A multi-dimensional cooperative constraint ultrasonic phased array adaptive focusing imaging method

Abstract

This invention relates to the field of ultrasonic nondestructive testing imaging technology, specifically to a multi-dimensional collaboratively constrained ultrasonic phased array adaptive focusing imaging method, comprising: emitting ultrasonic waves towards the material to be tested and acquiring echo data; preprocessing the echo data; constructing an effective imaging interval in the depth direction based on the preprocessed echo data, and establishing a depth weighting function within the effective imaging interval; constructing a candidate set of sound velocities, the candidate set including the sound velocity of the target material; calculating the propagation time and total propagation distance of the preprocessed echo data; calculating the weighting coefficients of the transmitting and receiving array elements respectively, and obtaining an imaging energy value based on the weighting coefficients, propagation time, and total propagation distance of the transmitting and receiving array elements under the constraint of the depth weighting function; evaluating the imaging energy value using an imaging quality evaluation function, and obtaining the optimal imaging after evaluation. This invention can improve defect location accuracy and image contrast.

Description

Technical Field

[0001] This invention relates to the field of ultrasonic nondestructive testing imaging technology, specifically to a multi-dimensional collaboratively constrained ultrasonic phased array adaptive focusing imaging method. Background Technology

[0002] Ultrasonic phased array total focusing imaging (TFM), a high-resolution non-destructive testing method, is based on the matching calculation of full matrix acquisition (FMC) data and sound wave propagation time model. By constructing a bidirectional propagation time matrix from the transmitting and receiving array elements to each pixel in the imaging region, the multi-channel echo signals are delayed, aligned, and coherently superimposed to ultimately form an energy distribution image reflecting the scattering characteristics of defects. The imaging quality of this technique directly depends on the accuracy of the propagation sound velocity parameters, the rationality of the array spatial weights, and the signal-to-noise ratio characteristics of the echo signals. These factors collectively determine the effectiveness of sound field energy focusing and the reliability of the imaging results.

[0003] Existing improved technologies, such as patent CN108693253A, use frequency domain focusing operations to replace traditional time domain calculations, significantly improving computational efficiency through submatrix decomposition and frequency domain weighting. However, these methods still have three inherent limitations: First, they assume that all channel echoes have equal contribution weights, failing to consider the negative impact of non-uniform echoes such as near-field interference and interface reflections on coherent superposition in actual detection; second, fixed propagation velocity parameters can lead to systematic deviations in the time matrix when faced with changes in the acoustic properties of the medium, causing focusing position shifts; finally, the array spatial weighting model cannot adaptively adjust the sidelobe energy distribution, exacerbating energy diffusion under the combined effects of sound velocity errors and complex echo environments. These defects make traditional TFM technology prone to problems such as decreased contrast, increased artifacts, and deteriorated positioning accuracy in non-ideal detection environments. Summary of the Invention

[0004] To address the aforementioned technical problems of uneven echo contributions despite equal-weighted superposition, focusing deviations caused by sound velocity variations, and a lack of sidelobe adaptive control, this invention provides a multi-dimensional collaboratively constrained ultrasonic phased array adaptive focusing imaging method. This invention primarily utilizes a ROI depth weighting function, continuous weighting of array element space, and an image quality evaluation feedback mechanism to effectively suppress focus drift and improve defect localization accuracy and image contrast in complex echo environments.

[0005] The technical means employed in this invention are as follows:

[0006] A multi-dimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays includes the following steps: Ultrasonic waves are emitted toward the material to be tested, and echo data is collected. The echo data is preprocessed; An effective imaging range in the depth direction is constructed based on the preprocessed echo data, and a depth weighting function is established within the effective imaging range. Construct a candidate set of sound speeds, the candidate set of sound speeds including the sound speeds of the target material; Calculate the propagation time and total propagation distance of the preprocessed echo data; The weighting coefficients of the transmitting and receiving array elements are calculated separately. Under the constraint of the depth weighting function, the imaging energy value is obtained based on the weighting coefficients of the transmitting and receiving array elements, the propagation time, and the total propagation distance. The imaging energy value is evaluated using an imaging quality evaluation function, and the optimal imaging is obtained after the evaluation.

[0007] Furthermore, the construction of the effective imaging range in the depth direction based on the preprocessed echo data includes: Envelope extraction is performed on the preprocessed echo data to obtain the echo data envelopment line; The upper limit of the effective imaging depth is determined by the timing of the bottom surface echo. The echo data envelope lines are fused to obtain the average envelope energy distribution curve that varies along the depth direction; Find M consecutive sampling points that satisfy the first condition in the average envelope energy distribution curve, and take the first depth position among the M sampling points as the lower limit of the effective imaging depth. The first condition is:

[0008] in, Let z be the average envelope energy at depth z. This is the energy proportion coefficient. This represents the peak energy level. An effective imaging range is constructed based on the upper limit of the effective imaging depth and the lower limit of the effective imaging depth.

[0009] Further, establishing a depth-weighted function within the effective imaging range includes: Find the energy peak of the echo data network line within the effective imaging range, and take the depth corresponding to the energy peak as the reflection center; The average envelope energy distribution curve is normalized to obtain the normalized echo energy. The width parameter is calculated based on the normalized echo energy corresponding to the reflection center and depth coordinates. The formula for calculating the width parameter is as follows:

[0010] in, For width parameter, For depth coordinates, As the center of reflection, The normalized echo energy; Based on the reflection center and width parameters, a weighting function is constructed, and the calculation formula of the weighting function is as follows:

[0011] in, For weighting functions; Based on the depth gating function and the weighting function, a depth-weighted function is constructed, and the calculation formula of the depth-weighted function is as follows:

[0012] in, For depth-weighted functions, For depth-gated functions, These are the weighting coefficients.

[0013] Furthermore, the calculation steps for the total propagation distance include: Calculate the transmission distance based on the position of the transmitting array elements and the pixels within the effective imaging range:

[0014] in, For launch distance, The x-coordinate of the pixel is The x-coordinate of the transmitting array element The ordinate of the pixel; Calculate the receiving distance based on the position of the receiving array element and the pixels within the effective imaging range:

[0015] in, For receiving distance, The x-coordinate of the receiving array element; The total propagation distance is calculated by adding the transmission distance and the reception distance.

[0016] Furthermore, the propagation time is obtained by dividing the total propagation distance by the speed of sound.

[0017] Furthermore, the formula for calculating the weighting coefficients of the transmitting array elements is as follows:

[0018] in, The weighting coefficients for the transmitting array elements are... i For the serial number of the launch array element ，N The total number of array elements, The formula for calculating the weighting coefficients of the receiving array element is as follows:

[0019] in, The weighting coefficients for the receiving array elements. j This is the sequence number of the receiving array element.

[0020] Furthermore, the formula for calculating the imaging energy value is as follows:

[0021] in, This represents the imaging energy value. for, For the time of dissemination, The weighting coefficients for the transmitting array elements are... The weighting coefficients for the receiving array elements. The x-coordinate of the pixel is The vertical coordinate of the pixel is denoted as y.

[0022] Furthermore, the calculation formula for the imaging quality evaluation function is as follows:

[0023] in, For image quality evaluation function, Total number of pixels This represents the imaging energy value corresponding to a pixel. This represents the image mean.

[0024] Further, the preprocessing of the echo data includes: Amplitude baseline correction is performed on the echo data; Apply bandpass filtering to the corrected echo signal; Time-domain smoothing is performed on the echo signal after bandpass filtering to obtain preprocessed echo data.

[0025] Compared with the prior art, the present invention has the following advantages: 1. This invention uses a depth-domain continuous weighting function to physically constrain the echo participation range, thereby suppressing echoes from non-target depth regions during the delay superposition process, reducing the impact of near-field interference and strong interface reflection echoes on the pixel energy structure, and thus improving the signal-to-noise ratio of effective defect echoes.

[0026] 2. This invention modulates the array aperture response and echo participation range by continuously weighting the array element space and coordinating the ROI depth weighting function, thereby optimizing the array spatial spectrum distribution, reducing the sidelobe energy leakage intensity, and enabling the target echo to maintain high coherent superposition consistency even when there is a deviation in sound velocity, thus reducing the spatial misalignment superposition phenomenon.

[0027] 3. This invention establishes a closed-loop adjustment relationship between the selection of propagation parameters and imaging quality through a sound velocity candidate traversal and image quality evaluation feedback mechanism. It automatically compensates for sound velocity estimation errors without requiring precise prior sound velocity information, thereby reducing the impact of systematic deviations in the propagation time matrix on focusing accuracy.

[0028] 4. Under the above-mentioned three-dimensional collaborative constraint, the ROI depth participation constraint, array spatial response modulation and propagation parameter adaptive optimization are executed synchronously under each candidate sound velocity condition during the imaging process. This makes the data participation range, array spatial response and propagation parameters no longer fixed assumptions, but form an adjustable coupling relationship, thereby effectively suppressing focus drift in complex echo environments and improving defect location accuracy and image contrast.

[0029] 5. The overall process of this invention does not require additional modeling or complex parameter calibration of the object to be detected, reducing the manual debugging and intervention process before imaging. It can be stably applied in ultrasonic probes with different numbers of array elements and various target material detection scenarios. At the same time, it does not require changes to the existing FMC / TFM data acquisition structure and can be directly embedded into the existing phased array system, which has good system compatibility and implementation feasibility.

[0030] Based on the above reasons, this invention can be widely applied in fields such as ultrasonic nondestructive testing and imaging. Attached Figure Description

[0031] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 This is a schematic diagram of internal defects in materials and sound wave scattering in the prior art.

[0033] Figure 2 This is a flowchart of the algorithm for a multi-dimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays according to the present invention.

[0034] Figure 3 This is a flowchart illustrating a multi-dimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays according to the present invention.

[0035] Figure 4 This is a schematic diagram of the effective imaging depth range and the Gaussian depth weighting function of the ROI in an embodiment of the present invention.

[0036] Figure 5 This is a schematic diagram comparing the original A-scan echo and the ROI-weighted echo in an embodiment of the present invention.

[0037] Figure 6 This is a schematic diagram of the propagation path of grid points in the ultrasonic medium in an embodiment of the present invention.

[0038] Figure 7 This is a comparison image of the defect imaging results in an embodiment of the present invention. Detailed Implementation

[0039] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0040] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0041] In real-world testing environments, the acoustic parameters of the propagation medium may be uncertain, resulting in a non-uniform distribution of echo signals along the depth direction. This includes near-field interference echoes, strong interface reflection echoes, multiple reflection echoes, and weak scattering defect echoes. When these echoes are processed with equal weights during time-delay superposition, non-target echoes may incoherently superimpose or abruptly enhance with the target echo, thereby altering the pixel energy distribution structure, reducing image contrast, and producing artifacts, such as... Figure 1 As shown.

[0042] On the other hand, when the propagation speed parameter deviates from the actual speed, the propagation time matrix constructed based on this parameter will produce a systematic error, causing a time mismatch in the echo signals that should be coherently superimposed, resulting in a focus energy shift. Furthermore, when the array spatial response adopts a fixed equal-weight model, the sidelobe energy distribution is difficult to adjust with changes in propagation conditions. Under the superposition of speed deviation and complex echo environment, the focus shift phenomenon may be further amplified.

[0043] This invention retains the basic physical mechanism of TFM based on propagation time compensation and delay superposition, and makes synergistic improvements to the data participation mode, array aperture response mode and propagation parameter selection mode, thereby solving problems such as focus drift caused by fixed assumptions.

[0044] Specifically, this invention first introduces an adjustable participation mechanism in the data dimension. Traditional TFM defaults to all acquisition channel data participating in delay stacking, while this invention analyzes the statistical characteristics of the echo envelope in the depth direction, takes the position corresponding to the first echo as the starting point of the depth search, and takes the first position of M consecutive depth sampling points that meet the preset energy criterion as the lower limit of the effective imaging depth. Combined with the bottom echo, the upper limit of the effective imaging depth is determined, and a continuous weighting function for ROI depth is further established to constrain the physical reliability interval of the echo data, so that delay stacking only occurs within the depth range that meets the physical validity.

[0045] In the spatial dimension, this invention constructs a continuous modulation mechanism for array element responses. By introducing a smooth array element weighting function within the imaging range constrained by the depth weighting function, the spatial spectral structure of the array aperture is controlled, reducing sidelobe energy and leakage intensity in non-target directions, making the coherent superposition process closer to ideal focusing conditions. This spatial modulation mechanism is coupled with the depth domain constraint, ensuring that the signals participating in the superposition have a consistent spatial distribution.

[0046] In terms of parameters, this invention no longer treats the propagation speed of sound as a fixed input parameter. Instead, it constructs a candidate set of sound speeds and repeatedly executes the aforementioned depth and spatial constraint imaging process under different sound speed conditions. A feedback selection mechanism is formed through an image quality evaluation function, establishing a closed-loop relationship between the sound speed parameter and the imaging quality. This mechanism transforms the propagation parameter from a "preset constant" into an "adaptive optimization variable."

[0047] Therefore, the core key point of this invention is that instead of treating ROI depth weighting, array element spatial weighting, and sound velocity search evaluation as independent sequential steps, the three are made to work together in the focusing imaging process under the same candidate sound velocity conditions, forming a collaborative closed-loop mechanism of data dimension selection, spatial dimension modulation, and parameter dimension feedback optimization, thereby suppressing the focusing drift caused by fixed assumptions from the mechanism level.

[0048] like Figure 2 and Figure 3 As shown, this invention provides a multi-dimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays, comprising the following steps: S1. Emit ultrasonic waves to the material to be tested and collect echo data.

[0049] Specifically, ultrasonic waves are emitted to the material under test through an ultrasonic phased array detection system, and raw FMC echo data are collected.

[0050] S2. Preprocess the echo data.

[0051] Specifically, the raw echo data is first subjected to amplitude baseline correction to eliminate the DC bias introduced by the acquisition system, so that the echo signal is represented in zero-mean form. Then, bandpass filtering is applied to the corrected echo signal to suppress low-frequency drift and high-frequency noise components exceeding the target operating frequency band through a preset frequency response, thus preserving the effective echo components. Based on the bandpass filtering, the echo signal is further smoothed in the time domain to reduce the impact of impulse noise and random interference on subsequent envelope extraction and energy statistics.

[0052] The acquired raw echo data undergoes amplitude baseline correction to eliminate the DC bias introduced by the acquisition system, ensuring the echo signal is represented in zero-mean form. A bandpass filter is then applied to the corrected echo signal, with a center frequency set to 2.5 MHz. This suppresses low-frequency drift and high-frequency noise components exceeding the target operating frequency band by using a preset frequency response, preserving effective echo components. Based on the bandpass filter, a median filter is performed on the echo signal to reduce the impact of impulse noise and random interference on subsequent envelope extraction and energy statistics. The median-filtered signal is then interpolated by a factor of 32 to improve the signal's temporal resolution, providing a more refined data foundation for subsequent focusing, time delay calculation, or envelope detection.

[0053] S3. Construct an effective imaging range in the depth direction based on the preprocessed echo data, and establish a depth weighting function within the effective imaging range.

[0054] Here, the initial starting point is based on the first depth that satisfies the decision, and the ending point is based on the maximum peak depth near the maximum input depth. Therefore, multiple candidate reflection centers may be contained within an effective imaging range.

[0055] S3 specifically includes: S31. Extract the envelope from the preprocessed echo data and perform a preliminary analysis of the echo propagation time to obtain the echo data envelope line.

[0056] S32. Determine the upper limit of the effective imaging depth by using the bottom surface echo time.

[0057] The bottom echo time is determined by searching for the main peak of the bottom echo within a time window corresponding to the preset maximum detection depth. The time corresponding to the main peak is then identified as the bottom echo time, and this time is converted into the upper limit of the effective imaging depth based on the sound velocity parameter. For example, if the channel data sampling points are 2000, and the bottom echo is estimated to be around point 700 based on the sound velocity, then a precise point is found in the surrounding area. This precise point is then used to calculate the time it takes for the sound wave to travel to the bottom, thus obtaining the accurate depth position.

[0058] The upper limit of the effective imaging depth is first determined by the time of the bottom surface echo, and then the time is mapped to the corresponding depth position according to the propagation speed of sound, thus obtaining the upper limit of the depth.

[0059] S33. Statistically fuse the envelope signals of each “transmit-receive” channel along the channel dimension to obtain the average envelope energy distribution curve that varies along the depth direction.

[0060] Fusion refers to the process of averaging or weighted averaging the envelope signals of each transmit-receive channel at the corresponding depth sampling points to obtain the average envelope energy distribution curve that varies along the depth direction.

[0061] S34. Find M consecutive sampling points that satisfy the first condition in the average envelope energy distribution curve, determine that the sound wave has left the near-field interference region, and take the first depth position that satisfies the condition as the lower limit of the effective imaging depth. The first condition is:

[0062] in, Let z be the average envelope energy at depth z. This is the energy proportion coefficient. This represents the peak energy level.

[0063] Let the initial imaging depth range be:

[0064] in, The initial depth search starting point is determined by the first echo. The upper limit of the imaging depth is determined by the bottom surface echo. z For depth.

[0065] like Figure 4 As shown in (a), based on the defect model, the effective imaging depth lower limit determined by the continuous M-point criterion and the imaging depth upper limit determined by the bottom surface echo time are combined to form a physically reliable effective depth interval, and echo envelope energy distribution analysis is performed only within this interval. The depth position corresponding to the energy peak satisfying the local peak criterion within the effective depth interval is determined as the candidate reflection center.

[0066] S35. Construct the effective imaging interval based on the upper limit and lower limit of the effective imaging depth.

[0067] S36. For example Figure 4 As shown in (b), the energy peak of the echo data network line within the effective imaging range is found, and the depth corresponding to the energy peak is taken as the reflection center. .

[0068] For each candidate region, take its center depth as the center. Statistical analysis is performed using a local depth window centered on a preset physical scale.

[0069] The search term refers to the local energy peak of the average envelope energy distribution curve within the effective imaging range, rather than the peak value of the original envelope curve of a single channel.

[0070] S37. Normalize the average envelope energy distribution curve to obtain the normalized echo energy.

[0071] S38. Based on the normalized echo energy corresponding to the reflection center and depth coordinates, the width parameter is calculated, which reflects the energy dispersion of the defect echo in the depth direction. The formula for calculating the width parameter is: (Calculated using weighted standard deviation).

[0072] in, For width parameter, For depth coordinates, As the center of reflection, This refers to the normalized echo energy.

[0073] To prevent defect information from being lost due to an excessively narrow ROI window, set... Lower bound constraint: when the calculated lower bound constraint is obtained When the depth is smaller than the smallest resolvable unit (i.e., the physical depth corresponding to a single pixel) of the system imaging, that smallest resolvable unit is taken as the depth. The final value of .

[0074] S39. Based on the reflection center and width parameters, a weighting function is constructed, which is defined in Gaussian window form as follows:

[0075] in, This is the weighting function.

[0076] S310. Construct a depth-weighted function based on the depth gating function and the weighting function.

[0077] Specifically, when multiple potential defects exist, the Gaussian depth weight functions corresponding to each candidate reflection center are linearly superimposed and normalized. To further limit invalid depth participation, a depth gating function can be introduced. This gating function takes the value 1 within the effective depth interval and a preset suppression factor outside the interval. The two together constitute the maximum ROI depth weight function. The formula for calculating the depth weighting function is as follows:

[0078] in, For depth-weighted functions, For depth-gated functions, These are the weighting coefficients.

[0079] To illustrate the effect of ROI construction, Figure 5 (a) presents the A-scan results of the single-channel raw echo signal after bandpass filtering and interpolation. The horizontal axis represents the imaging depth mapped from the time axis, and the vertical axis represents the normalized echo amplitude. It can be seen that in the raw echo, in addition to the surface echo, there are both defect echoes and bottom surface echoes in the middle and later depth range, and the contrast between the defect echo and the background echo is relatively limited.

[0080] Based on this, according to the statistical results of the echo envelope energy distribution during the data statistical analysis phase, a ROI depth weighting function varying along the depth direction is constructed, and this depth weighting function is applied to the original echo signal to obtain the following result: Figure 5 (b) shows the ROI weighted echo results. In this result, the echo response near the depth of the candidate defect center is enhanced, while the echo in the non-target depth region is smoothed and suppressed.

[0081] S4. Construct a candidate set of sound speeds, which includes the sound speed of the target material.

[0082] In a preferred embodiment, the candidate set of sound velocities covers the typical range of longitudinal wave sound velocity variations of the target material, and its step size is preset according to the imaging resolution requirements and computational complexity requirements.

[0083] S5. Calculate the propagation time and total propagation distance of the preprocessed echo data.

[0084] The steps for calculating the total propagation distance include: The first step, specifically, is as follows: Figure 6 As shown, assuming the speed of sound in the medium is a constant c, the coordinates of the i-th element are... The coordinates of any pixel within the imaging area are The transmission distance is calculated based on the position of the transmitting array elements and the pixels within the effective imaging range:

[0085] in, For launch distance, The x-coordinate of the pixel is The x-coordinate of the transmitting array element The vertical coordinate of the pixel is denoted as y.

[0086] The second step, similarly, is to calculate the receiving distance based on the position of the receiving array elements and the pixels within the effective imaging range:

[0087] in, For receiving distance, The x-coordinate of the receiving array element.

[0088] The third step is to add the transmission distance and the reception distance to calculate the total propagation distance. The formula for calculating the total propagation distance is:

[0089] in, This represents the total propagation distance.

[0090] Step 4: The propagation time of the ultrasonic wave from the corresponding array element position to the target pixel and back to other array element positions is... :

[0091] in, c is the propagation time, and c is the speed of sound.

[0092] S6. Calculate the weighting coefficients of the transmitting and receiving array elements respectively. Under the constraint of the depth weighting function, obtain the imaging energy value based on the weighting coefficients of the transmitting and receiving array elements, the propagation time, and the total propagation distance.

[0093] Based on the above propagation time calculation results, the echo signals of the corresponding transmit-receive channels in the full matrix acquisition data are delayed and aligned, and the signals of each channel are superimposed within the imaging area to obtain the focusing energy value of the corresponding pixel. To improve the spectral characteristics of the array aperture and suppress sidelobe and grating lobe energy leakage, a continuous element weighting modulation function is introduced. This method preferably uses a Hann window to weight the transmit and receive elements separately. The formula for calculating the weighting coefficient of the transmit element is:

[0094] in, The weighting coefficients for the transmitting array elements are... i For the serial number of the launch array element ，N The total number of array elements, .

[0095] The formula for calculating the weighting coefficients of the receiving array elements is:

[0096] in, The weighting coefficients for the receiving array elements. j For the receiving array element number, .

[0097] For each candidate sound velocity condition, the ROI depth weighting function is used to constrain the depth domain participation of the echo data involved in delay alignment and stacking. At this time, any pixel point Under the current candidate sound velocity conditions, the TFM imaging energy value can be expressed as follows: The formula for calculating the imaging energy value is:

[0098] in, This represents the imaging energy value. for, For the time of dissemination, The weighting coefficients for the transmitting array elements are... The weighting coefficients for the receiving array elements. The x-coordinate of the pixel is The ordinate of the pixel is For channel The preprocessed echo signal.

[0099] The imaging energy value obtained in this step corresponds to the focusing result of each pixel in the imaging grid. Arranging the imaging energy values ​​of each pixel according to their spatial position will form the initial imaging result.

[0100] S7. The imaging energy value is evaluated using the imaging quality evaluation function, and the best imaging is obtained after the evaluation.

[0101] This invention preferably uses image variance as the imaging quality evaluation function, performing image statistical analysis on the imaging results corresponding to each candidate sound velocity. In other embodiments, image entropy, a custom sharpness function, or other image quality evaluation indicators may also be used. The calculation formula for the imaging quality evaluation function is:

[0102] in, For image quality evaluation function, Total number of pixels This represents the imaging energy value corresponding to a pixel. This represents the image mean.

[0103] If the evaluation result is unsatisfactory, return to step S5 until the result is satisfactory, and output the best image. The preferred embodiment of this invention does not use a fixed threshold for judgment, but instead calculates the evaluation function value for each candidate sound velocity corresponding to the imaging result, and selects the result with the largest evaluation function value as the best imaging result.

[0104] By maximizing the image variance evaluation function To achieve adaptive selection of sound speed parameters.

[0105] In a preferred embodiment, the array probe has 16 elements, a center frequency of 2.5 MHz, and a sampling frequency of 40 MHz; the candidate set step size for sound velocity is 50 m / s; the number of sampling points in the continuity criterion is 50; the half-width of the local depth window is 2 mm; and the out-of-area suppression factor is 0.2. These parameters can be adjusted according to the probe specifications, the acoustic properties of the target material, and the imaging resolution requirements. Figure 7 As shown, compared with traditional TFM imaging results, the method of the present invention exhibits better performance in terms of defect response focusing degree, background artifact suppression and image contrast.

[0106] The sequence numbers of the above embodiments of the present invention are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0107] In the above embodiments of the present invention, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0108] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features; and these modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A multi-dimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays, characterized in that, Includes the following steps: Ultrasonic waves are emitted toward the material to be tested, and echo data is collected. The echo data is preprocessed; An effective imaging range in the depth direction is constructed based on the preprocessed echo data, and a depth weighting function is established within the effective imaging range. Construct a candidate set of sound speeds, the candidate set of sound speeds including the sound speeds of the target material; Calculate the propagation time and total propagation distance of the preprocessed echo data; The weighting coefficients of the transmitting and receiving array elements are calculated separately. Under the constraint of the depth weighting function, the imaging energy value is obtained based on the weighting coefficients of the transmitting and receiving array elements, the propagation time, and the total propagation distance. The imaging energy value is evaluated using an imaging quality evaluation function, and the optimal imaging is obtained after the evaluation.

2. The multidimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays according to claim 1, characterized in that, The construction of an effective imaging region in the depth direction based on the preprocessed echo data includes: Envelope extraction is performed on the preprocessed echo data to obtain the echo data envelope line; The upper limit of the effective imaging depth is determined by the timing of the bottom surface echo. The echo data envelope lines are fused to obtain the average envelope energy distribution curve that varies along the depth direction; Find M consecutive sampling points that satisfy the first condition in the average envelope energy distribution curve, and take the first depth position among the M sampling points as the lower limit of the effective imaging depth. The first condition is: in, Let z be the average envelope energy at depth z. This is the energy proportion coefficient. This represents the peak energy level. An effective imaging range is constructed based on the upper limit of the effective imaging depth and the lower limit of the effective imaging depth.

3. The multidimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays according to claim 1, characterized in that, The establishment of a depth-weighted function within the effective imaging range includes: Find the energy peak of the echo data network line within the effective imaging range, and take the depth corresponding to the energy peak as the reflection center; The average envelope energy distribution curve is normalized to obtain the normalized echo energy. The width parameter is calculated based on the normalized echo energy corresponding to the reflection center and depth coordinates. The formula for calculating the width parameter is as follows: in, For width parameter, For depth coordinates, As the center of reflection, The normalized echo energy; Based on the reflection center and width parameters, a weighting function is constructed, and the calculation formula of the weighting function is as follows: in, For weighting functions; Based on the depth gating function and the weighting function, a depth-weighted function is constructed, and the calculation formula of the depth-weighted function is as follows: in, For depth-weighted functions, For depth-gated functions, These are the weighting coefficients.

4. The multidimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays according to claim 1, characterized in that, The steps for calculating the total propagation distance include: Calculate the transmission distance based on the position of the transmitting array elements and the pixels within the effective imaging range: in, For launch distance, The x-coordinate of the pixel is The x-coordinate of the transmitting array element The ordinate of the pixel; Calculate the receiving distance based on the position of the receiving array element and the pixels within the effective imaging range: in, For receiving distance, The x-coordinate of the receiving array element; The total propagation distance is calculated by adding the transmission distance and the reception distance.

5. The multidimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays according to claim 1, characterized in that, The propagation time is obtained by dividing the total propagation distance by the speed of sound.

6. The multidimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays according to claim 1, characterized in that, The formula for calculating the weighting coefficients of the transmitting array element is as follows: in, The weighting coefficients for the transmitting array elements are... i For the serial number of the launch array element ，N The total number of array elements, The formula for calculating the weighting coefficients of the receiving array element is as follows: in, The weighting coefficients for the receiving array elements. j This is the sequence number of the receiving array element.

7. The multidimensional cooperative constraint-based adaptive focusing imaging method for ultrasonic phased arrays according to claim 1, characterized in that, The formula for calculating the imaging energy value is: in, This represents the imaging energy value. For the total propagation distance, For the time of dissemination, The weighting coefficients for the transmitting array elements are... The weighting coefficients for the receiving array elements. The x-coordinate of the pixel is The vertical coordinate of the pixel is denoted as y.

8. The multidimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays according to claim 1, characterized in that, The calculation formula for the imaging quality evaluation function is as follows: in, For image quality evaluation function, Total number of pixels This represents the imaging energy value corresponding to a pixel. This represents the image mean.

9. The multidimensional collaboratively constrained adaptive focusing imaging method for ultrasonic phased arrays according to claim 1, characterized in that, The preprocessing of the echo data includes: Amplitude baseline correction is performed on the echo data; Apply bandpass filtering to the corrected echo signal; Time-domain smoothing is performed on the echo signal after bandpass filtering to obtain preprocessed echo data.

Images