Ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods

Abstract

The invention discloses an ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods, and the method comprises the steps: obtaining an ultrasonic cavitation signal passively received by an ultrasonic receiving transducer array, and carrying out the processing of a signal received by one array element through a Hansel-SVD method, three groups of data signals representing treatment ultrasonic energy, transient cavitation energy and steady cavitation energy are separated; respectively carrying out MV-DMAS method processing on one group of data signals in the three groups of separated data signals representing treatment ultrasonic energy, transient cavitation energy and steady state cavitation energy to obtain focusing signals, carrying out energy time integration to realize long pulse light imaging, and carrying out logarithmic compression processing and scanning conversion to obtain three corresponding ultrasonic cavitation passive imaging images. Passive imaging and feature detection are performed on the generated ultrasonic cavitation phenomenon so as to monitor the cavitation generation position of ultrasonic treatment and analyzer features, and the ultrasonic treatment safety is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic signal processing, in particular to an ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods. Background Art

[0002] Ultrasonic cavitation, the phenomenon in which a cavitation nucleus oscillates or suddenly collapses under ultrasonic stimulation, plays a vital role in a variety of industrial and biomedical applications. In recent years, high-intensity focused ultrasound (HIFU) has made significant progress as a non-invasive therapeutic technique, and acoustic cavitation is generally considered one of the key mechanisms of HIFU therapy. Effectively monitoring and analyzing cavitation is crucial for improving the safety of ultrasound therapy.

[0003] Acoustic cavitation can be divided into two modes: stable cavitation (SC) and transient cavitation (IC). Under low acoustic pressure, cavitation microbubbles typically oscillate in a linear, symmetrical manner. When the acoustic pressure increases to a certain level, the symmetry of the microbubble oscillation breaks down, and steady-state cavitation behavior, characterized by subharmonics and superharmonics, begins to emerge. As the acoustic pressure rises further, the microbubbles undergo violent asymmetric oscillations, rapidly expanding and then imploding to form transient cavitation, inducing a broad spectrum of cavitation noise signals.

[0004] Currently, the most effective acoustic cavitation monitoring technology commonly used in the field of medical ultrasound is passive cavitation detection (PCD). This method uses a broadband transducer to passively receive the scattered noise generated by the oscillation of acoustic cavitation microbubbles, achieving highly sensitive real-time cavitation monitoring. The PCD detection method can detect ultrasonic cavitation, but because a single ultrasonic transducer lacks the ability to detect the direction of the signal source, it can only detect the occurrence of ultrasonic cavitation within the receiving range, but cannot determine the location of the ultrasonic cavitation. In response to this, the Chinese invention patent "Fast Delay Multiplication and Adaptive Beamforming Method for Passive Acoustic Imaging (202211220827.4)" develops a passive imaging method using an ultrasonic transducer array to detect the location of ultrasonic cavitation. However, this method cannot distinguish whether the ultrasonic cavitation phenomenon is transient or steady-state cavitation. It can only detect the occurrence of ultrasonic cavitation at a certain location, and cannot accurately distinguish the time-domain evolution of ultrasonic cavitation. This method still has shortcomings in ultrasonic cavitation detection. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes an ultrasonic cavitation passive imaging method that combines the H-SVD and MV-DMAS methods, which can obtain ultrasonic cavitation signals with significant characteristics and accurately distinguish the time domain evolution behaviors of steady-state cavitation and transient cavitation within a certain irradiation time range; it can passively image and detect the characteristics of the ultrasonic cavitation phenomenon generated during ultrasonic treatment to monitor the location and analyzer characteristics of cavitation generated during ultrasonic treatment, thereby improving the safety of ultrasonic treatment.

[0006] In order to achieve the above object, the technical solution of the present invention is as follows:

[0007] The ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods includes the following steps:

[0008] Step 1: Acquire an ultrasonic cavitation signal passively received by an ultrasonic receiving transducer array, process the signal received by one of the array elements using the Hankel-SVD method, and separate three sets of data signals representing therapeutic ultrasound energy, transient cavitation energy, and steady-state cavitation energy;

[0009] Step 2: Perform the minimum variance-multiplication delay superposition (MV-DMAS) method on one group of data signals from the three groups of data signals representing therapeutic ultrasound energy, transient cavitation energy, and steady-state cavitation energy to obtain a focused signal. After energy-time integration to achieve long-pulse light imaging, the corresponding three types of ultrasonic cavitation passive imaging images are obtained through logarithmic compression processing and scan conversion.

[0010] Preferably, the step 1 comprises the following steps:

[0011] Step 101, collecting ultrasonic cavitation signals passively received by a single ultrasonic receiving transducer;

[0012] Step 102: The received ultrasonic cavitation signal is cut into an observation window of a certain length, and the ultrasonic cavitation signal within the observation window is rearranged to obtain a two-dimensional Hankel matrix; the two-dimensional Hankel matrix is ​​subjected to singular value decomposition and anti-diagonal averaging to reconstruct a one-dimensional signal array;

[0013] Step 103, calculating the average frequency of each order component based on the one-dimensional signal array;

[0014] Step 104 , filtering the calculated average frequency of each order component, and filtering out data points corresponding to the therapeutic ultrasound energy, steady-state cavitation energy, and transient cavitation energy;

[0015] Step 105: Sum up the data point results corresponding to the therapeutic ultrasound energy, stable cavitation energy, and transient cavitation energy respectively. The corresponding state of acoustic cavitation at present is the one corresponding to the maximum summation result.

[0016] Preferably, step 102 includes the following steps:

[0017] The acoustic cavitation signal has the specific form of:

[0018] s = [s(0), s(1), …, s(N D -1)] T

[0019] where N D is the number of sampling points, and N D is an even number;

[0020] Obtain a two-dimensional Hankel matrix, which has the specific form of:

[0021]

[0022] where P = N D / 2 is the signal window length;

[0023] The rank of the two-dimensional Hankel matrix is P, and it is decomposed into the sum of P orthogonal components through SVD, which has the specific form of:

[0024]

[0025] where S k is the k-th order Hankel component after decomposition, and σ k , u k , are the corresponding singular value, left singular vector, and right singular vector respectively;

[0026] Reconstruct the P two-dimensional matrices after SVD decomposition into P one-dimensional matrices: Assume that for the k-th order Hankel component S k , each element in its matrix is expressed as S kij (1 ≤ i ≤ P, 1 ≤ j ≤ N D -P + 1), let N D -P + 1 = K, P * = min(P, K), K * = max(P, K); when P < K, Otherwise [[ID=********]] i, j, m, n represent the subscripts of matrix elements. Through diagonal averaging, transform the matrix S k into a sequence with the required length of N D The specific reconstruction method is described as: Specifically, the reconstruction method is described as:

[0027]

[0028] Thus, a one-dimensional signal array s' is obtained, the number of which is the same as the number of points of the original ultrasonic cavitation signal, expressed as:

[0029] s'=[s'(0),s'(1),…,s'(N D -1)] T .

[0030] Preferably, the average frequency f of each order component is calculated based on the one-dimensional signal array. mean , the specific form is:

[0031]

[0032] where N D is the number of Fourier transform points, f s is the sampling rate, is the actual frequency value of each component in the discrete frequency domain, and P[k] is the power spectral density of each frequency component, which is obtained by Fourier transform of the one-dimensional signal array.

[0033] Preferably, step 104 includes the following steps:

[0034] The screening criteria for therapeutic ultrasound energy are:

[0035]

[0036] Where k is a positive integer, f c is the center frequency of the signal component corresponding to the maximum singular value, bws is half of the -6dB bandwidth of the spectrum of each order signal component, and bws0 is half of the -6dB bandwidth of the selected threshold;

[0037] The screening conditions for steady-state cavitation energy are:

[0038]

[0039] The screening conditions for transient cavitation energy are:

[0040] s inertial ={bws>bws0}.

[0041] Preferably, the step 2 comprises the following steps:

[0042] Step 201: For each position point in each sound field, the corresponding receiving delay value τ(x,z) of each ultrasonic transducer array element is calculated according to the sound speed in the medium. The specific form is:

[0043]

[0044] Where: x is the coordinate parallel to the probe surface, z is the coordinate perpendicular to the probe surface, and i represents the i-th transducer array element;

[0045] Step 202: Time-shift the signal corresponding to each ultrasonic transducer array element according to the receiving delay value of the array element. The specific form is:

[0046] S' i (x,z,t)=s' i (t+τ(x,z))

[0047] Where: s' i (t) is the starting signal corresponding to the i-th array element in the transducer array;

[0048] Step 203: Based on the time-shifted signal S' i (x, z, t), calculate the weight W and perform weighting, the specific form is:

[0049]

[0050] in: a is the identity matrix; H is the transpose; thus we can get S' i The optimal weighted delayed signal of (x,z,t) Specifically expressed as:

[0051]

[0052] Step 204: Calculate the focus signal q(x,z,t) based on the MV-DMAS algorithm MV-DMAS , specifically:

[0053]

[0054] Where l is the total number of array elements of the ultrasonic transducer array;

[0055] Step 205: Simplify the focused signal q to obtain q * (x,z,t) MV-DMAS , specifically:

[0056]

[0057] in, To preserve the sign of the square root,

[0058] Step 206: Obtain the sound intensity I of the passive sound field imaging by time integration. MV-DMAS (x,z), specifically:

[0059]

[0060] Where ρ0 is the average density of the human body; c0 is the average speed of sound in the human body; T is the integration time;

[0061] Step 207: Time-average the sound intensity I calculated for each transmission and reception as the pixel value corresponding to each position in the sound field;

[0062] Step 208: Perform logarithmic compression and gain adjustment on each pixel value.

[0063] Preferably, in step 209 , coordinate transformation is performed on the image of the entire sound field, from a rectangular coordinate system to a polar coordinate system.

[0064] Preferably, the three corresponding ultrasonic cavitation passive imaging images are displayed separately or superimposed.

[0065] Based on the above technical solution, the beneficial effects of the present invention are as follows: the present invention provides a method for passive ultrasonic cavitation imaging that combines H-SVD and MV-DMAS methods, obtains an ultrasonic cavitation signal passively received by an ultrasonic receiving transducer array, processes the signal received by one of the array elements using the Hankel-SVD method, and separates three groups of data signals representing therapeutic ultrasonic energy, transient cavitation energy, and steady-state cavitation energy; and processes one of the three groups of data signals representing therapeutic ultrasonic energy, transient cavitation energy, and steady-state cavitation energy using the MV-DMAS method to obtain a focused signal. After energy-time integration to achieve long-pulse light imaging, three corresponding ultrasonic cavitation passive imaging images are obtained through logarithmic compression processing and scan conversion. The present invention can obtain ultrasonic cavitation signals with significant characteristics and can accurately distinguish the time-domain evolution behavior of steady-state cavitation and transient cavitation within a certain irradiation time range. The present invention can also passively image and detect the characteristics of ultrasonic cavitation phenomena generated during ultrasonic therapy, thereby monitoring the location and analyzer characteristics of cavitation generated during ultrasonic therapy and improving the safety of ultrasonic therapy. BRIEF DESCRIPTION OF THE DRAWINGS

[0066] Figure 1 is a flow chart of a method for ultrasonic cavitation passive imaging combining H-SVD and MV-DMAS methods in one embodiment;

[0067] Figure 2 is a schematic diagram of a Hankel-SVD analysis process in one embodiment;

[0068] Figure 3 is a schematic diagram of the change of the signal array during the Hankel-SVD analysis process in an embodiment;

[0069] Figure 4is a schematic diagram of an ultrasonic cavitation passive imaging measurement system in one embodiment;

[0070] Figure 5 The present invention is an ultrasonic cavitation passive imaging superposition image after screening transient cavitation and steady-state cavitation in one embodiment. DETAILED DESCRIPTION

[0071] The technical solutions in the embodiments of the present invention will be described clearly and completely below with reference to the accompanying drawings in the embodiments of the present invention.

[0072] like Figures 1 to 3 As shown, the ultrasonic cavitation passive imaging method combining the H-SVD and MV-DMAS methods includes the following steps:

[0073] Step 1: Acquire an ultrasonic cavitation signal passively received by an ultrasonic receiving transducer array, process the signal received by one of the array elements using the Hankel-SVD method, and separate three sets of data signals representing therapeutic ultrasound energy, transient cavitation energy, and steady-state cavitation energy;

[0074] Step 2: Perform MV-DMAS processing on one group of data signals from the three groups of data signals representing therapeutic ultrasound energy, transient cavitation energy, and steady-state cavitation energy to obtain a focused signal. After energy-time integration to achieve long-pulse light imaging, the corresponding three types of ultrasonic cavitation passive imaging images are obtained through logarithmic compression processing and scan conversion.

[0075] In an embodiment of the ultrasonic cavitation passive imaging method combining the H-SVD and MV-DMAS methods, a detailed processing procedure of step 1 is also provided, including the following steps:

[0076] Step 101: During ultrasound treatment, the ultrasonic cavitation raw signal is collected for a period of time using the transducer elements in the ultrasonic transducer array. The data points collected by one of the transducer elements are set to be represented in the form of:

[0077] s=[s(0),s(1),…,s(N D -1)] T

[0078] where N D is the number of sampling points, and N D Should be an even number;

[0079] Step 102: rearrange the one-dimensional original signal into a two-dimensional Hankel matrix. The specific form is:

[0080]

[0081] Where P = N D / 2 is the preferred signal window length;

[0082] The rank of the aforementioned Hankel matrix is P and can be decomposed into the sum of P orthogonal components by SVD. The specific form is:

[0083]

[0084] where S k is the k-th order Hankel component after decomposition, σ k , u k , are the corresponding singular value, left singular vector and right singular vector respectively.

[0085] Reconstruct the P two-dimensional matrices after SVD decomposition into P one-dimensional matrices. Assume that for the k-th order Hankel component S k , each element in its matrix is represented as S kij (1 ≤ i ≤ P, 1 ≤ j ≤ N D -P + 1). Let N D -P + 1 = L, P * = min(P, K), K * = max(P, K). When P < K, Otherwise i, j, m, n represent the subscripts of matrix elements. The matrix S k can be transformed into a sequence with the required length of N D through anti-diagonal averaging The specific reconstruction method can be described as:

[0086]

[0087] Thus, a one-dimensional signal array can be obtained again, with the same number of points as the original signal, and can be expressed as:

[0088] s' = [s'(0), s'(1), …, s'(N D -1)] T

[0089] Step 103, based on the new signal array, calculate the average frequency of each order component. It can be obtained by calculating the average value of the frequency weighted by the power spectral density. The specific form is:

[0090]

[0091] where N is the number of points of the Fourier transform, f s is the sampling rate, is the actual frequency value of each component in the discrete frequency domain, and P[k] is the power spectral density of each frequency component, which is obtained by Fourier transform of the new signal array, and its -6dB bandwidth can be calculated in the power spectrum.

[0092] Step 104 , screening the calculated average frequency of each order component, and screening out the data points corresponding to the therapeutic ultrasound energy, the steady-state cavitation energy, and the transient cavitation energy.

[0093] 1) The screening conditions for therapeutic ultrasound energy are:

[0094]

[0095] Where k is a positive integer, f c is the center frequency of the signal component corresponding to the maximum singular value, bws is half of the -6dB bandwidth of the spectrum of each order signal component, and bws0 is half of the -6dB bandwidth of the selected threshold;

[0096] 2) The screening conditions for steady-state cavitation energy are:

[0097]

[0098] 3) The screening conditions for transient cavitation energy are:

[0099] s inertial ={bws>bws0};

[0100] In step 105, all new signal data points that meet a certain condition, obtained by screening according to different conditions in step 104, are retained to represent the corresponding cavitation state. These new signal data points are set to zero, resulting in a set of signal data points that primarily represent the acoustic cavitation state.

[0101] After completing step 104, three sets of signal data point arrays are obtained, each primarily representing the therapeutic ultrasound energy, the steady-state cavitation energy, and the transient cavitation energy. These data points represent the three sets of data after separation of the acoustic cavitation signal received by an array element in the ultrasonic transducer array.

[0102] Therefore, the method of steps 101 to 105 is repeated, and the ultrasonic cavitation signals collected by other elements in the ultrasonic transducer array are processed in the same way to enhance the representation of the ultrasonic cavitation signal characteristics. These signals processed by the H-SVD method are used as the starting signals for subsequent passive ultrasonic imaging to complete the subsequent signal processing process.

[0103] In an embodiment of the ultrasonic cavitation passive imaging method combining the H-SVD and MV-DMAS methods, a detailed processing procedure of step 2 is also provided, including the following steps:

[0104] Step 201: For each position point in each sound field, the corresponding receiving delay of each ultrasonic transducer array element is calculated according to the sound speed in the medium, specifically:

[0105]

[0106] Where: x is the coordinate parallel to the probe surface, z is the coordinate perpendicular to the probe surface, and i represents the i-th transducer array element.

[0107] Step 202: Time-shift the signal corresponding to each ultrasonic transducer array element according to the receiving delay value of the array element, specifically:

[0108] S' i (x,z,t)=s' i (t+τ(x,z))

[0109] Where: s' i (t) is the starting signal corresponding to the i-th array element in the transducer array.

[0110] Step 203: Calculate the weight W based on the time-shifted signal S' and perform weighting, specifically:

[0111]

[0112] in: a is the identity matrix; H is the transpose. Thus we can get S' i The optimal weighted delay signal of (x, z, t) can be expressed as:

[0113]

[0114] Step 204: Calculate the focus signal q(x,z,t) based on the MV-DMAS algorithm MV-DMAS , specifically:

[0115]

[0116] Where l is the total number of array elements of the ultrasonic transducer array; S' after weighting W i (x,z,t).

[0117] Step 205: Simplify the focused signal q to obtain q*, specifically:

[0118]

[0119] in, To preserve the sign of the square root,

[0120] Step 206: Obtain the sound intensity I of the passive sound field imaging by time integration, specifically:

[0121]

[0122] Where ρ0 is the average density of the human body; c0 is the average speed of sound in the human body; T is the integration time.

[0123] Step 207: Time-average the sound intensity I calculated for each transmission and reception as the pixel value corresponding to each position in the sound field.

[0124] Step 208: Perform logarithmic compression and gain adjustment on each pixel value.

[0125] Since logarithmic compression and gain adjustment are conventional processing methods in this field, they will not be further explained here.

[0126] Step 209 , performing coordinate transformation on the image of the entire sound field, from a rectangular coordinate system to a polar coordinate system (required when using a convex array or a phased array ultrasonic transducer array, but not required when using a linear array ultrasonic transducer array).

[0127] The resulting passive ultrasound cavitation imaging image is displayed. Repeat steps 201 through 208 or 209 to perform passive cavitation imaging on the signals primarily representing therapeutic ultrasound energy, steady-state cavitation energy, and transient cavitation energy. The resulting images can be displayed individually or overlaid in the same location.

[0128] It should be understood that, although the various steps in the above flow chart are shown in sequence as indicated by the arrows, these steps are not necessarily performed in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and these steps can be performed in other orders. Moreover, at least a portion of the steps in the above flow chart may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily performed at the same time, but can be performed at different times, and the execution order of these sub-steps or stages is not necessarily to be performed in sequence, but can be performed in turn or alternately with other steps or at least a portion of the sub-steps or stages of other steps.

[0129] Specific implementation process:

[0130] Build a Figure 4The passive imaging measurement system for ultrasonic cavitation signals shown in the figure changes the parameters of the drive signal for the ultrasonic transmitting transducer by adjusting the parameters of the signal generator. After receiving the ultrasonic cavitation signal, the ultrasonic receiving transducer array transmits it to the ultrasonic data processing device. This device completes basic processing steps such as data digitization and simple pre-processing. The data is then processed and displayed using a data processing program developed based on the technical solution developed in this patent.

[0131] In such Figure 4 In the experimental system shown, Sonovue contrast agent microbubbles are continuously injected into the silicone tube to promote ultrasonic cavitation within the tube. A signal generator is set to a specific output voltage (e.g., 100mV). This voltage is then amplified by a power amplifier with a fixed gain of 50dB before driving the ultrasonic transmitting transducer. The receiving transducer array and ultrasonic data processing equipment continuously collect ultrasonic cavitation signals for acoustic cavitation feature analysis and passive imaging within the sound field.

[0132] Since the focal position of the transmitting transducer is in the silicone tube, the sound field in the tube is unevenly distributed, the sound pressure at the focal point is larger, and the sound pressure around it is smaller. Therefore, the ultrasonic cavitation conditions at different positions in the silicone tube are different. Through the technical solution of the present invention, the sound field in the silicone tube can be divided into therapeutic ultrasonic energy distribution images, steady-state cavitation energy distribution images, and transient cavitation energy distribution images according to different ultrasonic cavitation characteristics. Since transient cavitation and steady-state cavitation have a greater impact on the treatment effect during actual ultrasonic treatment, the distribution images of the two are selected and the images are superimposed based on the position point. The following is obtained: Figure 5 In the acoustic field distribution image shown, the bright area in the upper left corner is the location of a steady-state cavitation microbubble group, while the bright area in the upper right corner is the location of a transient cavitation microbubble group.

[0133] This demonstrates that by combining the Hankel-SVD and MV-DMAS methods, and utilizing the ultrasound transducer array to receive signals, it is possible to effectively distinguish different states of ultrasonic cavitation and passively image the specific locations of characteristic cavitation. This significantly enhances monitoring capabilities during ultrasound therapy, improving both the accuracy and safety of treatment.

[0134] In the present technical solution, based on the process of screening different types of ultrasonic cavitation energy described in steps 101 to 105, the frequency range of the energy signal is determined by calculating the signal data after Hankel-SVD, and there is no need to set the frequency range a priori. This forms a detection method that is independent of system parameters and has better universality.

[0135] The foregoing description is merely a preferred embodiment of the ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods disclosed herein and is not intended to limit the scope of protection of the embodiments of this specification. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the embodiments of this specification shall be included within the scope of protection of the embodiments of this specification.

[0136] It should also be noted that the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, commodity, or apparatus that includes a series of elements includes not only those elements but also other elements not explicitly listed, or includes elements inherent to such process, method, commodity, or apparatus. In the absence of further limitations, an element defined by the phrase "comprises a ..." does not exclude the presence of other identical elements in the process, method, commodity, or apparatus that includes the element.

Claims

1. Ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods, characterized by: The steps include: Step 1: Acquire an ultrasonic cavitation signal passively received by an ultrasonic receiving transducer array, process the signal received by one of the array elements using the Hankel-SVD method, and separate three sets of data signals representing therapeutic ultrasound energy, transient cavitation energy, and steady-state cavitation energy; Step 2: Perform MV-DMAS processing on one group of data signals from the three groups of data signals representing therapeutic ultrasound energy, transient cavitation energy, and steady-state cavitation energy to obtain a focused signal. After energy-time integration to achieve long-pulse light imaging, the corresponding three types of ultrasonic cavitation passive imaging images are obtained through logarithmic compression processing and scan conversion.

2. The ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods according to claim 1, characterized in that: The step 1 comprises the following steps: Step 101, collecting ultrasonic cavitation signals passively received by a single ultrasonic receiving transducer; Step 102: The received ultrasonic cavitation signal is cut into an observation window of a certain length, and the ultrasonic cavitation signal within the observation window is rearranged to obtain a two-dimensional Hankel matrix; the two-dimensional Hankel matrix is ​​subjected to singular value decomposition and anti-diagonal averaging to reconstruct a one-dimensional signal array; Step 103, calculating the average frequency of each order component based on the one-dimensional signal array; Step 104 , filtering the calculated average frequency of each order component, and filtering out data points corresponding to the therapeutic ultrasound energy, steady-state cavitation energy, and transient cavitation energy; Step 105 , summing the data points corresponding to the therapeutic ultrasound energy, the steady-state cavitation energy, and the transient cavitation energy, respectively. The maximum summed result corresponds to the current acoustic cavitation state.

3. The ultrasonic cavitation characteristic analysis method based on the Hankel-SVD method according to claim 2, characterized in that: Step 102 includes the following steps: The ultrasonic cavitation signal is specifically in the form of: s=[s(0),s(1),…,s(N D -1)] T where N D is the number of sampling points, and N D is an even number; Get the two-dimensional Hankel matrix, the specific form is: Where P = N D / 2 is the signal window length; The rank of the two-dimensional Hankel matrix is ​​P, which is decomposed into the sum of P orthogonal components by SVD. The specific form is: Among them S k is the kth order Hankel component after decomposition, σ k 、u k 、 are the corresponding singular values, left singular vectors, and right singular vectors respectively; Reconstruct the P two-dimensional matrices after SVD decomposition into P one-dimensional matrices: Assume that for the k-th order Hankel component S k for which each element in its matrix is represented as S kij (1 ≤ i ≤ P, 1 ≤ j ≤ N D - P + 1), let N D - P + 1 = K, P * = min(P, K), K * = max(P, K); when P < K, otherwise where i, j, m, n represent the subscripts of matrix elements, and transform the matrix S k into a sequence of the required length N D through diagonal averaging The specific reconstruction method is described as follows: Thus, a one-dimensional signal array s' is obtained, the number of which is the same as the number of points of the original ultrasonic cavitation signal, expressed as: s'=[s'(0),s'(1),…,s'(N D -1)] T 。 4. The ultrasonic cavitation characteristic analysis method based on the Hankel-SVD method according to claim 3, characterized in that: Based on the one-dimensional signal array, the average frequency f of each order component is calculated mean , the specific form is: where N D is the number of Fourier transform points, f s is the sampling rate, is the actual frequency value of each component in the discrete frequency domain, and P[k] is the power spectral density of each frequency component, which is obtained by Fourier transform of the one-dimensional signal array.

5. The ultrasonic cavitation characteristic analysis method based on the Hankel-SVD method according to claim 4, characterized in that: Step 104 includes the following steps: The screening criteria for therapeutic ultrasound energy are: Where k is a positive integer, f c is the center frequency of the signal component corresponding to the maximum singular value, bws is half of the -6dB bandwidth of the spectrum of each order signal component, and bws0 is half of the -6dB bandwidth of the selected threshold; The screening conditions for steady-state cavitation energy are: The screening conditions for transient cavitation energy are: S inertial ={bws>bws0}。 6. The ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods according to claim 1, characterized in that: The step 2 comprises the following steps: Step 201: For each position point in each sound field, the corresponding receiving delay value τ(x,z) of each ultrasonic transducer array element is calculated according to the sound speed in the medium. The specific form is: Where: x is the coordinate parallel to the probe surface, z is the coordinate perpendicular to the probe surface, and i represents the i-th transducer array element; Step 202: Time-shift the signal corresponding to each ultrasonic transducer array element according to the receiving delay value of the array element. The specific form is: S' i (x,z,t)=s' i (t+τ(x,z)) Where: s' i (t) is the starting signal corresponding to the i-th array element in the transducer array; Step 203: Based on the time-shifted signal S' i (x, z, t), calculate the weight W and perform weighting, the specific form is: in: a is the identity matrix; H is the transpose; thus we get S' i The optimal weighted delayed signal of (x,z,t) Specifically expressed as: Step 204: Calculate the focus signal q(x, z, t) based on the MV-DMAS algorithm MV-DMAS , specifically: Where l is the total number of array elements of the ultrasonic transducer array; Step 205: Simplify the focused signal q to obtain q * (x,z,t) MV-DMAS , specifically: in, To preserve the sign of the square root, Step 206: Obtain the sound intensity I of the passive sound field imaging by time integration. MV-DMAS (x, z), specifically: Where ρ0 is the average density of the human body; c0 is the average speed of sound in the human body; T is the integration time; Step 207: Time-average the sound intensity I calculated for each transmission and reception as the pixel value corresponding to each position in the sound field; Step 208: Perform logarithmic compression and gain adjustment on each pixel value.

7. The ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods according to claim 6, characterized in that: Step 209 : performing coordinate transformation on the image of the entire sound field, from the rectangular coordinate system to the polar coordinate system.

8. The ultrasonic cavitation passive imaging method combining H-SVD and MV-DMAS methods according to claim 1, characterized in that: The three corresponding ultrasonic cavitation passive imaging images are displayed separately or superimposed.

Images