Bonding detection method and system for ultrasonic transducer

Abstract

The invention relates to the technical field of material testing or analysis, in particular to a bonding detection method and system for an ultrasonic transducer. The method comprises the following steps: firstly, acquiring thickness ultrasonic data of a bonding layer by using an ultrasonic transducer, and generating thickness fusion data through an acousto-optic weighted average algorithm in combination with thickness laser data acquired by a laser interferometer; secondly, different transmitting power gradients are set in the medium, receiving power is measured, and a power error rate is calculated; meanwhile, ICE images under multi-angle and periodic motion are collected through the bionic cardiac cavity model, a static-dynamic imaging conjoint analysis method is adopted, the static and dynamic imaging error rate is calculated, and the imaging error rate is obtained; finally, according to the thickness fusion data, the power error rate and the imaging error rate, accurate correction of the bonding thickness is achieved through a bonding thickness correction formula.

Description

Technical Field

[0001] The 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 important equipment for modern medical imaging. Their core is to convert electrical signals into sound wave signals, thereby achieving non-destructive testing inside tissues. As a special type of ultrasonic transducer, ICE (intracavitary ultrasound) transducers are widely used in cardiovascular interventional surgery due to their compactness and flexibility. They provide real-time surgical navigation support by obtaining high-resolution tissue images in the heart cavity. Due to their complex structure and high precision requirements, the manufacture and testing of ICE transducers has become one of the important links in technological development.

[0003] The bonding layer is a key component of the ultrasonic transducer, and its thickness has a significant impact on the performance of the transducer. If the bonding layer is too thick, it may increase the attenuation of ultrasound and reduce the energy transfer efficiency; while if the thickness is too small, it may cause acoustic impedance mismatch and affect the imaging quality. Especially for ICE transducers, the thickness distribution of the bonding layer is directly related to the resolution and accuracy of intracardiac imaging. Therefore, accurately detecting and optimizing the thickness of the bonding layer is the key to improving the performance of ICE transducers.

[0004] At present, the detection method of bonding layer thickness mainly relies on a single ultrasonic detection or optical detection method, which can only obtain certain thickness information and is difficult to fully reflect the true state of the bonding layer. At the same time, most of the existing detection technologies are aimed at quality assessment and cannot provide effective bonding thickness optimization solutions, which limits the further improvement of transducer performance. To solve these problems, a comprehensive method that combines multiple detection methods and can support bonding thickness correction and optimization is urgently needed.

[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 a bonding 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 with laser interference, and 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 bonding thickness correction and optimization.

[0007] To achieve the above object, the present invention provides the following technical solutions: A bonding detection method for an ultrasonic transducer, comprising: Fixing the ultrasonic transducer sample, activating the ultrasonic probe inside the ultrasonic transducer sample to transmit and receive reflected waves, obtaining thickness ultrasonic data of the bonding layer, using a laser interferometer to detect the thickness distribution of the bonding layer, obtaining thickness laser data, and fusing the thickness ultrasonic data and the thickness ultrasonic data according to an acousto-optic weighted average algorithm to obtain thickness fusion data; 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; 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 by 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.

[0008] Furthermore, the specific steps of obtaining the thickness fusion data include: Fixing the ultrasonic transducer sample on a testing table; 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 the phase difference from transmitting the ultrasonic waves to receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data according to the phase difference and the material propagation speed 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 of the interference pattern between the reflected laser beam and the reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer according to the change of the interference pattern to obtain the thickness laser data; The acousto-optic weighted average algorithm is used to fuse the thickness ultrasonic data and the thickness laser data to obtain the thickness fusion data; The calculation formula of the acoustic-optic weighted average algorithm is:

[0009] 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.

[0010] Furthermore, the calculation formula for obtaining the power error rate is:

[0011] 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 ith test, Represents the test receiving power of the i-th test.

[0012] Furthermore, the static-dynamic imaging joint analysis method includes: Construct a bionic heart model that can simulate the periodic contraction and relaxation of heart chambers; Placing an ultrasonic transducer in a bionic heart chamber model, acquiring a static ICE image of the bionic heart chamber model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate; According to the bionic heart cavity movement cycle, an ICE image group of the interior of the bionic heart cavity model is collected at multiple angles to generate a set of dynamic image sequences, and the optical flow change characteristics of the dynamic image sequences are extracted using a dynamic texture analysis method, and the imaging similarity is calculated by comparing with a standard clinical image sequence; According to the imaging similarity, a dynamic imaging error rate is calculated; 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.

[0013] Furthermore, the bonding thickness correction formula is:

[0014] in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

[0015] An ultrasonic transducer bonding detection system, comprising: Thickness detection module: 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 average algorithm to obtain thickness fusion data; Power error analysis module: placing the ultrasonic transducer and the receiving probe in a medium, setting a first transmitting power and a second transmitting power, the ultrasonic transducer uses the first transmitting power as the initial power and gradually increases the transmitting power, recording the test receiving power received by the receiving probe at the recording point, and calculating and obtaining the power error rate according to the standard receiving power and the test receiving power; Imaging error analysis module: placing the ultrasonic transducer in the 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; 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.

[0016] Furthermore, the specific steps of obtaining the thickness fusion data include: Fixing the ultrasonic transducer sample on a testing table; 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 the phase difference from transmitting the ultrasonic waves to receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data according to the phase difference and the material propagation speed 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 of the interference pattern between the reflected laser beam and the reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer according to the change of the interference pattern to obtain the thickness laser data; The thickness ultrasonic data and the thickness laser data are fused by using the acousto-optic weighted averaging algorithm to obtain the thickness fusion data; the calculation formula of the acousto-optic weighted averaging algorithm is:

[0017] 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.

[0018] Furthermore, the calculation formula for obtaining the power error rate is:

[0019] 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 ith test, Represents the test receiving power of the i-th test.

[0020] Furthermore, the static-dynamic imaging joint analysis method includes: Construct a bionic heart model that can simulate the periodic contraction and relaxation of heart chambers; Placing an ultrasonic transducer in a bionic heart chamber model, acquiring a static ICE image of the bionic heart chamber model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate; According to the bionic heart cavity movement cycle, an ICE image group of the interior of the bionic heart cavity model is collected at multiple angles to generate a set of dynamic image sequences, and the optical flow change characteristics of the dynamic image sequences are extracted using a dynamic texture analysis method, and the imaging similarity is calculated by comparing with a standard clinical image sequence; According to the imaging similarity, a dynamic imaging error rate is calculated; 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.

[0021] Furthermore, the bonding thickness correction formula is:

[0022] in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

[0023] Compared with the prior art, the present invention has the following beneficial effects: 1. The present invention proposes a bonding thickness detection method based on acoustic-optic weighting, which effectively combines the advantages of ultrasonic detection and laser interferometry technology, and fuses the two measurement data through an acoustic-optic weighted averaging algorithm, thereby improving the accuracy and reliability of thickness detection, overcoming the limitation of insufficient accuracy of a single detection method in the complex internal environment of an ICE transducer, and ensuring the accuracy of core data in bonding detection.

[0024] 2. The present invention proposes a static-dynamic imaging joint analysis method, which comprehensively evaluates the difference between ICE images and standard clinical images by combining the static imaging error rate and the dynamic imaging error rate, effectively improving the accuracy and reliability of imaging error analysis. This method can capture the multi-dimensional characteristics of imaging under static and dynamic conditions, and provides a reliable data source for imaging quality evaluation and bonding layer status analysis in bonding detection, thereby achieving the accuracy of imaging detection in bonding detection.

[0025] 3. The present invention accurately corrects the thickness of the bonding layer through the bonding thickness correction formula based on the thickness fusion data, power error rate, and imaging error rate, which can comprehensively evaluate the actual deviation of the bonding layer thickness and achieve optimized correction based on the synergy of multi-dimensional data. This method effectively overcomes the limitations of a single detection method and improves the accuracy, stability, and diversity of bonding detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0026] Figure 1 A flow chart of a bonding detection method using an ultrasonic transducer provided in Example 1 of the present invention; Figure 2 This is a structural diagram of an ultrasonic transducer bonding detection system provided in Example 2 of the present invention. DETAILED DESCRIPTION

[0027] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

[0028] Embodiment 1 A company produces ICE transducers for monitoring the internal anatomical structure of the heart cavity, and the core production line is responsible for the packaging of ICE transducers. In order to achieve a more accurate monitoring effect, the company is currently preparing to upgrade the products of the two production lines. Therefore, a bonding detection method for ultrasonic transducers is used to detect and correct the thickness of the bonding layer between the Pebax tube and the transducer, thereby improving the performance of the ICE transducer.

[0029] Reference Figure 1Step S100 in: 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 an acousto-optic weighted average algorithm to obtain thickness fusion data; Furthermore, the specific steps of obtaining the thickness fusion data include: Fixing the ultrasonic transducer sample on a testing table; 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 the phase difference from transmitting the ultrasonic waves to receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data according to the phase difference and the material propagation speed 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 of the interference pattern between the reflected laser beam and the reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer according to the change of the interference pattern to obtain the thickness laser data; The acousto-optic weighted average algorithm is used to fuse the thickness ultrasonic data and the thickness laser data to obtain the thickness fusion data; The calculation formula of the acoustic-optic weighted average algorithm is:

[0030] 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.

[0031] The thickness ultrasonic data and the thickness laser data are respectively:

[0032] 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; The ultrasonic measurement weight and the laser measurement weight are respectively:

[0033] 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; The standard deviation of the ultrasonic measurement data and the standard deviation of the laser measurement data are respectively:

[0034] 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.

[0035] Specifically, the transducer sample is fixed on the test bench to ensure its stability; the ultrasonic probe inside the transducer is activated, and the probe starts to transmit ultrasonic waves and receive reflected waves. The ultrasonic waves emitted by different electronic phase-controlled units inside the ultrasonic probe are reflected back after passing through the adhesive layer. The probe captures these reflected waves and records the phase difference from transmission to reception. To obtain the phase difference, the ultrasonic wave and the reflected wave need to be converted from time domain signals to frequency domain signals. The calculation formula is as follows:

[0036] 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:

[0037] 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, and the calculation process is:

[0038] in represents the phase difference, represents the phase of the emitted 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:

[0039] in, represents the thickness ultrasonic data obtained by measuring the i-th electronic phase-controlled unit, and then the thickness ultrasonic data is obtained:

[0040] 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, and the vertical thickness of the adhesive layer from different electronic phase-controlled units of the adhesive layer to 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 fused data.

[0041] The ultrasonic method calculates the thickness of the adhesive layer through phase difference and propagation speed, which is suitable for rapid detection of the thickness of the adhesive layer; while the laser interferometry method captures surface deformation through precise interference patterns and provides high-precision surface thickness data. Combining the two and using the acousto-optic weighted average algorithm to fuse ultrasonic data and laser data can effectively improve measurement accuracy and efficiency.

[0042] Reference Figure 1 Step S200 in: The ultrasonic transducer and the receiving probe are placed in the 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; Furthermore, the calculation formula for obtaining the power error rate is:

[0043] 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 ith test, Represents the test receiving power of the i-th test.

[0044] 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 5MHz, the second transmission power is 10MHz, and the single increase gradient is 0.5MHz. 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.

[0045] By gradient-boosting the transmitting power of the ultrasonic transducer and recording the test receiving power of the receiving probe, the power error rate is calculated. This can accurately evaluate 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.

[0046] Reference Figure 1 Step S300 in: The ultrasonic transducer was placed in the bionic heart cavity model, and the 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 by the static-dynamic imaging joint analysis method to obtain the imaging error rate. Furthermore, the static-dynamic imaging joint analysis method includes: Construct a bionic heart model that can simulate the periodic contraction and relaxation of heart chambers; Placing an ultrasonic transducer in a bionic heart chamber model, acquiring a static ICE image of the bionic heart chamber model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate; According to the bionic heart cavity movement cycle, an ICE image group of the interior of the bionic heart cavity model is collected at multiple angles to generate a set of dynamic image sequences, and the optical flow change characteristics of the dynamic image sequences are extracted using a dynamic texture analysis method, and the imaging similarity is calculated by comparing with a standard clinical image sequence; According to the imaging similarity, a dynamic imaging error rate is calculated; 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.

[0047] Specifically, a cardiac bionic model that can simulate the periodic contraction and relaxation of the cardiac chamber is constructed; the ICE images of the bionic cardiac chamber model in the diastolic state are collected and compared with the 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 the standard clinical images are spatially aligned, and the static imaging error rate between the two images is calculated by calculating the deviation value of each pixel. The calculation formula is as follows:

[0048] 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 index pixel point on the standard clinical image; in the bionic heart cavity motion cycle, ICE images of multiple angles are collected at each time point to generate a set of dynamic image sequences, and 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:

[0049] 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, It 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 the cosine similarity is:

[0050] in, Represents the cosine similarity of the optical flow change characteristics of ICE images and standard clinical images, Represents the optical flow change characteristics of ICE images, Represents the optical flow variation 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:

[0051] 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: ; in, represents the imaging error rate, represents the static imaging error rate, represents the dynamic imaging error rate.

[0052] The imaging performance of the ultrasound transducer can be comprehensively evaluated by combining the bionic heart cavity model with static and dynamic error analysis. The static error reflects the accuracy of the transducer in the diastolic state, and the dynamic error quantifies its ability under motion conditions through optical flow feature comparison. The comprehensive analysis improves the overall evaluation of the transducer performance in the real heart cavity scene and provides accurate imaging error numbers for the bonding detection and correction of the ultrasound transducer.

[0053] Reference Figure 1 Step S400 in: Furthermore, the bonding thickness correction formula is:

[0054] in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

[0055] 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.

[0056] The bonding 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 results of the comparative experiment are shown in Table 1, where the bonding 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 apply to high-precision cardiac imaging. The bonding 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 improved, which shows that the bonding 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, and correct the thickness of the bonding layer by analyzing and calculating the power error rate and the imaging error rate, thereby improving the performance of the ultrasonic transducer.

[0057] Table 1. Comparison of an ultrasonic transducer bonding detection method before and after implementation

[0058] Through multimodal data fusion and multi-level error analysis, the accuracy and reliability of the bonding layer thickness measurement have been significantly improved. Combining the advantages of ultrasound probes and laser interferometry technology, the thickness fusion data is obtained using the acousto-optic weighted average algorithm to ensure the accuracy of thickness measurement. At the same time, through power error rate analysis and static-dynamic imaging joint analysis methods in bionic heart cavity models, the effects of bonding thickness on ICE transducer power and imaging are systematically detected and evaluated, and these multi-dimensional error information are integrated to achieve accurate correction of bonding layer thickness using the bonding thickness correction formula. This method provides strong technical support for the bonding thickness and performance control of ICE transducers, and helps to improve the application value of ICE transducers in the field of clinical medicine.

[0059] Embodiment 2 An ultrasonic transducer bonding detection system, comprising: Thickness detection module: 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 average algorithm to obtain thickness fusion data; Power error analysis module: placing the ultrasonic transducer and the receiving probe in a medium, setting a first transmitting power and a second transmitting power, the ultrasonic transducer uses the first transmitting power as the initial power and gradually increases the transmitting power, recording the test receiving power received by the receiving probe at the recording point, and calculating and obtaining the power error rate according to the standard receiving power and the test receiving power; Imaging error analysis module: placing the ultrasonic transducer in the 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; 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.

[0060] Furthermore, the specific steps of obtaining the thickness fusion data include: Fixing the ultrasonic transducer sample on a testing table; 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 the phase difference from transmitting the ultrasonic waves to receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data according to the phase difference and the material propagation speed 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 of the interference pattern between the reflected laser beam and the reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer according to the change of the interference pattern to obtain the thickness laser data; The acousto-optic weighted average algorithm is used to fuse the thickness ultrasonic data and the thickness laser data to obtain the thickness fusion data; The calculation formula of the acoustic-optic weighted average algorithm is:

[0061] 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.

[0062] Furthermore, the calculation formula for obtaining the power error rate is:

[0063] 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 ith test, Indicates the test receiving power of the i-th test Furthermore, the static-dynamic imaging joint analysis method includes: Construct a bionic heart model that can simulate the periodic contraction and relaxation of heart chambers; Placing an ultrasonic transducer in a bionic heart chamber model, acquiring a static ICE image of the bionic heart chamber model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate; According to the bionic heart cavity movement cycle, an ICE image group of the interior of the bionic heart cavity model is collected at multiple angles to generate a set of dynamic image sequences, and the optical flow change characteristics of the dynamic image sequences are extracted using a dynamic texture analysis method, and the imaging similarity is calculated by comparing with a standard clinical image sequence; According to the imaging similarity, a dynamic imaging error rate is calculated; 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.

[0064] Furthermore, the bonding thickness correction formula is:

[0065] in, Indicates the corrected bonding thickness, represents the thickness fusion data, represents the power error, represents the imaging error rate.

[0066] Although 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 the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A bonding detection method of an ultrasonic transducer, characterized in that: include: Fixing the ultrasonic transducer sample, activating the ultrasonic probe inside the ultrasonic transducer sample to transmit and receive reflected waves, obtaining thickness ultrasonic data of the bonding layer, using a laser interferometer to detect the thickness distribution of the bonding layer, obtaining thickness laser data, and fusing the thickness ultrasonic data and the thickness ultrasonic data according to an acousto-optic weighted average algorithm to obtain thickness fusion data; 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; 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 by 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 bonding detection method of an ultrasonic transducer according to claim 1, characterized in that: The specific steps of obtaining the thickness fusion data include: Fixing the ultrasonic transducer sample on a testing table; 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 the phase difference from transmitting the ultrasonic waves to receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data according to the phase difference and the material propagation speed 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 of the interference pattern between the reflected laser beam and the reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer according to the change of the interference pattern to obtain the thickness laser data; The acousto-optic weighted average algorithm is used to fuse the thickness ultrasonic data and the thickness laser data to obtain the thickness fusion data; The calculation formula of the acoustic-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 bonding detection method of an ultrasonic transducer according to claim 1, 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 ith test, Represents the test receiving power of the i-th test.

4. The bonding detection method of an ultrasonic transducer according to claim 1, characterized in that: The static-dynamic imaging joint analysis method comprises: Construct a bionic heart model that can simulate the periodic contraction and relaxation of heart chambers; Placing an ultrasonic transducer in a bionic heart chamber model, acquiring a static ICE image of the bionic heart chamber model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate; According to the bionic heart cavity movement cycle, an ICE image group of the interior of the bionic heart cavity model is collected at multiple angles to generate a set of dynamic image sequences, and the optical flow change characteristics of the dynamic image sequences are extracted using a dynamic texture analysis method, and the imaging similarity is calculated by comparing with a standard clinical image sequence; According to the imaging similarity, a dynamic imaging error rate is calculated; 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 bonding detection method of an ultrasonic transducer according to claim 1, 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.

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 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 average algorithm to obtain thickness fusion data; Power error analysis module: placing the ultrasonic transducer and the receiving probe in a medium, setting a first transmitting power and a second transmitting power, the ultrasonic transducer uses the first transmitting power as the initial power and gradually increases the transmitting power, recording the test receiving power received by the receiving probe at the recording point, and calculating and obtaining the power error rate according to the standard receiving power and the test receiving power; Imaging error analysis module: placing the ultrasonic transducer in the 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: according to the thickness fusion data, the power error rate, and the imaging error rate, a thickness correction formula is used to correct the thickness of the adhesive layer.

7. The bonding 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 table; 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 the phase difference from transmitting the ultrasonic waves to receiving the reflected waves, and calculating and obtaining the thickness ultrasonic data according to the phase difference and the material propagation speed 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 of the interference pattern between the reflected laser beam and the reference beam to obtain the surface deformation of the adhesive layer, and calculating the thickness of different positions of the adhesive layer according to the change of the interference pattern to obtain the thickness laser data; The acousto-optic weighted average algorithm is used to fuse the thickness ultrasonic data and the thickness laser data to obtain the thickness fusion data; The calculation formula of the acoustic-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 bonding 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 ith test, Represents the test receiving power of the i-th test.

9. The bonding 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 periodic contraction and relaxation of heart chambers; Placing an ultrasonic transducer in a bionic heart chamber model, acquiring a static ICE image of the bionic heart chamber model in a diastolic state, comparing the static ICE image with a standard clinical image, and calculating a static imaging error rate; According to the bionic heart cavity movement cycle, an ICE image group of the interior of the bionic heart cavity model is collected at multiple angles to generate a set of dynamic image sequences, and the optical flow change characteristics of the dynamic image sequences are extracted using a dynamic texture analysis method, and the imaging similarity is calculated by comparing with a standard clinical image sequence; According to the imaging similarity, a dynamic imaging error rate is calculated; 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 bonding detection system of 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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