Ultrasonic cavitation characteristic analysis method based on Hansel-SVD (Singular Value Decomposition) method

Abstract

The invention discloses an ultrasonic cavitation characteristic analysis method based on a Hansel-SVD (Singular Value Decomposition) method. The ultrasonic cavitation characteristic analysis method comprises the following steps: acquiring an ultrasonic cavitation signal passively received by using a single ultrasonic receiving transducer; intercepting the received ultrasonic cavitation signals into an observation window with a certain length, and rearranging the ultrasonic cavitation signals in the observation window to obtain a two-dimensional Hankel matrix; performing singular value decomposition and reverse diagonal averaging processing on the two-dimensional Hankel matrix, and performing reconstruction to obtain a one-dimensional signal array; calculating the average frequency of each order component based on the one-dimensional signal array; and screening the calculated average frequency of each order of component, respectively screening data points corresponding to the treatment ultrasonic energy, the steady state cavitation energy and the transient state cavitation energy, and summing, the maximum summing result corresponding to the data points being the current acoustic cavitation state. According to the method, time domain evolution behaviors of steady-state cavitation and transient-state cavitation within a certain irradiation time range can be accurately distinguished.

Description

Technical Field

[0001] The present invention relates to the technical field of acoustic signal processing, and in particular to an ultrasonic cavitation characteristic analysis method based on a Hankel-SVD method. 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 technology 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. Spectral analysis of the collected acoustic cavitation scattered noise is then used to further distinguish between steady-state and transient cavitation. While traditional spectrum analysis methods can typically determine whether cavitation activity has occurred during a single pulse, they lack time-domain analysis capabilities and are unable to accurately distinguish the temporal evolution of steady-state and transient cavitation within a specific irradiation time range. Summary of the Invention

[0005] In order to solve the above technical problems, the present invention proposes an ultrasonic cavitation feature analysis method based on the Hankel-SVD method. In the method, an ultrasonic cavitation signal with significant characteristics can be obtained, and the time domain evolution behavior of steady-state cavitation and transient cavitation within a certain irradiation time range can be accurately distinguished.

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

[0007] A method for analyzing ultrasonic cavitation characteristics based on the Hankel-SVD method comprises the following steps:

[0008] Acquiring ultrasonic cavitation signals passively received by a single ultrasonic receiving transducer;

[0009] The received ultrasonic cavitation signal is cut into an observation window of a certain length, and the ultrasonic cavitation signal in the observation window is rearranged to obtain a two-dimensional Hankel matrix;

[0010] Perform singular value decomposition and anti-diagonal averaging on the two-dimensional Hankel matrix to reconstruct a one-dimensional signal array;

[0011] Based on a one-dimensional signal array, calculate the average frequency of each order component;

[0012] The calculated average frequency of each order component is screened, and the data points corresponding to the therapeutic ultrasound energy, steady-state cavitation energy and transient cavitation energy are respectively screened out;

[0013] The data point results corresponding to the therapeutic ultrasound energy, steady-state cavitation energy and transient cavitation energy are summed respectively, and the maximum sum result corresponds to the current acoustic cavitation state.

[0014] Preferably, the ultrasonic cavitation signal is in the form of:

[0015] s=[s(0),s(1),…,s(N D -1)] T

[0016] where N D is the number of sampling points, and N D An even number.

[0017] Preferably, a two-dimensional Hankel matrix is ​​obtained, specifically in the form of:

[0018]

[0019] Where P = N D / 2 is the signal window length.

[0020] Preferably, the step of performing singular value decomposition and anti-diagonal averaging on the two-dimensional Hankel matrix to reconstruct a one-dimensional signal array comprises the following steps:

[0021] 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:

[0022]

[0023] Among them Sk is the Hankel component of the k-th order after decomposition, σ k , u k , are the corresponding singular value, left singular vector and right singular vector respectively;

[0024] 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 = K, 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 is transformed into a sequence with the required length of N D through diagonal averaging The specific reconstruction method is described as:

[0025]

[0026] Thus, a one-dimensional signal array s' is obtained, and the number of points is the same as that of the original ultrasonic cavitation signal, which is expressed as:

[0027] s' = [s'(0), s'(1), …, s'(N D -1)] T .

[0028] Preferably, based on the one-dimensional signal array, calculate the average frequency f mean of each order component, and the specific form is:

[0029]

[0030] where N D is the number of points of the Fourier transform, f s is the sampling rate, <​​​​​​​​

[0033]

[0034] 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;

[0035] The screening conditions for steady-state cavitation energy are:

[0036]

[0037] The screening conditions for transient cavitation energy are:

[0038] s inertial ={bws>bws0}.

[0039] Based on the above technical solution, the beneficial effects of the present invention are as follows: the present invention provides an ultrasonic cavitation feature analysis method based on the Hankel-SVD method, which collects ultrasonic cavitation signals passively received by a single ultrasonic receiving transducer; cuts the received ultrasonic cavitation signal into an observation window of a certain length, rearranges the ultrasonic cavitation signal within the observation window to obtain a two-dimensional Hankel matrix; performs singular value decomposition and anti-diagonal averaging on the two-dimensional Hankel matrix to reconstruct a one-dimensional signal array; calculates the average frequency of each order component based on the one-dimensional signal array; filters the calculated average frequency of each order component and selects data points corresponding to therapeutic ultrasonic energy, steady-state cavitation energy, and transient cavitation energy; sums the data points corresponding to the therapeutic ultrasonic energy, steady-state cavitation energy, and transient cavitation energy, and the maximum sum corresponds to the current acoustic cavitation state. The present invention can obtain ultrasonic cavitation signals with significant characteristics and can accurately distinguish the time-domain evolution behaviors of steady-state cavitation and transient cavitation within a certain irradiation time range. BRIEF DESCRIPTION OF THE DRAWINGS

[0040] Figure 1 is a flow chart of an ultrasonic cavitation characteristic analysis method based on the Hankel-SVD method in one embodiment;

[0041] Figure 2 is a schematic diagram of the change of a signal array in an embodiment;

[0042] Figure 3 is a schematic diagram of a passive measurement system for ultrasonic cavitation signals in one embodiment;

[0043] Figure 4 FIG. 1 is a schematic diagram of changes in ultrasonic cavitation characteristics caused by different sound pressures in an embodiment. DETAILED DESCRIPTION

[0044] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention.

[0045] As Figure 1 , 2 shown, this embodiment provides an ultrasonic cavitation feature analysis method based on the Hankel - SVD method, including the following steps:

[0046] Step 1, during the ultrasonic treatment process, use a passive cavitation detection ultrasonic probe (PCD probe) to collect the PCD original signal for a period of time, and the specific form is:

[0047] s = [s(0), s(1), …, s(N D - 1)] T

[0048] where N D is the number of sampling points, and N D should be an even number;

[0049] Step 2, rearrange the one - dimensional PCD original signal into the form of a two - dimensional Hankel matrix, and the specific form is:

[0050]

[0051] where P = N D / 2 is the preferred signal window length;

[0052] Step 3, the rank of the aforementioned Hankel matrix is P, and it can be decomposed by SVD into the sum of P orthogonal components, and the specific form is:

[0053]

[0054] 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.

[0055] Step 4, 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 = K, P * = min(P, K), K * = max(P, K). When P < k, otherwise i, j, m, n represent the subscripts of the matrix elements. By anti-diagonal averaging, the matrix S k Transform to the required length N D sequence The specific reconstruction method can be described as:

[0056]

[0057] Thus, we can obtain a one-dimensional signal array again, which has the same number of points as the original signal and can be expressed as:

[0058] s'=[s'(0),s'(1),…,s'(N D -1)] T

[0059] Step 5: Calculate the average frequency of each order component based on the one-dimensional signal array. This can be obtained by calculating the average value of the power spectrum density weighted frequency, as follows:

[0060]

[0061] 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 new signal array, and its -6dB bandwidth can be calculated in the power spectrum.

[0062] Step 6: Filter the calculated average frequency of each order component, and filter out the data points corresponding to the therapeutic ultrasound energy, steady-state cavitation energy, and transient cavitation energy.

[0063] Step 6-1, screening conditions for therapeutic ultrasound energy are:

[0064]

[0065] 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;

[0066] Step 6-2, the screening conditions for steady-state cavitation energy are:

[0067]

[0068] Step 6-3, the screening conditions for transient cavitation energy are:

[0069] S inertial ={bws>bws0}.

[0070] In step 7, all new signal data points that meet a certain condition, obtained by filtering according to different conditions in step 6, are summed. When the sum corresponding to a certain condition is relatively large, it indicates that the corresponding cavitation state is relatively strong, and there are many ultrasonic cavitation bubbles in this state in the sound field. Therefore, the current acoustic cavitation state can be more clearly understood.

[0071] 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.

[0072] Specific implementation process:

[0073] Build a Figure 3 The passive ultrasonic cavitation signal measurement system shown here changes the parameters of the drive signal for the ultrasonic transmitting transducer by adjusting the parameters of a signal generator. After receiving the ultrasonic cavitation signal, the ultrasonic receiving transducer digitizes it using an oscilloscope and then captures it on a control computer. The data is then processed and displayed using a data processing program developed based on the technical solution developed in this patent.

[0074] In such Figure 3 In the experimental system shown, Sonovue contrast agent microbubbles were continuously injected into the silicone tubing to promote ultrasonic cavitation within the tubing. The signal generator was set to automatically adjust its output voltage, increasing it from an initial level (0mV) in 1mV steps per second over 200 seconds. After power amplification at a fixed gain of 50dB, the power amplifier drove the ultrasonic transmitting transducer. Signals from the ultrasonic receiving transducer were continuously collected and analyzed for acoustic cavitation characteristics.

[0075] In the initial stage, since the acoustic driving pressure is very low (for example, P-=10kPa, which is the maximum negative sound pressure at the focus measured by the hydrophone when the ultrasonic transmitting transducer parameters are calibrated in advance, the same below), the microbubbles only oscillate linearly, so only the fundamental frequency component can be observed in the spectrum. As the excitation sound pressure increases, the intensity of the fundamental frequency and harmonic components increases, which corresponds to the increase in the oscillation amplitude of the microbubbles. When the excitation sound pressure rises above a certain threshold (for example, P-=60kPa), the microbubbles will exhibit stable cavitation phenomena, and the nonlinear vibration will be enhanced, causing subharmonic and superharmonic components to appear simultaneously in the spectrum. As the sound pressure is further increased to a higher level (such as P-=140kPa), the microbubbles will collapse asymmetric and lead to the formation of jets. The broadband noise generated by the violently active microbubbles dominates the spectrum, and the subharmonic and superharmonic components become less obvious or even submerged. The characteristics of ultrasonic cavitation at different stages are as follows: Figure 4 As shown in the figure, after processing with the Hankel-SVD method, the ultrasonic cavitation signals driven by different sound pressures have distinct characteristics, which can clearly determine the current ultrasonic cavitation state in the sound field. This provides a technical means for real-time monitoring of the acoustic cavitation state during medical ultrasound treatment.

[0076] In the present technical solution, the process of screening different types of ultrasonic cavitation energy described in steps 5 and 6 is to determine the frequency range of the energy signal based on the signal data after Hankel-SVD calculation, and does not require a priori setting of the frequency range. This forms a detection method that is independent of system parameters and has better universality.

[0077] The above description is merely a preferred embodiment of the Hankel-SVD method for ultrasonic cavitation feature analysis 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.

Claims

1. A method for ultrasonic cavitation characteristic analysis based on the Hankel-SVD method, characterized in that: The steps include: Acquiring ultrasonic cavitation signals passively received by a single ultrasonic receiving transducer; The received ultrasonic cavitation signal is cut into an observation window of a certain length, and the ultrasonic cavitation signal in the observation window is rearranged to obtain a two-dimensional Hankel matrix; Perform singular value decomposition and anti-diagonal averaging on the two-dimensional Hankel matrix to reconstruct a one-dimensional signal array; Based on a one-dimensional signal array, calculate the average frequency of each order component; The calculated average frequency of each order component is screened, and the data points corresponding to the therapeutic ultrasound energy, steady-state cavitation energy and transient cavitation energy are respectively screened out; The data point results corresponding to the therapeutic ultrasound energy, steady-state cavitation energy and transient cavitation energy are summed respectively, and the maximum sum result corresponds to the current acoustic cavitation state.

2. The ultrasonic cavitation characteristic analysis method based on the Hankel-SVD method according to claim 1, characterized in that: 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 An even number.

3. The ultrasonic cavitation characteristic analysis method based on the Hankel-SVD method according to claim 2, characterized in that: Get the two-dimensional Hankel matrix, the specific form is: Where P = N D / 2 is the signal window length.

4. The ultrasonic cavitation characteristic analysis method based on the Hankel-SVD method according to claim 3, characterized in that: The method of performing singular value decomposition and anti-diagonal averaging on the two-dimensional Hankel matrix to reconstruct a one-dimensional signal array includes the following steps: 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 it, each element in the 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 i, j, m, n represent the subscripts of matrix elements. The matrix S is transformed into a sequence with the required length N k through diagonal averaging D The specific reconstruction method is described as follows: Specifically, the reconstruction method is described as: 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 。 5. The ultrasonic cavitation characteristic analysis method based on the Hankel-SVD method according to claim 4, 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.

6. The ultrasonic cavitation characteristic analysis method based on the Hankel-SVD method according to claim 5, characterized in that: The data point filtering conditions corresponding to therapeutic ultrasound energy, steady-state cavitation energy, and transient cavitation energy are as follows: 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}。

Images