Amplitude filtering for phase coherence imaging.

Abstract

Phase-based approaches can be used for one or more of the acquisition, storage, or subsequent analysis of acquired acoustic imaging data. Such phase-based approaches can include coherent addition of normalized or quantized representations of A-scan data corresponding to phase information. Such approaches can be referred to as "phase coherence imaging" (PCI) beamforming techniques. Amplitude filtering can be applied to phase coherence imaging, such as weighting features in such imaging associated with scattering to enhance defect contrast, suppress noise, or both.

Description

[Technical Field]

[0001] Priority claims This patent application claims the benefit of priority to U.S. Provisional Patent Application No. 63,477,243 (Attorney Docket No. 6409.233PRV) to Chi-Hang Kwan, entitled "AMPLITUDE FILTERING FOR PHASE-COHERENCE IMAGING," filed December 27, 2022, which is incorporated herein by reference in its entirety.

[0002] This document relates generally to non-destructive evaluation, and more particularly to apparatus and techniques for providing phase-coherence imaging (PCI) based on acquired acoustic inspection data, where the phase-coherence imaging can be enhanced through the use of amplitude filtering techniques. [Background technology]

[0003] Non-destructive testing (NDT) can refer to the use of one or more different techniques to inspect an area on or within an object, for example, to determine whether flaws or defects are present in the inspected object, or to otherwise characterize the inspected object. Examples of non-destructive testing approaches include the use of eddy current testing approaches in which electromagnetic energy is applied to an object by one or more probes, and the resulting induced current is detected on or within the object, with the value of the detected current (or associated impedance) providing an indication of the structure of the object under test, such as indicating the presence of cracks, voids, porosity, or other inhomogeneities.

[0004] Another approach for NDT can involve the use of acoustic inspection techniques, such as using one or more electroacoustic transducers to irradiate an area on or within an object under test with ultrasonic waves, and detecting and processing scattered or reflected acoustic energy. Such scattered or reflected energy can be referred to as an acoustic echo signal. Generally, such acoustic inspection schemes involve the use of acoustic frequencies in the ultrasonic range of frequencies, including pulses having energy within a specified range, which can include values ​​from hundreds of kilohertz to tens of megahertz, by way of example. Summary of the Invention

[0005] Acoustic testing, such as ultrasound-based inspection, can involve focusing or beamforming techniques to aid in constructing data plots or images representative of regions of interest within a test specimen. The use of an array of ultrasonic transducer elements can involve the use of a phased array beamforming approach, which can be referred to as phased array ultrasonic testing (PAUT). For example, a delay-and-sum beamforming technique can be used, which involves coherently summing time-domain representations of received acoustic signals from each transducer element or aperture. Another approach can use a total focusing method (TFM) beamforming technique, in which one or more elements (or apertures defined by such elements) in the array are used to transmit acoustic pulses, while other elements are used to receive scattered or reflected acoustic energy, and a matrix of time-series (e.g., A-scan) representations is constructed corresponding to a sequence of transmit-receive cycles in which transmissions occur from different elements (or corresponding apertures) in the array. Such a TFM approach, in which A-scan data is obtained for each element (or each defined aperture) in the array, may be referred to as a "full matrix capture" (FMC) technique.

[0006] Similar to TFM imaging, a phase-based approach can be used for one or more of the acquisition, storage, or subsequent analysis. Such a phase-based approach can include coherent addition of normalized or quantized representations of the A-scan data corresponding to the phase information. Such an approach can be referred to as a "phase coherence imaging" (PCI) beamforming technique.

[0007] The inventors have recognized, among other things, that the use of phase-based imaging approaches is associated with various attributes and several potential drawbacks. Phase-based imaging may be more sensitive to small omnidirectional scatterers, which may be advantageous in terms of detecting crack tips or porosity. However, such sensitivity to scattering may also enhance the presence of particle echoes (e.g., in stainless steel or associated with modified grain structures in welds), which may clutter the imaging or hinder analysis of such imaging, such as by obscuring features corresponding to defects. The inventors have recognized, among other things, that amplitude information suppressed in phase-based imaging would otherwise indicate the difference between omnidirectional scatterers associated with particle noise and features of interest, such as crack tips. Thus, the inventors have recognized that some amplitude information can be reintroduced by using amplitude data from a separate image, such as one constructed using total focusing method (TFM) beamforming. In this way, for example, a first image corresponding to a phase-based imaging approach can be filtered, e.g., pixel-wise or voxel-wise, using a second image constructed using TFM beamforming (or a representation thereof). In this way, the sensitivity of phase-based imaging approaches is preserved, but image features associated with particle noise or other scatterers of no interest can be suppressed.

[0008] In one example, a machine-implemented method may include acquiring acoustic echo data indicative of scattered or reflected acoustic energy corresponding to each transmit / receive pair of an aperture; forming a first image by coherently adding representations of phase information from the acoustic echo data to establish respective first image pixel or voxel element values; forming a second image by coherently adding representations of phase information and amplitude information from the acoustic echo data to establish respective second image pixel or voxel element values; weighting each second image pixel or voxel element value using an amplitude filter function to establish respective weighted second image pixel or voxel element values; and adjusting each first image pixel or voxel element value of the first image using each weighted second image pixel or voxel element value from a corresponding location in the second image to establish a weighted first image.

[0009] In one example, a non-destructive testing system may perform a machine-implemented method. For example, the system may include a test instrument having a transmitter circuit and a receiver circuit, at least one processor circuit communicatively coupled to or included as part of the test instrument, and at least one memory circuit communicatively coupled to or included as part of the test instrument, the at least one memory circuit including instructions that, when executed by the at least one processor circuit, cause the non-destructive testing system to acquire, using the transmitter circuit and the receiver circuit, acoustic echo data indicative of scattered or reflected acoustic energy corresponding to each transmit / receive pair of an aperture, and coherently combine representations of phase information from the acoustic echo data to establish respective first image pixel or voxel element values. forming a first image by coherently adding a representation of the phase information and the amplitude information from the acoustic echo data to establish respective second image pixel or voxel element values; forming a second image by coherently adding a representation of the phase information and the amplitude information from the acoustic echo data to establish respective weighted second image pixel or voxel element values; weighting each second image pixel or voxel element value using an amplitude filter function to establish respective weighted second image pixel or voxel element values; adjusting each first image pixel or voxel element value of the first image using each weighted second image pixel or voxel element value from a corresponding location in the second image to establish a weighted first image; and transmitting or presenting the weighted first image to a user. In any of the examples herein, the amplitude filter function can establish a nonlinear mapping from input amplitude values ​​to output amplitude values ​​used to weight each second image pixel or voxel element value, such as having a sigmoid shape. For example, a hyperbolic tangent function can be used as the amplitude filter function.

[0010] In the drawings, which are not necessarily drawn to scale, like numerals may describe like components in different views. Like numerals with different letter suffixes may represent different instances of similar components. The drawings generally illustrate, by way of example, various embodiments discussed in this document, although not by way of limitation. [Brief explanation of the drawings]

[0011] [Figure 1] An example is generally shown that includes an acoustic inspection system, such as may be used to implement at least a portion of one or more of the techniques shown and described herein. [Figure 2] We generally illustrate techniques, such as machine-implemented methods, that can include a phase-based acoustic beamforming approach for acoustic imaging that is weighted using amplitude information from another beamforming approach. [Figure 3A] 1 shows generally an example of an image constructed using total focusing method (TFM) beamforming technique. [Figure 3B] 1 illustrates generally an example of an image constructed using phase-based beamforming techniques. [Figure 3C] 1 shows generally an example of a weighted TFM image with an amplitude filter applied. [Figure 4A] 10 generally illustrates one example of a function that may be used to perform amplitude filtering of an image constructed using phase-based acoustic beamforming, where the filter function may include a scaling factor. [Figure 4B] 4B generally illustrates an example including a technique for parameterizing the scaling factors used in the amplitude filtering function of FIG. 4A. [Figure 4C] An example is provided generally including other sigmoid-like functions that can be used to implement amplitude filtering. [Figure 5A] 1 shows an example of an image constructed using a phase-based beamforming technique without amplitude-based filtering. [Figure 5B] 1 shows an example of an image constructed using a phase-based beamforming technique, where the image is weighted using amplitude information from another beamforming approach to provide amplitude-based filtering. [Figure 5C] 1 shows an example of an image constructed using a phase-based beamforming technique without amplitude-based filtering. [Figure 5D] 1 shows an example of an image constructed using a phase-based beamforming technique, where the image is weighted using amplitude information from another beamforming approach to provide amplitude-based filtering. [Figure 5E] 1 shows an example of an image constructed using a phase-based beamforming technique without amplitude-based filtering. [Figure 5F] 1 shows an example of an image constructed using a phase-based beamforming technique, where the image is weighted using amplitude information from another beamforming approach to provide amplitude-based filtering. [Figure 6] 6 shows an example block diagram comprising a machine 600 upon which any one or more of the techniques (eg, methods) discussed herein may be implemented. DETAILED DESCRIPTION OF THE INVENTION

[0012] Acoustic inspection can include the use of total focusing method (TFM) beamforming techniques, as described above, in which one or more elements (or apertures defined by such elements) in an array are used to transmit acoustic pulses, while other elements are used to receive scattered or reflected acoustic energy, and a matrix of time-series (e.g., A-scan) representations is constructed corresponding to a sequence of transmit-receive cycles in which transmissions occur from different elements (or corresponding apertures) in the array. Similar to TFM imaging, a phase-based approach can be used for one or more of the acquisition, storage, or subsequent analysis. Such a phase-based approach can include coherent addition of normalized or quantized representations of A-scan data corresponding to phase information. Such an approach can be referred to as a “phase coherence imaging” (PCI) beamforming technique.

[0013] As discussed above, phase-based approaches such as phase coherence imaging (PCI) can enhance sensitivity to small omnidirectional scatterers, such as crack tips and porosity, by normalizing and factoring out the amplitude contributions to the received signal before coherent summation. The present subject matter can be used to provide filtering of noise, such as grain noise, in PCI images using information from corresponding amplitude- and phase-based imaging approaches, such as TFM images (e.g., images constructed using TFM beamforming). Illustratively, PCI and TFM images can be constructed from acquired acoustic echo data corresponding to each transmit-receive aperture pair. The TFM images are weighted using an amplitude filter function that nonlinearly adjusts the amplitude values ​​(e.g., envelope magnitude values ​​corresponding to each pixel or voxel).

[0014] The weighted TFM image can be used to adjust the values ​​(e.g., pixel or voxel values) of the corresponding PCI image to obtain an enhanced image (e.g., one with reduced particle noise or one with a focus on scatterers of interest, such as crack tips or porosity). Generally, the amplitude filter function applied to the TFM image generally corresponds to a sigmoid-shaped nonlinear mapping of amplitude values. The arguments to the filter function can be normalized or scaled representations of the TFM image pixel or voxel values. This allows particle noise and other low-amplitude features to be suppressed in the weighted TFM image before the weighted TFM image is used to adjust the PCI image. The resulting weighted PCI image retains the enhanced sensitivity of PCI while reducing noise, such as particle noise.

[0015] FIG. 1 generally illustrates an example including an acoustic inspection system 100 that can be used to implement at least a portion of one or more of the techniques shown and described herein. The acoustic inspection system 100 can include a test instrument 140, such as a handheld or portable assembly. The test instrument 140 can be electrically coupled to a probe assembly 150, such as using a multi-conductor interconnect 130. The probe assembly 150 can include one or more electroacoustic transducers, such as a transducer array 152 including respective transducers 154A-154N. The transducer array can follow a linear or curved contour, or can include an array of elements extending in two axes, such as providing a matrix of transducer elements. The elements need not have a square footprint or be arranged along a linear axis. The size and pitch of the elements can vary depending on the inspection application.

[0016] A modular probe assembly 150 configuration can be used to allow the test instrument 140 to be used with a variety of different probe assemblies. Generally, the transducer array 152 includes, for example, a piezoelectric transducer that can be acoustically coupled to a target 158 ​​(e.g., a test sample or “object under test”) via a coupling medium 156. The coupling medium can include a fluid or gel, or a solid membrane (e.g., an elastomer or other polymeric material), or a combination of fluid, gel, or solid structures. For example, the acoustic transducer assembly can include a transducer array coupled to a wedge structure comprising a rigid thermosetting polymer with known acoustic propagation properties (e.g., Rexolite® available from C-Lec Plastics Inc.), and water can be injected between the wedge and the structure under test as the coupling medium 156 during testing, or testing can be performed by otherwise immersing the interface between the probe assembly 150 and the target 158 ​​in the coupling medium.

[0017] The test instrument 140 may include digital and analog circuitry, such as a front-end circuit 122 including one or more transmit signal chains, receive signal chains, or switching circuitry (e.g., transmit / receive switching circuitry). The transmit signal chain may include amplifier and filter circuitry to provide transmit pulses for delivery to the probe assembly 150 via the interconnect 130 for insonification of the target 158, and to receive scattered or reflected acoustic energy elicited in response to the insonification, thereby imaging or otherwise detecting defects 160 on or within the target 158 ​​structure.

[0018] 1 shows a single probe assembly 150 and a single transducer array 152, other configurations can be used, such as multiple probe assemblies connected to a single test fixture 140, or multiple transducer arrays 152 used with a single probe assembly 150 or multiple probe assemblies for pitch / catch inspection. Similarly, test protocols can be implemented using coordination among multiple test fixtures 140, for example, in response to an overall test scheme established from a master test fixture 140 or established by another remote system, such as computing equipment 108, or a general-purpose computing device, such as a laptop 132, tablet, smartphone, or desktop computer. The test scheme can be established in accordance with published standards or regulatory requirements and can be implemented repeatedly, by way of example, at initial production or for ongoing monitoring.

[0019] The receive signal chain of the front-end circuitry 122 may include one or more filter or amplifier circuits, along with analog-to-digital conversion facilities, to digitize echo signals received using the probe assembly 150. The digitization may be performed coherently to provide multiple channels of digitized data that are aligned or referenced to each other in time or phase. The front-end circuitry may be coupled to and controlled by one or more processor circuits, such as the processor circuit 102 included as part of the test instrument 140. The processor circuit may be coupled to the memory circuit 104, for example, to execute instructions that cause the test instrument 140 to perform one or more of acoustic transmission, acoustic acquisition, processing, or storage of data related to an acoustic test, or otherwise implement techniques such as those shown and described herein. The test instrument 140 may be communicatively coupled to other portions of the system 100, such as using a wired or wireless communication interface 120.

[0020] For example, performance of one or more techniques as shown and described herein can be achieved on the test instrument 140 or using other processing or storage facilities, such as using the computing equipment 108 or general-purpose computing devices, such as a laptop 132, tablet, smartphone, desktop computer, etc. For example, processing tasks that would be unnecessarily slow if performed on the test instrument 140 or beyond the capabilities of the test instrument 140 may be performed remotely (e.g., on a separate system), e.g., in response to a request from the test instrument 140. Similarly, storage of intermediate data, such as, e.g., A-scan matrices of imaging data or time series data, or other representations of such data, may be achieved using remote facilities communicatively coupled to the test instrument 140. The test instrument may include a display 110, such as for presenting configuration information or results, and input devices 112, including one or more of a keyboard, trackball, function keys or softkeys, a mouse interface, a touch screen, a stylus, etc., for receiving operator commands, configuration information, or responses to queries.

[0021] 2 generally illustrates a technique 200, such as a machine-implemented method, that may include a phase-based acoustic beamforming approach for acoustic imaging that is weighted using amplitude information from another beamforming approach. For example, acoustic echo data may be acquired at 205. Such acoustic echo data may include a time-series analytical signal representation that may be processed to extract amplitude and phase terms.

[0022] Each pixel value may be determined at 210 by forming a first image that includes coherently summing representations of phase information (e.g., quantized time series data in which the amplitude indicates normalized phase). Such an approach may be similar to delay-and-sum beamforming, but with the amplitude information normalized. By way of example, analytical notation indicates the coherent summation shown below in Equation 1, which corresponds to TFM beamforming: where TFM(x) can be used to establish pixel or voxel values ​​for spatial locations described by vector x, and s tr represents the complex-valued analytic signal response for the corresponding transmit / receive pair at element indices t and r. TFM(x)=Σ t Σ r |s tr (x)|exp(i∠s tr (x)) (Formula 1)

[0023] In a phase-based approach, the amplitude terms can be factored out of the summation process and the phase-related coherence terms can be added (exp(i∠s tr (x))"). PCI(x)=Σ t Σ r exp(i∠s tr (x)) (Formula 2)

[0024] By using such factorization, factors that affect amplitude are suppressed (because such factors may affect terms now moved “outside” the sum), but phase-related terms (e.g., associated with scatterers or other features of interest) remain. Images may be constructed using a similar approach to TFM beamforming, but using normalized amplitude values ​​for each transmit / receive pair (t, r) and performing summation in a manner similar to that shown in Equation 2. Such an approach for the first image formed in 210 may be referred to as phase coherence imaging (PCI). In one example implementation of phase coherence imaging, a binarized representation of the phase-related coherence terms still allows for coherent summation, which may reduce one or more of computational complexity or data storage burden. The use of such binarization may be represented by a summation process as follows:

number

[0025] In Equation 3, the following binarization approach is used:

number

[0026] As an example, Figure 3B generally shows an image constructed using such a phase-based beamforming technique. The PCI approach may be more sensitive to small, omnidirectional scatterers, such as crack tips and porosity, compared to TFM beamforming, which uses amplitude and phase information in coherent summation. In PCI, backwall and other specular echoes are generally less intense or saturated compared to TFM imaging, and the use of PCI can facilitate multimode imaging because each propagation mode has similar amplitude scaling through normalization.

[0027] As discussed elsewhere herein, PCI approaches may present drawbacks, such as enhancing particle echoes or other features that do not correspond to the defect of interest. To de-emphasize such noise, a filtering process can be used. Referring back to FIG. 2, at 215, a second image can be formed by coherently adding representations of phase and amplitude information. For example, at 215, a TFM beamforming approach can be used to generate the second image. FIG. 3A generally illustrates an example of such an image constructed using a total focusing method (TFM) beamforming technique.

[0028] In FIG. 2 , at 220, a second image (e.g., formed using TFM imaging including amplitude and phase information) may be weighted using an amplitude filter function. Generally, the weighting may include a sigmoid-shaped function. For example, a hyperbolic tangent may be applied to the envelope TFM image on a pixel-by-pixel or voxel-by-voxel basis, etc. Various examples of amplitude filter functions are discussed below in connection with FIGS. 4A, 4B, and 4C. FIG. 3C generally illustrates an example of a weighted TFM image to which an amplitude filter such as that provided at 225 as shown in FIG. 2 has been applied. In FIG. 2 , at 225, the first image may be adjusted using the weighted second image established at 220 (e.g., constructed using TFM and weighted using an amplitude filter), such as by multiplying pixel values ​​in the first image (e.g., constructed using phase-based imaging) by the weighted second image (e.g., constructed using TFM and weighted using an amplitude filter). This may be referred to as an amplitude-filtered PCI image. At 230, the image (or other image or data) established at 225 can be stored, transmitted, or presented. For example, the amplitude-filtered PCI image can be presented to a user on the display of the acquisition instrument, or can be presented using another device, such as a tablet, laptop, mobile device, or more generally, other client (e.g., a web-based client or other client coupled to another processing facility or cloud facility).

[0029] As described further below, the amplitude filter function may be established using a variety of functions, such as including scaling factors in arguments of such functions, or parameterization of scaling factors. Optionally, technique 200 may include establishing parameters of the amplitude filter function at 240. For example, such parameters may include, by way of example, scaling factor values, parameters used to establish such scaling factors, or selection of a filter shape or function.

[0030] 4A generally illustrates an example of a function that can be used to perform amplitude filtering of an image constructed using phase-based acoustic beamforming, where the amplitude filter function can include a scaling factor. As discussed above, the amplitude filter function can be sigmoidal, and FIG. 4A illustrates an example of an amplitude filter function including a hyperbolic tangent that can be applied to an envelope TFM image, such as on a pixel-by-pixel or voxel-by-voxel basis. For example, as shown in the analytical formula below, max|TFM| represents a scalar value of the maximum envelope amplitude in the TFM image, |TFM(x)| represents the envelope amplitude at each pixel or voxel location, and the argument to the hyperbolic tangent can be the ratio of |TFM(x)| to max|TFM|.

number

[0031] The formula for amplitude filtered PCI image is shown below, and PCI filt (x) represents the pixel or voxel value at location x in the amplitude filtered image, and PCI(x) represents the corresponding pixel or voxel value in the unfiltered PCI image multiplied by the corresponding weighted TFM image pixel or voxel value.

number

[0032] In general, the hyperbolic tangent function can be expressed as:

number

[0033] The hyperbolic tangent function has the property of being near-linear for small z values ​​(e.g., |z|<0.5) and saturated for large z values ​​(e.g., |z|>2.0). Therefore, an amplitude filter function based on the hyperbolic tangent function can be used to filter echoes with very small amplitudes (e.g., particle noise) without attenuating the medium (e.g., crack tips) or large amplitude features (e.g., backwall reflections) in the TFM image.

number

[0034] By way of illustration, in the limit as c approaches zero, the hyperbolic tangent function is approximately linear and the equation is approximated as:

number

[0035] In such instances where c approaches zero, the above equation represents the scaled product of PCI with amplitude TFM. Conversely, in the limit c is very large, the equation becomes filt (r) = PCI(r). Without being bound by theory, a "c" value of about 8 is an intermediate value that appeared to work well for test cases, as shown in FIG. 5A and subsequent figures. The scaling factor c itself can be parameterized by another variable, such as represented by s, to linearize the scaling response. An example of such a parameterization can be expressed as follows: c(s)=10 2.3s -0.5 (Equation 9)

[0036] FIG. 4C generally illustrates an example including other sigmoid-like functions that can be used to implement amplitude filtering. The hyperbolic tangent (shown in FIG. 4C as tanh(x)) is not the only sigmoid-like function that can be used as an amplitude filter function. Simpler algebraic expressions can be used to speed up the computation of operations involving applying an amplitude filter. Examples shown in FIG. 4C include the hyperbolic tangent, the error function (erf), the arctangent function, the Gudelmann function (gd), and the expressions x / (1 + |x|) and x / (sqrt(1 + x 2 )). If the linear region near zero (corresponding to the dashed line) is deemed unnecessary, other functions that smoothly transition from zero (e.g., defining a filtering zone) to a constant value (e.g., a saturation zone) can be used. This can include a piecewise amplitude filter function or other shapes.

[0037] Figures 5A, 5C, and 5E show examples of images constructed using phase-based beamforming techniques without amplitude-based filtering. Figures 5A, 5C, and 5E were acquired at different physical locations along the sample and show signs of stress corrosion cracking. The acoustic inspection data was acquired using a 10DL64-Rex1-HC-PR probe and wedge combination (available from Evident Scientific, Inc.). In each of Figures 5A, 5C, and 5E, grain noise or other scatterers appear in the image in addition to features corresponding to stress corrosion cracking.

[0038] Figures 5B, 5D, and 5F show examples of images constructed using a phase-based beamforming technique, where the image is weighted using amplitude information from another beamforming approach to provide amplitude-based filtering. In each of Figures 5B, 5D, and 5F, a c value of 8 was used, reducing the grain noise of Figures 5A, 5C, and 5E while still preserving most (if not all) of the features indicative of stress corrosion cracking. In this way, the signal-to-noise ratio was enhanced while still preserving the enhanced sensitivity associated with PCI imaging.

[0039] Generally, the approach described herein involves establishing a first image using a phase-based approach (e.g., PCI), establishing a second image using an amplitude and phase-based approach (e.g., TFM beamforming), then applying an amplitude filter to the second image and adjusting the first image using the weighted second image. Such an approach involves two separate coherent summation operations corresponding to each image. However, other implementations are possible. For example, amplitude filtering can be performed on the amplitude terms in the coherent summation to achieve equivalent results, with only a single image being constructed from the coherent summation. In this approach, the amplitude filter is embedded in the coherent summation, as exemplarily shown in the following equation:

number

[0040] The approach of Equation 10 may include a scaling factor c, as in the example above. Because different inspection configurations (or inspection modes) generally involve different c values, it may be very difficult to determine the value of c a priori. Therefore, the approach of Equation 10 may eliminate post-summation adjustments, unlike approaches in which amplitude filtering is performed by applying a filter to the summed image data (as exemplarily shown in FIG. 2). The approach of Equation 10 will generally be more computationally intensive than other approaches.

[0041] 6 shows an example block diagram including a machine 600 on which any one or more of the techniques (e.g., methods) discussed herein may be implemented. The machine 600 (e.g., a computer system) may include a hardware processor 602 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 604, and a static memory 606, which are connected via an interlink 630 (e.g., a link or bus), and some or all of these components may constitute hardware for the systems and related implementations discussed above.

[0042] Specific examples of main memory 604 include semiconductor memory devices, which may include storage locations in a semiconductor, such as random access memory (RAM) and registers. Specific examples of static memory 606 include non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices, magnetic disks, such as internal hard disks and removable disks, magneto-optical disks, RAM, or optical media, such as CD-ROM and DVD-ROM disks.

[0043] The machine 600 may further include a display device 610, an input device 612 (e.g., a keyboard), and a user interface (UI) navigation device 614 (e.g., a mouse). In one example, the display device 610, the input device 612, and the UI navigation device 614 may be touchscreen displays. The machine 600 may further include a mass storage device (e.g., a drive unit) 608, a signal generating device 618 (e.g., a speaker), a network interface device 620, and one or more sensors 616, such as a global positioning system (GPS) sensor, a compass, an accelerometer, or other sensors. The machine 600 may include an output controller 628, such as a serial (e.g., Universal Serial Bus (USB), parallel, or other wired or wireless (e.g., infrared (IR), near field communication (NFC), etc.) connection, for communicating with or controlling one or more peripheral devices (e.g., a printer, a card reader, etc.).

[0044] The mass storage device 608 may comprise a machine-readable medium 622 having stored thereon one or more sets of data structures or instructions 624 (e.g., software) that embody or are utilized by any one or more of the techniques or functions described herein. The instructions 624 may also reside, completely or at least partially, within the main memory 604, within the static memory 606, or within the hardware processor 602 during execution thereof by the machine 600. In one example, one or any combination of the hardware processor 602, the main memory 604, the static memory 606, or the mass storage device 608 comprises a machine-readable medium.

[0045] Specific examples of machine-readable media include one or more of non-volatile memory, such as semiconductor memory devices (e.g., EPROM or EEPROM) and flash memory devices, magnetic disks, such as internal hard disks and removable disks, magneto-optical disks, RAM, or optical media, such as CD-ROM and DVD-ROM disks. Although the machine-readable medium is illustrated as a single medium, the term "machine-readable medium" may include a single medium or multiple media (e.g., a centralized or distributed database, or associated caches and servers) configured to store one or more instructions 624.

[0046] The device of machine 600 includes one or more of a hardware processor 602 (e.g., a central processing unit (CPU), a graphics processing unit (GPU), a hardware processor core, or any combination thereof), a main memory 604 and static memory 606, a sensor 616, a network interface device 620, an antenna, a display device 610, an input device 612, a UI navigation device 614, a mass storage device 608, an instruction 624, a signal generation device 618, or an output controller 628. The device may be configured to perform one or more of the methods or operations disclosed herein.

[0047] The term "machine-readable medium" includes any medium capable of storing, encoding, or carrying instructions for execution by machine 600, causing machine 600 to perform any one or more of the techniques of this disclosure, or causing another device or system to perform any one or more of the techniques, or capable of storing, encoding, or carrying data structures used by or associated with such instructions. Non-limiting examples of machine-readable media include solid-state memory, and optical or magnetic media. Specific examples of machine-readable media include non-volatile memory, such as semiconductor memory devices (e.g., electrically programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM)) and flash memory devices, magnetic disks, such as internal hard disks and removable disks, magneto-optical disks, random access memory (RAM), or optical media, such as CD-ROM and DVD-ROM disks. In some embodiments, machine-readable media include non-transitory machine-readable media. In some embodiments, machine-readable media include machine-readable media that are not transitory, propagating signals.

[0048] The instructions 624 may be transmitted or received over a communications network 626 using a transmission medium via the network interface device 620, for example, utilizing any one of several transport protocols (e.g., frame relay, Internet Protocol (IP), Transmission Control Protocol (TCP), User Datagram Protocol (UDP), Hypertext Transfer Protocol (HTTP), etc.). Exemplary communications networks include, among others, a local area network (LAN), a wide area network (WAN), a packet data network (e.g., the Internet), a mobile telephone network (e.g., a cellular network), a plain old telephone service (POTS) network, and a wireless data network (e.g., the Institute of Electrical and Electronics Engineers (IEEE) 802.11 family of standards known as Wi-Fi®), the IEEE 802.15.4 family of standards, the Long Term Evolution (LTE) 4G or 5G family of standards, the Universal Mobile Telecommunications System (UMTS) family of standards, a peer-to-peer (P2P) network, and a satellite network.

[0049] In one example, network interface device 620 includes one or more physical jacks (e.g., Ethernet, coaxial, or interconnect) or one or more antennas for accessing communications network 626. In one example, network interface device 620 includes one or more antennas that communicate wirelessly using at least one of single-input multiple-output (SIMO), multiple-input multiple-output (MIMO), or multiple-input single-output (MISO) technologies. In some embodiments, network interface device 620 communicates wirelessly using multi-user MIMO technologies. The term “transmission medium” should be understood to include any intangible medium capable of storing, encoding, or carrying instructions for execution by machine 600, including digital or analog communications signals or other intangible media for facilitating the communication of such software.

[0050] Various notes Each of the above non-limiting aspects may stand alone or may be combined in various permutations or combinations with one or more of the other aspects or other subject matter described herein.

[0051] The above detailed description includes references to the accompanying drawings, which form a part of the detailed description. The drawings show, by way of illustration, specific embodiments in which the invention may be practiced. These embodiments are also generally referred to as "examples." Such examples may include elements in addition to those shown or described. However, the inventors also contemplate examples in which only those elements shown or described are provided. Furthermore, the inventors also contemplate examples using any combination or permutation of those elements (or one or more aspects thereof) shown or described with respect to the particular example (or one or more aspects thereof) shown or described herein or with respect to other examples (or one or more aspects thereof).

[0052] In the event of a conflict of usage between this document and any document so incorporated by reference, the usage in this document shall take precedence.

[0053] As used herein, the terms "a" or "an" are used, as is common in patent documents, to include one or more, regardless of any other instance or usage of "at least one" or "one or more." As used herein, the term "or" is used to refer to a non-exclusive or, unless otherwise indicated, such that "A or B" includes "A but not B," "B but not A," and "A and B." As used herein, the terms "including" and "in which" are used as the plain-English equivalents of the respective terms "comprising" and "wherein." Also, in the following claims, the terms "including" and "comprising" are open-ended, i.e., systems, devices, articles, compositions, formulations, or processes that include elements in addition to those recited after such terms in a claim are still deemed to be within the scope of that claim. Moreover, in the following claims, the terms "first," "second," and "third," etc., are used merely as labels and are not intended to impose numerical requirements on their objects.

[0054] Examples of the methods described herein may be at least partially machine- or computer-implemented. Some examples may include a computer-readable or machine-readable medium encoded with instructions operable to configure an electronic device to perform the methods described in the examples. Implementations of such methods may include code, such as microcode, assembly language code, higher-level language code, etc. Such code may include computer-readable instructions for implementing various methods. The code may form part of a computer program product. Such instructions may be readable and executed by one or more processors to enable performance of operations, including methods. The instructions may be in any suitable form, such as, but not limited to, source code, compiled code, interpreted code, executable code, static code, dynamic code, etc. Further, in one example, the code may be tangibly stored, such as during execution or at other times, on one or more volatile, non-transitory, or non-volatile tangible computer-readable media. Examples of these tangible computer-readable media may include, but are not limited to, hard disks, removable magnetic disks, removable optical disks (e.g., compact disks and digital video disks), magnetic cassettes, memory cards or sticks, random access memory (RAM), read-only memory (ROM), etc.

[0055] The above description is intended to be illustrative, not limiting. For example, the examples described above (or one or more aspects thereof) may be used in combination with each other. Other embodiments may be used, for example, by one of ordinary skill in the art upon reviewing the above description. The Abstract is provided to allow the reader to quickly ascertain the nature of the technical disclosure. It is submitted with the understanding that it will not be used to interpret or limit the scope or meaning of the claims. Also, in the above Detailed Description, various features may be grouped together to streamline the disclosure. This should not be construed as intending that any unclaimed disclosed feature is essential to any claim. Rather, inventive subject matter may lie in less than all features of a particular disclosed embodiment. Accordingly, the following claims are incorporated into the Detailed Description as an example or embodiment, with each claim standing on its own as a separate embodiment, and it is contemplated that such embodiments can be combined with each other in various combinations or permutations. The scope of the invention should be determined with reference to the appended claims, along with the full range of equivalents to which such claims are entitled.

Claims

1. 1. A machine-implemented method comprising: acquiring acoustic echo data indicative of scattered or reflected acoustic energy corresponding to each transmit / receive pair of the aperture; forming a first image by coherently adding representations of phase information from the acoustic echo data to establish respective first image pixel or voxel element values; forming a second image by coherently adding representations of the phase information and amplitude information from the acoustic echo data to establish respective second image pixel or voxel element values; weighting each second image pixel or voxel element value using an amplitude filter function to establish a respective weighted second image pixel or voxel element value; adjusting each first image pixel or voxel element value of the first image using each weighted second image pixel or voxel element value from a corresponding location in the second image to establish a weighted first image; 10. A machine-implemented method comprising:

2. 2. The machine-implemented method of claim 1, wherein forming the first image by coherently adding the representations of the phase information includes normalizing amplitudes of received acoustic echo data formed from received portions of each transmit-receive pair of apertures before coherently adding.

3. 3. The machine-implemented method of claim 1 or 2, wherein the amplitude filter function comprises a sigmoid-shaped function with a non-linear mapping from input amplitude values ​​to output amplitude values ​​used to weight each second image pixel or voxel element value.

4. The machine-implemented method of claim 1 or 2, wherein the magnitude filter function comprises a hyperbolic tangent function.

5. The machine-implemented method of any one of claims 1 to 4, wherein arguments of the amplitude filter function comprise normalized representations of respective second image pixel or voxel values.

6. The machine-implemented method of claim 5 , wherein the argument of the amplitude filter function is scaled using a scaling factor.

7. The machine-implemented method of claim 6 including adjusting the scaling factor in response to user input.

8. The machine-implemented method of claim 6 or 7, wherein the scaling factor is parameterized.

9. 9. The machine-implemented method of claim 1, wherein acquiring acoustic echo data indicative of scattered or reflected acoustic energy corresponding to each transmit / receive pair of the aperture comprises performing a full matrix capture (FMC) acquisition.

10. forming the first image by coherently adding the representations comprises a phase coherence imaging (PCI) technique; 10. The machine-implemented method of claim 9, wherein forming the second image includes coherently adding the representations includes a total focusing method (TFM) technique.

11. 11. The machine-implemented method of claim 1, wherein adjusting each first image pixel or voxel element value of the first image using the respective weighted second image pixel or voxel element value from the corresponding location in the second image comprises scaling the pixel or voxel element amplitude value of the first image using pixel or voxel element amplitude value from the corresponding location in the second image.

12. 12. The machine-implemented method of claim 11, wherein each pixel or voxel element value of the first image corresponds to one of a maximum magnitude of a complex-valued time series representation of a coherently added representation of phase information from the acoustic echo data or a maximum amplitude of a substantial portion of a complex-valued time series representation of a coherently added representation of the phase information.

13. The machine-implemented method of any one of claims 1 to 12, comprising presenting the weighted first image to a user via a display.

14. 1. A non-destructive testing system comprising: a test instrument including a transmitter circuit and a receiver circuit; at least one processor circuit communicatively coupled to or included as part of the test instrument; at least one memory circuit communicatively coupled to or included as part of the test instrument; the at least one memory circuit containing instructions that, when executed by the at least one processor circuit, cause the non-destructive testing system to: acquiring acoustic echo data indicative of scattered or reflected acoustic energy corresponding to each transmit / receive pair of apertures using the transmitter circuitry and the receiver circuitry; forming a first image by coherently adding representations of phase information from the acoustic echo data to establish respective first image pixel or voxel element values; forming a second image by coherently adding representations of the phase information and amplitude information from the acoustic echo data to establish respective second image pixel or voxel element values; weighting each second image pixel or voxel element value using an amplitude filter function to establish a respective weighted second image pixel or voxel element value; adjusting each first image pixel or voxel element value of the first image using each weighted second image pixel or voxel element value from a corresponding location in the second image to establish a weighted first image; transmitting or presenting the weighted first image to a user; A non-destructive testing system that performs the following:

15. the at least one processor circuit and the at least one memory circuit are included as part of the test instrument; 15. The non-destructive testing system of claim 14, wherein the non-destructive testing system comprises a test probe assembly including an electro-acoustic transducer array, the transmitter circuitry and receiver circuitry being communicatively coupled to the electro-acoustic transducer array to generate acoustic transmit pulses and receive the scattered or reflected acoustic energy in response to the generated acoustic transmit pulses, respectively.

16. 16. A non-destructive testing system according to claim 14 or 15, comprising a display configured to present the weighted first image.

17. 17. A non-destructive testing system according to any one of claims 14 to 16, wherein the amplitude filter function comprises a sigmoid-shaped function having a non-linear mapping from input amplitude values ​​to output amplitude values ​​used to weight each second image pixel or voxel element value.

18. A non-destructive testing system according to any one of claims 14 to 17, wherein an argument of the amplitude filter function comprises a normalised representation of each second image pixel or voxel value scaled using a configurable scaling factor.

19. The non-destructive testing system of claim 18 , wherein the configurable scaling factor is parameterized.

20. 1. A non-destructive inspection system, comprising: means for acquiring acoustic echo data indicative of scattered or reflected acoustic energy corresponding to each transmit / receive pair of the aperture, the acoustic echo data including a time series corresponding to each transmit / receive pair of the aperture; means for forming a first image using phase coherence imaging techniques, using phase information from the acoustic echo data to establish respective first image pixel or voxel element values; means for forming a second image using a total focusing imaging technique using the phase information and amplitude information from the acoustic echo data to establish respective second image pixel or voxel element values; and means for adjusting each first image pixel or voxel element value of the first image using each weighted second image pixel or voxel element value from a corresponding location in the second image to establish a weighted first image, wherein the each weighted second image pixel or voxel element value is established using an amplitude filter function applied to the second image.

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