Systems and methods for adaptive full focus methods

Abstract

A system and method for an adaptive full focus method (TFM) includes obtaining full matrix capture (FMC) data of a region of interest of an article, the region of interest being made of a material; for each unit of a set of units in the article, evaluating the FMC data to obtain a parameter for relating to a material property of the material; associating the parameters of each cell in the set of cells with a state model of the material to determine an anisotropic state of each cell in the set of cells in the article; generating a distribution parameter model of the material anisotropy of the product by using the anisotropy state of each unit in the group of units in the product; and operating the beamformer with the distributed parameter model using the TFM to image the region of interest.

Description

Cross-referencing related applications

[0001] This application claims the benefit and priority of U.S. Provisional Application No. 63 / 591,235, filed October 18, 2023, by reference and inclusion. Technical Field

[0002] This disclosure relates to ultrasound detection, and more particularly, to systems and methods for adaptive total focusing (TFM). Background Technology

[0003] Sometimes, there is some confusion between the terms "adaptive TFM" and "iterative TFM." The difference between adaptive TFM and iterative TFM lies in the following: Fundamentally, adaptive TFM modifies an established model of the material based on information derived from a full matrix capture (FMC) dataset before applying TFM beamforming; while iterative TFM applies a few separate cycles of the TFM beamformer to identify and adapt to changes in the sample geometry. These geometric changes may occur at reflective and refractive interfaces.

[0004] When applied to anisotropic materials, TFM beamforming capabilities face challenges. These difficulties stem from two main conditions. The first is the velocity dependence relative to the path reaching the reflector. The sum of these conditions leads to poor reconstruction of the reflector image. The second is the difference between the group velocity and phase velocity that may occur in anisotropic materials. This difference causes the signal phase to no longer be consistent with the group velocity. TFM beamformers rely on the sum of the analyzed waveforms achieved through Hilbert transform. The fundamental assumption of TFM is that the phase always remains consistent with group propagation. If the signal phase varies within the group envelope, the TFM signal sum may exhibit poor performance.

[0005] Adaptive TFM is useful when the longitudinal and transverse wave velocities of a material are unknown at the required accuracy and / or can vary relative to position and time within the sample. This situation describes the challenges posed when anisotropy is induced in the material by environmental factors (heat, temperature gradients, molecular absorption, radiation, etc.) and stress. Summary of the Invention

[0006] According to a first aspect of this disclosure, a method for an adaptive total focusing method (TFM) is provided, comprising: acquiring full matrix capture (FMC) data of a region of interest in an article of fabrication, the region of interest being made of a material; evaluating the FMC data for each of a set of cells in the article of fabrication to obtain parameters relating to material properties of the material; associating the parameters of each of the set of cells with a state model of the material to determine an anisotropic state for each of the set of cells in the article of fabrication; generating a distribution parameter model of the material anisotropy of the article of fabrication using the anisotropic state of each of the set of cells in the article of fabrication; and operating a beamformer using the distribution parameter model with TFM to image the region of interest.

[0007] In some or all exemplary embodiments of the first aspect of this disclosure, the state model includes one or more Christofel matrices.

[0008] In some or all exemplary embodiments of the first aspect of this disclosure, the state model includes establishing a velocity range relative to direction for the material from an isotropic state to a fully anisotropic state.

[0009] In some or all exemplary embodiments of the first aspect of this disclosure, the range of speeds is established for both longitudinal and transverse waves.

[0010] In some or all exemplary embodiments of the first aspect of this disclosure, the generation includes associating parameters of adjacent units in the set of units.

[0011] In some or all exemplary embodiments of the first aspect of this disclosure, the association includes, for each of the plurality of cells, solving for the orientation and magnitude of the phase velocity and group velocity in the FMC data.

[0012] In some or all exemplary embodiments of the first aspect of this disclosure, the cell size in the plurality of cells is determined based on the gradient of the anisotropic properties in adjacent cells of the plurality of cells.

[0013] In some or all exemplary embodiments of the first aspect of this disclosure, the method further includes merging one of the plurality of units with an adjacent unit of the plurality of units having a corresponding parameter.

[0014] According to a second aspect of this disclosure, a system for an adaptive total focusing method (TFM) is provided, comprising: at least one processor; and a memory storing computer-readable instructions, which, when executed by the at least one processor, cause the system to perform the following operations: acquire full matrix capture (FMC) data of a region of interest of an article of fabrication, the region of interest being made of a material; evaluate the FMC data for each of a set of cells in the article of fabrication to obtain parameters associated with material properties of the material; correlate the parameters for each of the set of cells with a state model of the material to determine an anisotropic state of each of the set of cells in the article of fabrication; generate a distribution parameter model of the material anisotropy of the article of fabrication using the anisotropic state of each of the set of cells in the article of fabrication; and operate a beamformer using the distribution parameter model with TFM to image the region of interest.

[0015] In some or all exemplary embodiments of the second aspect of this disclosure, the state model includes one or more Christofel matrices.

[0016] In some or all exemplary embodiments of the second aspect of this disclosure, the state model includes establishing a velocity range relative to direction for the material from an isotropic state to a fully anisotropic state.

[0017] In some or all exemplary embodiments of the second aspect of this disclosure, the velocity range is established for longitudinal and transverse waves.

[0018] In some or all exemplary embodiments of the second aspect of this disclosure, the computer-readable instructions, when executed by the at least one processor, cause the system to associate parameters of adjacent units in the set of units.

[0019] In some or all exemplary embodiments of the second aspect of this disclosure, the computer-readable instructions, when executed by the at least one processor, cause the system to solve for the orientation and magnitude of the phase velocity and group velocity in the FMC data for each of the plurality of cells.

[0020] In some or all exemplary embodiments of the second aspect of this disclosure, the cell size in the plurality of cells is determined based on the gradient of the anisotropic properties in adjacent cells of the plurality of cells.

[0021] In some or all exemplary embodiments of the second aspect of this disclosure, the computer-readable instructions, when executed by the at least one processor, cause the system to merge one of the plurality of units with an adjacent unit of the plurality of units having corresponding parameters.

[0022] Other aspects and features of this disclosure will become apparent to those skilled in the art upon review of the following detailed description in conjunction with the accompanying drawings. Attached Figure Description

[0023] Reference is now made to the accompanying drawings by way of example, which illustrate exemplary embodiments of the present application, wherein: Figure 1 The diagram illustrates an adaptive TFM process according to an embodiment of the present disclosure.

[0024] Figure 2 A framework for a distributed parameter model for a hypothetical configuration is shown.

[0025] Figure 3 This is a flowchart of a method for adaptive TFM according to an embodiment of the present disclosure.

[0026] Figure 4A The Cauchy tensor, which describes the stretching and shearing vectors associated with a unit volume, is shown.

[0027] Figure 4B It shows Figure 4A The matrix representation of the Cauchy tensor.

[0028] Figure 5A An example of the phase slowness curve is shown, in which the velocity remains constant regardless of direction.

[0029] Figure 5B Another example of the phase slowness curve for anisotropic materials is shown, where velocity varies with direction.

[0030] Figure 6 The phase velocity vector and group velocity vector are shown plotted on the slowness curve, where the magnitude of the variation has been exaggerated for illustrative purposes.

[0031] Figure 7A The left side shows an isotropic hydride sample, and the right side shows an A-scan of the resulting immersion pattern.

[0032] Figure 7B The left side shows an anisotropic hydride sample, and the right side shows an A-scan of the resulting immersion pattern.

[0033] Figures 8A to 8C Three different examples of solving the conditions are shown.

[0034] Figure 9 A system for adaptive TFM is shown according to an embodiment of the present disclosure.

[0035] Similar reference numerals may have been used in different accompanying drawings to indicate similar parts. Unless otherwise specified, the articles shown in the accompanying drawings are not necessarily drawn to scale. Detailed Implementation

[0036] This disclosure is described with reference to the accompanying drawings, in which embodiments are illustrated. However, many different embodiments may be used, and therefore this specification should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to make this application thorough and complete. In the drawings and the following description, the same reference numerals are used wherever possible to refer to the same elements, and apostrophe notation is used to indicate similar elements, operations, or steps in alternative embodiments. The separate blocks or separate illustrations of functional elements of the illustrated systems and devices do not necessarily require physical separation of these functions, as communication between these elements can be achieved through message passing, function calls, shared memory spaces, etc., without any such physical separation. Therefore, individual functions are not necessarily implemented on physically or logically separate platforms, although they are illustrated separately here for ease of explanation. Different devices may have different designs such that while some devices implement certain functions in fixed-function hardware, others may implement those functions in a programmable processor using code obtained from a machine-readable medium. Finally, unless the context explicitly or implicitly indicates otherwise, elements appearing in the singular may also include the plural form, and vice versa.

[0037] Adaptive TFM is useful when the known P-wave and S-wave velocities of the material are not accurate enough, and / or when the P-wave and S-wave velocities of the material can vary relative to position and time within the sample. This situation describes the challenges posed by materials in which anisotropy is induced by environmental factors (heat, temperature gradients, molecular absorption, radiation, etc.) and stress.

[0038] Figure 1 The diagram illustrates the process for adaptive TFM according to an embodiment. The main inputs come from metallurgical research—a Christofel matrix segmentation and a signal analysis segmentation. These segments are described below. In this process, adaptive TFM is implemented as follows: Obtain the FMC dataset. Then evaluate the FMC dataset to obtain parameters associated with material properties. Associate the identified parameters with the material model. Update the distributed parameter model and apply the TFM beamformer.

[0039] Figure 2The weld region 200 in the article 204 to be tested is shown. The weld region 200 is sensitized and exposed to a substance that induces anisotropy. The article 204 has isotropic or known material properties in region 208 away from the weld region 200. The material of the article 204 exhibits variable anisotropic properties in the weld region 200. The method disclosed herein can compensate for the anisotropic properties of the weld region 200 during nondestructive testing (NDT) of the article 204 for impurities, defects, etc.

[0040] Figure 3 A method 300 for adaptive TFM according to an embodiment is shown, the method 300 generally corresponding to Figure 1 The process is illustrated in Method 300. Method 300 begins with a detailed metallurgical study of the material (one or more) to be examined (310). The study has two objectives. First, a velocity range is established relative to the orientation, starting from the material's normal state and progressing to a fully anisotropic state. This requirement applies to both longitudinal and transverse wave modes. The second objective is to determine the orientation and rate or variation of anisotropy (gradient) relative to the sample geometry. The gradient and the expected potential variation of the gradient over the lifetime are used as inputs to the model. Alternatively, if properties within the state range have already been determined based on other effects, a predictive or statistical model of the material can be applied instead of the metallurgical study.

[0041] Then, the Christofel matrix representation of the material (320) is generated. The Christofel matrix representation is a state model of the material, representing a series of states of the material from isotropic to anisotropic forms. Anisotropic properties in the material can be generated by manipulating the environment by introducing foreign substances into the target material matrix. The concentration of the foreign substance can vary with exposure time and can also vary along the distance of the target component. Anisotropic behavior may propagate over time to eventually reach an equilibrium state. Similarly, the anisotropic gradient can vary with the time of exposure to the same environmental factor. The model needs to accommodate a range of anisotropy from the initial state to the maximum anisotropic state. The size and spacing of the cells in the model will be determined based on the gradient in the anisotropic properties.

[0042] Although one or more Christofel matrices are referenced in this specification, other types of state models can also be used to represent the state range of a material.

[0043] For the material properties and wave propagation in an elastic medium, the general form of the elastic wave equation is: .

[0044] Furthermore, it can be shown that material properties are related to wave propagation through the following formula: , or , Where E is the tensile modulus, G is the shear modulus, and ρ is the material density.

[0045] Based on the aforementioned equations, the following relationship is established: , and , Where VL and VS are the longitudinal wave phase velocities and transverse wave phase velocities, respectively. When the material properties are constant relative to direction, i.e., isotropic, the phase velocity and group velocity are the same.

[0046] In practice, the measured speeds are typically within the following range: 0.995 ≤ Vn ≤ 1.005 Processing methods, heat treatment, and minor variations in alloy content typically lead to normal variations in speed.

[0047] Once the Christofel matrix representation of the material is obtained, cells (330) within the region of interest of the article are selected. The region of interest can be a portion of the article or the entire article. For example, in Figure 2 In the illustrated article 204, the region of interest may include, for example, the welded area 200 and the surrounding area. The surrounding area may be selected, for example, to provide a buffer zone at a certain distance from the welded area.

[0048] FMC data (340) of the selected cell is obtained. FMC data can be obtained before or during method 300. The FMC dataset can be acquired using conventional methods, such as contact detection or local immersion detection. Any suitable conventional method for adaptive FMC / TFM data acquisition can be used. However, the selection of transducer parameters may be influenced by a material anisotropy distribution parameter model developed to describe the inspected area. In this embodiment, the FMC data is acquired by the system, but in other embodiments it may also be received from another system.

[0049] The distribution parameter model (350) for the region of interest is then updated. The model evolution process begins by examining locations on the sample that have or are assumed to have known properties (i.e., normal material properties). A distribution parameter model is developed based on the maximum gradient of anisotropic properties identified in metallurgical studies and represented by the Christoffel matrix. Assuming the element is located in the region of maximum gradient, the element size in this model is determined by the step size that causes a change at the detection threshold. In other words, the location of subsequent elements is determined based on the detected material properties. Element properties are key parameters obtained from the solution of the Christoffel matrix.

[0050] Determine if there are any remaining cells to be analyzed in the region of interest (360). If there are remaining cells to be analyzed, method 300 returns to 330, selecting adjacent cells for analysis.

[0051] The material's distributed parameter model is updated or adjusted by evaluating unknown cells adjacent to known regions (one or more). This process assumes the waveform traverses both the known regions (one or more) and the unknown cells, resolving the direction and magnitude of phase and group velocities and vectors within the cells. Note that a single measurement is insufficient to determine parameter values. More than one point on the slow-motion surface can provide a valid solution. This solution is constrained by providing separate estimates of complementary parameters and / or from independent paths traversing the same cell. This evaluation can be derived from the same wave mode propagating at an angle spaced apart from the first wave mode in direction, or from complementary wave modes when the relationship between the two wave modes is known. When a cell is evaluated and found to have characteristics corresponding to a known region, the cell is included in the known region, and the model is discarded. When a cell differs from the known region, the model is updated with the parameter values ​​determined during the path-dependent adaptation process.

[0052] In most cases, the ultrasonic testing (UT) data acquired by the inspection system is observed to show the group velocity. When the material is isotropic (within a given plane) or at the maximum and minimum of the phase velocity, the group velocity and phase velocity are equal. These conditions are called stagnation points. When the phase velocity is not at its maximum or minimum, the difference between the phase velocity and the group velocity is observed as a shift in the signal phase relative to the group. The detection of anisotropy depends on the nature of the effect and is material-specific. In some materials, the shear component is most affected, while in others, the P-wave mode may be affected. Ideally, the detection strategy should extract the most sensitive modes to obtain a reliable estimate of the material's anisotropy.

[0053] P-wave group velocities are typically obtained using autocorrelation techniques over a known distance. These measurements provide information on both group velocity and phase vector direction. Similarly, provided there is no phase change relative to the group, S-wave group velocities can be measured via autocorrelation along the same path relative to the P-wave group velocity.

[0054] Compared to detecting changes in the velocity of a component, the velocity ratio between P-wave and S-wave modes can be used to enhance the sensitivity of anisotropic property detection. Changes in tensile modulus and shear modulus in opposite directions are common. These events have a corresponding effect on P-wave and S-wave velocities. Changes in properties are more easily detected when monitoring the ratio between the two modes.

[0055] Another method for assessing local variations in wave propagation characteristics is to detect and measure the phase displacement within the group. A reference signal and a target signal are chosen, and a Fourier transform is applied. The difference between the weighted phase components of the two signals provides the angle between the phase vector and the group vector. The phase velocity can also be calculated relative to the group velocity.

[0056] Cauchy tensors, or Cauchy matrices, are effective tools for representing the distribution of mechanical properties of materials. Information derived from metallurgical research can be expressed in the form of Cauchy tensors, for example... Figure 4A As shown in the diagram. In the case of isotropic materials, the material properties are constant with respect to direction. The tensile modulus values ​​are all equal, i.e. T 1 = T 2 = T 3 And the shear modulus values ​​are all equal, that is S 12 = S 21 = S 13 = S 31 = S 23 = S 32 In the case of anisotropic materials, typically T 1 ≠ T 2 ≠ T 3 And S 12 ≠ S 21 ≠ S 13 ≠ S 31 ≠ S 23 ≠ S 32 .

[0057] Since the relationship between modulus and corresponding velocity has been established, the longitudinal and transverse wave velocities can be derived as functions of the tensile and shear moduli of the material. In the case of transverse waves, the polarization of each transverse wave vector is considered. The resulting representation is constructed using a notation called the Christoffel matrix. Figure 4B An example is shown, which corresponds to Figure 4AThe Cauchy tensor is shown. The examination plane is not necessarily aligned with the principal axes of the Christoffel matrix and may be offset by arbitrary angles from the basis of the Christoffel matrix. Because material properties evolve with increasing exposure to anisotropic materials, the Christoffel representation will change in a similar manner. In other words, the Christoffel matrix represents a state of material properties.

[0058] Slowness surfaces and their corresponding slowness curves are an effective means of visualizing phase vectors and group vectors. Phase slowness is defined as the reciprocal of the phase velocity. Faster wave modes are closer to the origin than slower wave modes. Wave slowness can be plotted as three-dimensional volumes. These three-dimensional shapes are called slowness surfaces and can take many shapes, typically variations of ellipsoids and spheres, and in special cases, they can fold themselves. Slowness curves represent the phase velocities of planes passing through slowness surfaces. Typically, these planes lie on the principal axes of the coordinate system, i.e., the XY, XZ, and YZ planes.

[0059] Figure 5A and Figure 5B An example of a phase slowness curve is shown. Specifically, Figure 5A The phase slowness curve of an isotropic material is shown, in which the velocity remains constant regardless of direction; Figure 5B The phase slowness curves of anisotropic materials are shown, where the velocity varies with direction.

[0060] When dealing with anisotropic materials, four parameters are defined: the phase vector, phase velocity, group vector, and group velocity. The phase vector is the independent variable representing the direction through the material. The group vector is the curl of the phase velocity, with its direction perpendicular to the phase velocity slowdown curve. In practice, the phase vector represents the wave propagation direction, and the group vector represents the wavefront orientation. Based on the expansion of the two-dimensional elastic wave equation, the phase velocity within the medium can be expressed as: , Where ω is the angular frequency expressed in radians per second. k It is a wavenumber vector expressed in radians per meter.

[0061] When the vector quantity k, the above relationship holds for both P-wave and S-wave modes.

[0062] Group velocity is defined as: .

[0063] like Figure 6 As shown, when plotted on a slow-speed curve, the vectors and magnitudes of the phase velocity and group velocity can be visualized. For ease of explanation, the magnitudes of change have been exaggerated.

[0064] Figure 7AThe left side shows an anisotropic hydride sample article, with the vertical beam A-scan response generated in immersion mode shown in the center. Note that the phase remains consistent in each multiple echo. The first reflection, following the interface signal, is the reference signal. All subsequent echoes have the same phase-group relationship as the reference signal. The right side shows a grayscale time-domain representation of a single pulse emitted from one element and received by multiple elements.

[0065] Figure 7B The sample artifact is shown on the left, and the generated vertical beam A-scan response is shown in the center. The A-scan at the center indicates the throw-and-hold configuration. The scan includes the LL signal and the LS signal. Note that the reference signal (first LL reflection) remains consistent with... Figure 6 The same phase-group relation exists in A, but the subsequent LS signal shows a significant change in the phase-group relation. This behavior is an indication of anisotropy.

[0066] The results of the probing process for a given element are applied equally to the corresponding variables in the Christoffel matrix (e.g., L-wave phase vector, L-wave group velocity, group and phase vector angles, etc.). Eigenvector solutions identify the orientation of the Christoffel matrix that can be applied to the element and the corresponding degree of anisotropy. Each application will have different degrees of freedom due to material properties. Furthermore, due to the symmetry of the Christoffel matrix, more than one valid solution may be applicable to the element. Further evaluation (input) is needed to address cases involving symmetry.

[0067] This process is repeated using parameters from the substrate material and solved elements, combined with parameters obtained from the FMC dataset, to determine the properties of new elements. If an element's parameters are unique, it is retained as an independent entity. If the parameters correspond to those of neighboring materials, the element is incorporated into the adjacent structure. In this way, the model evolves from a fine structure containing many small elements to a structure with fewer but larger elements. The behavior of element merging is analogous to the annealing behavior of materials exposed to high temperatures.

[0068] As mentioned above, several independent paths need to be traversed through the elements under consideration to identify parameters linked to material properties. These paths are considered independent when they do not propagate along parallel or near-parallel vectors, or when they employ different modes. Figure 8A The poorly configured path is shown, where the L-wave is a nearly parallel vector. Figure 8B The well-conditioned path is shown, in which the L-waves are almost perpendicular to each other. Figure 8C The well-conditioned path is shown, in which the L-wave and S-wave belong to different modes.

[0069] This requirement imposes constraints on transducer spacing (array size), sample geometry, scanning mode, and any scanning limitations that may be encountered. If the material properties are highly sensitive to orientation in all wave modes, or if the range of anisotropic behavior from the substrate state to a fully anisotropic state is large, then the number of degrees of freedom required to resolve the different states will increase. The extent of the number of degrees of freedom will vary depending on the specific application.

[0070] Under adaptive TFM, the TFM process remains essentially unchanged compared to the traditional TFM process. The propagation time through each cell is determined by the group velocity of the vector being evaluated. The correct path is one that conforms to Fermat's principle (i.e., minimum propagation time). When the group velocity and phase velocity are not equal, a phase shift relative to the group occurs. For any pixel where the signal passes through such a cell, the difference between the phase velocity and the group velocity must be considered and compensated for before summing. Failure to do so will result in the addition of various phase shifts, leading to poor imaging results.

[0071] Conversely, if all elements in the region of interest have been analyzed, the distribution parameter model of the material anisotropy is complete, and the TFM beamformer can be operated to analyze the article (370).

[0072] Figure 9 Various physical and logical components of an exemplary system 400 for adaptive TFM according to embodiments of this disclosure are illustrated. Although exemplary embodiments of system 400 are shown and discussed below, other embodiments may be used to implement the examples disclosed herein, and these embodiments may include components different from those shown. Figure 9 A single instance of each component of system 400 is shown, but each component shown may have multiple instances.

[0073] System 400 includes one or more processors 404, such as a central processing unit, microprocessor, application-specific integrated circuit (ASIC), field-programmable gate array (FPGA), application-specific logic circuit, tensor processing unit, neural processing unit, dedicated artificial intelligence processing unit, or a combination thereof. One or more processors 404 may be collectively referred to as processor 404. System 400 may include a display 408 for outputting data and / or information in some applications, but may not include a display 408 in other applications.

[0074] System 400 includes one or more memories 412 (collectively referred to as "memory 412"), which may include volatile or non-volatile memories (e.g., flash memory, random access memory (RAM), and / or read-only memory (ROM)). Non-transitory memory 412 may store machine-executable instructions for execution by processor 404. A set of machine-executable instructions 416 defining the process for adaptive TFM is shown stored in memory 412, which processor 404 may execute to perform various steps of the method for adaptive TFM described herein. Memory 412 may include other machine-executable instructions executed by processor 404, such as machine-executable instructions for implementing an operating system and other applications or functions.

[0075] The memory 412 stores or is capable of storing metallurgical data 420 from metallurgical research. Furthermore, the memory 412 stores a material anisotropy distribution parameter model 420.

[0076] Scan array 424 can be connected to processor 404, enabling processor 404 to control scan array 424 to capture ultrasound data from the region of interest of the artifact. Processor 404 can control scan array 424 to capture TFM data, FMC data, etc. Alternatively, processor 404 can be connected to an external scan array to control the external scan array and capture data from that scan array.

[0077] In some examples, system 400 may also include one or more electronic storage units (not shown), such as solid-state drives, hard disk drives, disk drives, and / or optical disk drives. In some examples, one or more datasets and / or modules may be provided by external memory (e.g., an external drive that is wired or wirelessly connected to system 400), or may be provided by transient or non-transitory computer-readable media. Examples of non-transitory computer-readable media include RAM, ROM, erasable programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), flash memory, CD-ROM, or other portable storage memories. The storage units and / or external memory may be used in conjunction with memory 412 to enable the data storage, retrieval, and caching functions of system 400.

[0078] Components of system 400 may communicate with each other, for example, via a bus. In some embodiments, system 400 is a distributed computing system and may include multiple computing devices communicating with each other via a network, as well as one or more optional additional components. The various operations described herein may be performed by different computing devices of the distributed system in some embodiments. In some embodiments, system 400 is a virtual machine provided by a cloud computing platform.

[0079] The steps (also referred to as operations) in the flowcharts and figures of this document are for illustrative purposes only. These steps / operations can be varied in many ways without departing from the teachings of this disclosure. For example, steps may be performed in a different order, or steps may be added, deleted, or modified as appropriate.

[0080] In other embodiments, the same method described herein can be used for other modalities.

[0081] Overview Based on the foregoing description of the embodiments, the present invention can be implemented solely by hardware, or by software and a necessary general-purpose hardware platform, or by a combination of hardware and software. The software coding for implementing the methods described above is within the skill of those skilled in the art after reading this disclosure. Based on these understandings, the technical solutions of the present invention can be embodied in the form of a software product. The software product can be stored in a non-volatile or non-transitory storage medium, such as an optical storage medium, a flash drive, or a hard disk. The software product includes several instructions that cause a computing device (personal computer, server, or network device) to execute the methods provided in the embodiments of this disclosure.

[0082] All values ​​and subranges within the scope of disclosure are also disclosed. Furthermore, although the systems, devices, and methods disclosed and illustrated herein may include a specific plurality of elements, such systems, devices, and components may be modified to include more or fewer of these elements. While several exemplary embodiments are described herein, modifications, adaptations, and other implementations are possible. For example, elements shown in the figures may be replaced, added, or modified, and the exemplary methods described herein may be modified by replacing, reordering, or adding steps to the disclosed methods.

[0083] Features can be selected from one or more of the foregoing embodiments to create alternative embodiments consisting of feature sub-combinations that may not be explicitly described above. Furthermore, features can be selected and combined from one or more of the foregoing embodiments to create alternative embodiments consisting of feature combinations that may not be explicitly described above. Those skilled in the art will readily recognize suitable features for such combinations and sub-combinations upon reading the full disclosure.

[0084] Furthermore, numerous specific details are set forth to provide a thorough understanding of the exemplary embodiments described herein. However, those skilled in the art will understand that the exemplary embodiments described herein can be practiced without these specific details. Moreover, well-known methods, processes, and elements have not been described in detail so as not to obscure the exemplary embodiments described herein. The subject matter described herein and the scope of the claims are intended to cover and include all appropriate variations in the technology.

[0085] Although the invention and its advantages have been described in detail, it should be understood that various changes, substitutions and alterations can be made without departing from the invention as defined in the appended claims.

[0086] This invention may be practiced in other specific forms without departing from the subject matter of the claims. The exemplary embodiments described are to be considered illustrative rather than restrictive in all respects. This disclosure is intended to cover and include all suitable variations in the technology. Therefore, the scope of this disclosure is described by the appended claims, not by the foregoing description. The scope of the claims should not be limited to the embodiments set forth in the examples, but should be given the broadest interpretation consistent with the overall description.

Claims

1. A method for adaptive total focusing (TFM) methods, comprising: Full matrix capture (FMC) data of the region of interest of the article, the region of interest being made of the material; For each of the units in a set of units in the article of manufacture, the FMC data is evaluated to obtain parameters associated with the material properties of the material; The parameters of each unit in the set of units are associated with the state model of the material to determine the anisotropic state of each unit in the set of units in the article; Using the anisotropic state of each unit in the set of units in the article, a distribution parameter model of the material anisotropy of the article is generated; and The beamformer is operated using the distributed parameter model via TFM to image the region of interest.

2. The method according to claim 1, wherein, The state model includes one or more Christofel matrices.

3. The method according to claim 2, wherein, The state model includes establishing a velocity range relative to direction for the material from its isotropic state to its fully anisotropic state.

4. The method according to claim 3, wherein, The speed range is established for both longitudinal and transverse waves.

5. The method according to claim 1, wherein, The generation includes associating parameters of adjacent units in the set of units.

6. The method according to claim 1, wherein, The correlation includes, for each of the plurality of cells, solving for the orientation and magnitude of the phase velocity and group velocity in the FMC data.

7. The method according to claim 6, wherein, The cell size in the plurality of cells is determined based on the gradient of the anisotropic properties in the adjacent cells of the plurality of cells.

8. The method according to claim 7, further comprising: One of the plurality of units is merged with an adjacent unit in the plurality of units that has the corresponding parameters.

9. A system for an adaptive total focusing method (TFM), comprising: At least one processor; as well as A memory storing computer-readable instructions that, when executed by the at least one processor, cause the system to perform the following operations: Full matrix capture (FMC) data of the region of interest of the article, the region of interest being made of the material; For each of the units in a set of units in the article of manufacture, the FMC data is evaluated to obtain parameters associated with the material properties of the material; The parameters of each unit in the set of units are associated with the state model of the material to determine the anisotropic state of each unit in the set of units in the article; Using the anisotropic state of each unit in the set of units in the article, a distribution parameter model of the material anisotropy of the article is generated; and The beamformer is operated using the distributed parameter model via TFM to image the region of interest.

10. The system according to claim 9, wherein, The state model includes one or more Christofel matrices.

11. The system according to claim 10, wherein, The state model includes establishing a velocity range relative to direction for the material from its isotropic state to its fully anisotropic state.

12. The system according to claim 11, wherein, The speed range is established for both longitudinal and transverse waves.

13. The system according to claim 9, wherein, When executed by the at least one processor, the computer-readable instructions cause the system to associate parameters of adjacent units in the set of units.

14. The system according to claim 9, wherein, When executed by the at least one processor, the computer-readable instructions cause the system to solve for the orientation and magnitude of the phase velocity and group velocity in the FMC data for each of the plurality of cells.

15. The system according to claim 14, wherein, The cell size in the plurality of cells is determined based on the gradient of the anisotropic properties in the adjacent cells of the plurality of cells.

16. The system according to claim 15, wherein, When executed by the at least one processor, the computer-readable instructions cause the system to merge one of the plurality of units with an adjacent unit of the plurality of units having corresponding parameters.

Images