A bonding detection method and system for ultrasonic transducer

Abstract

The present invention relates to the technical field of material testing or analysis, and specifically to a bonding detection method and system using an ultrasonic transducer. First, ultrasonic transducer thickness data of the bonding layer is collected, and combined with thickness laser data acquired by a laser interferometer, thickness fusion data is generated using an acousto-optic weighted averaging algorithm. Second, different transmission power gradients are set in the medium, the received power is measured, and the power error rate is calculated. Simultaneously, ICE images under multiple angles and periodic motion are collected using a bionic heart cavity model. A static-dynamic imaging joint analysis method is used to calculate the static and dynamic imaging error rates and derive the imaging error rate. Finally, based on the thickness fusion data, power error rate, and imaging error rate, a bonding thickness correction formula is used to accurately calibrate the bonding thickness.

Description

Technical Field

[0001] The present invention relates to the technical field of material testing or analysis, and in particular to a bonding detection method and system of an ultrasonic transducer. Background Art

[0002] Ultrasonic transducers are essential devices for modern medical imaging. Their core function is to convert electrical signals into acoustic signals, enabling nondestructive testing within tissues. As a specialized type of ultrasonic transducer, the ICE (Intracavity Ultrasound) transducer, with its compactness and flexibility, is widely used in cardiovascular interventional procedures. By acquiring high-resolution tissue images within the cardiac cavity, it provides real-time surgical navigation support. Due to its complex structure and high precision requirements, the manufacturing and testing of ICE transducers has become a crucial component of technological development.

[0003] The adhesive layer is a critical component of ultrasound transducers, and its thickness significantly impacts transducer performance. Excessive adhesive layer thickness can increase ultrasound attenuation and reduce energy transfer efficiency, while too little can cause acoustic impedance mismatch, impacting imaging quality. For ICE transducers in particular, the thickness distribution of the adhesive layer directly impacts the resolution and accuracy of intracardiac imaging. Therefore, accurately measuring and optimizing adhesive layer thickness is key to improving ICE transducer performance.

[0004] Currently, adhesive layer thickness testing relies primarily on single ultrasonic or optical methods, which often only provide a limited understanding of thickness and fail to fully reflect the true condition of the adhesive layer. Furthermore, existing testing technologies, mostly focused on quality assessment, fail to provide effective solutions for optimizing adhesive thickness, limiting further improvements in transducer performance. To address these issues, a comprehensive approach combining multiple testing methods is urgently needed to support adhesive thickness correction and optimization.

[0005] To this end, a bonding detection method and system using an ultrasonic transducer are proposed. Summary of the Invention

[0006] The purpose of the present invention is to provide an adhesion detection method and system for an ultrasonic transducer. First, the thickness fusion data of the bonding layer is obtained by an acousto-optic weighted algorithm combining ultrasonic detection and laser interference. Then, the power error rate is calculated by power gradient measurement. Then, the static and dynamic imaging error rates are analyzed by a bionic heart cavity model. Finally, the above data are combined and a correction formula is used to accurately correct the thickness of the bonding layer, thereby realizing a comprehensive method that combines multiple detection methods and can support adhesion thickness correction and optimization.

[0007] To achieve the above object, the present invention provides the following technical solutions:

[0008] A method for detecting adhesion of an ultrasonic transducer, comprising:

[0009] Fixing an ultrasonic transducer sample, activating an ultrasonic probe inside the ultrasonic transducer sample to transmit and receive reflected waves, obtaining thickness ultrasonic data of the adhesive layer, detecting the thickness distribution of the adhesive layer using a laser interferometer to obtain thickness laser data, and fusing the thickness ultrasonic data with the thickness ultrasonic data according to an acousto-optic weighted averaging algorithm to obtain thickness fusion data;

[0010] Placing the ultrasonic transducer and the receiving probe in a medium, setting a first transmitting power and a second transmitting power, using the first transmitting power as the initial power and gradually increasing the transmitting power of the ultrasonic transducer, recording the test receiving power received by the receiving probe at the recording point, and calculating and obtaining a power error rate based on the standard receiving power and the test receiving power;

[0011] Placing the ultrasonic transducer in a bionic heart cavity model, collecting ICE images of the bionic heart cavity model at different angles and motion cycles, and calculating the static imaging error rate and the dynamic imaging error rate of the standard clinical image and the ICE image using a static-dynamic imaging joint analysis method to obtain an imaging error rate;

[0012] The thickness of the adhesive layer is corrected using an adhesive thickness correction formula according to the thickness fusion data, the power error rate, and the imaging error rate.

[0013] Furthermore, the specific steps of obtaining the thickness fusion data include:

[0014] fixing the ultrasonic transducer sample on a testing platform;

[0015] activating the ultrasonic probe inside the transducer, the ultrasonic probe transmitting ultrasonic waves and receiving reflected waves generated after the ultrasonic waves pass through the adhesive layer, recording a phase difference between transmitting the ultrasonic waves and receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data based on the phase difference and the material propagation velocity of the adhesive layer;

[0016] Using a laser interferometer to emit a laser beam, the laser beam is irradiated on the surface of the adhesive layer to generate a reflected laser beam, analyzing the change in the interference pattern between the reflected laser beam and a reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer based on the change in the interference pattern to obtain the thickness laser data;

[0017] fusing the thickness ultrasonic data and the thickness laser data using the acousto-optic weighted averaging algorithm to obtain the thickness fusion data;

[0018] The calculation formula of the acousto-optic weighted average algorithm is:

[0019]

[0020] in, represents the thickness fusion data, represents the thickness ultrasonic data, represents the thickness laser data, represents the ultrasonic measurement weight, Represents the laser measurement weight.

[0021] Furthermore, the calculation formula for obtaining the power error rate is:

[0022]

[0023] in, represents the power error rate, m represents the number of tests, i represents the index of the number of tests, represents the standard received power of the i-th test, Indicates the test receiving power of the i-th test.

[0024] Furthermore, the static-dynamic imaging joint analysis method includes:

[0025] Construct a bionic heart model that can simulate the cyclic contraction and relaxation of cardiac chambers;

[0026] placing an ultrasonic transducer in a bionic heart cavity model, acquiring a static ICE image of the bionic heart cavity model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate;

[0027] Collect ICE image groups from multiple angles inside the bionic heart cavity model according to the bionic heart cavity motion cycle to generate a set of dynamic image sequences. Use dynamic texture analysis to extract the optical flow change characteristics of the dynamic image sequences, and calculate the imaging similarity by comparing with standard clinical image sequences.

[0028] Calculating a dynamic imaging error rate based on the imaging similarity;

[0029] The imaging error rate is calculated by combining the static imaging error rate and the dynamic imaging error rate;

[0030] The calculation formula of the imaging error rate is:

[0031] ;

[0032] in, represents the imaging error rate, represents the static imaging error rate, represents the dynamic imaging error rate.

[0033] Furthermore, the bonding thickness correction formula is:

[0034]

[0035] in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

[0036] An ultrasonic transducer bonding detection system, comprising:

[0037] Thickness detection module: fix the ultrasonic transducer sample, activate the ultrasonic probe inside the ultrasonic transducer sample to transmit and receive reflected waves, obtain ultrasonic thickness data of the adhesive layer, use a laser interferometer to detect the thickness distribution of the adhesive layer, obtain thickness laser data, and fuse the thickness ultrasonic data and the thickness ultrasonic data according to the acousto-optic weighted averaging algorithm to obtain thickness fusion data;

[0038] Power error analysis module: placing the ultrasonic transducer and receiving probe in a medium, setting a first transmission power and a second transmission power, using the first transmission power as the initial power and gradually increasing the transmission power of the ultrasonic transducer, recording the test receiving power received by the receiving probe at the recording point, and calculating the power error rate based on the standard receiving power and the test receiving power;

[0039] Imaging error analysis module: placing the ultrasonic transducer in a bionic heart cavity model, collecting ICE images of the bionic heart cavity model at different angles and motion cycles, and calculating the static imaging error rate and dynamic imaging error rate of the standard clinical image and the ICE image through a static-dynamic imaging joint analysis method to obtain the imaging error;

[0040] Thickness correction module: corrects the thickness of the adhesive layer using an adhesive thickness correction formula according to the thickness fusion data, the power error rate, and the imaging error rate.

[0041] Furthermore, the specific steps of obtaining the thickness fusion data include:

[0042] fixing the ultrasonic transducer sample on a testing platform;

[0043] activating the ultrasonic probe inside the transducer, the ultrasonic probe transmitting ultrasonic waves and receiving reflected waves generated after the ultrasonic waves pass through the adhesive layer, recording a phase difference between transmitting the ultrasonic waves and receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data based on the phase difference and the material propagation velocity of the adhesive layer;

[0044] Using a laser interferometer to emit a laser beam, the laser beam is irradiated on the surface of the adhesive layer to generate a reflected laser beam, analyzing the change in the interference pattern between the reflected laser beam and a reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer based on the change in the interference pattern to obtain the thickness laser data;

[0045] The thickness ultrasonic data and the thickness laser data are fused using the acousto-optic weighted averaging algorithm to obtain the thickness fusion data. The calculation formula of the acousto-optic weighted averaging algorithm is:

[0046]

[0047] in, represents the thickness fusion data, represents the thickness ultrasonic data, represents the thickness laser data, represents the ultrasonic measurement weight, Represents the laser measurement weight.

[0048] Furthermore, the calculation formula for obtaining the power error rate is:

[0049]

[0050] in, represents the power error rate, m represents the number of tests, i represents the index of the number of tests, represents the standard received power of the i-th test, Indicates the test receiving power of the i-th test.

[0051] Furthermore, the static-dynamic imaging joint analysis method includes:

[0052] Construct a bionic heart model that can simulate the cyclic contraction and relaxation of cardiac chambers;

[0053] placing an ultrasonic transducer in a bionic heart cavity model, acquiring a static ICE image of the bionic heart cavity model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate;

[0054] Collect ICE image groups from multiple angles inside the bionic heart cavity model according to the bionic heart cavity motion cycle to generate a set of dynamic image sequences. Use dynamic texture analysis to extract the optical flow change characteristics of the dynamic image sequences, and calculate the imaging similarity by comparing with standard clinical image sequences.

[0055] Calculating a dynamic imaging error rate based on the imaging similarity;

[0056] The imaging error rate is calculated by combining the static imaging error rate and the dynamic imaging error rate;

[0057] The calculation formula of the imaging error rate is:

[0058] ;

[0059] in, represents the imaging error rate, represents the static imaging error rate, represents the dynamic imaging error rate.

[0060] Furthermore, the bonding thickness correction formula is:

[0061]

[0062] in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

[0063] Compared with the prior art, the present invention has the following beneficial effects:

[0064] 1. The present invention proposes a bonding thickness detection method based on acousto-optic weighting, which effectively combines the advantages of ultrasonic detection and laser interferometry technology. The two measurement data are fused through an acousto-optic weighted averaging algorithm, which improves the accuracy and reliability of thickness detection. It overcomes the limitation of insufficient accuracy of a single detection method in the complex internal environment of an ICE transducer and ensures the accuracy of core data in bonding detection.

[0065] 2. The present invention proposes a static-dynamic imaging joint analysis method. By combining the static imaging error rate and the dynamic imaging error rate, the difference between ICE images and standard clinical images is comprehensively evaluated, effectively improving the accuracy and reliability of imaging error analysis. This method can capture the multidimensional characteristics of imaging under static and dynamic conditions, providing a reliable data source for imaging quality assessment and bonding layer status analysis in bonding testing, and ensuring the accuracy of imaging detection in bonding testing.

[0066] 3. This method accurately corrects the adhesive layer thickness using a bonding thickness correction formula based on thickness fusion data, power error rate, and imaging error rate. This method comprehensively assesses the actual deviation in adhesive layer thickness and optimizes correction based on the synergistic effect of multidimensional data. This method effectively overcomes the limitations of a single detection method and improves the accuracy, stability, and diversity of adhesive testing. BRIEF DESCRIPTION OF THE DRAWINGS

[0067] Figure 1A flow chart of a bonding detection method using an ultrasonic transducer provided in Example 1 of the present invention;

[0068] Figure 2 This is a structural diagram of an ultrasonic transducer bonding detection system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0069] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0070] Example 1

[0071] A company produces ICE transducers for monitoring the internal anatomy of cardiac chambers. Its core production line is responsible for packaging ICE transducers. To achieve higher-precision monitoring, the company is currently preparing to upgrade products on two production lines. Therefore, they are implementing an ultrasonic transducer bonding test method. This method measures and corrects the thickness of the bond between the Pebax tube and the transducer, thereby improving ICE transducer performance.

[0072] Reference Figure 1 Step S100 in:

[0073] Fix the ultrasonic transducer sample, activate the ultrasonic probe inside the ultrasonic transducer sample to transmit and receive reflected waves, obtain thickness ultrasonic data of the bonding layer, use a laser interferometer to detect the thickness distribution of the bonding layer, obtain thickness laser data, and fuse the thickness ultrasonic data and the thickness ultrasonic data according to the acousto-optic weighted averaging algorithm to obtain thickness fusion data;

[0074] Furthermore, the specific steps of obtaining the thickness fusion data include:

[0075] fixing the ultrasonic transducer sample on a testing platform;

[0076] activating the ultrasonic probe inside the transducer, the ultrasonic probe transmitting ultrasonic waves and receiving reflected waves generated after the ultrasonic waves pass through the adhesive layer, recording a phase difference between transmitting the ultrasonic waves and receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data based on the phase difference and the material propagation velocity of the adhesive layer;

[0077] Using a laser interferometer to emit a laser beam, the laser beam is irradiated on the surface of the adhesive layer to generate a reflected laser beam, analyzing the change in the interference pattern between the reflected laser beam and a reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer based on the change in the interference pattern to obtain the thickness laser data;

[0078] fusing the thickness ultrasonic data and the thickness laser data using the acousto-optic weighted averaging algorithm to obtain the thickness fusion data;

[0079] The calculation formula of the acousto-optic weighted average algorithm is:

[0080]

[0081] in, represents the thickness fusion data, represents the thickness ultrasonic data, represents the thickness laser data, represents the ultrasonic measurement weight, Represents the laser measurement weight.

[0082] The thickness ultrasonic data and the thickness laser data are respectively:

[0083]

[0084] Where n represents the number of electronic phase-controlled units in the transducer, i represents the index number of the electronic phase-controlled unit, represents the thickness ultrasonic data obtained by the i-th electronic phase control unit. represents the vertical thickness of the bonding layer between the i-th electronic phase-controlled unit and the Pebax tube;

[0085] The ultrasonic measurement weight and the laser measurement weight are respectively:

[0086]

[0087] in, represents the standard deviation of the ultrasonic measurement data, represents the standard deviation of the laser measurement data, represents a non-zero correction factor;

[0088] The standard deviation of the ultrasonic measurement data and the standard deviation of the laser measurement data are respectively:

[0089]

[0090] Where n represents the number of electronic phase-controlled units in the transducer, i represents the index number of the electronic phase-controlled unit, represents the thickness ultrasonic data obtained by the i-th electronic phase control unit. represents the vertical thickness of the bonding layer between the i-th electronic phase-controlled unit and the Pebax tube.

[0091] Specifically, the transducer sample is fixed on the test table to ensure its stability. The ultrasonic probe inside the transducer is activated, and the probe begins to transmit ultrasonic waves and receive reflected waves. The ultrasonic waves emitted by different electronic phase-controlled units inside the ultrasonic probe pass through the adhesive layer and are reflected back. The probe captures these reflected waves and records the phase difference from transmission to reception. To obtain the phase difference, the ultrasonic wave and reflected wave need to be converted from time domain signals to frequency domain signals. The calculation formula is as follows:

[0092]

[0093] Where S(f) represents the frequency domain signal, s(t) represents the time domain signal, and j represents the complex unit, whose value is , f represents the signal frequency, t represents the time; the phase is obtained according to the frequency domain signal, and the calculation formula is as follows:

[0094]

[0095] in represents the phase of the frequency domain signal, represents the imaginary part of the frequency domain signal, Represents the real part of the frequency domain signal; the phase difference between the ultrasonic wave and the reflected wave is calculated by the phase of the frequency domain signal. The calculation process is:

[0096]

[0097] in represents the phase difference, represents the phase of the transmitted ultrasonic wave, Indicates the phase of the received reflected wave. The ultrasonic thickness is obtained by using the phase difference and wavelength. The calculation formula is:

[0098]

[0099] in, represents the thickness ultrasonic data obtained by the i-th electronic phase-controlled unit, and then the thickness ultrasonic data is obtained:

[0100]

[0101] A laser interferometer is used to emit a laser beam, which is irradiated on the surface of the adhesive layer to generate a reflected laser beam. The interference pattern change between the reflected laser beam and the reference beam is analyzed to obtain the surface deformation of the adhesive layer. The vertical thickness of the adhesive layer between different electronic phase-controlled units of the adhesive layer and the Pebax tube is calculated based on the interference pattern change to obtain thickness laser data. Based on the thickness ultrasonic data and the thickness laser data, the two data are fused using the acousto-optic weighted averaging algorithm to obtain the thickness fusion data.

[0102] Ultrasonic methods calculate adhesive layer thickness through phase difference and propagation velocity, making them suitable for rapid testing. Laser interferometry, on the other hand, captures surface deformation through precise interference patterns, providing highly accurate surface thickness data. Combining these two methods, using an acousto-optic weighted averaging algorithm to fuse ultrasonic and laser data, can significantly improve measurement accuracy and efficiency.

[0103] Reference Figure 1 Step S200 in:

[0104] The ultrasonic transducer and the receiving probe are placed in a medium, a first transmitting power and a second transmitting power are set, the ultrasonic transducer uses the first transmitting power as the initial power and gradually increases the transmitting power, the test receiving power received by the receiving probe at the recording point is recorded, and the power error rate is calculated based on the standard receiving power and the test receiving power;

[0105] Furthermore, the calculation formula for obtaining the power error rate is:

[0106]

[0107] in, represents the power error rate, m represents the number of tests, i represents the index of the number of tests, represents the standard received power of the i-th test, Indicates the test receiving power of the i-th test.

[0108] Specifically, the first transmission power is the minimum transmission power of the ICE transducer, and the second transmission power is the maximum transmission power of the ICE transducer. In a feasible implementation scheme, the first transmission power is 5 MHz, the second transmission power is 10 MHz, and the single increase gradient is 0.5 MHz. Therefore, it can be seen that the number of tests m is 10 times; the standard receiving power refers to the receiving power obtained after testing using recognized equipment in a specific clinical or experimental environment.

[0109] By gradient-raising the transmitting power of the ultrasonic transducer and recording the test receiving power of the receiving probe, the power error rate is calculated. This allows for an accurate evaluation of the power transfer efficiency of the ultrasonic system's bonding thickness at different powers, thereby providing power error data for bonding detection and correction of the ultrasonic transducer.

[0110] Reference Figure 1 Step S300 in:

[0111] The ultrasound transducer was placed in the bionic heart cavity model, and ICE images of the bionic heart cavity model at different angles and motion cycles were collected. The static imaging error rate and dynamic imaging error rate of the standard clinical images and ICE images were calculated using the static-dynamic imaging joint analysis method to obtain the imaging error rate.

[0112] Furthermore, the static-dynamic imaging joint analysis method includes:

[0113] Construct a bionic heart model that can simulate the cyclic contraction and relaxation of cardiac chambers;

[0114] placing an ultrasonic transducer in a bionic heart cavity model, acquiring a static ICE image of the bionic heart cavity model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate;

[0115] Collect ICE image groups from multiple angles inside the bionic heart cavity model according to the bionic heart cavity motion cycle to generate a set of dynamic image sequences. Use dynamic texture analysis to extract the optical flow change characteristics of the dynamic image sequences, and calculate the imaging similarity by comparing with standard clinical image sequences.

[0116] Calculating a dynamic imaging error rate based on the imaging similarity;

[0117] The imaging error rate is calculated by combining the static imaging error rate and the dynamic imaging error rate;

[0118] The calculation formula of the imaging error rate is:

[0119] ;

[0120] in, represents the imaging error rate, represents the static imaging error rate, represents the dynamic imaging error rate.

[0121] Specifically, a bionic cardiac model capable of simulating the cyclical contraction and relaxation of cardiac chambers was constructed. ICE images of the bionic cardiac model in the diastolic state were collected and compared with standard clinical images (standard images refer to high-quality images obtained using recognized equipment and techniques in a specific clinical or experimental environment). The ICE images and standard clinical images were spatially aligned, and the static imaging error rate between the two images was calculated by calculating the deviation value of each pixel. The calculation formula is as follows:

[0122]

[0123] in, Represents the static error rate, M represents the number of pixels in the horizontal direction of the ICE image, N represents the number of pixels in the vertical direction of the ICE image, x represents the horizontal coordinate of the index pixel, and y represents the vertical coordinate of the index pixel. Represents the coordinates of the indexed pixel on the ICE image, represents the coordinates of the indexed pixel points on the standard clinical image; during the bionic cardiac cavity motion cycle, ICE images from multiple angles are collected at each time point to generate a set of dynamic image sequences. The dynamic texture analysis method is used to extract the optical flow change characteristics of the dynamic image sequence. The calculation formula of the optical flow change characteristics is:

[0124]

[0125] in, Represents the optical flow change characteristics, Represents the partial derivative of the image with respect to the pixel in the horizontal direction, Represents the partial derivative of the image in the vertical direction of the pixel point, Represents the partial derivative of the image to the pixel point in the time series, uses dynamic texture analysis to obtain the optical flow change characteristics of each frame, and calculates the cosine similarity of the optical flow field. The calculation formula of cosine similarity is:

[0126]

[0127] in, Represents the cosine similarity of the optical flow change features of ICE images and standard clinical images, Represents the optical flow change characteristics of the ICE image, Represents the optical flow change characteristics of standard clinical images, Indicates the characteristic modulus length of the optical flow change of the ICE image, represents the characteristic modulus length of the optical flow change of the standard clinical image, and t represents the length of the dynamic image sequence. The dynamic error rate is obtained by calculating the cosine similarity, and the calculation formula is:

[0128]

[0129] in, Represents the dynamic error rate. The imaging error rate is calculated by combining the static imaging error rate and the dynamic imaging error rate. The calculation formula is:

[0130] ;

[0131] in, represents the imaging error rate, represents the static imaging error rate, represents the dynamic imaging error rate.

[0132] By combining static and dynamic error analysis with a biomimetic cardiac cavity model, the imaging performance of ultrasound transducers can be comprehensively evaluated. Static error reflects the transducer's accuracy in the diastolic state, while dynamic error quantifies its performance under motion conditions by comparing optical flow features. This combined analysis improves the overall evaluation of transducer performance in real cardiac cavity scenarios and provides accurate imaging error data for ultrasonic transducer adhesion detection and correction.

[0133] Reference Figure 1 Step S400 in:

[0134] Furthermore, the bonding thickness correction formula is:

[0135]

[0136] in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

[0137] By combining thickness fusion data, power error rate and imaging error rate, the multiple effects of the bonding layer on thickness distribution, power variation and imaging accuracy are fully considered. While effectively improving the accuracy and reliability of the bonding layer thickness measurement, the bonding thickness correction formula is used for correction, and then indicative correction data is given, providing a bonding optimization solution for designing ultrasonic transducers with better performance.

[0138] The adhesion detection method of an ultrasonic transducer in this embodiment is compared before and after implementation to verify the effectiveness of the method of this embodiment. The comparative experimental results are shown in Table 1, wherein the adhesion thickness detection result of the ICE transducer of production line A before implementation is 0.312mm, the power error with the standard receiving power is 9%, and the imaging error with the standard clinical image is 6%, which is difficult to be applied to high-precision cardiac imaging. The adhesion thickness detection result of the ICE transducer of production line A after implementation is 0.305mm, the power error with the standard receiving power is reduced to 2%, and the imaging error with the standard clinical image is reduced to 2%. In addition, various errors before and after the implementation of production line B have also been improved, which shows that the adhesion detection method of the ultrasonic transducer proposed in the present invention can accurately detect various indicators of the bonding layer, including thickness, power and imaging. By analyzing and calculating the power error rate and imaging error rate, the thickness of the bonding layer is corrected, thereby improving the performance of the ultrasonic transducer.

[0139] Table 1. Comparison of an ultrasonic transducer bonding detection method before and after implementation

[0140]

[0141] Through multimodal data fusion and multi-level error analysis, the accuracy and reliability of bonding layer thickness measurement have been significantly improved. Combining the advantages of ultrasound probes and laser interferometry technology, an acousto-optic weighted averaging algorithm is used to obtain thickness fusion data, ensuring the accuracy of thickness measurement. At the same time, through power error rate analysis and a combined static-dynamic imaging analysis method in a bionic cardiac cavity model, the effect of bonding thickness on ICE transducer power and imaging was systematically detected and evaluated. By integrating this multidimensional error information, a bonding thickness correction formula was used to achieve precise correction of the bonding layer thickness. This method provides strong technical support for the control of bonding thickness and performance of ICE transducers, helping to enhance the application value of ICE transducers in the clinical medical field.

[0142] Example 2

[0143] An ultrasonic transducer bonding detection system, comprising:

[0144] Thickness detection module: fix the ultrasonic transducer sample, activate the ultrasonic probe inside the ultrasonic transducer sample to transmit and receive reflected waves, obtain ultrasonic thickness data of the adhesive layer, use a laser interferometer to detect the thickness distribution of the adhesive layer, obtain thickness laser data, and fuse the thickness ultrasonic data and the thickness ultrasonic data according to the acousto-optic weighted averaging algorithm to obtain thickness fusion data;

[0145] Power error analysis module: placing the ultrasonic transducer and receiving probe in a medium, setting a first transmission power and a second transmission power, using the first transmission power as the initial power and gradually increasing the transmission power of the ultrasonic transducer, recording the test receiving power received by the receiving probe at the recording point, and calculating the power error rate based on the standard receiving power and the test receiving power;

[0146] Imaging error analysis module: placing the ultrasonic transducer in a bionic heart cavity model, collecting ICE images of the bionic heart cavity model at different angles and motion cycles, and calculating the static imaging error rate and dynamic imaging error rate of the standard clinical image and the ICE image through a static-dynamic imaging joint analysis method to obtain the imaging error;

[0147] Thickness correction module: corrects the thickness of the adhesive layer using an adhesive thickness correction formula according to the thickness fusion data, the power error rate, and the imaging error rate.

[0148] Furthermore, the specific steps of obtaining the thickness fusion data include:

[0149] fixing the ultrasonic transducer sample on a testing platform;

[0150] activating the ultrasonic probe inside the transducer, the ultrasonic probe transmitting ultrasonic waves and receiving reflected waves generated after the ultrasonic waves pass through the adhesive layer, recording a phase difference between transmitting the ultrasonic waves and receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data based on the phase difference and the material propagation velocity of the adhesive layer;

[0151] Using a laser interferometer to emit a laser beam, the laser beam is irradiated on the surface of the adhesive layer to generate a reflected laser beam, analyzing the change in the interference pattern between the reflected laser beam and a reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer based on the change in the interference pattern to obtain the thickness laser data;

[0152] fusing the thickness ultrasonic data and the thickness laser data using the acousto-optic weighted averaging algorithm to obtain the thickness fusion data;

[0153] The calculation formula of the acousto-optic weighted average algorithm is:

[0154]

[0155] in, represents the thickness fusion data, represents the thickness ultrasonic data, represents the thickness laser data, represents the ultrasonic measurement weight, Represents the laser measurement weight.

[0156] Furthermore, the calculation formula for obtaining the power error rate is:

[0157]

[0158] in, represents the power error rate, m represents the number of tests, i represents the index of the number of tests, represents the standard received power of the i-th test, Indicates the test receiving power of the i-th test

[0159] Furthermore, the static-dynamic imaging joint analysis method includes:

[0160] Construct a bionic heart model that can simulate the cyclic contraction and relaxation of cardiac chambers;

[0161] placing an ultrasonic transducer in a bionic heart cavity model, acquiring a static ICE image of the bionic heart cavity model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate;

[0162] Collect ICE image groups from multiple angles inside the bionic heart cavity model according to the bionic heart cavity motion cycle to generate a set of dynamic image sequences. Use dynamic texture analysis to extract the optical flow change characteristics of the dynamic image sequences, and calculate the imaging similarity by comparing with standard clinical image sequences.

[0163] Calculating a dynamic imaging error rate based on the imaging similarity;

[0164] The imaging error rate is calculated by combining the static imaging error rate and the dynamic imaging error rate;

[0165] The calculation formula of the imaging error rate is:

[0166] ;

[0167] in, represents the imaging error rate, represents the static imaging error rate, represents the dynamic imaging error rate.

[0168] Furthermore, the bonding thickness correction formula is:

[0169]

[0170] in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

[0171] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for detecting adhesion of an ultrasonic transducer, characterized in that: include: Fixing an ultrasonic transducer sample, activating an ultrasonic probe inside the ultrasonic transducer sample to transmit and receive reflected waves, obtaining ultrasonic thickness data of the adhesive layer, detecting the thickness distribution of the adhesive layer using a laser interferometer to obtain thickness laser data, and fusing the thickness ultrasonic data and the thickness laser data according to an acousto-optic weighted averaging algorithm to obtain thickness fusion data; Placing the ultrasonic transducer and the receiving probe in a medium, setting a first transmitting power and a second transmitting power, using the first transmitting power as the initial power and gradually increasing the transmitting power of the ultrasonic transducer, recording the test receiving power received by the receiving probe at the recording point, and calculating and obtaining a power error rate based on the standard receiving power and the test receiving power; Placing the ultrasonic transducer in a bionic heart cavity model, collecting ICE images of the bionic heart cavity model at different angles and motion cycles, and calculating the static imaging error rate and the dynamic imaging error rate of the standard clinical image and the ICE image using a static-dynamic imaging joint analysis method to obtain an imaging error rate; The thickness of the adhesive layer is corrected using an adhesive thickness correction formula according to the thickness fusion data, the power error rate, and the imaging error rate.

2. The method for detecting adhesion of an ultrasonic transducer according to claim 1, wherein: The specific steps of obtaining the thickness fusion data include: fixing the ultrasonic transducer sample on a testing platform; activating the ultrasonic probe inside the transducer, the ultrasonic probe transmitting ultrasonic waves and receiving reflected waves generated after the ultrasonic waves pass through the adhesive layer, recording a phase difference between transmitting the ultrasonic waves and receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data based on the phase difference and the material propagation velocity of the adhesive layer; Using a laser interferometer to emit a laser beam, the laser beam is irradiated on the surface of the adhesive layer to generate a reflected laser beam, analyzing the change in the interference pattern between the reflected laser beam and a reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer based on the change in the interference pattern to obtain the thickness laser data; fusing the thickness ultrasonic data and the thickness laser data using the acousto-optic weighted averaging algorithm to obtain the thickness fusion data; The calculation formula of the acousto-optic weighted average algorithm is: ； in, represents the thickness fusion data, represents the thickness ultrasonic data, represents the thickness laser data, represents the ultrasonic measurement weight, Represents the laser measurement weight.

3. The method for detecting adhesion of an ultrasonic transducer according to claim 1, wherein: The calculation formula for obtaining the power error rate is: ； in, represents the power error rate, m represents the number of tests, i represents the index of the number of tests, represents the standard received power of the i-th test, Indicates the test receiving power of the i-th test.

4. The method for detecting adhesion of an ultrasonic transducer according to claim 1, wherein: The static-dynamic imaging joint analysis method comprises: Construct a bionic heart model that can simulate the cyclic contraction and relaxation of cardiac chambers; placing an ultrasonic transducer in a bionic heart cavity model, acquiring a static ICE image of the bionic heart cavity model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate; Collect ICE image groups from multiple angles inside the bionic heart cavity model according to the bionic heart cavity motion cycle to generate a set of dynamic image sequences. Use dynamic texture analysis to extract the optical flow change characteristics of the dynamic image sequences, and calculate the imaging similarity by comparing with standard clinical image sequences. Calculating a dynamic imaging error rate based on the imaging similarity; The imaging error rate is calculated by combining the static imaging error rate and the dynamic imaging error rate; The calculation formula of the imaging error rate is: ； in, represents the imaging error rate, represents the static imaging error rate, represents the dynamic imaging error rate.

5. The method for detecting adhesion of an ultrasonic transducer according to claim 1, wherein: The bonding thickness correction formula is: ； in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

6. An ultrasonic transducer bonding detection system, characterized in that: include: Thickness detection module: fix the ultrasonic transducer sample, activate the ultrasonic probe inside the ultrasonic transducer sample to transmit and receive reflected waves, obtain ultrasonic thickness data of the adhesive layer, use a laser interferometer to detect the thickness distribution of the adhesive layer, obtain thickness laser data, and fuse the thickness ultrasonic data and the thickness laser data according to the acousto-optic weighted averaging algorithm to obtain thickness fusion data; Power error analysis module: placing the ultrasonic transducer and receiving probe in a medium, setting a first transmission power and a second transmission power, using the first transmission power as the initial power and gradually increasing the transmission power of the ultrasonic transducer, recording the test receiving power received by the receiving probe at the recording point, and calculating the power error rate based on the standard receiving power and the test receiving power; Imaging error analysis module: placing the ultrasonic transducer in a bionic heart cavity model, collecting ICE images of the bionic heart cavity model at different angles and motion cycles, and calculating the static imaging error rate and dynamic imaging error rate of the standard clinical image and the ICE image through a static-dynamic imaging joint analysis method to obtain an imaging error rate; Thickness correction module: corrects the thickness of the adhesive layer using an adhesive thickness correction formula according to the thickness fusion data, the power error rate, and the imaging error rate.

7. The adhesion detection system of an ultrasonic transducer according to claim 6, characterized in that: The specific steps of obtaining the thickness fusion data include: fixing the ultrasonic transducer sample on a testing platform; activating the ultrasonic probe inside the transducer, the ultrasonic probe transmitting ultrasonic waves and receiving reflected waves generated after the ultrasonic waves pass through the adhesive layer, recording a phase difference between transmitting the ultrasonic waves and receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data based on the phase difference and the material propagation velocity of the adhesive layer; Using a laser interferometer to emit a laser beam, the laser beam is irradiated on the surface of the adhesive layer to generate a reflected laser beam, analyzing the change in the interference pattern between the reflected laser beam and a reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer based on the change in the interference pattern to obtain the thickness laser data; fusing the thickness ultrasonic data and the thickness laser data using the acousto-optic weighted averaging algorithm to obtain the thickness fusion data; The calculation formula of the acousto-optic weighted average algorithm is: ； in, represents the thickness fusion data, represents the thickness ultrasonic data, represents the thickness laser data, represents the ultrasonic measurement weight, Represents the laser measurement weight.

8. The adhesion detection system of an ultrasonic transducer according to claim 6, characterized in that: The calculation formula for obtaining the power error rate is: ； in, represents the power error rate, m represents the number of tests, i represents the index of the number of tests, represents the standard received power of the i-th test, Indicates the test receiving power of the i-th test.

9. The adhesion detection system of an ultrasonic transducer according to claim 6, characterized in that: The static-dynamic imaging joint analysis method comprises: Construct a bionic heart model that can simulate the cyclic contraction and relaxation of cardiac chambers; placing an ultrasonic transducer in a bionic heart cavity model, acquiring a static ICE image of the bionic heart cavity model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate; Collect ICE image groups from multiple angles inside the bionic heart cavity model according to the bionic heart cavity motion cycle to generate a set of dynamic image sequences. Use dynamic texture analysis to extract the optical flow change characteristics of the dynamic image sequences, and calculate the imaging similarity by comparing with standard clinical image sequences. Calculating a dynamic imaging error rate based on the imaging similarity; The imaging error rate is calculated by combining the static imaging error rate and the dynamic imaging error rate; The calculation formula of the imaging error rate is: ； in, represents the imaging error rate, represents the static imaging error rate, represents the dynamic imaging error rate.

10. The adhesion detection system of an ultrasonic transducer according to claim 6, characterized in that: The bonding thickness correction formula is: ； in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

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