial signal coding individual. Among them, the above-mentioned individual space search control factor can characterize the movement behavior of the initial signal coding individual in the search space. The value range of the above-mentioned individual space search control factor can be (0, 2). It should be noted that the above-mentioned individual space search control factor has the characteristics of nonlinear decreasing. In the early stage of the iterative update of the update step, the individual space search control factor maintains a large value for a long time and the value changes greatly, which can expand the spatial search range of the initial signal coding individual and increase the possibility of finding the global optimal solution. In the later stage of the iterative update of the update step, the individual space search control factor gradually decreases and the value changes slowly, which can enhance the initial signal coding individual's detailed exploration of its own search space, and reduce the probability of converging to the local optimal solution to a certain extent, thereby improving the convergence speed of the initial signal coding individual and the probability of searching for the global optimal solution. The above-mentioned second preset execution threshold can be the maximum number of times that the above-mentioned update step can be executed.

[0065] As an example, the execution subject may first determine the ratio of the difference between the number of executions of the update step and the second preset execution threshold to the second preset execution threshold as an exponential value. Secondly, determine the sum of an exponential function with e as the base and the exponential value as the exponent and 1 as the denominator value. Subsequently, determine the ratio of 2 and the denominator value as the first value. Then, determine the sum of 0.5 and a beta-distributed random number as the second value. Among them, the beta-distributed random number may be a value that conforms to the beta distribution and has a value range of (0, 1). Finally, the sum of the first value and the second value is determined as the individual space search control factor.

[0066] Sub-step 3, determining the migration update signal coding individual of the above-mentioned initial signal coding individual according to the above-mentioned individual space search control factor and the above-mentioned initial signal coding average individual. Among them, the above-mentioned migration update signal coding individual can represent the individual at the updated position where there is no position conflict between the above-mentioned initial signal coding individual and the initial signal coding individuals other than the initial signal coding individual in the above-mentioned initial signal coding population. It should be noted that by determining the migration update signal coding individual after the migration update through the individual space search control factor and the initial signal coding average individual, the initial signal coding individual can learn from the initial signal coding average individual, and the search range of the initial signal coding individual can be expanded in the early stage of iterative update, so that the coding searches for the local optimal solution too early.

[0067] As an example, the execution subject may first determine the difference between the initial signal coding average individual and the initial signal coding individual as the difference coding individual. Then, determine the product of the random number and the difference coding individual and the sum of the initial signal coding individual as the random coding individual. The random number may be a value in the range of (0, 1). Finally, determine the product of the random coding individual and the individual space search control factor as the migration update signal coding individual.

[0068] Sub-step 4, screening out the initial signal coding individuals whose corresponding initial fitness values ​​meet the preset conditions from the above-mentioned initial signal coding population, and obtaining the target initial signal coding individuals. The above-mentioned preset conditions may be the initial fitness value with the largest value in the initial fitness value set. The above-mentioned target initial signal coding individual may be the coding individual with the largest initial fitness value in the above-mentioned initial signal coding population.

[0069] Sub-step 5, generating a migration update direction vector for the initial signal coding individual according to the target initial signal coding individual and the individual space search control factor, wherein the migration update direction vector may represent the update direction of the initial signal coding individual when it is updated under the influence of the target initial signal coding individual.

[0070] As an example, the execution subject may first determine the difference between the target initial signal coding individual and the initial signal coding individual as the target difference coding individual. Then, the square of the individual space search control factor and the product of the random number and 2 are determined as the migration update value. Finally, the product of the migration update value and the target difference coding individual is determined as the migration update direction vector.

[0071] Sub-step 6, determining the sum of the above-mentioned migration update signal encoding individual and the above-mentioned migration update direction vector as the individual relative distance vector between the above-mentioned target initial signal encoding individual and the above-mentioned migration update signal encoding individual.

[0072] Sub-step 7, determining the spiral flight radius and the random angle of spiral flight of the initial signal encoding individual. The random angle of spiral flight can be a randomly determined angle with a value range of [0, 2π]. The spiral flight radius can be a radius obtained by multiplying an exponential function with e as the base and the product of the random angle of spiral flight and the flight trajectory factor as the exponent and a flight range factor. The flight trajectory factor can characterize the flight trajectory of the spiral flight of the initial signal encoding individual. The flight range factor can characterize the flight range of the spiral flight of the initial signal encoding individual. The flight trajectory factor and the flight range factor can be factors obtained by a particle swarm optimization algorithm.

[0073] Sub-step 8, determining the horizontal axis flight height, longitudinal axis flight height and vertical axis flight height of the initial signal encoding individual according to the spiral flight radius and the spiral flight random angle.

[0074] As an example, the execution subject may determine the product of the spiral flight radius and the cosine value of the spiral flight random angle as the horizontal axis flight height, determine the product of the spiral flight radius and the sine value of the spiral flight random angle as the vertical axis flight height, and determine the product of the spiral flight radius and the spiral flight random angle as the vertical axis flight height.

[0075] Sub-step 9, determines the sum of the individual relative distance vector, the horizontal axis flight height, the longitudinal axis flight height and the vertical axis flight height, and the target initial signal coding individual as the updated signal coding individual of the initial signal coding individual.

[0076] In the process of adopting technical solutions to solve the above-mentioned technical problem 1, the following technical problem 2 is often accompanied: Since there are a large number of nonlinear and non-stationary signals in the superimposed echo signal, how to accurately modally decompose the superimposed echo signal to obtain a relatively pure component modal signal and improve the quality of the generated ultrasonic harmonic image of the target area. For the above-mentioned technical problem 2, the conventional solution is generally: adopt an empirical mode decomposition algorithm to perform modal decomposition processing on the echo signal set to obtain a decomposed intrinsic mode signal set. However, the above conventional solution still has the following problems: Since the empirical mode decomposition algorithm uses all extreme points of the echo signal to interpolate and iteratively filter signal components, an endpoint effect will appear as the number of iterations increases, the decomposed component signal will be deformed, and the echo signal contains signals with similar time characteristic scales. When performing modal decomposition, modal aliasing problems will occur, resulting in a large number of noise signals and aliasing signals in the decomposed signal set, which reduces the quality of the generated ultrasonic harmonic image of the target area and increases transmission and computing resources. The inventors have considered the shortcomings of the above conventional solutions and combined the advantages / technical status of harmonic microscopy in this field owned by the applicant and the entrusted cooperative enterprises. We have decided to adopt the following solution:

[0077] In some optional implementations of some embodiments, performing modal decomposition processing on the superimposed echo signal according to the target number of filtered signals and the target signal bandwidth control factor to obtain a decomposed intrinsic mode signal set may include the following steps:

[0078] The first step is to perform waveform mirror extension processing on the superimposed echo signal through a waveform extension regression model to obtain an extended echo signal. The extended echo signal may be a signal obtained by mirroring and extending the signal at the endpoint of the superimposed echo signal. The waveform extension regression model may be a model that performs extended signal prediction on the input superimposed echo signal and outputs the signal at the endpoint of the superimposed echo signal that needs to be extended so that the endpoint of the extended signal is an extreme point. The waveform extension regression model may be a support vector regression machine whose kernel function is a linear kernel function.

[0079] The second step is to determine the initial signal frequency of each modal signal in the modal signals of the target number of filtered signals according to the extended echo signal, and obtain an initial signal frequency set, wherein the modal signal is a signal obtained by preliminary decomposition of the superimposed echo signal according to the target number of filtered signals and the target signal bandwidth control factor. The initial signal frequency can represent the center frequency of the modal signal.

[0080] As an example, the execution subject may perform the following signal determination steps for each modal signal in the target number of modal signals of the target filtered signals: first, determine the maximum signal frequency and the minimum signal frequency of the extended echo signal. Secondly, determine the ratio of the difference between the maximum signal frequency and the minimum signal frequency and the difference between the target number of filtered signals and 1 as the signal frequency difference. Then, determine the position sequence value of the modal signal in the target number of filtered signals. Finally, determine the product of the position sequence value and the signal frequency difference and the sum of the minimum signal frequency as the initial signal frequency.

[0081] The third step is to generate a signal bandwidth optimization model based on the above-mentioned target number of modal signals of the filtered signal, the above-mentioned initial signal frequency set and the above-mentioned extended echo signal, wherein the above-mentioned signal bandwidth optimization model includes: a signal bandwidth objective function and a signal bandwidth constraint function. Among them, the above-mentioned signal bandwidth optimization model can represent a planning model that minimizes the sum of the bandwidths of the filtered number of modal signals under constraints. The above-mentioned signal bandwidth objective function can be a function that minimizes the sum of the bandwidths of the filtered number of modal signals. The above-mentioned signal bandwidth constraint function can be a constraint function that the sum of the modes of the above-mentioned target number of modal signals of the filtered signal is the same as the above-mentioned extended echo signal.

[0082] As an example, the execution subject may first perform Hilbert transform processing on the modal signals of the target filtered signal number to obtain an analytical signal set. Then, the analytical signal set is spectrally modulated to obtain a modulated analytical signal set. Subsequently, the modulated analytical signal set is Gaussian-smoothed signal demodulated to obtain a modal bandwidth function set. Finally, the sum of the modal bandwidth function set is minimized to obtain a signal bandwidth target function, and the sum of the modal signals of the target filtered signal number is equal to the constraint function of the echo signal after the extension, which is determined as the signal bandwidth constraint function.

[0083] The fourth step is to transform the signal bandwidth optimization model according to the target signal bandwidth control factor and the Lagrange multiplier to obtain the transformed signal bandwidth optimization model. The signal bandwidth control factor can represent the degree of penalty for violating the signal bandwidth constraint function. The Lagrange multiplier can represent the weight value for integrating the signal bandwidth constraint function into the signal bandwidth target function.

[0084] As an example, the execution subject may first determine the product of the signal bandwidth control factor and the signal bandwidth objective function as the penalty signal bandwidth objective function. Then, the square of the Euclidean norm (L2 norm) of the difference between the extended echo signal and the sum of the modal signals of the target filtered signal number is determined as the constraint square penalty function. Subsequently, the inner product of the Lagrange multiplier and the constraint square penalty function is determined to obtain the signal bandwidth inner product constraint function. Finally, the sum of the penalty signal bandwidth objective function, the constraint square penalty function and the signal bandwidth inner product constraint function is determined as the converted signal bandwidth optimization model.

[0085] The fifth step is to perform saddle point solution on the above-mentioned converted signal bandwidth optimization model to obtain the minimized modal signal function, the minimized signal bandwidth function and the maximized Lagrange multiplier function. Among them, the above-mentioned saddle point solution can be a solution using the alternating multiplier method. The above-mentioned minimized modal signal function can be a function that minimizes the modal signal parameters by fixing the signal frequency parameters and the Lagrange multiplier parameters. The above-mentioned minimized signal bandwidth function can be a function that minimizes the signal frequency parameters by fixing the modal signal parameters and the Lagrange multiplier parameters. The above-mentioned maximized Lagrange multiplier function can be a function that maximizes the Lagrange multiplier parameters by fixing the modal signal parameters and the signal frequency parameters.

[0086] The sixth step is to solve the above-mentioned minimized modal signal function, the above-mentioned minimized signal bandwidth function and the above-mentioned maximized Lagrangian multiplier function to obtain the modal signal update function, the signal bandwidth update function and the Lagrangian multiplier update function. Among them, the solution of the above-mentioned minimized modal signal function and the above-mentioned minimized signal bandwidth function can be to first convert the minimized modal signal function and the minimized signal bandwidth function into frequency by Fourier transform, and then solve and derive the modal signal update function and the signal bandwidth update function using the Pasaval theorem strategy. The solution of the above-mentioned maximized Lagrangian multiplier function can be solved using the dual ascent method to obtain the Lagrangian multiplier update function.

[0087] In the seventh step, based on each modal signal in the target number of modal signals, perform the following input steps:

[0088] Sub-step 1, determining the number of times the input step of the above input step has been executed.

[0089] Sub-step 2: input the modal signal into the above modal signal update function to obtain an updated modal signal.

[0090] Sub-step 3: inputting the initial signal frequency corresponding to the above modal signal and the updated modal signal into the above signal bandwidth update function to obtain the updated signal bandwidth.

[0091] Sub-step 4, in response to determining that the number of times the input step has been executed is greater than or equal to the number of target filtered signals, inputting the Lagrangian multiplier, the updated modal signal and the updated signal bandwidth into the above-mentioned Lagrangian multiplier update function to obtain the updated Lagrangian multiplier.

[0092] In the seventh step, in response to determining that the number of times the input step has been executed is less than the above-mentioned target filter signal quantity, the updated modal signal is determined as the modal signal, and the sum of the number of times the input step has been executed and the above-mentioned preset value is determined as the number of times the input step has been executed, so as to execute the above-mentioned input step again.

[0093] In step 8, in response to determining that the sum of the square of the two-norm form of the difference between the updated modal signal corresponding to the updated Lagrange multiplier and the last updated modal signal and the square of the two-norm form of the last updated modal signal is less than or equal to a preset positive threshold, the obtained updated modal signal set is determined as the decomposed eigenmodal signal set. The preset positive threshold may be a pre-set positive number. For example, the preset positive threshold may be e to the power of -6.

[0094] In step 9, in response to determining that the cumulative sum of the ratios of the square of the second norm of the difference between the updated modal signal corresponding to the updated Lagrange multiplier and the last updated modal signal to the square of the second norm of the last updated modal signal is greater than a preset positive threshold, the number of times the input step has been executed is set to the preset value to execute the input step again.

[0095] The above technical solution and its related contents, combined with step "step 108", as an inventive point of an embodiment of the present disclosure, solves the second technical problem mentioned in the background technology: "Since the empirical mode decomposition algorithm uses all extreme points of the echo signal to interpolate and iteratively filter signal components, an endpoint effect will appear as the number of iterations increases, the decomposed component signals will be deformed, and the echo signal contains signals with similar time characteristic scales, and modal aliasing problems will occur when modal decomposition is performed, resulting in a large number of noise signals and aliasing signals in the decomposed signal set, reducing the quality of the generated ultrasonic harmonic image of the target area, and increasing transmission and computing resources." The factors that lead to the presence of a large number of noise signals and aliasing signals in the decomposed signal set, reducing the quality of the generated ultrasonic harmonic image of the target area, and increasing transmission and computing resources are often as follows: Since the empirical mode decomposition algorithm uses all extreme points of the echo signal to interpolate and iteratively filter signal components, an endpoint effect will appear as the number of iterations increases, the decomposed component signals will be deformed, and the echo signal contains signals with similar time characteristic scales, and modal aliasing problems will occur when modal decomposition is performed, resulting in a large number of noise signals and aliasing signals in the decomposed signal set. If the above factors are solved, the quality of the generated ultrasonic harmonic image of the target area can be improved, and the transmission and computing resources can be reduced. In order to achieve this effect, the present invention first uses a waveform extension regression model to perform waveform mirror extension on the superimposed echo signal, which can make the echo signal after extension continuously closed in the time domain space, avoiding the endpoint effect problem in the signal modal decomposition. Secondly, determine the initial signal frequency set of the modal signal of the number of filtered signals. Since the number of filtered signals is determined by a heuristic algorithm, the uncertainty of subjective selection is avoided, and the accuracy of modal decomposition and signal frequency can be improved. Subsequently, a signal bandwidth optimization model including a signal bandwidth objective function and a signal bandwidth constraint function is generated, and the signal bandwidth optimization model is transformed into a function, and the signal modal decomposition is converted into a variational problem. At the same time, the objective function and the constraint function are satisfied, so that the signal obtained by the subsequent modal decomposition has locality and sparsity in frequency, avoiding the modal aliasing problem and improving the accuracy of decomposition. Then, the bandwidth optimization model of the converted signal is solved, and each solved function is iteratively updated. The function approximation iterative solution can quickly converge to the optimal solution, improve the solution efficiency and reduce the waste of computing resources, as well as the independence and accuracy of each signal after decomposition. Finally, according to the filtered echo signal, the ultrasonic harmonic image set of the target area is generated and sent to the target terminal for display, which can improve the quality of the ultrasonic harmonic image of the target area and reduce the waste of transmission resources.

[0096] Step 107: Perform envelope detection processing on the superharmonic signal set to obtain an echo amplitude information set.

[0097] In some embodiments, the execution subject may perform envelope detection processing on the filtered echo signal to obtain echo amplitude information. The echo amplitude information may be information on the peak value of the filtered echo signal. The envelope detection processing may be detection processing using a wavelet transform method.

[0098] Step 108: Generate a target area ultrasound harmonic image set according to the echo amplitude information set and the ultrasonic harmonic signal set, and send the target area ultrasound harmonic image set to a target terminal to display the target area ultrasound harmonic image set.

[0099] In some embodiments, the execution subject may generate a target area ultrasound harmonic image set according to the echo amplitude information set and the ultrasonic harmonic signal set, and send the target area ultrasound harmonic image set to a target terminal to display the target area ultrasound harmonic image set. The ultrasound harmonic image in the target area ultrasound harmonic image set may be a two-dimensional image of the skin of the target area to be detected. The target terminal may be a terminal display screen used by a doctor to observe and analyze the target area ultrasound harmonic image. Figure 6 An ultrasound harmonic diagram of a schematic internal test showing the generated ultrasound harmonic image of the target region is shown.

[0100] As an example, the execution subject may first perform missing data filling processing on the filtered echo signal according to the echo amplitude information using a low-rank matrix filling algorithm to obtain a filled echo signal, and then perform a matrix transposition transformation on the filled echo signal to obtain an ultrasonic harmonic image set of the target area.

[0101] In some optional implementations of some embodiments, generating a target area ultrasound harmonic image set according to the echo amplitude information set and the superharmonic signal set may include the following steps:

[0102] In the first step, the echo amplitude set corresponding to the echo amplitude information set is normalized to obtain a normalized echo amplitude set, wherein the normalization process may be to transform the complex echo amplitude into data between 0 and 1.

[0103] In the second step, according to the above normalized echo amplitude set, the above superharmonic signal set is processed by completing and restoring the missing data to obtain the completed superharmonic signal set. Among them, the completed superharmonic signal in the above completed superharmonic signal set can be an echo signal obtained by completing the time series information of the above superharmonic signal. It should be noted that the normalized echo amplitude information set can provide local features of the signal, which helps to more accurately estimate the position of the actual data point, improve the quality and integrity of the ultrasonic harmonic image, and reduce the number of focusing times during sampling to increase the sampling frame rate.

[0104] As an example, the execution subject may utilize an ADMM (Alternating Direction Method of Multipliers) algorithm to perform missing data completion and recovery processing on the superharmonic signal set according to the normalized echo amplitude set to obtain a completed superharmonic signal set.

[0105] The third step is to dynamically compress the above-mentioned completed superharmonic signal set to obtain a compressed superharmonic signal set. The compressed superharmonic signal in the above-mentioned compressed superharmonic signal set can be an echo signal obtained by compressing the numerical range of the completed superharmonic signal to within the range of 8 bits. The above-mentioned dynamic compression processing can be dynamic compression performed using logarithmic compression processing.

[0106] The fourth step is to interpolate the compressed superharmonic signal set to obtain an interpolated superharmonic signal set. The interpolated superharmonic signal in the interpolated superharmonic signal set can be a signal obtained by interpolating and amplifying the compressed superharmonic signal by 10 to improve the quality and details of the image after imaging.

[0107] The fifth step is to map the above-mentioned interpolated super harmonic signal set to image pixels to obtain an initial target area harmonic image set. The initial target area harmonic image in the above-mentioned initial target area harmonic image set can be an image representing the target area to be detected, which is obtained by converting the amplitude of the interpolated echo signal and the depth information of the focus point corresponding to the amplitude into the horizontal and vertical values ​​of the image pixel value.

[0108] In the sixth step, image enhancement processing is performed on the initial target region harmonic image set to obtain an enhanced target region ultrasonic harmonic image set as the target region ultrasonic harmonic image set.

[0109] The above-mentioned embodiments of the present disclosure have the following beneficial effects: the ultrasonic harmonic image generation method of some embodiments of the present disclosure can reduce the attenuation and signal penetration depth of the received signal by transmitting a low-frequency signal and receiving a superharmonic signal, improve the purity of the received signal, and the quality and resolution of the generated ultrasonic harmonic image, and reduce the waste of storage resources and transmission resources. Specifically, the resolution and clarity of the generated ultrasonic harmonic image are low, and the received echo signal includes a large number of noise signals, and the storage resources and transmission resources are wasted because: the high-frequency ultrasonic signal is more attenuated than the low-frequency signal during the propagation process, is more easily absorbed and scattered, and has a low penetration depth, so that the received signal contains fewer regional signals and some areas cannot be reached, resulting in the resolution and clarity of the generated ultrasonic harmonic image is low, and the received echo signal includes a large number of noise signals, which wastes storage resources and transmission resources. Based on this, the ultrasonic harmonic image generation method of some embodiments of the present disclosure can first control the ultrasonic transmitting probe to transmit a low-frequency ultrasonic signal to the target area to be detected of the target patient. Here, it is convenient for the subsequent target area to be detected to reflect the ultrasonic echo signal back. Secondly, the ultrasonic receiving probe is controlled to receive the echo signal set to be detected that reflects the low-frequency ultrasonic signal in the target area to be detected, wherein the ultrasonic transmitting probe and the ultrasonic receiving probe are probes synchronized in time and phase. Here, the ultrasonic transmitting probe and the ultrasonic receiving probe synchronized in time and phase can ensure that the received signal corresponds to the transmitted signal and can accurately reflect the characteristics of the tissue in the target area to be detected. Again, the echo signal set to be detected is compressed and sampled to obtain the echo signal set to be detected after sampling. Here, the main features of the echo signal can be maintained while reducing the amount of data of the received echo signal, and the focus sampling efficiency can be improved to increase the frame rate and reduce the waste of storage resources. The echo signal set to be detected after sampling is subjected to signal reconstruction processing to obtain the reconstructed echo signal set. Here, high-quality signal reconstruction can be achieved through a small amount of echo signals, the reconstruction efficiency can be improved, and high resolution can be provided for the subsequently generated ultrasonic harmonic image of the target area. Subsequently, the reconstructed echo signal set is subjected to delayed superposition processing to obtain the superimposed echo signal. Here, the signal attenuation of the received superharmonic signal set can be reduced, the detailed information of the target area to be detected included in the received superharmonic signal set can be expanded, and the signal strength and imaging resolution can be enhanced so as to facilitate subsequent modal decomposition. Afterwards, the above-mentioned superimposed echo signal is subjected to signal filtering and decomposition processing to obtain a filtered echo signal set as a superharmonic signal set. Here, the noise of the superharmonic signal can be removed and the purity of the superharmonic signal set can be improved so as to improve the quality of subsequent harmonic imaging. Then, the above-mentioned superharmonic signal set is subjected to envelope detection processing to obtain an echo amplitude information set.Here, we can fully understand the change of the signal amplitude of the echo signal after filtering over time, as well as the characteristics such as the boundary and structure of the target area to be detected. Finally, according to the above echo amplitude information set and the above superharmonic signal set, the target area ultrasonic harmonic image set is generated, and the above target area ultrasonic harmonic image set is sent to the target terminal to display the above target area ultrasonic harmonic image set. Here, the quality of the target area ultrasonic harmonic image can be improved and the transmission resources can be reduced, and accurate amplitude can be provided for medical staff. It can be obtained that this ultrasonic harmonic image generation method can reduce the attenuation and signal penetration depth of the received signal by transmitting low-frequency signals and receiving superharmonic signals, improve the purity of the received signal, and the quality and resolution of the generated ultrasonic harmonic image, and reduce the waste of storage resources and transmission resources.

[0110] Further references Figure 2 As an implementation of the methods shown in the above figures, the present disclosure provides some embodiments of an ultrasonic harmonic image generation device. These device embodiments are similar to Figure 1 Corresponding to the method embodiments shown, the ultrasonic harmonic image generating device can be specifically applied to various electronic devices.

[0111] like Figure 2As shown, an ultrasonic harmonic image generating device 200 includes: a first control unit 201, a second control unit 202, a compression sampling unit 203, a signal reconstruction unit 204, a delay superposition unit 205, a signal filtering and decomposition unit 206, an envelope detection unit 207 and a generating unit 208. The first control unit 201 is configured to: control the ultrasonic transmitting probe to transmit a low-frequency ultrasonic signal to the target area to be detected of the target patient. The second control unit 202 is configured to: control the ultrasonic receiving probe to receive the echo signal set to be detected that reflects the low-frequency ultrasonic signal from the target area to be detected, wherein the ultrasonic transmitting probe and the ultrasonic receiving probe are probes synchronized in time and phase. The compression sampling unit 203 is configured to: perform compression sampling processing on the echo signal set to be detected to obtain a sampled echo signal set to be detected. The signal reconstruction unit 204 is configured to: perform signal reconstruction processing on the sampled echo signal set to be detected to obtain a reconstructed echo signal set. The delay superposition unit 205 is configured to perform delay superposition processing on the above-mentioned reconstructed echo signal set to obtain a superimposed echo signal. The signal filtering decomposition unit 206 is configured to perform signal filtering decomposition processing on the above-mentioned superimposed echo signal to obtain a filtered echo signal set as a superharmonic signal set. The envelope detection unit 207 is configured to perform envelope detection processing on the above-mentioned superharmonic signal set to obtain an echo amplitude information set. The generation unit 208 is configured to generate a target area ultrasound harmonic image set according to the above-mentioned echo amplitude information set and the above-mentioned superharmonic signal set, and send the above-mentioned target area ultrasound harmonic image set to the target terminal to display the above-mentioned target area ultrasound harmonic image set.

[0112] It can be understood that the units described in the ultrasonic harmonic image generating device 200 are similar to those described in the reference Figure 1 Therefore, the operations, features and beneficial effects described above for the method are also applicable to the ultrasonic harmonic image generating device 200 and the units included therein, and will not be described in detail here.

[0113] Reference below Figure 3 , which shows a structural schematic diagram of an electronic device (eg, an electronic device) 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is only an example and should not bring any limitation to the functions and scope of use of the embodiments of the present disclosure.

[0114] like Figure 3As shown, the electronic device 300 may include a processing device (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. In the RAM 303, various programs and data required for the operation of the electronic device 300 are also stored. The processing device 301, the ROM 302, and the RAM 303 are connected to each other via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0115] Typically, the following devices may be connected to the I / O interface 305: input devices 306 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; output devices 307 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; storage devices 308 including, for example, a magnetic tape, a hard disk, etc.; and communication devices 309. The communication devices 309 may allow the electronic device 300 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 3 The electronic device 300 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead. Figure 3 Each block shown in the figure may represent one device, or may represent multiple devices as required.

[0116] In particular, according to some embodiments of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, some embodiments of the present disclosure include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program includes a program code for executing the method shown in the flowchart. In some such embodiments, the computer program can be downloaded and installed from the network through the communication device 309, or installed from the storage device 308, or installed from the ROM 302. When the computer program is executed by the processing device 301, the above-mentioned functions defined in the method of some embodiments of the present disclosure are executed.

[0117] It should be noted that the computer-readable medium in some embodiments of the present disclosure may be a computer-readable signal medium or a computer-readable storage medium or any combination of the two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In some embodiments of the present disclosure, the computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, device or device. In some embodiments of the present disclosure, the computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0118] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (Hyper Text Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0119] The computer-readable medium may be included in the electronic device; or it may exist independently without being installed in the electronic device. The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device: controls the ultrasonic transmitting probe to transmit a low-frequency ultrasonic signal to the target area to be detected of the target patient; controls the ultrasonic receiving probe to receive the echo signal set to be detected that reflects the low-frequency ultrasonic signal from the target area to be detected, wherein the ultrasonic transmitting probe and the ultrasonic receiving probe are probes synchronized in time and phase; performs compression sampling processing on the echo signal set to be detected to obtain the sampled echo signal set to be detected; and performs compression sampling processing on the echo signal set to be detected to obtain the sampled echo signal set to be detected. The signal set is subjected to signal reconstruction processing to obtain a reconstructed echo signal set; the reconstructed echo signal set is subjected to delay superposition processing to obtain a superposed echo signal; the superposed echo signal is subjected to signal filtering and decomposition processing to obtain a filtered echo signal set as a superharmonic signal set; the superharmonic signal set is subjected to envelope detection processing to obtain an echo amplitude information set; a target area ultrasonic harmonic image set is generated according to the echo amplitude information set and the superharmonic signal set, and the target area ultrasonic harmonic image set is sent to a target terminal to display the target area ultrasonic harmonic image set.

[0120] Computer program code for performing the operations of some embodiments of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, C++, and conventional procedural programming languages ​​such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer via any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0121] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0122] The units described in some embodiments of the present disclosure may be implemented by software or by hardware. The described units may also be arranged in a processor, for example, may be described as: a processor including a first control unit, a second control unit, a compression sampling unit, a signal reconstruction unit, a delay superposition unit, a signal filtering and decomposition unit, an envelope detection unit and a generation unit. The names of these units do not constitute limitations on the units themselves in certain cases, for example, the first control unit may also be described as a "unit that controls the ultrasound transmitting probe to transmit low-frequency ultrasound signals to the target area to be detected of the target patient".

[0123] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0124] The above descriptions are only some preferred embodiments of the present disclosure and an explanation of the technical principles used. Those skilled in the art should understand that the scope of the invention involved in the embodiments of the present disclosure is not limited to the technical solutions formed by a specific combination of the above-mentioned technical features, but should also cover other technical solutions formed by any combination of the above-mentioned technical features or their equivalent features without departing from the above-mentioned inventive concept. For example, the above-mentioned features are replaced with the technical features with similar functions disclosed in the embodiments of the present disclosure (but not limited to) and the technical solutions formed.

Claims

1. A method for generating an ultrasonic harmonic image, comprising: Controlling the ultrasonic transmitting probe to transmit a low-frequency ultrasonic signal to a target area to be detected of a target patient; Controlling the ultrasonic receiving probe to receive a set of echo signals to be detected that are the reflections of the low-frequency ultrasonic signal from the target area to be detected, wherein the ultrasonic transmitting probe and the ultrasonic receiving probe are probes synchronized in time and phase; Performing compression sampling processing on the echo signal set to be detected to obtain a sampled echo signal set to be detected; Performing signal reconstruction processing on the sampled to-be-detected echo signal set to obtain a reconstructed echo signal set; Performing delay and stacking processing on the reconstructed echo signal set to obtain a stacked echo signal; Performing signal filtering and decomposition processing on the superimposed echo signals to obtain a filtered echo signal set as a superharmonic signal set; Performing envelope detection processing on the superharmonic signal set to obtain an echo amplitude information set; A target area ultrasound harmonic image set is generated according to the echo amplitude information set and the super harmonic signal set, and the target area ultrasound harmonic image set is sent to a target terminal to display the target area ultrasound harmonic image set.

2. The method according to claim 1, wherein: The signal filtering and decomposition processing is performed on the superimposed echo signal to obtain a filtered echo signal set as a superharmonic signal set, including: Determining the number of filtered signals and the signal bandwidth control factor of the filtered echo signal set corresponding to the superimposed echo signal as the target number of filtered signals and the target signal bandwidth control factor; According to the number of target filter signals and the target signal bandwidth control factor, the stacked echo signal is subjected to modal decomposition processing to obtain a decomposed intrinsic modal signal set; Determine a signal cloud similarity value of each decomposed intrinsic mode signal in the decomposed intrinsic mode signal set and the superimposed echo signal to obtain a signal cloud similarity value set; Screening out at least one decomposed intrinsic mode signal whose corresponding signal cloud similarity value is greater than or equal to a preset cloud similarity threshold from the decomposed intrinsic mode signal set to obtain a target decomposed intrinsic mode signal set; Performing singular spectrum filtering on the target decomposed intrinsic mode signal set to obtain a filtered intrinsic mode signal set; Signal extraction is performed on the filtered intrinsic mode signal set to obtain a target intrinsic mode signal set as a superharmonic signal set.

3. The method according to claim 2, wherein: The step of determining the number of filtered signals and the signal bandwidth control factor of the filtered echo signal set corresponding to the superimposed echo signal as the target number of filtered signals and the target signal bandwidth control factor comprises: Performing chaos initialization coding processing on the superimposed echo signal to obtain an initial signal coding population; generating a filter fitness function for the initial signal coding population; Inputting the initial signal coding population into the filter fitness function to obtain an initial fitness value set; Based on the initial signal encoding population, the following determination steps are performed: determining a number of times the determining step of the determining step has been performed; According to the initial fitness value set and the number of times the determination step has been executed, each initial signal coding individual in the initial signal coding population is updated to generate an updated signal coding individual, thereby obtaining an updated signal coding population; Performing individual mutation processing on each updated signal coding individual in the updated signal coding population to generate a mutated signal coding individual, thereby obtaining a mutated signal coding population; Determine the reverse learning coding individual of each mutated signal individual in the mutated signal coding population to obtain a reverse learning coding population; Inputting the mutated signal coding population and the reverse learning coding population into the filtering fitness function to obtain an updated fitness value set and a reverse learning fitness value set; According to the updated fitness value set, the reverse learning fitness value set and the initial fitness value, the mutated signal coding population, the reverse learning coding population and the initial signal coding population are screened to obtain the target coding individual; In response to determining that the determining step has been performed a number of times greater than or equal to a first preset execution threshold, determining the number of filtered signals and the signal bandwidth control factor corresponding to the target coding individual as the target number of filtered signals and the target signal bandwidth control factor; In response to determining that the number of times the determination step has been executed is less than the first preset execution threshold, the mutated signal coding population, the reverse learning coding population and the initial signal coding population are screened to obtain the screened coding population as the initial signal coding population, and the sum of the number of times the determination step has been executed and the preset value is determined as the number of times the determination step has been executed, so as to execute the determination step again.

4. The method according to claim 3, wherein: The updating process is performed on each initial signal coding individual in the initial signal coding population according to the initial fitness value set and the number of times the determination step has been executed to generate an updated signal coding individual, and obtain the updated signal coding population, including: For each initial signal coding individual in the initial signal coding population, the following update steps are performed: Determining the average individual of the initial signal coding population; Determining an individual spatial search control factor of the initial signal encoding individual according to the number of times the updating step has been executed and a second preset execution threshold; Determine the migration update signal coding individual of the initial signal coding individual according to the individual space search control factor and the initial signal coding average individual; Selecting an initial signal coding individual whose corresponding initial fitness value meets a preset condition from the initial signal coding population to obtain a target initial signal coding individual; Generate a migration update direction vector for the initial signal encoding individual according to the target initial signal encoding individual and the individual space search control factor; Determine the sum of the migration update signal encoding individual and the migration update direction vector as the individual relative distance vector between the target initial signal encoding individual and the migration update signal encoding individual; Determining the spiral flight radius and the spiral flight random angle of the initial signal encoding individual; Determining the horizontal axis flight height, the longitudinal axis flight height and the vertical axis flight height of the initial signal encoding individual according to the spiral flight radius and the spiral flight random angle; Determine the sum of the individual relative distance vector, the horizontal axis flight height, the product of the longitudinal axis flight height and the vertical axis flight height, and the target initial signal coding individual as the updated signal coding individual of the initial signal coding individual.

5. The method according to claim 1, wherein: The step of generating a target area ultrasonic harmonic image set according to the echo amplitude information set and the ultrasonic harmonic signal set comprises: Normalizing the echo amplitude set corresponding to the echo amplitude information set to obtain a normalized echo amplitude set; According to the normalized echo amplitude set, the superharmonic signal set is processed by completing and restoring missing data to obtain a completed superharmonic signal set; Performing dynamic compression processing on the completed superharmonic signal set to obtain a compressed superharmonic signal set; Performing interpolation processing on the compressed superharmonic signal set to obtain an interpolated superharmonic signal set; Performing image pixel mapping on the interpolated superharmonic signal set to obtain an initial target area harmonic image set; The initial target region harmonic image set is subjected to image enhancement processing to obtain an enhanced target region ultrasonic harmonic image set as the target region ultrasonic harmonic image set.

6. An ultrasonic harmonic image generating device, comprising: A first control unit is configured to control the ultrasound transmitting probe to transmit a low-frequency ultrasound signal to a target area to be detected of a target patient; A second control unit is configured to control the ultrasonic receiving probe to receive a set of echo signals to be detected that reflect the low-frequency ultrasonic signal from the target area to be detected, wherein the ultrasonic transmitting probe and the ultrasonic receiving probe are probes synchronized in time and phase; A compression sampling unit is configured to perform compression sampling processing on the echo signal set to be detected to obtain a sampled echo signal set to be detected; A signal reconstruction unit is configured to perform signal reconstruction processing on the sampled to-be-detected echo signal set to obtain a reconstructed echo signal set; A delay and superposition unit is configured to perform delay and superposition processing on the reconstructed echo signal set to obtain a superpositioned echo signal; a signal filtering and decomposition unit configured to perform signal filtering and decomposition processing on the superimposed echo signal to obtain a filtered echo signal set as a superharmonic signal set; An envelope detection unit is configured to perform envelope detection processing on the superharmonic signal set to obtain an echo amplitude information set; The generating unit is configured to generate a target area ultrasound harmonic image set according to the echo amplitude information set and the super harmonic signal set, and send the target area ultrasound harmonic image set to a target terminal to display the target area ultrasound harmonic image set.

7. An electronic device comprising: one or more processors; a storage device having one or more programs stored thereon, When the one or more programs are executed by the one or more processors, the one or more processors implement the method according to any one of claims 1 to 5.

8. A computer readable medium having a computer program stored thereon, wherein: When the computer program is executed by a processor, the method according to any one of claims 1 to 5 is implemented.

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