A method for estimating the size distribution of an onset cavitation microbubble based on a non-monotonic decreasing time-intensity curve

Abstract

The application discloses the technical field of cavitation microbubble size detection, and relates to an effective cavitation microbubble size distribution estimation method based on a non-monotonous decrease time-intensity curve. recyc The microbubble size range participating in cavitation activity in the "effective window" within the pulse interval time ΔT is 0~R recyc The microbubble size range participating in cavitation activity in the "effective window" within the pulse interval time ΔT is 0~R n The microbubble size range participating in cavitation activity in the "effective window" within the pulse interval time ΔT is 0~R The microbubble size distribution obtained by fitting the time-cavitation intensity curve by changing the pulse interval time is the effective cavitation microbubble size distribution under the action of the pulse duration T. The application fills the technical gap, and can accurately calculate the size distribution of the effective cavitation microbubble by accurately simulating the dynamic behavior of the microbubble under the action of the ultrasonic pulse and combining the parameter fitting method.

Description

Technical Field

[0001] This invention belongs to the field of cavitation microbubble size detection technology, and specifically relates to a method for estimating the size distribution of effective cavitation microbubbles based on a non-monotonic descent time intensity curve. Background Technology

[0002] Cavitation refers to the series of dynamic processes, including oscillation, expansion, contraction, and even implosion, exhibited by cavitation nuclei in a liquid under certain forms of energy interaction (such as ultrasound, microwaves, and lasers). It is a major physical mechanism in ultrasound therapy. The size of cavitation microbubbles determines their resonant frequency, which is beneficial for controlling cavitation activity. Therefore, studying the size of cavitation microbubbles is of great value in fundamental cavitation research.

[0003] However, due to the randomness and complexity of the cavitation process, it is difficult to determine the size of cavitation microbubbles. Existing research shows that current methods for estimating the size distribution of effective cavitation microbubbles are only applicable to estimating the size distribution of effective cavitation microbubbles corresponding to monotonically decreasing time-intensity curves. In some cases, such as when the pulse interval time and the scattered sound intensity increase, i.e. when there is no monotonically decreasing time-intensity curve, existing methods cannot accurately calculate the size distribution of effective cavitation microbubbles. Summary of the Invention

[0004] The present invention aims to provide a method for estimating the size distribution of effective cavitation microbubbles based on non-monotonic descent time intensity curves, in order to solve the problem that current methods for estimating the size distribution of cavitation microbubbles cannot accurately calculate the size distribution of effective cavitation microbubbles under non-monotonic descent time intensity curves.

[0005] One method in this scheme for estimating the size distribution of cavitation microbubbles based on a non-monotonic descent time-intensity curve sets the size range of microbubbles participating in cavitation activity within the "effectiveness window" of the pulse interval ΔT to be 0 to R. recyc In the range of 0 to R recyc The size of microbubbles within the specified range will contribute to the arbitrary pulse interval time ΔT. n The cavitation intensity obtained from the internal sampling is proportional to the number of microbubbles within the "effective window". The fitting parameters are obtained by least square fitting the integral of these parameters. Based on this, the microbubble size distribution obtained by fitting the time-cavitation intensity curve by changing the pulse interval is the effective cavitation microbubble size distribution under the action of pulse duration T.

[0006] The working principle of this scheme is as follows: the upper and lower limits of the size of the microbubbles participating in cavitation activities within the microbubble "recycling station" are R... recycWhen the pulse stops, microbubbles within the "effective window" will dissipate into the microbubble "recycling station" during the pulse interval. When the next pulse arrives, the microbubbles in the "recycling station" can grow again and scatter the corresponding acoustic signal. During the pulse interval ΔT, the microbubbles dissipate into R. recyc The dimensions mentioned above will not participate in subsequent cavitation activities, for any pulse interval (ΔT). n The collected cavitation intensity n It is proportional to the integral of the number of microbubbles within the "effective window" of the pulse interval.

[0007] Therefore, it is assumed that the size distribution of the microbubble swarm to be estimated follows a certain distribution, with the parameter representing the shape of this distribution plus a constant coefficient C and the upper limit of the "recycling bin" R. recyc Several parameters were set as undetermined parameters. The cavitation intensity (Intensity) acquired under arbitrary pulse interval conditions was then calculated using a least-squares fitting method. n By integral fitting with the number of microbubbles within the "effective window," several fitting parameters can be obtained. Finally, the microbubble size distribution obtained by fitting the time-cavitation intensity curve obtained by changing ΔT is the effective microbubble size distribution under the action of the pulse duration T.

[0008] The beneficial technical effects of this solution are:

[0009] 1. This solution is specifically designed for calculating the size distribution of effective cavitation microbubbles under non-monotonic descent time intensity curves, filling a gap in existing technology.

[0010] 2. This method demonstrates high accuracy in calculating the size distribution of effective cavitation microbubbles based on non-monotonic descent time-intensity curves. By precisely simulating the dynamic behavior of microbubbles under ultrasonic pulses and combining this with parameter fitting methods, the size distribution of effective cavitation microbubbles can be accurately calculated.

[0011] 3. This solution has a wide range of applications. It is not only applicable to the field of medical ultrasound (such as ultrasound therapy and ultrasound imaging), but also to other fields involving microbubble cavitation phenomena (such as chemical engineering and environmental engineering).

[0012] In summary, this scheme achieves accurate estimation of the size distribution of effective cavitation microbubbles under non-monotonic descent time-intensity curves, providing strong support for research and applications in related fields.

[0013] Furthermore, the time-cavitation intensity curve is the relationship between the intensity of the backscattered signal generated when microbubbles cavitate and time, under a certain pulse duration T.

[0014] Furthermore, the time-cavitation intensity curve is obtained by using frequency-domain beamforming to synthesize the backscattered signal channel data during microbubble cavitation, resulting in a passive cavitation image. Then, the total backscattered signal intensity of each frame of passive cavitation images acquired under certain pulse intervals and different pulse intervals is calculated. Finally, the relationship between the backscattered signal intensity generated during microbubble cavitation and time is obtained under a certain pulse duration T, thus yielding the time-cavitation intensity curve. Frequency-domain beamforming effectively enhances the signal-to-noise ratio of the backscattered signal, thereby improving the quality of passive cavitation imaging. The obtained time-cavitation intensity curve is beneficial for accurately evaluating the influence of different acoustic parameters on the microbubble cavitation effect and for more precisely calculating the effective cavitation microbubble size distribution of the non-monotonic decreasing time intensity curve. Attached Figure Description

[0015] Figure 1 This is a schematic diagram of the process for obtaining a non-monotonic descent time-intensity curve according to the present invention;

[0016] Figure 2 This is a schematic diagram illustrating the principle of how microbubbles dissipate to the "recycling station" during the pulse interval in this invention.

[0017] Figure 3 This is the estimation result of the effective cavitation microbubble size distribution based on the non-monotonic descent time intensity curve in this embodiment of the invention. Detailed Implementation

[0018] The following detailed description illustrates the specific implementation method:

[0019] A method for estimating the size distribution of effective cavitation microbubbles based on non-monotonic descent time-intensity curves, combined with Figure 1 The process shown is as follows:

[0020] I. Passive cavitation imaging involves acquiring reflected echoes from focused ultrasound pulses (i.e., backscattered signals during microbubble cavitation) using a programmable diagnostic ultrasound system and linear array transducers, and then performing beamforming on the backscattered signal channel data using frequency domain beamforming. Figure 1 (b). Calculate the total backscattered signal intensity (Intensity1) of each frame of passively cavitation images under a certain pulse time interval ΔT1. Figure 1 (b). Subsequently, by adjusting the focused ultrasound pulse time interval, different pulse time intervals (ΔT2, ΔT3, ΔT4, ...) were calculated. Figure 1The total backscattered signal intensity (Intensity2, Intensity3, Intensity4...) of each frame of passive cavitation images acquired under condition (a)) is obtained. Finally, the relationship between the backscattered signal intensity generated by microbubble cavitation and time can be obtained under a certain pulse duration T, i.e., the time-cavitation intensity curve. Figure 1 (c)

[0021] The frequency-domain beamforming method described above involves: transforming the backscattered signal data from the time domain to the frequency domain, and passing the data through a bandpass filter to reduce the acoustic signal from the dominant frequency of focused ultrasound. Based on the distance between the receiving array elements and the field point, a phase delay (i.e., a time delay) is applied to the data of each channel in the frequency domain. For each pixel in the imaging field of view, the signals from all array elements that have undergone the frequency-domain phase delay are superimposed, and the square of the superimposed value is taken as the acoustic energy of that pixel.

[0022] Second, the theory of microbubbles dissipating to the "recycling station" during the pulse interval is introduced, such as... Figure 2 As shown, the upper and lower limits of the "Recycle Bin" are set to R respectively. recyc At the moment the pulse stops, microbubbles within the "effective window" (shaded area on the right) dissipate into the microbubble "recycling bin" (rectangular area on the left) during the pulse interval. When the next pulse arrives, these microbubbles can grow again and scatter the corresponding acoustic signal. During the pulse interval ΔT, the microbubbles dissipate into R. recyc The dimensions mentioned above will not be used in subsequent cavitation activities.

[0023] For any pulse interval time (ΔT) n The cavitation intensity (Intensity) collected under this pulse interval condition n The integral is proportional to the number of microbubbles within the "effective window".

[0024] Assume the size distribution of the microbubble swarm to be estimated follows a certain distribution, with parameters representing the shape of this distribution and an upper limit R for the "recycling bin". recyc Several parameters are included as undetermined parameters in the cavitation intensity obtained under arbitrary pulse interval conditions. n Several fitting parameters can be obtained by least-squares fitting of the integral of the number of microbubbles within the "effective window". Finally, the microbubble size distribution obtained by fitting the time-cavitation intensity curve obtained by changing ΔT is the effective microbubble size distribution under the action of the pulse duration T.

[0025] Example: The size distribution of effective cavitation microbubbles in tap water based on a non-monotonic fall time intensity curve was calculated using the above method.

[0026] A focused ultrasound pulse of 1.6 MHz and 14 W was applied to tap water for 12 μs. A 5 MHz single-element transducer was used to acquire backscattered signals during cavitation at a sampling rate of 20 MHz. Subsequent operations were performed using the same method. Results: Figure 3 (a) shows several backscattered signals (time domain) with a pulse interval of 500 μs. Figure 3 (b) for Figure 3 (a) The corresponding frequency domain signal. Figure 3 (c) The corresponding time-intensity curve was obtained. Figure 3 (d) is the size distribution of effective cavitation microbubbles estimated using the method proposed in this invention.

Claims

1. A method for estimating the size distribution of effective cavitation microbubbles based on a non-monotonic descent time-intensity curve, characterized in that: Within the set pulse interval ΔT, the microbubble size range participating in cavitation activities is 0 to R. recyc In the range of 0 to R recyc The size of microbubbles within the specified range will contribute to the arbitrary pulse interval time ΔT. n The cavitation intensity obtained from internal sampling is proportional to the number of microbubbles within the "effective window". The fitting parameters are obtained by least squares fitting of the integral. Based on this, the microbubble size distribution obtained by fitting the time-cavitation intensity curve by changing the pulse interval is the effective cavitation microbubble size distribution under the action of pulse duration T.

2. The method for estimating the size distribution of effective cavitation microbubbles based on a non-monotonic descent time-intensity curve according to claim 1, characterized in that: The time-cavitation intensity curve is the relationship between the intensity of the backscattered signal generated when microbubbles cavitate and time, under a certain pulse duration T.

3. The method for estimating the size distribution of effective cavitation microbubbles based on a non-monotonic descent time-intensity curve according to claim 2, characterized in that: The time-cavitation intensity curve is obtained by using frequency domain beamforming to synthesize the backscattered signal channel data when microbubbles cavitate, thus obtaining a passive cavitation image. Then, the total backscattered signal intensity of each frame of passive cavitation images acquired under certain pulse interval time conditions and under different pulse interval time conditions is calculated. Finally, the relationship between the backscattered signal intensity generated when microbubbles cavitate and time is obtained under a certain pulse duration T, i.e., the time-cavitation intensity curve.

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