Systems and methods for synthetic aperture ultrasound imaging of an object

Abstract

Techniques, systems, and devices for synthetic aperture ultrasound imaging using a beamformer incorporating an object model are disclosed. In some aspects, a system includes a transducer array to transmit and / or receive acoustic signals to an object with a synthetic aperture formed by the object forming system, an object beamformer unit to (i) coherently beamform the object according to a position, orientation, and / or geometry of the transducers relative to an object model, and (ii) produce a beamformed output signal comprising spatial information about the object from beamforming acoustic echoes, a data processing unit to process data and produce an image of the object based on a rendering of a position, orientation, geometry, and / or surface properties of the object relative to a coordinate system of the array determined by the data processing unit.

Description

[0001] Cross-references to related applications

[0002] This patent document claims priority and interest in U.S. Provisional Patent Application No. 63 / 113536, filed November 13, 2020, entitled “SYSTEMS AND METHODS FOR TOMOGRAPHIC SYNTHETIC APERTURE ULTRASOUND IMAGING OF AN OBJECT PRODUCINGSURFACE REFLECTIONS”. The entire contents of the aforementioned patent application are incorporated herein by reference as part of the disclosure of this patent document. Technical Field

[0003] This patent document relates to the formation of multidimensional acoustic images. Background Technology

[0004] Acoustic imaging is an imaging modality that uses the characteristics of sound waves propagating through a medium and the interaction between sound energy and the medium to obtain images of the medium or a region of the medium in various applications, including medical imaging. Summary of the Invention

[0005] Disclosed are apparatuses, systems, and methods for generating an image of an entire object using an aperture composed of multiple spatially separated transmitters, receivers, and / or transmitters and receivers (i.e., transceivers), compared to conventional imaging of point scatterers. This allows for the efficient generation of a synthetic aperture of the object by combining acoustic scattering from the object with optimized methods for object beamforming and object localization. In some embodiments, the disclosed systems, apparatuses, and methods provide sophisticated object feature detectors and locators capable of identifying specific points or regions on the object surface, enabling the measurement of the distances and angles of these specific points or regions on the object relative to each other and / or relative to a transducer array. In some embodiments, the disclosed systems, apparatuses, and methods provide the position and orientation of the entire object relative to a reference frame of the system.

[0006] In some embodiments, following standard practice in synthetic aperture imaging and tomography, the imaging aperture forms a tomographic aperture that partially or completely surrounds the object to be imaged, such that the object is acoustically penetrated from many directions and the returned echoes are received from many directions. However, the disclosed systems, apparatus, and methods form an image of the object using a combination of synthetic aperture tomography transmission and reception methods with an object beamformer and optimized methods for locating the object in three dimensions, rather than forming a 3D image of the object via tomography, for example, reconstructing the object from two or more 2D cross-sectional beamformed images of the object generated in a conventional manner.

[0007] In some embodiments, a tomographic synthetic aperture acoustic imaging system includes: (i) a transducer element array operable for transmitting, receiving, and / or transmitting and receiving acoustic signals to form a synthetic aperture using an object; (ii) an object beamforming unit for generating a beamformer that (a) coherently positions one or more regions of an object based on the geometry, position, and orientation of the transducer element array and an object model, and (b) generates one or more beamforming output signals as a function of all inputs; and (iii) a display unit for displaying the positioned object, for example, such that a representation of the object can be visualized and is useful in the array's reference frame or any external reference frame(s), and operable for updating and visualizing the relative motion between the object and the array. In some embodiments, for example, the tomographic synthetic aperture acoustic imaging system may be configured to position one or more regions on one or more object surfaces and provide means for measuring distances and angles between two or more regions.

[0008] In some aspects, a tomographic synthetic aperture acoustic imaging system includes: a transducer element array operable for transmitting, receiving, and / or transmitting and receiving acoustic signals to an object, which utilizes the object to form a synthetic aperture of the acoustic imaging system, wherein the acoustic signals include transmitted acoustic signals and received acoustic echoes returned from the object; an object beamforming unit including one or more processors and one or more memories, and configured to (i) beamform the object toward one or more regions of the object based on the position, orientation, and / or geometry of the transducer element array relative to an object model, the object model including information representing the object, and (ii) generate a digitally formatted... One or more beamforming output signals, the one or more beamforming output signals including spatial information about one or more regions of an object obtained from beamforming acoustic echoes; a data processing unit, including a processor and a memory, communicating with the object beamforming unit and the transducer element array, and configured to optimize one or more beamforming output signals to determine one or more of a set of position, orientation, geometry, or physical properties; and a display unit operable to generate an image of the object based on a reproduction of one or more of the set of position, orientation, geometry, or physical properties determined by the data processing unit relative to the transducer element array.

[0009] In some embodiments of the present technology (Example B36), a method for synthetic aperture acoustic imaging for tomography includes: transmitting acoustic signals to and receiving acoustic signals from an object by forming a synthetic aperture based on transmitting acoustic waveforms transduced to the object and receiving acoustic echoes returned from the object by transducer elements of an array of transducer elements; performing beamforming on the object using echo samples of returned acoustic echoes returned from one or more regions of the object to generate one or more beamforming output signals, wherein the one or more beamforming output signals are a function of one or more inputs for beamforming, wherein the one or more inputs for beamforming include information representing the object; optimizing the one or more beamforming output signals to determine one or more of a set of position, orientation, geometry, or physical properties of the object; and generating an image of the object based on the reproduction of one or more of the set of position, orientation, geometry, or physical properties of the object. Attached Figure Description

[0010] Figure 1A A simple example of a synthetic aperture is shown, which includes a transmitter element that also operates as a receiver and a separate receiver element.

[0011] Figure 1B The concept of virtual elements created using synthetic apertures is illustrated.

[0012] Figure 1C It shows the relationship with Figure 1A This contrasts with the concept of spatial sampling on the surface of an object, which is a function of the object's positional changes.

[0013] Figure 1D The concept of a synthetic aperture, comprising three elements acting as both a transmitter and a receiver, is shown for sampling six spatially separated scattering regions marked “A”-“F” on the surface of an object.

[0014] Figure 1E A diagram illustrating an example embodiment of a tomographic synthetic aperture ultrasound system according to the present technology is shown.

[0015] Figures 1F-1H A schematic diagram of an example embodiment of a transducer element array for an example tomographic synthetic aperture ultrasound system according to the present technology is shown.

[0016] Figure 1I A diagram illustrating an example embodiment of transducer array segmentation according to the present technology is shown.

[0017] Figure 2 A diagram illustrates an example embodiment of a method for beamforming an object surface using ultrasonic signals transmitted and received via a synthetic aperture according to the present technology.

[0018] Figure 3 A diagram depicts the geometry used in an example implementation of a numerical simulation of an example object beamformer technology according to this technique.

[0019] Figure 4 A graph depicting the received echoes from the simulation is shown, where the vertical axis represents time in microseconds and the horizontal axis represents the echo index.

[0020] Figure 5 A data graph depicting the output of an example object beamformer used for rotation and translation in an example implementation is shown.

[0021] Figure 6 The diagram depicts the range of rotation and translation amplitudes. Figure 5 The example beamformer shown is a graph of the normalized integral power of its output.

[0022] Figure 7 A diagram of another example embodiment of a tomographic synthetic aperture object imaging system according to the present technology is shown.

[0023] Figure 8A A diagram illustrating an example embodiment of a method for performing tomographic synthetic aperture imaging of an object by beamforming the entire object according to the present technology is shown.

[0024] Figure 8B The description is shown according to Figure 8A The method in Figure 7 The diagram shows a data and variable flowchart of an example embodiment of the front-end hardware and software modules operable on a tomographic synthetic aperture object imaging system.

[0025] Figure 8C The description is shown according to Figure 8A The method and continued Figure 8B In the embodiments Figure 7 Another data and variable flowchart of an exemplary embodiment of the back-end hardware and software modules operable on the tomographic synthetic aperture object imaging system shown.

[0026] Figure 8D It shows according to Figure 8A The method and continued Figure 8C In the embodiments Figure 7 Example embodiments of operable back-end hardware and software modules on a tomographic synthetic aperture object imaging system are shown below. Figure 8C Another diagram is shown.

[0027] Figure 9Figures depicting example full array geometries and 4×4 sparse array geometries used in example object beamformers according to this technology are shown.

[0028] Figure 10 The illustration shows a combination similar to Figure 9 A diagram of the sphere object model used in the example object beamformer discussed.

[0029] Figure 11 The illustration shows a combination similar to Figure 9 A diagram of the complex object model used in the example object beamformer discussed.

[0030] Figure 12A A data plot depicting the STA RF echo specific to the example spherical object is shown.

[0031] Figure 12B A data plot depicting the STA RF echo specific to an example complex object is shown.

[0032] Figure 13 A graph depicting the X, Y, and Z positions of an example sphere object estimated by SAOI and measured by the robot after 100 trials is shown.

[0033] Figure 14A A graph depicting the X, Y, and Z positions of an example complex object estimated by SAOI and measured by the robot after 100 trials is shown.

[0034] Figure 14B A graph depicting the SAOI-estimated and robot-measured EAX, EAY, and EAZ angles of an example complex object from 100 trials is shown. Detailed Implementation

[0035] The acoustic imaging technology disclosed in this patent document can be used in various imaging applications, including acoustic imaging for medical diagnostic purposes and for imaging-guided surgical systems, where acoustic imaging information is used to guide the surgical system to locate the target surgical area and perform surgery. The disclosed acoustic imaging technology can be used for high-frequency acoustic imaging, including viewing and imaging the internal structures and functions of animals and humans. High-frequency sound waves can be at various frequencies, such as between 1 and 20 MHz, or even higher, and are commonly referred to as ultrasound. Several factors, including insufficient spatial resolution, contrast resolution, and image signal-to-noise ratio, can lead to suboptimal image quality in ultrasound imaging, which may limit its use in many clinical indications or applications. The disclosed acoustic imaging technology can be implemented to improve ultrasound image quality using tomography and / or synthetic aperture imaging techniques.

[0036] When imaging various objects, image formation can be achieved by interrogating multiple voxels using certain means (e.g., acoustic, electromagnetic, etc.), thereby acquiring information from one or more measurements within each voxel. This information can be presented as a one-dimensional or higher-dimensional spatial distribution, producing an image in which the object can be visualized and detected intelligently. In this way, conventional medical ultrasound imaging systems, for example, utilize amplitude and phase information contained within voxels as a result of interrogating scatterers within voxels using multiple sound waves and processing multiple received echoes, so as to summarize information about the interrogated scatterers in the form of a 2D or 3D image of the object. The presented systems and methods are applied to imaging objects in a different way compared to conventional imaging techniques.

[0037] To image an object as a whole, spatial and temporal information about the object is constructed as a question that can be articulated in different ways. For example, from a set of queries, an imaging system can use knowledge about the object and the query method to determine whether the object exists in space and where it exists in space. In acoustic imaging, for example, the problem can be articulated as imaging an object in space using a synthetic aperture imaging method that utilizes: (i) a known transducer array geometry of the transmitter and receiver elements when the object is observed, (ii) a known model of the object to be imaged, (iii) waveforms transmitted and received by some combination of transmitters and receivers within the array, (iv) processing of the received echo waveforms to simultaneously detect the object and locate its position (e.g., Cartesian coordinates) and orientation (e.g., Euler angles) using a model of the object itself, and (v) displaying a representative model of the object in the form of an image.

[0038] In contemporary methods for acoustically imaging objects in space, the processing of the received echoes occurs in a beamformer that summarizes coherent information about voxels in space centered at a point, and the object is visualized relative to an array within an image composed of many voxels. One drawback of imaging objects in this way is that the object may not scatter sound waves with the high coherence required for visualization, because the coherent information of each voxel is located at a point in space, and this coherent information is typically not accompanied by the sum of coherent information scattered by the object.

[0039] The challenge lies in the fact that scattering from the surface or volume of an object may not occur in the observable direction. For example, in conventional real-beam imaging using a focused beam, the incident waveform arrives at the object (e.g., the object's surface) from a specific direction because it is guided and focused, and the scattered waveform returns to one or more specific directions based on specular scattering or other scattering from the object (e.g., the surface), but one or more receivers may not exist in one or more directions of the scattered waveform. In this way, imaging of the object relies solely on monostatic scattering or the direct scattering of components of the transmitted waveform back to the juxtaposed receivers. In synthetic transmit aperture operating modes, the source is unfocused or weakly focused; therefore, the scattered waveform has a much higher chance of reaching the receivers because the scattered waveform is incident on many points on the object from a series of incident directions (e.g., created by a diverging wavefront), thus creating scattered waveforms from many points in many directions. Thus, imaging of the object relies on both monostatic and bistatic scattering, where bistatic scattering is the scattering of the transmitted waveform toward one or more receivers not located in the same place as the transmitter. Another challenge is that objects (e.g., surfaces) may exhibit complex scattering properties, such as roughness, texture, porosity, attenuation, absorption, layers, inhomogeneities, heterogeneity, etc., which cause the scattered waveform to disperse and / or phase shift. Therefore, a new method is needed to generate high-resolution surface images regardless of the surface's location, orientation, or scattering properties.

[0040] In the described method of imaging an object (using monostatic and bistatic scattering from spatially separated and unfocused sources and receivers), i.e., the disclosed method can be considered a form of reflection tomography (also known as reflection imaging, reflection tomography, synthetic focusing, and diffraction tomography). In conventional reflection tomography, the area to be imaged is typically surrounded by a circular aperture, with the transmitter and receiver pointing inward (i.e., roughly towards the center of the circle). For an object within the aperture, a cross-section of the object is imaged. In the case of a spatially limited set of transmitters and receivers, smaller apertures tend to be scanned mechanically (e.g., along a circle) to synthetically create a much larger aperture. In the case of a physically large aperture (e.g., a circular aperture) but with a limited number of electrical transmitters and receivers, sub-apertures within the larger aperture can be operated using the same principle, except that electronic scanning is used instead of mechanical scanning, for example, by multiplexing a limited number of transmitters and receivers to operate multiple sub-apertures spanning the entire aperture. The received echoes generated from tomographic scans can be processed in various ways, but two main traditional approaches are (i) delaying and summing beamforming of points confined to a plane, and (ii) (e.g., using methods such as filtered back projection or inverse Radon transform) solving the inverse problem of determining the structure and / or shape of an object based on the received echoes. A significant challenge in image formation using this approach is due to diffraction in the elevation direction of the aperture (i.e., perpendicular to the imaging plane). Lobe energy returned from the scatterer outside the imaging plane can produce artifacts that degrade image quality within the image plane. For in-plane image formation, scattered energy from outside the plane is undesirable; however, understanding the object that generates the scattered energy helps to suppress it. Furthermore, understanding the object helps in imaging it when considering the superposition (e.g., summation) of all coherent energies reflected and scattered from it. New techniques are needed for tomographic imaging of objects, particularly for acoustic imaging.

[0041] Systems, apparatuses, and methods for reconstructing objects using synthetic aperture tomography (SAR) based on object formation are disclosed, wherein coherent beamforming and processing are performed on the received echoes to detect and locate the object relative to the imaging aperture based on matching a known or parameterized model of the object with the received echoes. In some embodiments, the disclosed systems, apparatuses, and methods for synthetic aperture imaging of objects include techniques for beamforming echoes acquired in synthetic aperture transmit and receive patterns in a manner employing: (i) bistatic scattering (e.g., in contrast to typical monostatic scattering) or a combination of bistatic and monostatic scattering based on a physical object model (object model), which may be a pre-existing model stored in system memory or a model generated and / or updated in real time (e.g., a parameterized object model); and (ii) a model of an array (array model), which may be a pre-existing model stored in system memory or a model generated and / or updated in real time (e.g., a parameterized array model).

[0042] In this document, a physical object model (object model) refers to any possible mathematical and / or numerical definition of scattering from the surface (surface scattering) or volume (volume scattering) of any three-dimensional object in Euclidean space. While the disclosed embodiments and example implementations described herein primarily relate to surface scattering from object models, it should be understood that this is for ease of understanding of the basic concepts of the art, and the disclosed embodiments of the art may also include volume scattering or other scattering (e.g., internal reflection, resonance) using object models. An object can be defined as a whole object, i.e., a single object, for example, it can be defined without loss of generality as a sphere. An object can be defined as a partial whole object, i.e., a portion of an object, for example, it can be defined without loss of generality as a hemisphere of a sphere. An object can also be defined as multiple sub-regions of a whole object or a partial object, i.e., an object consists of multiple scattering regions, for example, it can be defined without loss of generality as a cube composed of six faces. An object model consisting of multiple scattering regions can approximate the mathematical definition of an object model, or it can approximate the measured values ​​of an object.

[0043] A scattering region can be a surface that is not necessarily flat (i.e., its curvature is not necessarily zero). For example, a scattering surface can be infinite, for instance, it can be an infinite plane without loss of generality. A scattering surface can be arbitrarily constrained in space, for instance, it can be constrained to be a triangle without loss of generality. For ease of computation, a scattering surface can be generalized, for instance, it can be generalized to the point where a triangle can be represented as a point, a unit normal vector, and an area. In this way, an object model consisting of multiple scattering surfaces can be generalized to a list of points, unit normal vectors, and areas.

[0044] In some example implementations, the object model can be defined parametrically; for example, it can be defined without loss of generality as a spherical object parametrically defined based on its radius and 3D position coordinates. In this way, the object model can be variable, but constrained to a specific type of shape; for example, it can be constrained to a sphere without loss of generality.

[0045] In this document, the aperture model (aperture model, array model) refers to any possible mathematical and / or numerical definition that precisely, approximately, parametrically, and / or functionally represents a physical aperture operable for transmitting and receiving sound waves. In some embodiments, for example, the aperture may consist of multiple discrete transducer elements geometrically arranged within a larger array (e.g., a transducer array). In some embodiments, for example, the elements themselves may be parametrically defined (e.g., square elements with variable areas), and / or the array geometry may be parametrically defined (e.g., a circular array with variable radii).

[0046] For example, compared to conventional imaging of points in space (e.g., beamforming), the disclosed systems, apparatuses, and methods use a model of a physical object to beamform synthetic aperture echoes received from that object to generate an image of the object. In some examples of the disclosed synthetic aperture object beamforming techniques, the synthetic aperture system processes bistatic scattering or a combination of bistatic and monostatic scattering from one or more regions (e.g., a surface) of the object, along with information about the system, to create an effective aperture for imaging the object. For example, novel synthetic apertures for imaging the object are generated by using knowledge of the object's geometry and the parameters of the system's transducer elements.

[0047] For example, to conceptually illustrate the disclosed technique for creating an array of effective apertures for an acoustic imaging system using an object to be imaged, consider what an optically reflecting object looks like from the perspective of a finite aperture (e.g., similar to the aperture of a camera). From the viewpoint of the aperture, the object reflects a laterally inverted image of the aperture (e.g., a mirror reflection from a camera). For example, the reflected image can be a distorted copy of the aperture (e.g., similar to a funhouse mirror), or it can be an accurate copy of the aperture (e.g., as in a plane mirror). In either case, the aperture is now inextricably linked to the object because the reflection observed by the aperture is a function of the object, the aperture, and their relative orientation. A practical example is the classic blind spot side mirror problem when driving a car. If the driver cannot see the other driver's eye in their own side mirror, then the other driver is in the driver's blind spot. If we imagine it as the two eyeballs of another driver (i.e., the imaging apertures of two cameras) together with their mirrors (i.e., the objects) and the two eyeballs of the main driver (i.e., the second imaging apertures of two different cameras) forming a synthetic aperture, then in this example, the mirrors are inseparably linked to each imaging aperture through the propagation of light, and the position, orientation, shape, size, curvature, surface properties, etc., of the mirrors affect the response (i.e., the image) seen by either aperture. Continuing this conceptual example, the driver / side-view mirror / driver problem is an example of a bistationary synthetic aperture, where mirrors produce a larger effective aperture by becoming part of the aperture; that is, the physical apertures of the two pairs of eyeballs together with the objects (i.e., the side-view mirrors) become the synthetic aperture.

[0048] The disclosed synthetic aperture object beamforming technique differs from certain implementations of real aperture imaging or other modal synthetic aperture imaging based on imaging multiple points in space, where the knowledge of point localization, apart from location information, does not include information about the object or prior information about the object (which may affect monostatic or bistatic scattering from the point). The systems, apparatus, and methods disclosed in this patent document are designed to achieve synthetic apertures using knowledge of the object and aperture in image formation, which can be substantially larger and contain more elements than the physical extent and physical number of elements in the aperture.

[0049] In some embodiments, the disclosed imaging techniques use an object model as part of a beamformer to beamform the object. For example, instead of beamforming points to form an image on a Cartesian grid or other spatial sampling, the spatial information of the image is then matched with the object model to use a registration algorithm to detect and spatially register objects within the image. Registration algorithms include, but are not limited to, correlation-based alignment, point cloud registration, and machine learning algorithms. Importantly, the disclosed techniques can directly detect and register objects, whereas previous techniques used registration algorithms to indirectly detect and register objects within images.

[0050] In the disclosed image formation method for objects in space, beamforming is performed on the object itself. That is, when the echo received from the object achieves coherence with the object model within the beamformer, and when the object is simultaneously positioned in both location and orientation, the representation of the object in its location and orientation relative to the transducer element array can be displayed as a visualized image of the object for beamforming. The object model includes any information representing the physical object being penetrated by sound, such as, but not limited to, physical properties (e.g., density, bulk modulus, acoustic impedance, etc.), point surfaces (e.g., topology, geometry), and / or volumetric features (e.g., material distribution, inner layers, hollowness, etc.). This method addresses a problem associated with a class of problems called inverse problems, such as calculating the causal factors that produce them from a set of observations. For example, calculating the object (i.e., the causal factor) that reflects a waveform from a set of waveforms measured (i.e., observed) from a transmitted waveform. However, this problem is not precisely an inverse problem because the object model is a priori, i.e., the causal factor is known; however, in an inverse problem, the solution is to determine the object that leads to the set of observations. More precisely, the solution to the proposed problem is a fitting optimization problem in a high-dimensional space of time-domain echoes constrained by the array model, the object model, the spatial transformations associated with the array model and the object model, and the propagation and diffraction of sound waves.

[0051] The proposed problem of object imaging and localization can also be viewed in the context of spatial filtering. The function of a beamformer is to spatially filter the echo in order to locate information corresponding to a specific location or point in space relative to the transducer array. Instead of spatial filtering used to locate information at a point in space (i.e., a point spatial filter), spatial filtering can be applied to locate information about objects in space (e.g., an object spatial filter). Here, the spatial filter is a function of the object model, its position, its orientation, the array model, the position of the array model, the orientation of the array model, and / or transmission and / or scattering parameters derived from (i) the object model, (ii) diffraction physics, (iii) scattering physics, (iv) acoustic wave propagation physics, and / or (v) electromechanical transduction physics. In this disclosure, to distinguish the object spatial filter from the point spatial filter, the object spatial filter is hereinafter referred to as an object beamformer.

[0052] An object space filter (object beamformer) can also be viewed as a matched filter, whereby the output of the object beamformer is maximized and the physical object is matched and detected when the experimental relative positions and orientations of the transducer element array model (i.e., array model) and the physical object model (i.e., object model) contained in the object beamformer are matched with the actual relative positions and orientations of the physical array and the physical object.

[0053] In some embodiments, the acoustic imaging system is configured to beamform echoes from an object and generate an image of it, wherein the acoustic imaging system includes a transducer array, transmitter and receiver circuitry, a data processing device including an object beamformer module incorporating an object model and a transducer array model, a processing module for finding an optimal solution to an object fitting problem, and a control module for ordering synthetic aperture transmission and reception. In some embodiments, the acoustic imaging system may optionally include a display device for visualizing the image of the object relative to a known or reference coordinate system (e.g., the coordinate system of the array model).

[0054] In some embodiments, a method of beamforming an echo from a physical object using an acoustic imaging system to generate an image of the object involves manipulating an array model to optimize the position and orientation applied to transmitter and receiver coordinates using a homogeneous transformation matrix or any equivalent series of spatial transformations, wherein manipulations (e.g., rotation and / or translation) applied in the object model reference frame utilize the array model and the object model to optimize the output of an object beamformer. A similar functionally equivalent embodiment involves manipulating an object model (rather than an array model) to optimize the position and orientation applied to object model coordinates using a homogeneous transformation matrix or any equivalent series of spatial transformations, wherein manipulations (e.g., rotation and / or translation) applied in the array model reference frame utilize the array model and the object model to optimize the output of an object beamformer. For example, a common set of parameters includes the relative position and orientation of the array model and the object model, which can be viewed from either the array model reference frame or the object model reference frame.

[0055] Figures 1A-1D Figures illustrating an example implementation of tomographic synthetic aperture imaging and object beamforming techniques according to the present invention are shown.

[0056] Figure 1A The concept of an example synthetic aperture is illustrated. This example synthetic aperture can be implemented by the disclosed system and method for two spatially separated transducer elements in one dimension, and without loss of generality for more than one dimension. In the example, element 1 is both a transmitter and a receiver, while element 2 operates solely as a receiver. It is assumed that both elements are point sources / receivers, such that they emit spherical wavefronts; therefore, the ray tracing indicated by the arrows is sufficient to illustrate a representative scattering path. For the illustrated one-dimensional object surface, the transmission on element 1 results in an echo received from scattering region A on element 1 and an echo received from scattering region B on element 2. The echo received on element 1 is the result of monostatic scattering, while the echo received on element 2 is the result of bistatic scattering. The aperture produced by the reception on element 2 is shown to be much larger than the aperture produced by the transmission and reception on element 1 alone. The synthetic aperture is produced in this way. More transmitter and / or receiver elements can be added to further extend the synthetic aperture, e.g., to enclose or partially enclose the object to be imaged. Here, for example, the advantage of synthetic aperture is that a relatively large aperture can be formed by a relatively small number of transducer elements.

[0057] Figure 1B The process of adding the creation of a virtual component between component 1 and component 2 demonstrates the use of... Figure 1AThe synthetic aperture sensing operation shown uses the same hardware, with the virtual element performing monostatic sampling of a scattering region B with perpendicular incidence, compared to bistatic sampling generated by elements 1 and 2. Assuming scattering is independent of the incident angle, for example, the information from the scattering region B will be largely the same for both monostatic and bistatic sampling. In this way, the synthetic aperture, consisting of the transmission on element 1 and the reception on elements 1 and 2, is practically the same size, as if the transmission and reception were performed on element 1 and subsequently on the virtual elements at positions 1-2. In other words, the aperture generated by the reception on element 2 is physically expanded by a fraction of the distance from the transmitting element to the receiving element, in this case exactly half.

[0058] Figure 1C It shows the relationship with Figure 1B The geometry shown is simply rotated 10 degrees relative to the object surface to illustrate the positional changes of scattering regions A and B on the object surface and the resulting changes in the position of the virtual element. The position of the virtual element is no longer... Figure 1B The distance between elements 1 and 2 is half that shown. In this way, the effective synthetic aperture generated by transmitting on element 1 and receiving on elements 1 and 2 is inseparably linked to the object's shape, position, and orientation, and the resulting synthetic aperture is considered to be formed using the object. While some definitions of synthetic aperture are generally considered to be target-independent, for example, a synthetic aperture combined with a beamformer according to this technique is operable for locating coherent energy in space that may or may not coincide with the object's surface. According to the disclosed technique, a synthetic aperture combined with a beamformer is operable for detecting and locating objects based on coherent energy scattered from the object's surface.

[0059] Figure 1D An example of a fully synthetic transmit aperture operable for both transmitting and receiving from three spatially separated transceiver elements is shown. For example, in a fully synthetic transmit aperture, a transmission event occurs on transceiver element 1, followed by reception from all three transceiver elements; then, a transmission on transceiver element 2 is followed by reception from all three transceiver elements; and finally, a transmission on transceiver element 3 is followed by reception from all three transceiver elements. In this way, three monostatic scattering regions labeled A, C, and F on the object surface are sampled, and three bistatic scattering regions labeled B, D, and E on the object surface are sampled. Similarly, the number of object surface-specific samples can be increased by increasing the number of transmitters and / or receivers.

[0060] In addition, such as Figure 1DAs illustrated in the example, regions A through F consist of an infinite number of infinitesimal scattering points, each contributing a component to the echo received by each of the three transceiver elements for each transmission via acoustic diffraction. In this way, when all such paths are considered, there is a sharp multiplication of scattering paths, as shown for five locations including scattering region A. For each scattering path shown in region A, there is a graded decrease in grayscale of the path as the incident angle deviates from 0 degrees, indicating a decrease in echo intensity. Therefore, the disclosed method for imaging an object considers monostatic and / or bistatic scattering generated by a synthetic aperture comprising separate transmitters, receivers, and / or transmitters and receivers combined with scattering paths described according to the geometry of the object model. In this example, given five samples of the object surface for each scattering region, the number of scattering paths increases from 6 to 30 for this simple one-dimensional object surface. An equivalent two-dimensional object surface can be represented using 25 samples per scattering region, resulting in 150 scattering paths for a synthetic aperture composed of three spatially separated physical elements. Thus, the disclosed synthetic aperture technique is integrated with object beamforming technology, as discussed and illustrated in the example embodiments below.

[0061] In the examples disclosed in this patent document, the terms “synthetic aperture,” “synthetic aperture array,” and “synthetic aperture technology” refer to (i) a physical aperture and / or physical array operable according to synthetic aperture technology (e.g., including but not limited to synthetic transmit aperture imaging, non-focused transmit, non-focused receive), and / or (ii) any virtual aperture and / or virtual array that can be derived from a physical aperture and / or physical array and generated according to synthetic aperture technology, and / or (iii) any virtual aperture and / or virtual array generated using an object according to synthetic aperture technology applied to a physical aperture and / or physical array, and (iv) any spatial sampling on an object generated by physical and / or virtual apertures and arrays according to synthetic aperture technology.

[0062] In some embodiments, the object beamformer may be embodied as a hardware and / or software module of a computing device, including instructions executable by a computer processor and memory to calculate echo delay and weights based on array model coordinates and object model according to surface scattering, wherein some combination of linear and nonlinear operations applied to the delayed and weighted echoes produces one or more beamformed echoes as output to the object beamformer.

[0063] The output of the target beamformer can be processed by a computing device to integrate the power of the beamformer echo(s) within a time window; the duration of this time window is proportional to the duration of the bidirectional impulse response of the acoustic imaging system. A data processing unit (e.g., including a computer processor and memory), which may be embodied in the computing device, can be configured to determine the position and orientation of the array coordinates utilized within the target beamformer by using optimization algorithms (e.g., stored in the memory of the data processing unit) to modify parameters to maximize the beamforming echo power.

[0064] Optimized array position and orientation can be used to generate an object display within the array's reference frame, allowing the object to be visualized (e.g., on a display unit communicating with a computing device or on a display component of the computing device's user interface). Additionally, the object's position and orientation, along with an object model, can be displayed or output for use. For example, in some embodiments of the disclosed systems and methods for synthetic aperture beamforming object imaging (also known as synthetic aperture object imaging (SAOI)), position and / or orientation data associated with specific points or regions on the object surface can be accurately identified, for example, relative to a reference frame of a transducer array that can operate as a synthetic or real aperture. The identified position and / or orientation of one or more points or regions on the object surface can be implemented using periodic position and / or orientation updates within the reference frame.

[0065] In various embodiments, the disclosed systems and methods are primarily intended for imaging objects with significant impedance differences, thereby achieving specular reflection from the object's surface. For example, the systems and methods can be applied to imaging any type of tissue and / or implantable objects with reflective and / or scattering characteristics that facilitate the detection and differentiation of surrounding hard or soft matter. For instance, arrays and objects can be co-designed such that the array fully utilizes the specular reflective surface of the particular object, designed to scatter in specific directions within the object's frame of reference.

[0066] In addition to specular reflection (also known as non-scattering reflection and specular scattering), there is diffuse reflection (also known as diffuse reflection (e.g., Lambertian reflection)) and non-spectral scattering. Different degrees of diffuse and non-scattering acoustic reflection are possible for an object, depending primarily on (a) the size of the object relative to the wavelength, and (b) the size of the object's surface features relative to the wavelength. Objects much smaller than the wavelength exhibit Rayleigh scattering, where scattered waves from such a set of objects arrive at a point in space with a random set of phases. Objects on the order of wavelength produce Mie scattering, which results in interference effects in the scattered waves due to phase variations on the object's surface. The disclosed techniques are primarily applied to objects equal to or greater than the wavelength and to objects having surface features equal to or greater than the wavelength; however, without loss of generality, the disclosed systems, apparatus, and methods can be applied to objects that produce both non-scattering and diffuse acoustic reflections. The general terms for the two types of scattered waves, specular scattering and diffuse scattering, are hereafter interchangeably referred to as reflection and scattering, unless one or the other component is specifically mentioned, such as specular reflection.

[0067] Figure 1E An example embodiment of a synthetic aperture ultrasonic system 150 according to the present technology is shown. System 150 includes multiple acoustic transducer devices 151 (e.g., in...). Figure 1E The synthetic aperture array (shown in Figure 157 as the positions of eight points arranged in three-dimensional space relative to object 161) comprises acoustic transducer devices 151, which can be configured as transmitting transducers, receiving transducers, and / or transmit-receive transducers or acoustic transceivers (configured to transmit and receive acoustic signals). Each of the plurality of acoustic transducer devices 151 may include an array matrix of different transmitting or receiving acoustic transducer elements, and the collection of acoustic transducer devices 151 may be implemented as a transmitter-receiver unit as a synthetic aperture array for improving imaging of object 161. System 150 includes a transmitter-receiver unit 152 that interfaces with the synthetic aperture array, wherein the transmitter-receiver unit 152 is configured to generate and drive transmitted waveforms on specific array elements, and to receive echoes on specific elements and convert signals to the digital domain. Acoustic transducer devices 151 are positioned in space at a known location relative to the target object and at certain relative positions to each other within the synthetic aperture array to direct sound waves to the target object and receive acoustic signals from the target object for imaging. Depending on the application, in some imaging system designs, acoustic transducer devices 151 may be in a fixed position, while in others they may be placed on one or more actuator arms or frames to allow their position and relative position within the synthetic aperture array to be adjusted. A variety of motion actuators and rotation devices can be used.

[0068] Figures 1F-1H A diagram illustrating an example embodiment of an array of acoustic transducer devices 151 for imaging a sample synthetic aperture ultrasound system 150 for a target object 161 is shown. The array of acoustic transducer devices 151 may include transducer array segments. In these examples, each acoustic transducer device 151 comprises a two-dimensional array of acoustic transmitting or receiving or transmit-receiving acoustic transducer elements. The object 161 may be any object having at least three determinable degrees of freedom.

[0069] Figure 1F An example embodiment of an acoustic transducer device array 151F is shown, comprising eight rigidly connected transducer array segments 151F1, 151F2, ..., 151F8 arranged along eight points around a cube surrounding an object 161, wherein each transducer array segment points toward the object 161. The acoustic transducer device array 151F includes connection structures 192F1, 192F2, ..., 192F8 that electrically connect the transducer array segments 151F1, 151F2, ..., 151F8 to a transmitter-receiver unit 152, respectively. In some embodiments, for example, the connection structures 192F1, 192F2, ..., 192F8 include a rigid frame, housing, or enclosure such that the respective transducer array segments 151F1, 151F2, ..., 151F8 do not move relative to 192F1, 192F2, ..., 192F8. The acoustic transducer array 151F may optionally include a rigid structure 191F, which rigidly couples at least some of the transducer array segments 151F1, 151F2, ..., 151F8. In some embodiments, for example, the acoustic transducer array 151F may include a positioning system (e.g., an electric stepper motor, etc.) that can move some or all of the three-dimensional positions of the transducer array segments 151F1, 151F2, ..., 151F8 relative to another transducer array segment and / or relative to the object 161. In an example embodiment, for example, depending on the object and its orientation relative to the acoustic transducer array 151F and the repositioning of the transducer array segments 151F1, 151F2, ..., 151F8, the acoustic transducer array 151F can be used by the system 150 to determine the six degrees of freedom of the ellipsoid (e.g., as shown in the figure). Figure 1F (As shown in the figure). During this implementation of system 150, the number of individual transducer elements selected for transmission and / or reception, the geometry, orientation, etc. of the transducer array segments 151F1, 151F2, ..., 151F8, are known to system 150 relative to a reference frame. In some embodiments of the example transducer device array 151F, for example, functionally, a single array element of each segment in segments 151F1, 151F2, ..., 151F3 may be represented by a point source and a receiver.

[0070] Figure 1G An example embodiment of an acoustic transducer device array 151G is shown, comprising three rigidly connected transducer array segments 151G1, 151G2, and 151G3 arranged along three points surrounding an object 161, wherein each transducer array segment points toward the object 161, such that the three rigidly connected transducer array segments 151G1, 151G2, and 151G3 face the object 161 in three different directions. In the example embodiment, for example, depending on the object and its orientation relative to the acoustic transducer device array 151G, and the repositioning of the transducer array segments 151G1, 151G2, and 151G3, the acoustic transducer device array 151G can be used by the system 150 to determine spherical objects (e.g., such as...). Figure 1G At least three degrees of freedom for objects (as shown in the figure) and up to six degrees of freedom for other types of objects (e.g., cubes, ellipsoids, etc.).

[0071] Figure 1H An exemplary embodiment of an acoustic transducer device array 151H is shown, comprising three rigidly connected transducer array segments 151H1, 151H2, and 151H3 arranged along three vertical planes xy, xz, and yz surrounding an object 161 (e.g., a cube object in this example), wherein each transducer array segment points toward the object 161. In the example embodiment, the acoustic transducer device array 151H can be used by system 150 to determine, for example, six degrees of freedom (e.g., of a cube having array segments pointing toward three orthogonal planes) of the object and its orientation relative to the acoustic transducer device array 151H, as well as the repositioning of the transducer array segments 151H1, 151H2, and 151H3, depending on the object and the repositioning of the transducer array segments 151H1, 151H2, and 151H3.

[0072] In some embodiments, for example, in Figure 1F , Figure 1G and Figure 1H In the example, one or more rigid rods or frames used to connect and hold the different acoustic transducer devices 151 can be replaced by one or more adjustable rods or frames, or a combination of rigid rods or frames and actuators, to allow their positions and relative positions to be adjusted to accommodate different target objects of different shapes. In this design, once the position and orientation of the acoustic transducer device 151 are adjusted to the desired position and fixed, the spatial position and certain relative positions of the acoustic transducer device 151 with respect to the target object are known.

[0073] In example implementations, example transducer device arrays 151F, 151G, and 151H include various array segments containing combinations of various transducer elements spanning one or more subarrays, which can be used to generate various acoustic waveforms to be transmitted at object 161, such as coded acoustic waveforms for generating one or more composite acoustic waveforms (e.g., ultrasonic beams).

[0074] Figure 1I A diagram illustrating a non-limiting example of transducer array segments of example transducer device arrays 151F, 151G, and 151H is shown, illustrated as transducer segment array 151I. In this example, transducer segment array 151I includes 64 individual transducer elements arranged in four transducer segments (e.g., transducer segments 1, 2, 3, and 4). Although the diagram shows four transducer segments spatially close together, it should be understood that the four transducer segments can be arranged spatially separately, for example, including separate, rigidly connected transducer segments, such as... Figure 1F , Figure 1G and Figure 1H As shown. In Figure 1I In this example, one or more subarrays, comprising any one of 64 individual transducer elements (e.g., transducer elements from one or more of four transducer segments), can transmit (e.g., sequentially, simultaneously, or randomly) individual acoustic waveforms (e.g., individual coded acoustic waveforms). A subarray can comprise a combination of individual transducer elements from one or more transducer segments. For example, subarray 1 includes transducer elements 2, 3, 6, 7, 10, 11, 14, and 15 of transducer segment 1 and transducer elements 2 and 3 of transducer segment 3; subarray 2 includes transducer elements 1, 2, 3, 5, 6, 7, 9, 10, and 11 of transducer segment 2; subarray 3 includes transducer elements 9, 10, 11, 12, 13, 14, 15, and 16 of transducer segment 3 and transducer elements 9 and 13 of transducer segment 4; and subarray 4 includes transducer elements 5, 6, 7, 9, 10, 11, 13, 14, and 15 of transducer segment 4. Switching elements (e.g., multiplexer units) can be used to generate the subarray configuration, and in some embodiments, switching elements may be included in the transmitter-receiver unit 152.

[0075] Refer back Figure 1ESystem 150 includes an object beamformer unit 154 implemented on a computing device, which includes one or more processors communicating with one or more memory devices (e.g., random-access memory, RAM). Outputs of transmitter-receiver unit(s) 152 are transmitted to object beamformer unit(s), which is configured to determine echo delay and weight calculations using an object model to be imaged (shown as object model 155) in conjunction with information about the array geometry prior to coherent combining within an object beamformer contained within object beamformer unit(s).

[0076] System 150 includes a data processing and control unit 156 (e.g., sometimes referred to as a data processing unit), which includes a processor and memory communicating with an object beamformer unit and a transmitter-receiver unit. In some embodiments, the object beamformer unit 154 is configured on the same computing device as the data processing and control unit 156; while in other embodiments, the object beamformer unit 154 is configured on a separate computing device communicating with the computing device including the data processing and control unit 156. The data processing and control unit 156 is configured to adjust parameters describing the position and orientation of the array geometry relative to an object model (as shown in array model 153) such that the object beamformer output is maximized (described in detail below). The data processing and control unit 156 is configured to control the transmitter-receiver unit 152 for sequencing the transmission and reception of acoustic signals.

[0077] For example, in some embodiments of the disclosed systems and methods, object detection occurs when the data processing and control unit 156 determines a match between the receiver synthetic aperture echo set formed in the object beamformer unit 154, the object model, the array model, and / or the position and orientation of the array relative to the model. In some embodiments, for example, a decision on whether a match has been achieved is determined by comparing one or more inputs and / or outputs (e.g., variables) or variations of one or more inputs and / or outputs (e.g., different variations between iterations) of an optimization process seeking a match with one or more thresholds. The values ​​of one or more thresholds can be determined based on empirical data, simulation data, Monte Carlo methods, perturbation theory, and / or various statistical or detection theory methods used to determine the thresholds (e.g., including but not limited to statistical hypothesis testing, t-tests, receiver operating characteristics of binary classifiers, Bayesian hypothesis testing, etc.). One or more thresholds and conditions required for one or more variable inputs and / or outputs of the optimization process (e.g., inputs or outputs being greater than or less than a threshold) are sometimes referred to herein as detection criteria. For example, for numerical optimization, such criteria may be referred to as tolerance criteria (e.g., thresholds) or stopping criteria (e.g., the optimization process stops when a threshold is exceeded). In this paper, detection criteria and stopping criteria refer to the same standards used to detect an object based on conditions applied to the inputs and / or outputs of the optimization process. In implementations of the disclosed methods and systems involving numerical optimization, for example, the core of the optimization process is an algorithm responsible for searching for solutions that maximize or minimize the objective function. The optimization algorithms mentioned herein are also referred to as solvers or optimizers. Furthermore, the specific algorithm responsible for finding solutions can be called a solver or optimizer. Many types of optimizers exist, and they are broadly classified as linear or nonlinear optimizers. In some embodiments according to this technology, a nonlinear optimizer is used to solve for a set of parameters that is variable until a match is found, i.e., one or more detection criteria are satisfied. Further details regarding the optimization process will be discussed below.

[0078] In some embodiments, system 150 may include a user interface (UI) and a display unit 158 ​​to allow a user to input into system 150 and to present an output 159 (e.g., an image of a physical object) based on parameters describing the object, including but not limited to the position and / or orientation of the object generated by a data processing and control unit 156 that operates in conjunction with object beamforming unit 154 and transmitter-receiver unit 152.

[0079] In some embodiments of system 150, for example, transmitter-receiver unit 152 is configured to operate an array of locations of multiple transducer elements around an object and is operable to generate sparse apertures, i.e., aperture regions of one or more instances having large voids and large physical spacing between elements, wherein the spacing is typically greater than one wavelength.

[0080] exist Figure 1E In the example, there are eight transducer element positions that produce a sparse aperture. Precise geometric placement of the eight transducer elements, shown as a cube, is not required; however, it is required that multiple transducer elements completely or at least partially surround the object and that the elements are largely pointed towards the object, for example, the surface normal vectors of the arrayed elements are parallel to at least one surface normal direction on the object. In this way, the aperture can effectively function as a much larger aperture, but with very few elements (e.g., a sparse aperture) compared to an aperture that would completely surround the object.

[0081] exist Figure 1E In the example of array model 153 shown, for example, monostatic scattering, i.e., reflection directly back to the transmitting element, is expected when the surface normals of the array elements (shown as arrows emanating from each element location) are parallel to at least one surface normal. Bistatic scattering occurs when a transmission from one element is reflected in the direction of a second element. The point or region on the object from which the monostatic or bistatic reflection originates generates reflection sampling locations on the object surface. The total number of elements is important because, depending on the shape of the object, there must be at least three to six object-specific monostatic or bistatic reflection samples with significant intervals to determine the object's determinable position and / or determinable orientation in space. Bistatic and monostatic samples can overlap, thus requiring significant intervals between elements or a large number of monostatic and bistatic samples, such that despite redundancy in the sampling, and depending on the object's shape, at least three to six object-specific samples are actually generated. For example, a spherical object has a determinable position but an indeterminate orientation, thus requiring at least three object-specific samples to determine the object's position. Other types of objects possess both determinable and indeterminate degrees of freedom, including but not limited to, examples such as perfect cylinders, infinite cylinders, cones, and ellipsoids. As a general requirement for any object with a determinable position and orientation, for example, at least six specific samples of the object are needed to determine its position and orientation. Exceptions include, but are not limited to, examples such as cubes, right-angled prisms, tetrahedrons, and regular pyramids, which have fewer than six determinable positions and orientations.

[0082] exist Figure 1EIn the example arrangement shown in the exemplary embodiment of system 150, there are at least eight single-station samples (i.e., a transmission at element index i and a reception at the same element index i). The number of bistation samples (i.e., a transmission at element index i and a reception at element index j) such that j is not equal to i depends on the position and orientation of the object relative to the array and the shape of the object, such that a transmission at element index i will generate a reflection to element index j. In other words, for an 8-element array, there are 28 bistation samples of the object, but some of these bistation samples are irrelevant because they may not reflect.

[0083] like Figure 1E As shown, transducer elements are connected to the transmitter and / or receiver devices of transmitter-receiver unit 152, which includes at least one transmitter channel and at least one receiver channel multiplexed across all eight elements. In some embodiments, for example, transmitter-receiver unit 152 includes eight transmitter channels and eight receiver channels, eliminating the need for multiplexing. In some implementations, for example, transmitter-receiver unit 152 uses an analog or digital amplifier to drive the transducer to produce an unfocused acoustic waveform with a fractional bandwidth of at least 1%, but preferably greater than 50%. This waveform may have a low or high time-bandwidth product, such as a Gaussian-shaped waveform with a low time-bandwidth product or an coded waveform, such as a linear frequency-modulated chirped waveform with a high time-bandwidth product. Here, depending on the nearest distance from the element to the target, a long-duration waveform may fold over itself, i.e., it may still be transmitting when the first echo is received from the beginning of the waveform. Here, the duration must be reduced so that transmission does not overlap with reception; that is, the physical length of the waveform in the medium is less than the shortest round-trip distance from the array element to the object and back to the receiver element.

[0084] In various embodiments of system 150, for example, the transducer element of transmitter-receiver unit 152 may be made of lead zirconate titanate (PZT), polyvinylidene fluoride (PVDF), capacitive micromachined ultrasonic transducer (CMUT), piezoelectric micromachined ultrasonic transducer (PMUT), or any other piezoelectric material or device that converts electrical energy into mechanical energy and mechanical energy into electrical energy. The transmitter channel in transmitter-receiver unit 152 is operable to drive the transducer using an electrical waveform to generate an acoustic waveform. The necessary circuitry is used to protect the receiver channel in the transmitter-receiver unit from the transmitter; this circuitry includes, but is not limited to, diode extenders and limiters, high-voltage switches, converters, and duplexers. The receiver channel is operable to amplify and filter the received echo signal, but it can also perform other operations, such as microwave beamforming or beamforming in the analog domain, where analog sampling and summation are used to combine analog signals from multiple channels.

[0085] The transmitter-receiver unit 152 may include circuitry and electronic components, including but not limited to power amplifiers, RF amplifiers, variable gain amplifiers, duplexers, multiplexers, digital-to-analog converters, analog-to-digital converters, mixers, demodulators, detectors, ASICs, FPGAs, DSPs, RF transformers, analog filters, digital filters, Ethernet circuitry, PCI circuitry, digital buffers, RAM, non-volatile memory, communication components, analog buses, digital buses, switches, and power supply electronics. The circuitry and electronic components of the transmitter-receiver unit 152 are communicatively coupled to the transducer elements and the data processing unit of the system 150.

[0086] In some implementations, for example, a sufficiently high sampling rate (at least twice that of the highest frequency component) is used to amplify the received echo signal and convert it into a digital signal to satisfy the Nyquist sampling theorem and the quantization bit depth, such that the effective but weak echo can be distinguished from the noise. In some implementations, for example, an analog anti-aliasing filter can be used before digital conversion to ensure that the Nyquist sampling theorem is satisfied.

[0087] In some implementations, for example, transmitter-receiver unit 152 receives sequencing information from data processing and control unit 156. Control units within data processing and control unit 156 transmit information about which elements(s) to transmit on and which receiver(s) to receive and convert into digital signals. In addition to which elements are used, the sequencing information also includes timing information, such as multiple transmission delays and multiple reception delays. For example, after a defined delay, transmission may occur on element 1, and after a defined delay, a second transmission may occur on element 2. Upon reception, analog-to-digital (ADC) conversion on element 1 begins after a defined delay, and the same applies to element 2.

[0088] In some embodiments of system 150, for example, the transducer element array may each comprise a collection of transducer elements such that they operate as virtual transmitters and / or virtual receivers—clearly different from the virtual aperture and virtual array discussed above regarding synthetic aperture techniques. In this case, the delay on each element must be independently controllable, i.e., facilitating a virtual source point via a transmit beamformer and / or a virtual receiver point via a receive beamformer, with the wavefront appearing to originate from and / or be observed from that receiver point. For example, since the virtual elements consist of more than one physical element, they are advantageous for increasing transmit power during transmission and gain during reception, thereby improving the overall signal-to-noise ratio compared to using a single element for transmission and reception. For example, functionally, system 150 operates with virtual sources and receivers in the same way it operates with single-element sources and receivers, although adapting additional signal and ADC channels within the receiver and target beamformer unit 154 introduces greater complexity.

[0089] In some embodiments of system 150, for example, the phase center or effective location of the virtual source is behind the array, thus generating a divergent transmitted wavefront approximating a point source. In some embodiments, after similar operation on the virtual transmitter behind the array, a virtual receiver can be formed by digital beamforming the echo as if reaching a point behind the array. In some embodiments, for example, the phase center or effective location of the virtual source is in front of the array, thus generating a sound source diverging beyond the phase center or source point (also approximating a point source). In some embodiments, after similar operation on the virtual transmitter in front of the array, a virtual receiver can be formed by digital beamforming the echo as if reaching a point in front of the array.

[0090] In some embodiments of system 150, for example, microwave beamformers can be used to beamform a virtual receiver consisting of multiple elements in the analog domain to generate one or more beamformed signals from the virtual receiver, thus eliminating the complexity of adapting additional receiver channels to each virtual receiver. For example, given a virtual receiver consisting of 64 elements and a set of eight 8:1 microwave beamformers focused on the same virtual receiver point, the resulting eight microwave beamformed signals require only eight receiver channels (e.g., composed of analog filters (ADCs), etc.) instead of 64 channels. The resulting microwave beamformed signals can be processed by the target beamformer in the same way as the echo from the physical receiver.

[0091] In some embodiments of system 150, for example, the array elements may each include a set of elements operating as focusing transmitters, such that the focused beam is transmitted at one or more focal points in space that may or may not correspond to a point on an object, but which, due to focusing, insonify a limited area of ​​the object. Focused transmission helps limit interference from unwanted objects or obstacles and increases transmitted energy, e.g., thus improving the signal-to-noise ratio of the received echo. As in the case of a virtual transmitter, the delay on each element of the focusing transmitter must be independently controllable to focus the beam arbitrarily in space. Furthermore, as in the case of a virtual receiver, beamforming can be performed on the received echo to further locate echoes from one or more directions, where the receiving focus may or may not correspond to a point on the object.

[0092] In some implementations, for example, the transmitter-receiver unit 152 may also include analog and / or digital filters; for instance, each ADC may integrate a bandpass or lowpass analog anti-aliasing filter before conversion. Each ADC may also integrate a digital filter for the output, or the transmitter-receiver unit 152 may incorporate such a digital filter.

[0093] In some implementations, for example, the transmitter-receiver unit 152 can down-convert or demodulate the signal in the analog domain before digital conversion. For example, in the case of analog demodulation at the center frequency of the received waveform, two ADC channels can simultaneously sample in-phase and quadrature (IQ) signals, or one ADC channel can perform interleaved sampling of the IQ signals, for example, in-phase sampling followed by quadrature sampling followed by in-phase sampling, and so on. The delay between in-phase and quadrature samples can be compensated (e.g., using a digital interpolator or a fixed-delay all-pass filter). For example, analog demodulation helps reduce the sampling rate of broadband echoes, thus reducing the number of samples that need to be stored and the amount of data transmitted to the beamformer.

[0094] In some implementations, digital IQ demodulation can be used instead of analog IQ demodulation to achieve similar results, but with the added advantage of being able to tune the demodulation frequency and bandwidth. Tuning the demodulation frequency and bandwidth helps compensate for frequency and depth-dependent attenuation, which effectively reduces the center frequency and bandwidth as a function of echo depth.

[0095] In some implementations, instead of IQ demodulation, more precise techniques, such as coherent IQ processing for broadband signals, involve applying a Hilbert transform to the signal after conversion to the digital domain to obtain an analytic signal, as follows:

[0096]

[0097] Where s a s(t) is the analytic signal, s(t) is the input signal or in-phase component, and It is the Hilbert transform signal or quadrature component, which is obtained by the following formula:

[0098]

[0099] Where * represents the convolution operator. The Hilbert transform can be computed using the fast Fourier transform (FFT), where a -90-degree phase shift is added to all positive frequency components and a +90-degree phase shift is added to all negative frequency components, as follows:

[0100]

[0101] in It is the Fourier transform operator. The Hilbert transform operator is sgn(x), where sgn(x) is the sign function of x, ω is the angular frequency, and S(ω) is the Fourier transform of s(t). Since the Hilbert transform is a multiplicative operator in the frequency domain, it can also be approximated using a finite impulse response (FIR) filter. Hilbert transform techniques are advantageous for IQ processing because they are unaffected by frequency and depth-dependent attenuation of the received echo. An approximation of the analytic signal can also be obtained through orthogonal sampling methods.

[0102] In some implementations, for example, the obtained digital echo signals (e.g., in IQ form or pure real number form) are transmitted to the target beamformer unit 154, where they are stored in random access memory (RAM) for beamforming. In some implementations, for example, in addition to target beamforming, the target beamformer unit 154 may also be configured to perform beamforming on a virtual receiver.

[0103] The object model is initially loaded into the data processing unit 156 by various means, such as reading a file from a storage system, transferring it from computer-aided design (CAD) software, generating it using a computer program based on a mathematical model, or generating it using external means of imaging the object and converting the image into a model. For example, the object model can consist of simple convex polygons, such as triangles, quadrilaterals, hexahedrons, and other shapes with complex reflectivity. For example, the object model can consist of a Delaunay triangulation model, which includes a vertex list and a face list, where each face includes three indices from the vertex list. The largest dimension of the largest element within the model should be one wavelength or smaller, but preferably half a wavelength or smaller, where the wavelength corresponds to the speed of sound in the medium surrounding the object divided by the center frequency of transmission. It is also preferred that the faces of the polygons lie on circles or circumcircles such that the diameters of all circumcircles on all faces are equal or approximately equal on all faces, and that the diameter of the largest circumcircle is one wavelength, but preferably half a wavelength or smaller. Triangular and quadrilateral models are preferred because efficient ray tracing algorithms exist for these geometries. In some implementations, for example, the center positions of faces are calculated and stored with the model; these positions are typically found by calculating the average coordinates across all vertices of the face, but other methods for calculating center positions are also possible. Additionally, the unit surface normal vector for each face is calculated and stored with the model. Furthermore, the vertex normal for each vertex can be calculated and stored with the model. Other quantities that can be calculated and stored with the model may include, for example, Plücker coordinates, which can be used for efficient ray tracing, and the complex surface reflectivity of low-contrast objects.

[0104] In some embodiments, the data processing unit 156 may reside on one or more computers, such as desktop computers, laptop computers, a network of computer devices that communicate with each other via the Internet (e.g., in the “cloud”), or other computing devices, including but not limited to smartphones, tablets, or wearable computing / communication devices. In some embodiments, the data processing unit may reside in a device structure (e.g., a housing) that also includes the transmitter-receiver unit 152 and / or the target beamformer unit 154. The transmitter-receiver unit 152 may communicate with the data processing unit via a digital interface, which may be any interface or set of interfaces, including but not limited to USB, FireWire, Ethernet, PCI, IEEE 1394 serial, Wi-Fi, Fibre Channel, fiber optic, wireless bus, serial bus, or parallel bus.

[0105] In some embodiments, the data processing unit 156 may include a programmable processing unit and a storage device, which may include, but is not limited to, the following components: one or more processors, serial processors, parallel processors, mathematical coprocessors, general-purpose graphics processing units (GPUs), FPGAs, ASICs, DSPs, non-volatile memory, RAM, digital buffers, storage devices, hard disk drives, USB, FireWire, Ethernet, PCI, IEEE 1394 serial, Wi-Fi, Fibre Channel, optical fiber, wireless bus, serial bus, external display adapter, external display driver, parallel bus, communication components, and power supply electronics.

[0106] In some embodiments, such as system 150, the UI and display device 158 may also include a monitor, a speaker, or other devices for generating a combination of visual, auditory, or tactile outputs. For example, in some embodiments, when the data processing unit resides on a computer, such as residing in a single unit or separately via a cable to an external display, the UI and display device 158 may be integrated with the data processing unit.

[0107] In various embodiments, the data processing and control unit 156 includes one or more processors for processing data and one or more memories that communicate with the processors to store the data. For example, the processor may include a central processing unit (CPU) or a microcontroller unit (MCU). The memory may include processor-executable code that, when executed by the processor, configures the data processing and control unit 156 to perform various operations, such as receiving information, commands, and / or data, processing information and data, and sending or providing information / data to another entity (e.g., an external device). To support the various functions of the data processing and control unit 156, the memory may store other information and data, such as instructions, software, values, images, and other data processed or referenced by the processor. Various types of random access memory (RAM) devices, read-only memory (ROM) devices, flash memory devices, and other suitable storage media can be used to implement the storage functions of the memory. The memory may store data and information of the data processing and control unit 156 and other units of the system. For example, the memory may store system unit parameters and hardware constraints, as well as software parameters and programs for operation on the system. In this example, the data processing and control unit 156 includes an input / output (I / O) unit that allows the data processing and control unit 156 to communicate with other units in the system. For example, the I / O unit can enable the data processing and control unit 156 to communicate with other devices or systems, for instance, using various types of wired or wireless interfaces compatible with typical data communication standards, such as, but not limited to, Universal Serial Bus (USB), IEEE 1394 (FireWire), Bluetooth, IEEE 802.111, Wireless Local Area Network (WLAN), Wireless Personal Area Network (WPAN), Wireless Wide Area Network (WWAN), WiMAX, IEEE 802.16 (WiMAX), 3G / 4G / LTE / 5G cellular communication methods, and parallel interfaces. The I / O unit can also provide communication connectivity between the data processing and control unit 156 and external interfaces (e.g., external devices), data storage sources, or display devices (e.g., UI and display device 158).The I / O unit of the data processing and control unit 156 can also interface with other external interfaces, data storage sources and / or video or audio display devices to retrieve and transmit data and information that can be processed by the processor, stored in memory or displayed on the output unit of the system 150 (e.g., UI and display device 158).

[0108] The object beamforming unit 154 includes software containing code and / or a list of coordinates and / or vector information representing a predetermined model of the object, and / or code for generating a new model of the object or modifying a new or existing model of the object based on new information about the object provided to the object beamforming unit 154 by the data processing and control unit 156. In some embodiments, the object beamforming unit 154 is a data processing module of the data processing and control unit 156, wherein the object beamforming unit 154 utilizes the hardware of the data processing and control unit 156, such as, but not limited to, one or more processors and one or more memories of the data processing and control unit 156. However, in some embodiments, the object beamforming unit 154 includes one or more processors coupled to one or more memory units, which are decoupled from one or more memories of the data processing and control unit 156.

[0109] In some implementations, for example, prior to beamforming, the object model is loaded from the data processing and control unit 156 into an addressable RAM cell within the object beamformer unit 154. Similarly, prior to beamforming, the transducer array element positions and normals are loaded from the data processing and control unit 156 into the object beamformer unit 154. The transducer array element positions and array normals are stored in the data processing and control unit 156 as a model in its own reference frame, such as in... Figure 1E The array model 153 in the example is depicted below. The positions and array normals of the transducer array elements transmitted from the data processing and control unit 156 to the target beamformer unit 154 are changed by applying a transformation consisting of rotational and translational components. The translational components are represented in three-dimensional Cartesian coordinates, i.e., x, y, and z. The rotational components are determined by successive rotations of the angles about the x, y, and z axes in various successive orders (e.g., xyz, yzx, zxy, xzy, zyx, yxz, also known as Tet Bryan angles).

[0110] Example 3×3 rotation matrix calculation is as follows:

[0111]

[0112] Where R represents the rotation of its yaw, pitch, and roll angles as α, β, and γ, respectively. More formally, it is its inherent rotation of its Tetbryan angles as α, β, and γ about the z, y, and x axes, respectively. Other methods for calculating the components of the 3×3 rotation matrix include Euler angles, axis-angle representation, Rodrigues rotation formula, and quaternions. Rotation and translation are applied to the array model coordinates as follows:

[0113] A c ′=RA c +h

[0114] Where A c ′ is the transformed set of coordinates, R is a 3×3 rotation matrix, and A c It is the input set of array coordinates, and h is a 3×1 translation vector. Array element A n The normal vector is also transformed as follows:

[0115] A n ′=RA n

[0116] Where S n ′ is the set of transformed array normals. The transformed coordinates and normals are transmitted to the beamforming unit for weight and delay calculations. The rotation and translation can also be expressed as a mathematically equivalent 4×4 rigid homogeneous transformation T:

[0117]

[0118] Where h1 is the x-translation, h2 is the y-translation, and h3 is the z-translation, and

[0119] and

[0120] T is an efficient way to perform translation and rotation transformations on a set of coordinates. For example, the set of coordinates is arranged as a 4×N matrix P, where the 4th row of P contains all 1s, as shown below:

[0121] P′=TP,

[0122] Where P′ is the transformed set of coordinates. The translation is simply omitted by setting the fourth row of P to zero. This operation is understandable in the fields of kinematics and computer graphics.

[0123] Figure 2The diagram illustrates an example implementation of an object beamformer method for beamforming an object surface using ultrasonic signals transmitted and received via a synthetic aperture, according to the present technology. In some example embodiments of system 150, the algorithm for beamforming the object is executed within object beamformer unit 154 and includes a set of example mathematical operations for the object beamformer. Figure 2 The figure also shows an example equation for calculating specific parameters using an algorithm that can be implemented by the object beamformer unit. This example equation may include a specular scattering weight parameter, a directional weight parameter, and a scattering delay parameter (also shown below).

[0124] Mirror scattering weight parameter, directionality weight parameter, scattering delay parameter

[0125]

[0126] In some implementations of object beamformers, for each transmitter and receiver combination, the object beamformer is configured to calculate several quantities that determine the delay and weights applied to the echo before summation. Figure 2 In the example, the geometry of the object model stored within the object beamformer is shown for a single triangular facet, where it should be understood that an object can consist of multiple such triangular facests, and each facet can be processed in the manner shown.

[0127] Similarly, in Figure 2 The diagram also illustrates the geometry of a single transmitter and a single receiver. It should be understood that the same geometry applies to multiple pairs of transmitters and / or receivers, including transducer arrays, and is not limited to transmitters and receivers occupying different physical or virtual locations. It should also be understood that the description of a single transmitter or single receiver may consist of one or more transducer elements that are beamformed to produce a single echo as if obtained from a single transmitter or receiver located and oriented as shown.

[0128] It is worth noting that the geometry of the transmitter and receiver used within the object beamformer is based on a transformation applied to the transducer array model, where the transformation is controllable and the model is manipulated in the object model's reference frame. In contrast, an equivalent transformation can be applied to the object model, and the object model can be manipulated in space in the array model's reference frame; however, the computational requirements of applying transformations to the object model are typically much greater than those of the array model, and can hinder real-time computation. It should be understood that if the two approaches are equivalent, then the former approach is assumed. The transformed coordinates are calculated within the object beamformer unit 154 or the data processing unit 156 based on the transformation determined by the data processing unit 156.

[0129] like Figure 2 As shown, for each transmitter location, the object beamformer is configured to calculate the distance (d) from the transmitter location to a location on one or more faces of the model. t Similarly, for each receiver location, the object beamformer is configured to calculate the distance (d) from the receiver location to a location on one or more faces of the model. r In this paper, the transmitter location is the known location of one or more transmitting transducer elements and / or the known location of the acoustic signal transmitted by transducer(s) with known acoustic transmission parameters, such as phase center localization; similarly, the receiver location is the known location of one or more receiving transducer elements and / or the known location of the acoustic echo received by transducer(s) with known acoustic parameters. The face location used for distance calculation can be the face center location, or it can be a different location determined (e.g., by ray tracing from the transmitter or receiver to the intersection point on the polygon). The face location can also be determined, for example, from a combination of adjacent vertices, face normals, and vertex normals to solve for the interpolated face location based on the local curvature of the model.

[0130] For the purposes of this example, assume the global average speed of sound of the medium surrounding the object is represented by the variable c. Therefore, the delay τ from the transmitter to a point on the model and then to the receiver is determined by the following equation using the round-trip path d. t +d r Calculated by dividing by the medium velocity:

[0131]

[0132] Apart from the delay, for each transmitter location, the beamforming unit is configured as the incident unit vector of one or more surfaces on the computational model. Similarly, the beamforming unit can be configured to calculate the specular reflection unit vector according to the following equation.

[0133]

[0134] in It is the unit normal vector of the surface. It is the incident vector from the transmitter location, and the quantity It is a unit vector and The dot product between them is the cosine of the angle.

[0135] Similarly, for each receiver location, the beamforming unit is configured to compute the receiver unit vector on one or more surfaces of the model. For each combination of transmitter location, surface location, and receiver location, the beamforming unit is configured to calculate the mirror scattering weight w according to the following equation. s :

[0136]

[0137] in It is a unit vector and The dot product between the angles is the cosine of the angle, and p is a power term, also known as the specular index, which usually has a value of at least 1, but can be increased to reduce the specular scattering angle, for example, to match the experimentally observed reflection intensity.

[0138] In this example, the beamformer unit is configured to... s Clamping to the range of 0 to 1 causes negative values ​​to be assigned the value 0, meaning specular reflection is impossible. If and If they are parallel, then w s =1. If and If it is vertical, then w s =0. If and If they are antiparallel, then ws = 0. This basic weighting term is only responsible for weighting the specular scattering contributions of the scattering elements on the model.

[0139] Other contributions to echo weighting may include transmit element directivity weighting w dt w dt It can be approximated as the transmitter unit normal vector With unit incident vector The absolute value of the dot product between the inverses is as follows:

[0140]

[0141] Where qt is a power term similar to p above, and its value depends on the directivity of a given element and is usually 1. In the definition above, the transmission directivity is mathematically equivalent to the cosine of the angle between the transmitter normal vector and its incident vector, which is consistent with the cos(θ) tilt factor in the first Rayleigh-Sommerfeld solution of a plane wave source.

[0142] Similarly, echo weighting can also include receiver element directional weighting. dr directional weighting of receiver elements w dr It can be approximated as the receiver unit normal vector With unit receive vector The absolute value of the dot product between the inverses is as follows:

[0143]

[0144] Where qr is a power term similar to p above, and its value depends on the directivity of the given element and is usually 1. In the definition above, the receiver directivity is mathematically equivalent to the cosine of the angle between the receiver normal and the receiving vector, which is consistent with the cos(θ) tilt factor in the first Rayleigh-Sommerfeld solution of a plane wave source.

[0145] Given the total directivity w of the transmitter and receiver pair dtr It is given by the product of the individual transmitter directivity and receiver directivity, as follows:

[0146] w dtr =w dt w dr ,

[0147] The separation of directionality allows for different spatial responses between transmission and reception. Note, for example, when considering the limiting case, w dtr The meaning becomes clearer, for example, when and When perpendicular, w dt =0, or if and If perpendicular, then w dr =0, any situation will result in w dtr =0. Similarly, when and When parallel, w dt =1, and if and If they are parallel, then w dr =1.

[0148] Directivity w of the element based on analytical formulas and / or measured directivity patterns of the transmitting and receiving elements. dt and w dr A more precise expression is also possible, for example, by using a polynomial fit to the field amplitude measurement as a function of the angle with respect to the element normal.

[0149] In some embodiments, other weighting factors may also be included, for example, to indicate the area of ​​a given face. If all faces have approximately the same area because the object model is uniformly meshed in terms of polygon side lengths, polygon areas, or circumcircle diameters, the contribution from some faces being higher or lower than that from others can be ignored, and the area weighting term can be omitted.

[0150] Examples of other weighting factors that may be included include the complex reflectance term w. r Complex reflectivity term w rThe scattering behaviors of real and complex numbers are aggregated. These behaviors can depend on frequency and angle of incidence, but also on the physical parameters of the object, including but not limited to sound velocity, density, acoustic impedance, compressibility, attenuation, surface texture, surface or subsurface inhomogeneities, and multiple layers. The complex reflectivity term can take the following form:

[0151] w r =α+jβ,

[0152] Where α is the real component of reflectivity, β is the complex component of reflectivity, and j is the unit imaginary number. It should be understood that both α and β can have complex dependencies on other quantities (e.g., frequency, angle of incidence, etc.). Here, w r It can include the effect of diffuse scattering, similar to Lambert's cosine law in optics.

[0153] Examples of other weighting factors that may be included include the compensation weight w c Compensation weight w c Compensation is provided for frequency-dependent and distance-dependent attenuation, as well as 1 / r amplitude spread loss (assuming a point source). For example, w c A high value is assigned to the longest propagation path, and a low value is assigned to the shortest propagation path. In this way, the compensated echo corresponding to the longest propagation path has a similar amplitude to the echo from the shortest propagation path.

[0154] For a given transmitter, receiver, and model surface, the global complex weight w is established as follows:

[0155] w = w s w dtr w r w c

[0156] In some implementations of an object beamformer, the object beamformer is configured to calculate the sum of echo samples corresponding to multiple transmitter locations, receiver locations, and model points according to the following equation:

[0157]

[0158] Where f(t, M) A T, M O ) is the beamforming signal, t is time, and M is... A It consists of N TX The coordinates of the active transmitter and N RX An array model consisting of various parameters representing the coordinates of an active receiver, where T is a homogeneous transformation matrix applied to the positions of the active transmitter and receiver, and is a function of three translational distances and three rotational angles, comprising six degrees of freedom, M... O It consists of NMP The object model consists of various parameters representing the locations of model points, where i is the active transmitter index, j is the active receiver index, k is the model point index, and w... ijk (M A T, M O ) is the overall complex beamformer weight, s ij (t) is the stored synthetic aperture echo signal, and τ ijk (M A T, M O ) is the calculated beamformer delay.

[0159] In the example object beamformer implementation above, the object beamformer can be mathematically compared to the delay-sum beamformer used in conventional synthetic aperture ultrasound image formation (e.g., synthetic aperture point beamforming), although they are different. For example, a more conventional generalized synthetic aperture point beamformer can take the following mathematical form:

[0160]

[0161] Where t is time, IP represents one or more image points, and M... A It is an array model, w ij (M A (IP) is a 2D weighted array, which may or may not be a function of image points and array models, and τ ij (M A IP is the round-trip delay to and from a given image point in the array. In conventional beamformers, the weights can be fixed for a given array; for example, the apodization function (e.g., w) can be used. ij (M A The array is a 2D weighted array chosen to have the same value for all beamforming points in space (e.g., for a given array model, w). ij (M A () is a constant), or the weights can be a variable 2D weight array, for example, a function of a given point in space relative to the array model, such as a dynamic apodization function, such as w. ij (M A , IP). Many possible apodization functions are possible. Compared with conventional delay-sum beamformers used in conventional imaging, the main difference between the disclosed object beamformer technique and conventional point beamformers lies in (i) how weights are calculated based on the array model, the object model, and their relative orientation, e.g., w ijk (M A T, M O (ii) how to sum the echoes on the object model, for example, in N MPThe third summation at each model point. For example, f(t, M) A T, M O ), f conv (t,M A , IP) and other similar mathematical formulas can all be considered as beamformers. Although the disclosed object beamformer is not a point beamformer in the strict sense, for example, in some embodiments, by setting N MP =1 (i.e., remove the third summation), set M O =IP, will w ijk (M A T, M O Redefining it as w ij (M A IP) and τ ijk (M A T, M O ) is redefined as τ ij (M A (IP) can redefine the object beamformer as a point beamformer.

[0162] In some implementations of the object beamformer, the object beamformer is configured to determine the overall complex beamformer weights and / or other variables and parameters of the beamforming signal based on a lookup table.

[0163] In some embodiments, the transducer element array comprises N transducer elements, wherein at least some of the transducer elements are used to fully synthesize both transmission and reception in transmit aperture operation (i.e., N). TX =NR X And i = j refers to the same element). Due to the acoustic reciprocity property between source and receiver pairs, for each reciprocal bistation transmitter and receiver pair, i.e., pairs satisfying (ij) and (j, i) such that i ≠ j (e.g., (ij) = (1, 2) and (i, j) = (2, 1)), only one calculation of weights and delays is required. Mathematically, the object beamformer can be modified as follows:

[0164]

[0165] Where, for i≠j, a ij =2, and for i=j, a ij =1. Equivalently, based on the reciprocity principle, the target beamformer can also be modified as follows:

[0166]

[0167] Where, for i≠j, a ij =2, and for i=j, a ij=1. The advantage of this implementation is that, from N 2 to The computation time was reduced by about half.

[0168] In some implementations, for example, monostation echoes or subsets of monostation echoes (e.g., at least some of the i = j above) and / or bistation echoes or subsets of bistation echoes (e.g., at least some of the i ≠ j above) can be summed in the target beamformer.

[0169] In some implementations, for example, the object beamformer output can be multiplied by a time-domain window function. For example,

[0170] f tdw (t,M A T, M O )=wf(t)f(t,M A T, M O ),

[0171] Where wf(t) is the time-domain window function, such as the Hann window, given by the following function:

[0172]

[0173] Where L is the window length in the time domain. Time-domain window functions can be used to suppress unwanted echo components far from t=0. Other window functions can also be used, including but not limited to: Hamming, Blackman, Nuttor, Gaussian, Tukey, Kaiser, Parsons, Welch, Slepian, hyperspherical, exponential, flat-top, triangular, sine, and rectangular.

[0174] The transmitted waveform can be spatially coded, time-coded, or both. Assume the echo s... ij (t) is decoded before beamforming. This implicitly assumes that the time variable t is sampled, for example, t = n dt, where n is an integer and dt = 1 / f. s , and f s It is the sampling rate of the stored echo samples. In the quantity τ ijk (M A T, M O If the value of M does not fall on an integer multiple of dt, the beamforming unit can be configured to interpolate the fractional delay values ​​from neighboring samples using any number of efficient interpolation algorithms and broadband beamforming methods. The summation shown above is merely one embodiment of a beamformer including a weighted delay summation. The function f(t, M) can be obtained through other combinations of linear and nonlinear operations. A T, M O This suppresses incoherent echo energy.

[0175] The output of the beamformer unit is transmitted to the data processing unit, where it is used in the objective function of the optimization process. The optimizer modifies each of the six degrees of freedom that determine the transformation T in order to minimize the objective function. An example of the objective function g(t) is given below:

[0176]

[0177] Where t1 is the start time and t2 is the end time. Another example of the objective function g(t) incorporating the above time window is given below:

[0178]

[0179] The optimizer aims to maximize g(M) as follows: A T, M O ):

[0180]

[0181] This involves changing each of the six degrees of freedom corresponding to T and the set of object model variables M. A To maximize the integrated power of the beamformer output over a time window ranging from t1 to t2, in order to find the optimal converter T. opt and the set of optimal object model variables The duration of the time window Δt = t2 - t1 is inversely proportional to the center frequency and bandwidth of the received echo. For example, for a waveform transmission modeled using a Gaussian amplitude-modulated sinusoidal pulse p(t), the following is an example:

[0182]

[0183]

[0184] Where bw is the -6dB bidirectional fractional bandwidth ratio, i.e., the bandwidth of the symmetrical spectrum divided by the center frequency, and f c The center frequency and the -6dB amplitude cutoff time are given by the following formula:

[0185]

[0186] here, It is inversely proportional to the center frequency and fractional bandwidth. It should be understood that other amplitude cutoffs and cutoff times can be used to increase or limit the window for integrating the beamforming echo power, but the analytical formula above provides guidance on how the window duration and integration limit must be varied according to the frequency and bandwidth.

[0187] As described, further illustrative implementations were carried out. Numerical simulations of the operation of the object beamformer were performed to demonstrate its ability to locate a simple object with 25 scattering positions using a sparse synthetic aperture comprising 5 elements. Figure 3 The geometric arrangement of the positions of the scatterers and array elements is shown.

[0188] Figure 3 A graph depicting the geometry used in an example implementation of a numerical simulation employing an example object beamforming technique is shown. This graph consists of the positions of object scatterers modeled as point targets (marked with "x" symbols) and the positions of array elements modeled as point sources and receivers. The array element positions, marked with "o" symbols, used within the object beamformer are transformed versions of the actual array element positions, marked with "+" symbols. The transformed coordinates are obtained by rotating the actual array element positions around a vector by an angle of 0 to 10 degrees, and then translating the rotated coordinates in the direction of the vector by an amount of 0 to 10 millimeters. Increasing distances from the actual positions are encoded using grayscale.

[0189] To demonstrate the functionality of the object beamformer, each array position can be both a transmitter and a receiver, thus considering both monostatic and bistatic scattering. Assume the transmitter and receiver are point sources and point receivers with cos(θ) sensitivity, for example, according to... Figure 2 The geometry and w defined above using qt=1 and qr=1 dr and w dt The definition of directional sensitivity for transmission is given by Given, and regarding the directional sensitivity of the receiver, by Given. The geometry of the array is 4cm. 2 A square sparse aperture, with a transmitter / receiver at each corner and a transmitter / receiver at the center. For example, according to Figure 2 The geometry and w defined above using p=1 s The definition assumes the scatterer is a point reflector with a scattering amplitude of cos(θ) of the incident wave. A Gaussian-modulated sinusoidal pressure waveform as defined above is used, with a center frequency f of 1 MHz. c A bidirectional -6dB fractional bandwidth (bw) was used to model the transmitted waveform generated from each source. The speed of sound was set to 1500 m / s, resulting in a wavelength of 1.5 mm.

[0190] For example, the echo can be numerically calculated using the approximate Rayleigh integral formula in the time domain of the pressure source, given by the following equation:

[0191]

[0192] Where p(x, t) is the sound pressure waveform at the vector position x and time t in space, c0 is the sound velocity of the medium, and p s (x, t) is the surface pressure component of the sound source, A is the integrand of the area, ||xx′|| is the Euclidean length of the vector xx′, and The tilt term is given by the angle between the vector xx′ and the normal to the sound source surface (equivalent to the Cos(θ) directionality shown above). Assuming reciprocity, a homogeneous medium, and the first-order Born approximation (i.e., no multiple scattering), the pulse echo field p pe (x, t) can be approximated by the following formula:

[0193] p pe (x, t) = p t (x, t)* t p r (x, t),

[0194] Where p t (x, t) is the field at the scatterer, p r (x, t) is the field at the receiver from the source at the scatterer, and * t The operator represents convolution in the time domain. For a set of point sources, receivers, and point scatterers, the pulse echo responses of the individual scatterers are summed by corresponding weights for each combination, e.g., weighted based on directionality and scattering amplitude. Using knowledge of the unit normal vectors of the transmitter and receiver, the directionality of the transmitter and receiver responses (e.g., the Cos(θ) term) can easily be included in each combination of transmitter, receiver, and scatterer locations as described above (e.g., w...). dtr Similarly, the directionality of the scattered wave can easily be included in the knowledge of the unit normal vector of the scatterer as described above (e.g., w). s Without loss of generality, for example, frequency and depth-dependent attenuation are neglected in the simulation.

[0195] To simulate the received synthetic aperture echo, for example, real array cell locations were used, such as... Figure 3 As shown, each location is marked with a "+". Based on synthetic transmitted aperture imaging, each source is excited individually, and the echo from each receiver is simulated by superimposing all scattering events from all point scatterers. Figure 3 As shown, each point scatterer is labeled "x". In this way, each transmission event generates five received echoes corresponding to five possible receivers, which is then repeated for all sources, resulting in a fully synthetic transmit aperture dataset of 25 echoes including every possible combination of source and receiver.

[0196] Figure 4A graph depicting the received echoes from the simulation is shown, with the vertical axis representing time in microseconds and the horizontal axis representing the echo index. The echo amplitude is encoded in grayscale values ​​from -0.38 to 0.38, as shown. The amplitude unit is the pressure unit of a 1 Pa peak pressure source. Figure 4 The echoes are organized according to a fully synthetic aperture dataset corresponding to 25 echoes, where echoes indices 1-5 are generated by transmissions at source index 1, echoes indices 6-10 are generated by transmissions at source index 2, and so on. This dataset is equivalent to s with echo indices based on 5(i-1)+j in the above equation. ij (t), where i is the source index, j is the receiver index, and i and j are integers spanning sets 1, 2, 3, 4, and 5.

[0197] For different degrees of rotation and translation of the actual array element position coordinates relative to the object scatterer position coordinates (index k), the object beamformer delay τ is calculated according to the method described above. ijk (M A T, M O This is to illustrate the sensitivity of the object beamformer to relative rotational and translational offsets between the array and the object. Here, transformation T represents a homogeneous transformation including rotation followed by translation, applied to the position coordinates of the actual array elements. In the simulation, Figure 3 The actual array element position coordinates shown are used as the array model, but the array model can also be represented in any reference frame. Similarly, Figure 3 The object position coordinates shown are also used as the object model, but the object model can also be represented in any reference frame. Importantly, the number of object beamformer delays used for this problem is 5 × 5 × 25 = 625, which is far greater than 25, or the number of synthetic aperture delay terms used to beamform points in space with the same aperture. In this way, as described above, objects are used to form synthetic apertures. It can also be seen here that incorporating the object model into the beamformer greatly increases the spatial diversity of the coherence conditions required for coherent detection.

[0198] In the simulation, the homogeneous transformation T includes a rotation of 0 to 10 degrees around the vector, followed by a translation of 0 to 10 millimeters in the direction of the vector. Figure 3 The range of array position coordinates used in the object beamformer is depicted, with each array position coordinate marked with "o" and shaded in gray from black to white to show the increasing distance from the actual array element position coordinates.

[0199] Given s ij (t) w calculated based on the directionality of the specular scattering element as described above ijk (M A T, M o), and τ ijk (M A T, M O The object beamformer summation is performed based on the transformation T with a rotation of 0.1 degrees and a translation of 0.1 mm increments (i.e., rotation and translation occur for each value of T).

[0200] Figure 5 The diagram shows a data graph depicting the output of the example object beamformer, that is, corresponding to the output of the beamformer. Figure 3 The actual array element positions shown are respectively equal to 0 degrees and 0 millimeters of rotation and translation, and for Figure 3 The actual array element positions shown are equal to rotations and translations of 1 degree and 1 millimeter, respectively, and the quantities f(t, M) given above are... A T, M O The amplitude unit is arbitrary. Note that the amplitude (in Pa) of the beamformer object echo for zero rotation and translation is much larger than the echo for 1 degree and 1 millimeter rotation and translation. The beamformer object echo shown is calculated in the time range of -2.32 to 2.32 microseconds, which roughly corresponds to the -60 dB envelope level of the transmitted Gaussian pulse.

[0201] As described above, as a method for detecting an object and determining its position and orientation, the power of the beamformer object echo can be determined according to g(M) as shown above. A T, M o Integrate over a time window. Such a time window is... Figure 5 As shown in the diagram, the corresponding times t1 and t2 are plotted as vertical lines. Here, and As per the above... The equations given and the Gaussian pulse parameters used in the simulation are used for calculation. It should be understood that t1 and t2 can also have other values ​​(e.g., ...). ), or any other value that produces an integral window centered at t=0.

[0202] Figure 6 The normalized integral power of the beamformer output depicting the object is shown for rotations around the vector from 0 to 10 degrees and translations in the direction from 0 to 10 millimeters, respectively. Figure 5 The data chart is shown for two example positions. The power is normalized by the power obtained for 0 rotation and 0 translation.

[0203] Figure 6 The chart shows the result through g(M) A T0, M O Normalized g(M) A T, MO ), where T0 corresponds to the case of zero rotation and translation (e.g., T0 equals a 4×4 identity matrix). This graph illustrates the rapid decrease in beamforming object echo power as a function of increasing transform values, thus demonstrating the sensitivity of methods used for coherently detecting and locating objects. For example, a suitable seeded optimizer can find... Figure 6 The peaks shown are used to simultaneously detect and locate the position and orientation of an object. Note that the half-power point is approximately 0.35 degrees / mm, which is much smaller than the wavelength of 1.5 mm (<λ / 4.3), thus demonstrating the extremely high sensitivity of this technique to relative motion between the array and the object, which in this example is a very small fraction of the wavelength. Considering its ability to locate objects much smaller than the wavelength, synthetic aperture object beamforming can be considered a super-resolution imaging method.

[0204] For example, without loss of generality, the assumptions of point sources / receivers and point scatterers are expected to apply to larger diffraction apertures and targets, which are typically approximated as multiple sets of point sources / receivers and multiple sets of point scatterers for faster computation. Furthermore, for example, without loss of generality, the object beamformer is represented for a set of possible, arbitrarily chosen rotational and translational trajectories; however, similar results can be expected for other arbitrary rotational and / or translational amplitudes and directions. Furthermore, for example, without loss of generality, the object beamformer is represented for a possible set of arbitrarily chosen source / receiver locations representing the array model; however, similar results are expected for other array geometries, if not better, especially if the array surrounds or partially surrounds the object. Furthermore, for example, without loss of generality, the object beamformer is represented for a possible set of arbitrary scatterers representing the object model; however, similar results are expected for a larger number of points and different geometries, if not better.

[0205] Example embodiments of the optimizer can include any type of nonlinear optimizer, including but not limited to: Gauss-Newton method, Neld-Mead method, Levenberg-Marquardt algorithm, sequential Monte Carlo method, particle filter method, genetic algorithm, interior point method, grid search method, and penalty function method. Once a feasible solution is found by satisfying the optimizer's stopping criterion (e.g., an object is detected), that solution can be used to seed and constrain the optimization for the next set of echoes obtained from the object. This can make the optimization process much faster because the next solution is likely to be very close to the previous solution, for example, if the object moves slightly relative to the array.

[0206] Other nonlinear objective functions are also possible, including finding the maximum value over the time window as follows:

[0207]

[0208] In some implementations, for example, the disclosed systems and methods for synthetic aperture acoustic imaging in tomography can pseudocoherently beamform the object. In such an implementation of pseudocoherent object beamforming, for example, system 150 is configured to separately and coherently beamform regions of the object model, and then incoherently combine the signals from each region in an objective function. For example, given an object model M consisting of more than one disjoint and / or intersecting but rigidly connected region... O Each region can have its own associated object model region. Where m is an integer index of a specific object model region, M0 can be defined as follows:

[0209]

[0210] Where U is the union operator and N is the number of regions. Each region can be (e.g., using the notation above) separated for object beamforming, modeling regions. The target beamformer output is from Given. The obtained objective function can be... Any linear and / or nonlinear combination thereof, for example, in some implementations, the objective function can be defined as follows:

[0211]

[0212] Each object model area Coherent beamforming is performed, the object beamformer power is integrated, and at least some or all of the model regions are incoherently combined with the final solution. The result of this objective function formulation is that, due to the incoherent summation, destructive interference between object beamformation echoes from different regions of the model is prevented, which can aid the optimization process due to the reduction in the number of local maxima around the optimal solution. A basic example of a multi-region object is a three-body object, consisting of three spheres of equal radius arranged in an equilateral triangle by rigid connections between them. The object is naturally divided into three regions, each corresponding to one of the three spheres (ignoring the rigid support structure of the spheres).

[0213] In some implementations, the object model M O It can consist of more than one non-intersecting and / or intersecting and possibly unconnected (e.g., randomly sampled) region, each of which can have its own associated object model region. Where m is an integer index of a specific object model region, such that M O It can be defined as follows:

[0214]

[0215] Where U is the union operator and N is the number of regions.

[0216] In implementations of coherent or pseudo-coherent object beamforming, for example, the optimizer uses an initial guess as a seed. This initial guess can be determined in various ways, such as through prior knowledge of the approximate location of the object relative to the array, or based on external measurements or observations. Random seed values ​​can be tested until one value yields a significantly better optimization result than others, for example, a lower minimum value at optimization termination. The seed value can also be tested using a grid search over a range of possible orientations and locations, which can be constrained to accelerate the process.

[0217] Once the optimizer determines the Top t The transformation represents the position of the array in the reference frame of the object model. To display the object model in the array's reference frame, the vertices of the object model must be transformed via To... pt The inverse (denoted as T) opt -1 Transform T opt -1 Mathematically equivalent to the rotational component R opt -1 =R opt T (R transpose) followed by translation component -R opt T h opt As shown below:

[0218]

[0219] in and

[0220] For example, such as Figure 1E As shown, the transformation and model can be transmitted to the UI and display unit 158, which performs the transformation on the model and displays the model relative to the array.

[0221] In some implementations, for example, the objective function can also be formulated such that the optimizer minimizes g(M). A T, M O The reciprocal of ) (i.e., This is to detect and locate the position and orientation of an object, for example, For example, most optimization algorithms are formulated as minimizing an objective function.

[0222] In some implementations, for example, the object beamformer can be configured to ignore object model parameters M that may change the geometry of the object model. OAnd ignore the array model parameter M that may change the array geometry. A However, changing these parameters is expected to affect the objective function g(M). A T, M O This has a similar effect. For example, by changing the scale of the object model, such as by multiplying the model coordinates by a constant scaling factor in each dimension, the optimizer can determine the optimal value of the scaling factor. As another example, if the object model is a sphere, the radius of the sphere can be changed by the optimizer to determine the optimal value of the radius. In some implementations, for example, the model parameters (i.e., M...) O and / or M A The optimization of model parameters (i.e., M) and the optimization of transformation T are performed simultaneously. In other implementations, for example, the optimization of model parameters (i.e., M) is performed independently. O and / or M A ) and optimization of transformation T.

[0223] In some implementations, for example, besides M O M A Besides T, other parameters related to acoustic properties can be modified and optimized, including but not limited to: global acoustic medium velocity, local acoustic medium velocity for each transmitter and receiver, global acoustic medium attenuation, local acoustic medium attenuation for each transmitter and receiver, global medium acoustic impedance and local acoustic impedance for each transmitter and receiver, acoustic impedance of the object, local acoustic impedance of the object for each transmitter and receiver, and acoustic impedance of the transducer array. For example, such parameters can be lumped into the object beamformer as follows:

[0224]

[0225] Here, the parameter set Z represents additional acoustic parameters that affect the output of the object beamformer, such as sound velocity, attenuation, acoustic impedance, etc. In this paper, acoustic properties related to the transducer array itself (e.g., matching layer, lens material, etc.), the medium between the transducer array and the object (e.g., water or other coupling medium), and the acoustic medium including the object itself (e.g., steel or other materials) are referred to as acoustic properties.

[0226] For example, when using an object beamformer to optimize the sound velocity of a medium, an initial guess of the sound velocity (e.g., 1500 m / s) can be used as a seed for the optimizer, or the sound velocity can be estimated based on pitch-catch measurements between spatially separated transmitters and receivers, and this estimate can be used as a seed for the optimizer. If an exact or very good approximation of the object model is known a priori, and the array geometry is known a priori, then the optimized sound velocity can be used to improve conventional synthetic aperture or real aperture imaging around the object.

[0227] In some implementations, for example, in addition to points, normals, etc., which are related to the geometry of the array, the array model M consists of... A Other parameters can be changed and optimized, including but not limited to: array element position, array element normal direction, array element dimension, array element shape, and array element rotation around the array element position. For example, extending the parameter set M A One such application would be the process of calibrating a given array with precisely known object geometry, object properties, and medium properties.

[0228] In some implementations, for example, in addition to points, normals, areas, curvatures, regions, etc., which are related to the geometry of the object, the object model M is composed of... O Other parameters can be changed and optimized, including but not limited to parameters associated with: rigid transformation, non-rigid transformation, affine transformation, perspective transformation, non-uniform transformation, statistical model of the object, mathematical model of the object (e.g., analytical model), and numerical model of the object (e.g., polynomial fitting).

[0229] In some implementations, for example, other parameters affecting the target beamformer output can be compared with the parameter M already included above. A T, M O The same approach as Z is included. For example, it includes, but is not limited to: parameters related to the electrical characteristics of the transducer, parameters related to the time-domain impulse response of the transducer, parameters related to the spatial impulse response of the transducer, parameters related to the center frequency of the transmitted waveform, parameters related to the bandwidth of the transmitted waveform, and parameters related to the delay of the transmitted waveform.

[0230] In some embodiments of the disclosed object imaging system, the synthetic aperture array geometry is pre-optimized for a given object such that small perturbations to the object's position and orientation produce maximum change in the object beamformer output, i.e., to optimize sensitivity to small translational and / or rotational displacements.

[0231] In some embodiments of the disclosed object imaging system, the synthetic aperture array geometry is pre-optimized for a given object such that the redundancy of spatial sampling on the object surface is minimized, i.e., the spatial samples are distributed approximately uniformly across the object. For example, the array geometry for a spherical object should ideally be spherical. Similarly, for example, the array geometry for a cylindrical object should ideally be cylindrical.

[0232] In some embodiments of the disclosed object imaging system, the synthetic aperture array geometry provides at least as many spatial samples as the number of unknowns solved by the optimizer.

[0233] Figure 7 A diagram illustrates an example embodiment of a synthetic aperture object imaging system (referred to as synthetic aperture object imaging system 700 or system 700) according to the present technology. Synthetic aperture object imaging system 700 includes hardware subsystems and software subsystems, including a synthetic aperture array unit 750 communicating with a transmitter / receiver unit 752 of system 700, wherein the transmitter / receiver unit 752 communicates with a processing and control unit 756 and an object beamformer unit 754 of system 700. In some embodiments, for example, system 700 optionally includes a user interface and a display unit communicating with the processing and control unit. In some embodiments, for example, system 700 is operable to implement the disclosed technology and / or include information regarding... Figure 1E The features discussed in system 150 shown.

[0234] Figure 8A A diagram illustrating an example embodiment of a method 800 for performing tomographic synthetic aperture imaging of an object by beamforming the entire object according to the present technology is shown. Method 800 includes process 810: transmitting, receiving, and / or transmitting and receiving acoustic signals at the object, which forms a synthetic aperture based on transmitting transduced acoustic waveforms at the object and receiving acoustic echoes returned from the object. In some embodiments of process 810, the transmitted acoustic waveforms may be formed as composite waveforms, which may be further spatially encoded, temporally encoded, or spatially and temporally encoded. In some embodiments of process 810, when the transmitted acoustic waveforms are spatially encoded, temporally encoded, or spatially and temporally encoded, process 810 may include decoding the encoded received waveforms.

[0235] Method 800 includes process 815: beamforming the object using echo samples of acoustic echoes returned from one or more regions of the object to generate one or more beamforming output signals. For example, the one or more beamforming output signals may be a function of one or more inputs for beamforming, wherein the one or more inputs for beamforming include information representing the object. In some embodiments of process 815, beamforming of the object is performed by coherently combining (e.g., summing and / or multiplying) delayed and weighted echo samples of acoustic echoes returned from one or more regions of the physical object to generate one or more beamforming output signals. In some embodiments of process 815, process 815 includes generating delayed echo samples (e.g., including real or complex radio frequency (RF) sample waveforms as described above). ij (t-τ ijk (M A T, M o ))) and weighting factors (e.g., including w as described above) ijk(M A T, M o ), which corresponds to: (i) multiple transmitter positions (indexed i) and receiver positions (indexed j) of the transducer array, at least partially including the array model M. A (ii) Multiple points (index k) of the object model, at least partially comprising the object model M. O ; and / or (iii) the relative position and orientation of the array are transformed by a property T related to the model. In some non-limiting embodiments, for example, the process 815 of generating object beamformer output samples includes a sum of delayed and weighted echo samples calculated by the following equation:

[0236]

[0237] It has the parameters discussed above. For example, one or more beamforming output signals generated at process 815 are functions of one or more inputs used in the beamforming process, which will be referenced below. Figure 8C and Figure 8D This will be discussed. In an embodiment of method 800, the process 815 of beamforming an object includes generating one or more coherent beamforming output signals in digital format (e.g., one or more object beamforming echoes). In an embodiment of method 800, the process 815 of beamforming an object includes generating one or more pseudo-coherent beamforming output signals in digital format (e.g., one or more object beamforming echoes).

[0238] Method 800 includes process 820: optimizing one or more beamforming output signals to determine the location, orientation, geometry, and / or physical properties or a set of physical properties of an object. Examples of physical properties or sets of physical properties include, but are not limited to, density, bulk modulus, acoustic properties or multiple acoustic properties such as acoustic impedance, surface reflection, volumetric or internal reflection, acoustic absorption, etc., or other physical properties of the object. In some embodiments of process 820, determining the location, orientation, geometry, and / or physical or acoustic properties of an object based on the optimization of one or more beamforming output signals includes: generating one or more scalar outputs based on one or more beamforming output signals; optimizing one or more scalar outputs based on at least some of the location, orientation, geometric properties, or physical properties of the object; and detecting the object by using an optimizer to determine the degree of optimization of one or more objective functions based on the values ​​or variations of the optimized inputs and outputs, compared with a detection criterion.

[0239] Method 800 includes process 840: generating an image of an object based on optimized reproduction of its position, orientation, geometric properties and / or object properties (e.g., physical properties, including surface properties, volume properties and / or acoustic properties).

[0240] For example, in some embodiments of method 800, process 820 may include process 821: generating one or more scalar outputs based on one or more beamforming output signals. In some embodiments of process 821, for example, one or more scalar outputs are generated by summing the power of one or more coherent beamforming output signals over a time window determined by the center frequency and bandwidth of the transmitted pulses as described above. (See reference...) Figure 8C and Figure 8D As discussed in further detail, one or more scalar outputs may correspond to the outputs of one or more objective functions. One or more inputs to the beamforming process 815 (e.g., parameters describing the object, array, and their relative position and orientation; acoustic properties) are independent variables of one or more objective functions.

[0241] In some embodiments of method 800, process 820 may include process 825: optimizing one or more scalar outputs based on at least some of the position, orientation, or geometry of the transducer array, and / or the position, orientation, one or more geometric and / or physical properties (e.g., surface or volumetric characteristics) and / or the acoustic properties of the object. For example, process 825 uses an optimizer (i.e., an optimization algorithm) to optimize one or more objective functions to produce one or more optimizer outputs. In some example embodiments, the one or more optimizer outputs produced at process 825 include, but are not limited to, the norm of the object parameters, the norm of the array parameters, one or more optimality metrics, one or more feasibility metrics, one or more residuals, and / or step size information. In embodiments of process 825, one or more scalar outputs are optimized, for example, by changing parameters related to the transducer element array and / or object model and / or transformations that associate the object with the array in space, and / or acoustic properties. In this way, one or more scalar outputs are functions of parameters associated with the transducer element array and / or the object model and / or the transformation that associates the object with the array in space and / or parameters associated with acoustic properties, i.e., forming an objective function optimized by an optimizer (i.e., an optimization algorithm) (e.g., at process 821). One or more optimization parameters may include, but are not limited to: (i) at least one output of the objective function, i.e., at least one dependent variable, such as the object beamforming echo power, which is an integral; and / or (ii) at least one output of the objective function, i.e., at least one independent variable, such as, but not limited to, parameters describing the position and orientation of the object, parameters describing the geometry of the object, parameters describing the properties of the object, parameters describing the position and orientation of the array, parameters describing the geometry of the array, and / or parameters describing acoustic properties.

[0242] In some embodiments of method 800, process 825 determines the position, orientation, one or more geometric properties and / or one or more physical properties and / or one or more acoustic properties of the object based on one or more beamforming output signals generated at process 815 and further refined in process 821 in response to changes in the properties of the transducer array and the object model, in order to optimize one or more properties of the one or more beamforming output signals.

[0243] In some embodiments of method 800, process 820 may include process 830: detecting an object by determining the degree of optimization of one or more objective functions (e.g., process 821) relative to (e.g., compared to) a detection criterion or stopping criterion (e.g., one or more thresholds) based on (i) the values ​​of the optimizer's inputs and outputs and / or (ii) changes in the optimizer's inputs and outputs. In some embodiments, process 830 detects an object by comparing one or more scalar outputs with a threshold (e.g., a value that satisfies the total integral power). In some embodiments, the determined degree of optimization may be based on a detection decision where optimized variables (e.g., object parameters, object position, object orientation) satisfy a threshold (e.g., a detection criterion or stopping criterion). In some embodiments, the determined degree of optimization may be based on a detection decision where changes in optimized variables (e.g., object parameters, object position, object orientation) between two or more iterations of the optimizer satisfy a threshold (e.g., a detection criterion or stopping criterion). In some implementations, object detection may include locating one or more regions of an object by maximizing the power contained within one or more beamforming output signals, based on the geometry, position, and / or orientation of an array of transducer elements (e.g., transducers, receivers, and / or transceivers) and the position and / or orientation of one or more regions (e.g., one or more distinct sets of points) on the object model. In this way, process 830 detects an object by determining at least some of its position, orientation, geometric and / or physical properties and / or acoustic properties based on one or more optimization parameters and optimization criteria applied to those parameters, for example, as referenced. Figure 8C Further detailed discussion is needed.

[0244] In some implementations, for example, process 830 detects an object by determining the degree of optimization based on the absolute values ​​of the optimizer's inputs and / or outputs and / or the changes between iterations of the optimizer's inputs and / or outputs. For example, the optimizer must iterate (e.g., maximize the objective function) while searching for and refining solutions. For example, the input may consist of one or more scalar outputs of one or more objective functions (e.g., from process 821), and the output may consist of the geometry, position, and / or orientation of an array and / or the geometry, position, and / or orientation of one or more regions (e.g., one or more distinct or overlapping sets of points) on the object model.

[0245] In some embodiments of process 830, during the optimization of one or more inputs, if the values ​​of one or more inputs exceed one or more corresponding absolute thresholds, thus satisfying the optimization requirement, process 830 may signal that an object has been detected. In some embodiments, during the optimization of one or more inputs, if the difference in the values ​​of one or more inputs between two or more iterations of the optimization algorithm falls below one or more corresponding thresholds, thus satisfying the optimization requirement, process 830 may signal that an object has been detected. In some embodiments, during the optimization of one or more inputs, an object may be detected by a combination of one or more inputs exceeding one or more thresholds and / or the difference in the values ​​of one or more inputs falling below one or more thresholds.

[0246] Since the output of the optimization algorithm is often unknown—for example, initially, the object has an unknown position and orientation relative to the array—a detection threshold based on the absolute value of one or more outputs from the optimization algorithm is generally not considered for detection unless it can be verified, for example, that the optimized position and orientation are within reasonable limits. For instance, if the optimized position and orientation are outside reasonable limits, the object cannot be detected. Therefore, in some embodiments of process 830, during the optimization of one or more outputs, if the value of one or more outputs falls outside one or more ranges or limits, and thus does not meet the optimization criteria, process 830 can signal the determination that no object was detected.

[0247] In some implementations of process 830, during the optimization of one or more outputs, if the difference between the values ​​of one or more outputs between two or more iterations of the optimization algorithm is less than one or more corresponding thresholds, thus satisfying the optimization requirement, process 830 can signal that an object has been detected. For example, if the change in the norm (e.g., vector length) of the optimizer's array position output is less than a specified amount, the optimization algorithm has converged, and an object has been detected.

[0248] In some implementations of process 830, the norm or function of a scalar metric of vectors operated on can be computed across all optimizer output variables (e.g., the Euclidean norm or length of a multidimensional vector) or one or more subsets of optimizer output variables (e.g., array positions are one subset, array orientations are another, and so on). One or more norms can be normalized and / or linearly or non-linearly combined to compute a composite norm or scalar value as a function of two or more optimizer outputs. According to the previously described implementation of process 830 and without limitation, one or more norms can be used instead of optimizer outputs to determine the degree of optimization, and without limitation, individual detection criteria can be associated with one or more norms as optimizer outputs. Vector norms can be used as optimizer convergence metrics, for example, when the objective function contains a large number of independent variables.

[0249] In some implementations of process 830, the norm can include any function applied to the vector that produces the optimized output of the scalar metric, which can include, but is not limited to, the Euclidean norm (also known as the L1 norm). 2 norm, l 2 Norms, 2-norm or square norm), Manhattan norm (also known as 1-norm, Manhattan distance, l1 norm, l1 distance or taxi norm), p-norm, maximal norm, composite norm, asymmetric norm and Mahalanobis distance.

[0250] Figure 8B The diagram illustrates an example embodiment of a front-end subsystem 850, which is an example embodiment of an object imaging system according to the disclosed technology. Figure 8A The method 800 shown illustrates data flow and example variables in communication between different functional subsystems executable in hardware and software.

[0251] For example, Figure 8B The diagram depicts a physical object 851 being acoustically queried by a transmitted acoustic waveform (e.g., through a medium supporting the sound wave), the acoustic waveform being transmitted by a transmitter 853A (e.g., according to...). Figure 8AThe process 810 discussed in the illustration generates and controls the signal and is transduced by the transducer element array 852. In some embodiments, the transducer element array 852 comprises an arrangement of transducer elements organized in a continuous, discontinuous, symmetrical, asymmetrical, one or more transducer segments or subarrays, or other arrangements. In some embodiments, for example, the transducer element array 852 is included in a single acoustic probe unit or device; while in some embodiments, for example, the transducer element array 852 is included in multiple acoustic probe units or devices. Furthermore, according to process 810, the returned echo (e.g., reflected and / or scattered echo) is transduced by the transducer element array 852 and processed by the receiver 853B (e.g., amplified, sampled, and / or filtered), which stores digital complex radio frequency (RF) samples in one or more memory units (e.g., in...). Figure 8B The diagram shows an RF echo memory 855. For example, the memory unit may include a non-transitory computer-readable medium, such as random access memory (RAM). The timing, multiplexing, and parameters associated with the waveform generated by the transmitter 853A are controlled by a sequencer unit 854, which also coordinates with the receiver 853B and performs time-base reference communication, ensuring that transmission and reception are coherent, for example, that reception occurs deterministically and within a known time period after transmission, according to a digital clock or timer. In this way, based on coherent transmission and reception, the real, complex, or quadrature digital samples stored in the RF echo memory 855 have known timing, such as timestamps and sampling rates.

[0252] Figure 8C The diagram illustrates an example embodiment of a rear subsystem 860 illustrating an example embodiment of an object imaging system according to the disclosed technology, depicting a hardware and software executable and operable for implementing the disclosed method (including...). Figure 8A The data flow and example variables in communication between different functional subsystems of the method shown in 800).

[0253] Figure 8C The picture depicts Figure 8B As described above, processed and sampled acoustic echoes, which are echo samples (e.g., digital complex RF samples) that can be stored in memory, can be transmitted as data to object beamformer 861 for processing by an object beamforming algorithm that implements at least some of the processes of method 800 described above, for example, according to the present technology.

[0254] For example, in reference Figure 8AIn some embodiments of the described processes 815-830, the beamforming output signal (e.g., a digital signal representing a non-scalar waveform) can be passed to an objective function 862 to generate an optimizable (scalar) quantity (e.g., the integral object beamforming echo power). For example, an optimizer 863 (e.g., an optimizer algorithm executable by the object beamformer unit and / or the data processing unit) can use the optimizable (scalar) quantity to change variables describing the array model, the object model, and their relative positions and orientations. For example, variables generated by the optimizer 863 describing the array model, the object model, and their relative positions and orientations (e.g., 4×4 transformations) can be passed to an array and object model generator 864, which can be embodied as an algorithm for modifying the array model and / or the object model. In such embodiments, a new or modified array model and / or a new or modified object model can be passed to the object beamformer 861 to re-beamform the object, i.e., iteratively repeating the object beamforming process. The resulting beamforming output signal (e.g., optimized or unoptimized) can be passed again through the objective function 862 and the optimizer 863. According to... Figure 8A In processes 815-830, the optimizer's output can be passed to object detector 865, which determines whether an object is detected based on described object detection criteria (e.g., threshold, tolerance, stopping criteria, optimizer convergence metric). If an object is detected, then according to process 840 described above, a representation of the detected object and its parameters can be displayed at display object and parameter module 866 (e.g., user interface and display unit).

[0255] Figure 8D The disclosed method is operable according to the disclosed technology for implementing the disclosed method (including...). Figure 8A Another figure shows an example embodiment of the rear terminal system 860D of the example embodiment of the object imaging system of the method. Figure 8D The diagram of the example rear terminal system 860D provides Figure 8C Further details are provided in some embodiments of the rear terminal system 860 shown.

[0256] like( Figure 8D As shown in the figure, in reference Figure 8AIn some embodiments of the described processes 815-830, multiple inputs can be provided to the target beamformer 861 to generate beamforming output signals(s). For example, the sequencer 854 provides sequencing parameters (e.g., timing information, echo information, acquisition status, or others) based on a sequencing algorithm utilizing, for example, inputs from a user and / or automatic inputs, such as commands like start scan, rescan, stop scan, and / or other parameters like transmitter power, receiver gain, etc. Furthermore, for example, the target beamformer 861 (e.g., from the RF echo memory 855 (e.g.) Figure 8B The receiver receives echo information, which includes synthetic aperture echoes, such as complex RF samples, analytical RF samples, IQ demodulated RF samples, orthogonal RF samples, and / or other echo samples. The beamforming output signal (e.g., the object beamforming echo, which may be a vector of complex echo samples and digitally represented) can be passed to an objective function 862 to generate optimizable (scalar) quantities (e.g., the integrated object beamforming echo power, maximum amplitude, or others) based on objective function parameters (e.g., integral limit, filter coefficients, weights, thresholds, etc.). For example, an optimizer 863 (e.g., an optimizer algorithm executable by the object beamformer unit and / or data processing unit) can use the optimizable (scalar) quantities to change variables describing the array model, the object model, and their relative positions and orientations (e.g., 4×4 transformations). Optimizer 863 uses optimizer parameters to determine optimization variables and / or optimization outputs. Optimizer parameters may include, for example, initial guesses (such as initial guesses of the first output), constraints, maximum iterations, maximum objective function evaluation, minimum / maximum step size, search grid, or other parameters. For example, variables describing the array model and object model generated by optimizer 863 (such as variable array position and orientation) can be passed to array and object model generator 864, which may be embodied as an algorithm for modifying the array model and / or object model. In such an implementation, a new or modified array model and / or a new or modified object model can be passed to object beamformer 861 to re-beamform the object, i.e., iteratively repeating the object beamforming process. The resulting beamforming output signal (e.g., optimized or unoptimized) can then be passed again through objective function 862 and optimizer 863. Figure 8AIn processes 815-825, the optimizer's output can be passed to object detector 865, which determines whether an object is detected based on object detection criteria (e.g., stopping criteria, tolerance, and / or threshold). If an object is detected, then according to process 840 described above, a representation of the detected object and its parameters can be displayed at display object and parameter module 866 (e.g., user interface and display unit). Furthermore, object detector 865 can provide optimized variables (e.g., object parameters, object position, and orientation information) to display object and parameter module 866 to generate an image. The generated image can also be used to update or modify the object model, for example, where object geometry in an array reference frame (e.g., (multiple) triangulated surfaces, (multiple) faces, one or more vertices, (multiple) normals, (multiple) areas, (multiple) textures, and / or (multiple) renderings) and / or array geometry information can be provided from display object and object parameter module 866 to array and object model generator 864.

[0257] Similar to Figure 1, Figure 7 and Figures 8A-8D Example features of embodiments of the synthetic aperture acoustic imaging system shown and / or discussed throughout this patent disclosure may include the following.

[0258] In some embodiments, for example, an acoustic imaging system for imaging the scattering properties of an object contained within a medium having an acoustic impedance different from that of the object, includes: (i) a transducer element array operable to transmit, receive, and / or transmit and receive acoustic signals by forming a synthetic aperture of the system; (ii) transmitter and receiver circuitry coupled to the transducer element array and configured to generate and / or process transmitted acoustic waveforms (e.g., digital signals) transduced and transmitted by selected transmitting transducer elements(s) of the array, and configured to convert received acoustic echoes (received at selected receiving transducer elements(s)) into digital signals representing acoustic echo waveforms; iii) a digital beamforming unit operable for storing and / or generating an object model, and calculating delays and weights based on array geometry and object model geometry, and generating a digital signal derived from beamforming of the received echoes based on the calculated delays and weights; (iv) a data processing unit including a processor and a memory to operate as a data processing and control unit of the system, configured to control transmit and receive sequencing, and configured to control the beamforming unit to determine surface properties based on the digital signals generated by the beamformer; (v) and a display unit operable for generating an image of the object based on the reproduction of the position, orientation, and surface properties determined by the data processing unit.

[0259] In some embodiments of an acoustic imaging system used to image the surface reflectivity of an object, for example, a digital beamforming unit is configured to calculate a delay determined from each transmitter position to a point on the model and back to each receiver position; calculate weights for specular scattering, acoustic field directivity, and complex reflectivity based on the incident vector, reflection vector, receiver vector, transducer normal vector, object surface normal vector, and / or prior information about the object related to complex reflectivity; and apply the calculated delays and calculated weights to a stored echo before summing the delayed and weighted echoes to generate a single beamforming echo.

[0260] In some embodiments, one or more beamforming echoes are processed using additional analysis and filtering, including but not limited to finite impulse response filters, infinite impulse response filters, low-pass filters, high-pass filters, band-pass filters, matched filters, autocorrelation, cross-correlation, envelope detection, demodulation, Wiener filters, nonlinear filters, causal filters, noncausal filters, digital filters, frequency domain filters, time domain filters, principal component analysis, wavelet analysis, Fourier transform, filter banks, time-frequency analysis, cyclostationary analysis, singular value decomposition, eigenvalue decomposition, interleaved decomposition, even / odd decomposition, adaptive filters, interpolators, deconvolution filters, inverse filters, and neural networks.

[0261] For example, in some embodiments of an acoustic imaging system used to image the reflectivity of an object, the data processing unit is configured to integrate the beamformer echo power over a time window and optimize the transformations applied to the array element positions and normal vectors that serve as inputs to the beamformer in order to maximize the beamformer echo power integrated over the time window.

[0262] For example, in some embodiments of an acoustic imaging system used to image the reflective properties of an object, the display unit is configured to visualize the object in an array of reference frames according to the inverse transformation of an optimized transformation.

[0263] In some embodiments of the system, for example, the transducer element array includes one or more transmitter transducer elements and one or more receiver transducer elements that are spatially separated from the object (e.g., located near the object). In some embodiments, for example, one or more transmitter transducer elements and one or more receiver transducer elements completely or at least partially surround the object.

[0264] In some embodiments of the system, for example, the transducer element array includes at least three transducer elements that generate at least three monostatic and at least three bistatic reflection samples of the object, such that the samples are significantly separated on the object surface.

[0265] In some embodiments of the system, for example, the transducer element array includes an array geometry configured to generate at least six independent monostatic or bistatic reflective samples of the object, such that the samples are significantly separated on the object surface.

[0266] In some embodiments of the system, for example, the object model includes vertices and faces that approximate objects, where the faces are no larger than one acoustic wavelength resolution. In some embodiments, for example, these faces have half or less of the acoustic wavelength resolution.

[0267] In some embodiments of the system, for example, the object model includes points and surface normals corresponding to each point, which approximate the object with an acoustic resolution of at most one wavelength. In some embodiments, for example, the points and surface normals corresponding to each point approximate the object with a resolution of less than half the acoustic wavelength.

[0268] In some embodiments of the system, for example, in addition to point and surface normal vectors, the object model also consists of surface curvature parameters related to the major and minor curvatures of the surface at that point.

[0269] In some embodiments of the system, for example, the object model consists of faces and face normals.

[0270] In some embodiments of the system, for example, the object model consists of vertices and vertex normals.

[0271] In some embodiments of the system, for example, the object model consists of faces and face normals, as well as vertices and vertex normals.

[0272] In some embodiments of the system, the object points and normals used within the beamformer are subsets representing the points and normals of the entire object.

[0273] In some embodiments of the system, for example, the beamforming unit is configured to calculate a first time delay from transducer element 1 to a surface or point on the model and back to transducer element 1.

[0274] In some embodiments of the system, for example, the beamforming unit is configured to calculate a second time delay from transducer element 1 to a surface or point on the model and back to transducer element 2.

[0275] In some embodiments of the system, for example, the beamforming unit is configured to calculate a first weighting factor based on the normal vector of transducer element 1, the incident vector from transducer element 1 to a surface or point on the model, the reflection vector from a surface or point on the model, and the receiving vector from a point or surface on the model to transducer element 1.

[0276] In some embodiments of the system, for example, the beamforming unit is configured to calculate a second weighting factor based on the normal vector of transducer element 1, the incident vector from transducer element 1 to a surface or point on the model, the reflection vector from a surface or point on the model, the receiving vector from a point or surface on the model to transducer element 2, and the normal vector of transducer element 2.

[0277] In some embodiments of the system, for example, transmission occurs on transducer element 1 of the array, and subsequent reception occurs on transducer elements 1 and 2 of the array. The echo received on transducer element 1 is delayed and weighted according to a first delay and a first weighting factor, and the echo received on transducer element 2 is delayed and weighted according to a second delay and a second weighting factor. The delayed and weighted echoes are then summed together in a beamformer to generate a single beamformed echo output signal.

[0278] In some embodiments of the system, for example, transmission occurs on transducer element 1 of the array, and after a delay, transmission occurs on transducer element 2 of the array, such that the delay is substantially less than the round-trip time from transducer element 1 to the nearest point on the object and back to transducer element 2.

[0279] In some embodiments of the system, for example, multiple weighted and delayed echoes determined for multiple surfaces or points on the model each contribute to the single beamforming echo output signal.

[0280] In some embodiments of the system, for example, the coordinates of multiple transducer elements of the array and their corresponding normal vectors are changed by applying a homogeneous transformation determined by an optimization algorithm in order to maximize the integrated power of the beamformer single echo signal over a time window duration inversely proportional to the center frequency and bandwidth of the received echo.

[0281] In some embodiments of the system, for example, the coordinates are transformed by using the inverse transformation of the optimized transformation to present the object model or equivalent representation of the object used within the beamformer unit on the display relative to the coordinate system of the array.

[0282] In some embodiments of the system, for example, the acoustic impedance of the object is significantly different from that of the surrounding medium, thereby producing monostatic and bistatic specular reflections observable from multiple transmitter and receiver locations.

[0283] In some embodiments of the system, for example, the system is operable such that the transmission event occurs on groups of transducer elements of an array, each group of transducer elements having an individual delay, so that the transmission appears to originate from a single point in space.

[0284] In some embodiments of the system, for example, the system is operable such that a received event occurs on groups of transducer elements of an array, wherein each group of transducer elements is beamformed separately to generate a single echo as if the receiver were located at a single point in space.

[0285] In some embodiments of the system, for example, the system can be operated such that the center frequency of the transmitted waveform is substantially less than 5 MHz.

[0286] In some embodiments of the system, for example, the center frequency of the transmitted waveform is adjustable across the transducer bandwidth to be operable to assist in the optimization of array position and orientation by reducing the sensitivity of the optimization to the array position and orientation.

[0287] In some embodiments of the system, for example, the bandwidth of the transmitted waveform is adjustable across the transducer bandwidth (e.g., from 10% to 100%) to be operable to assist the optimization of array position and orientation by reducing the sensitivity of the optimization to the array position and orientation.

[0288] In some embodiments of the system, for example, the system is operable such that the center frequency and / or bandwidth of the transmitted waveform can be increased to improve the spatial positioning of the object.

[0289] In some embodiments of the system, for example, the system can be operated such that specific transmitted waveforms or waveform types can be transmitted to improve the spatial localization of objects, improve the signal-to-noise ratio (SNR) of echoes, and / or reject interference sources, such as frequency-modulated waveforms, phase-modulated waveforms, orthogonal frequency-division multiplexing (OFDM) waveforms, direct-sequence spread spectrum (DSSS) modulated waveforms, pseudo-random noise waveforms, pseudo-random binary sequence waveforms, maximum-length sequence waveforms, binary coded waveforms, complementary waveforms, arbitrary coded sequence waveforms, range-compressible waveforms, arbitrary waveforms, etc.

[0290] In some embodiments of the system, multiple different waveforms are transmitted simultaneously from different sources, such as orthogonal waveforms, coded waveforms, and waveforms with different frequencies and / or bandwidths and / or time-bandwidth products and / or durations.

[0291] In some embodiments of the system, multiple different waveforms, such as orthogonal waveforms, coded waveforms, and waveforms with different frequencies and / or bandwidths and / or time-bandwidth products and / or durations, are transmitted from different sources at different times.

[0292] In some embodiments of the system, multiple different waveforms, such as orthogonal waveforms, coded waveforms, and waveforms with different frequencies and / or bandwidths and / or time-bandwidth products and / or durations, are transmitted from the same source at different times.

[0293] In some embodiments of the system, for example, the object is repeatedly positioned in real time so that the movement of the object relative to the array can be visualized on the display unit.

[0294] In some embodiments of the system, for example, the position and orientation coordinates of an object generated by the object imager are transmitted for use by a separate system.

[0295] In some embodiments of this system, the object imager output includes an estimate of the quality of the object detection.

[0296] In some embodiments of the system, the object imager is operable for beamforming multiple objects.

[0297] In some embodiments of the system, the object imager is operable to perform beamforming on specific features of an object.

[0298] In some embodiments of the system, the object imager is operable to perform beamforming on specific features of more than one object.

[0299] In some embodiments of the system, the object imager is operable to locate multiple features of an object, such that the distances and angles between the features can be quantized.

[0300] In some embodiments of the system, the object imager is operable to locate features on multiple objects, making it possible to determine the distances and angles between the features.

[0301] Other examples of synthetic aperture systems and methods for synthetic aperture acoustic imaging are described in U.S. Patent No. 9,844,359, which can be used to implement beamforming object imaging techniques according to the present technology. This patent is incorporated herein by reference for all purposes as part of the disclosure of this patent document. For example, any exemplary embodiments of the synthetic aperture object imaging systems and subsystems described herein (including, but not limited to, those described herein) Figure 1E , Figure 7 , Figure 8B , Figure 8C and / or Figure 8D The system may include and / or employ one or more features of the apparatus, system and method described in U.S. Patent No. 9,844,359, including structural features and / or functional features.

[0302] Further discussion and example data from example embodiments of the disclosed SAOI technology

[0303] Because the traditional limitations of acoustic imaging do not apply to the disclosed SAOI technology, it is foreseeable that the novel advantages and benefits from the disclosed implementations of synthetic aperture object imaging (SAOI) systems and methods for image formation, spatial sampling requirements, and spatial resolution will be profound. Conventional synthetic aperture imaging utilizing planar arrays of regularly sampled data is well-defined. The far-field spatial response of such a synthetic aperture array can be approximated as a Fourier transform of the convolution of the transmitting and receiving arrays. The spatial response (e.g., the response in azimuth or elevation angles) is typically characterized by beam characteristics (e.g., main lobe width, side lobe height, depth of field, Fresnel distance, etc.). The apodization function (e.g., a window) applied to the transmitting or receiving aperture modifies the beam characteristics and also the beam shape parameters. The focusing delay applied to the transmitting and / or receiving aperture of this aperture is used to bring the far-field response into the near field, which also modifies the beam shape parameters. For example, beamwidth is typically defined as proportional to the wavelength multiplied by the f-number, where the f-number is defined as the focal length divided by the effective aperture size (e.g., in the case of synthetic aperture, the effective aperture size can be larger than the physical aperture size). The element spacing of this aperture is also critical when considering high focusing and beam guiding. A requirement for at least half the wavelength sampling (e.g., defining the array spacing) is critical when the beam is focused and guided to avoid grating lobes (which can also be characterized for a given array).

[0304] The disclosed SAOI systems and methods do not have these or similar limitations on array pitch because grating lobe energy is almost always incoherent for objects with finite volumes. That is, due to the fact that any object with a finite volume must have at least some convexity and finite dimensions, grating lobe energy tends to be scattered elsewhere in unobservable directions and / or away from the object itself. This feature is extremely useful, for example, because it enables sparse synthetic apertures, low channel counts, and inexpensive systems for imaging objects. Furthermore, sparse apertures do not require regular sampling, which is used to further suppress grating lobe energy during beamforming. Additionally, sparse apertures formed using objects with surfaces modeled as irregularly sampled are used to further suppress grating lobe energy during beamforming.

[0305] In traditional synthetic aperture imaging, when the echo is coherently beamformed onto a spatial grid to form an image, all factors defining the beam characteristics at the transmit and receive ends affect the appearance of a point scatterer in space; for example, the spatial response is characterized as a point spread function (PSF). The PSF can be incoherent; for example, when beamforming a synthetic aperture echo onto a point scatterer, envelope detection is performed on the beamformed echo to remove all phase information, and the peak value of the envelope is stored for a given location of the scatterer in space, regardless of the peak's extent or the scatterer's extent. The resulting incoherent field amplitude is then mapped (e.g., onto the grid at the point scatterer's location) to form a beamchart or beampattern, which is often measured and / or simulated for a given array to characterize its performance.

[0306] In conventional synthetic aperture imaging, the PSF (Presentation-Synthetic-Aperture Filter) can also be coherent; for example, when synthetic aperture echoes are not beamformed, they are left as temporal echoes. In this way, the PSF represents the spatiotemporal response of the point scatterer, which can be very different for each transmitter-receiver pair because all spatial and temporal information is preserved. In other words, there is much more information in the spatiotemporal response of a given aperture compared to beamforming and incoherent summation, as described above. Existing imaging systems utilize this coherent information to improve imaging performance in terms of resolution, speckle reduction, and contrast, for example, by synthesizing multiple phase-coherent wavefronts for a set of known spatiotemporal responses. This has the effect of reducing phase-incoherent components (e.g., corresponding to sidelobes and grating lobes) and emphasizing phase-coherent information (e.g., corresponding to the main lobe).

[0307] The disclosed SAOI systems and methods do not strictly possess a PSF, except in the limiting case where the object becomes a point target, which is impossible for objects with finite volumes. Consider an object approximated as a set of point scatterers (see the extended discussion below). For a given transmitter and receiver pair, summing the PSF contributions for each scatterer (assuming linear acoustics and superposition); the resulting quantity is no longer the PSF, but instead, the object spread function (OSF) is a more appropriate term to describe it. Thus, for a given object, the OSF of each transmitter / receiver pair in the synthetic aperture is the sum of the PSFs of that pair. Therefore, the OSF of a given synthetic aperture array is the set of OSFs corresponding to all transmit-receive pairs within the aperture. A possible OSF for a given synthetic aperture can be defined as the set of all received synthetic aperture echoes, each received synthetic aperture echo corresponding to a different transmitting element; for example, the set of synthetic transmit aperture (STA) echoes from the object is the OSF of the aperture. Another possible definition of OSF can include all possible synthetic aperture echoes formed by one or more transmitting elements and one or more receiving elements. The number of possible combinations is virtually infinite. The disclosed SAOI technique provides a novel way to describe imaging systems considered to have broad and profound implications.

[0308] The disclosed SAOI system and method can utilize the space-time response from one or more elements of the array to an object and back to one or more elements of the array, and this space-time response is an OSF. For example, the OSF depends on the shape of the object, the physical properties of the object, the physical properties of the medium, its orientation relative to the transmitting and receiving array elements, the shape, size and geometry of the transmitting and receiving elements, and the diffraction and propagation of the sound waves.

[0309] The definition of the object spread function (OSF) can be used to quantify the spatial resolution of disclosed synthetic aperture object imaging systems and methods. Spatial resolution is a subjective characteristic of the imaging system, dependent on the accuracy definition. A commonly used definition is the full-width half maximum (FWHM) or -6 dB amplitude width, or the equivalent -3 dB power width. As mentioned above, the diffraction-limited spatial resolution of conventional imaging systems is proportional to the f-number multiplied by the wavelength. (For example, the spatiotemporal resolution measured using the coherent PSF described above) is often defined as proportional to the transmitted pulse length (or the compressed pulse length of the encoded waveform), which in turn is proportional to the wavelength divided by the fractional bandwidth. The f-number of the disclosed SAOI system is difficult to define because the synthetic aperture is formed using an object. For example, neglecting the effect of the object on the f-number (for illustrative purposes), if the aperture surrounds the object, the effective f-number of points on the object can be quite small, resulting in very high diffraction-limited spatial resolution (e.g., PSF) for points on the object in the conventional sense. In this way, the disclosed SAOI system and method can essentially be considered as a tomographic scan, and tomographic scan systems are known to achieve high spatial resolution. Furthermore, by considering the expansion of the aperture formed by the object, more of the aperture interrogates points on the object via bistatic reflection, since more of the aperture is visible for each point. The resulting larger effective aperture further reduces the effective f-number and increases the diffraction-limited spatial resolution (e.g., PSF) of points on the object in the conventional sense. It is readily apparent that the diffraction-limited resolution of points on the object in the conventional sense can be much smaller than the wavelength using the tomographic aperture.

[0310] Further steps can be taken beyond the above. Consider a given object aperture system with an OSF in one orientation, and only synthetic aperture echoes are observable; for example, the OSF of the object aperture system is a synthetic aperture dataset defined as OSF0. Now consider a second OSF measured for the same object aperture system, but with a small 6DoF perturbation (e.g., displacement) between the object and the aperture (called OSF(d)). OSF(d) will differ from OSF(0), and the degree of difference depends on many factors, including all aspects of the aperture and object, wavelength, bandwidth, and how the difference is quantified as a function of OSF(0) and OSF(d) (e.g., linear and / or nonlinear combinations of OSF(0) and OSF(d), cross-correlation, summation, etc.) to quantify the similarity. One definition of spatial resolution can be formulated as 2d, such that for a perturbation d in one or more spatial dimensions, |S(OSF(0),OSF(d))|=0.5, where a function S returning a scalar value in units of echo amplitude determines the similarity between two sets of synthetic aperture echoes. The resulting 2d value can be very small relative to the wavelength, at least half the wavelength of the aperture that essentially surrounds the object, in the same way that an interferometer can resolve very small displacements.

[0311] The disclosed SAOI system and method can be viewed as a multipath acoustic interferometer that considers all possible round-trip paths from the array to the object and back to the array, making the disclosed synthetic aperture object imaging system and method extremely sensitive to small displacements and suitable for locating objects in space. For example, Figure 6 The example results shown illustrate the potential sensitivity for small displacements in non-tomographic scanning apertures.

[0312] In some implementations, for example, the object beamformer of an example embodiment of a SAOI system operates in a manner similar to the function S described above. For instance, the measured coherent OSF is the input (e.g., synthetic aperture echo), the array model is the input (e.g., array model), the object model is the input (e.g., object model), the medium is the input (e.g., speed of sound, typically assumed to be constant), and the relative orientation of the array to the object is the input (i.e., d or 6DoF coordinates or a 4×4 transformation matrix as described above). The output (e.g., object beamformed echo) is calculated as a function of d to maximize some metric of the output (i.e., the integrated object beamformed echo power). In this way, the object beamformer can be used to estimate the spatial resolution of an object imaging system. For example, given a set of synthetic aperture echoes (e.g., OSF) recorded from an object at a known location, the object beamformer's response to displacement of the object (or array) away from the known location in one direction gives a metric of the spatial resolution in that direction (e.g., the displacement required to achieve half the maximum power). An example of such a spatial resolution metric can be found in... Figure 6 The peak width of the results shown is derived from the fact that it is approximately half the wavelength of the simulation problem (again, it is not the tomographic aperture).

[0313] In the foregoing discussion and description of object beamformers, it has been clearly shown that object beamformers attempt to match synthetic aperture observations with the problem model. Without loss of generality, for example, this is merely one possible conception for the object detection and localization problem using synthetic apertures, and other concepts are possible, for example, those within the framework of maximum likelihood estimation theory or other estimation and / or detection theories. However, the basis, principles, and objectives of alternative concepts remain the same, and such concepts cannot escape the novel concept of utilizing object-forming synthetic apertures as a means of detecting and localizing said objects.

[0314] The acoustic transmit / receive response of an object can be simulated by approximating the object and the array of transducer elements, for example, by representing the object and array as a set of point targets, point sources, and point receivers. In this way, the object is spatially sampled. Ideally, the spatial samples of the object and array are extremely small to generate the best approximation. Computational considerations require that the spatial samples be finite and roughly on the order of a wavelength based on simulations and experiments, but for a given object and array geometry, it is difficult to define the exact requirements. In some example implementations, the numerical integration of the Rayleigh-Sommerfeld integral can be made to converge accurately in the near field of the aperture (e.g., using a fast near-field method), or the spatial impulse response method can be applied to calculate the spatial impulse response of the object, which can then be convolved with the time-domain transmit / receive response. Similarly, similar spatial sampling requirements apply when beamforming an object; for example, the spatial sampling requirements for accurately simulating the echo from the object also require object beamforming of the echo from the object. Therefore, the spatial sampling requirements of the disclosed SAOI system and method are broadly defined as: (i) obtaining a sufficient number of spatial samples to determine one or more unknown parameters describing the array, the object, and their relationships, i.e., requiring at least as many independent measurements as the unknown quantity, and (ii) ensuring sufficient spatial sampling of the object itself so as to approximate the scattering process using discrete and computationally feasible scattering processes (e.g., according to established numerical methods for approximating the transmit / receive response of the object).

[0315] Example implementations, including proof-of-concept experiments, are performed to demonstrate certain aspects, features, and capabilities of the disclosed SAOI method. In the example described below, an example implementation of the SAOI method is used to locate two distinct objects in three-dimensional space, the first object having three degrees of freedom and the second object having six degrees of freedom. This is performed using commercial ultrasound imaging hardware and an off-the-shelf computing platform. The hardware used in the example implementation includes a Verasonics Vantage 256 research ultrasound system (Verasonics, Inc., Kirkland, WA) that interfaces with a Vermon 3MHz 32×32 matrix array probe (Verasonics, Inc., Kirkland, WA) using a Verasonics UTA ​​1024-MUX adapter (Verasonics, Inc., Kirkland, WA), a UR10 robotic arm (Universal Robots A / S, Odense, Denmark), and a Dell Precision Tower 7920 workstation (Dell, Inc., Round Rock, TX) containing an NVIDIA Quadro P620 GPU (NVIDIA Corp., Santa Clara, CA).

[0316] A robotic arm was programmed to manipulate an object within a 50-gallon tank filled with temperature-monitored distilled water. Acoustic energy was coupled and directed at the object through a thin TPX plastic acoustic window on the side of the tank. A custom retainer was used to keep the probe stationary against the acoustic window. Aquasonic 100 coupling gel (Parker Laboratories, Inc., Fairfield, NY) was applied to the probe to provide acoustic coupling into the TPX window. The software was written in MATLAB vR2019b (MathWorks, Inc., Natick, MA), Microsoft Visual Studio Community 2019 (Microsoft Corp., Redmond, WA), and CUDA v10.1 GPU (NVIDIA Corp., Santa Clara, CA), and executed in a Windows 10 operating system environment (Microsoft Corp., Redmond, WA) to coordinate data acquisition and processing.

[0317] The matrix array probe comprises a 32×32 element array (e.g., a total of 1024 elements) addressable in four 8×32 element subarrays (e.g., four 256-element subarrays) with a nominal center frequency of 3MHz and a nominal fractional bandwidth of 50%. The element spacing of the array is 0.3mm×0.3mm (e.g., 0.6 times the wavelength at c = 1500m / s, thus approximately a phased array), with three passive rows at the elevation angle, resulting in an array aperture size of 10.5mm at the elevation angle and 9.6mm at the azimuth angle.

[0318] The ultrasound system is programmed to transmit 30-volt peak-to-peak broadband single-cycle pulses across 16 elements, such as... Figure 9 The elements are sparsely arranged on a 4×4 grid within a 32×32 grid, with the element positions marked "o" and the sparse aperture positions marked "x" and numbered from 1 to 16.

[0319] Figure 9 The diagrams depict example full array geometry and 4×4 sparse array geometry used in the example object beamformer.

[0320] Each transmission receives echoes on 256 elements at a time, requiring four transmissions per transmitting element to cover reception on all 1024 receiving elements. 4-to-1 multiplexing is supported by the UTA 1024-MUX adapter, which pairs all 1024 elements to the 256 transmit / receive channels on the ultrasound system. A total of 64 transmissions are required to form a fully synthetic transmit aperture (STA) with a 16-element sparse aperture at a pulse-repetition-frequency (PRF) of 6.41 kHz.

[0321] The received echoes are bandwidth-limited to 5MHz before being sampled by a 14-bit ADC at 35.7143MHz, FIR-filtered using a 23-tap low-pass filter (-3dB at 4.885MHz), decimated by factors from 3 to 11.9048MHz, and then FIR-filtered again using a 41-tap band-pass filter (-3dB at 1.488MHz and 4.464MHz) before being stored as 1024 signed integer samples multiplied by 256 echoes in a 2D array (including a full STA with a 4×4 sparse aperture). Only the received echoes corresponding to the transmitting element are retained for processing, resulting in a total of 256 echoes for each complete STA acquisition.

[0322] Redundant reciprocal echoes are directly summed (as described above) to reduce data volume and processing overhead. This results in a 2D array of 1024 signed integer samples multiplied by 136 echoes, encompassing a fully proprietary STA dataset with a 4×4 sparse aperture. Before beamforming, the echoes are converted to floating-point form and upsampled by a factor of 3 to recover the original sampling rate of 35.7143 MHz.

[0323] Two objects were investigated: (i) steel spheres with a diameter of 6.35 mm, and (ii) a complex object comprising eight steel spheres with a diameter of 6.35 mm arranged on a spiral with a diameter of approximately 5 cm. The complex object consisted of multiple spheres because only one ultrasonic probe was available for inspection. The multiple spheres effectively simulated the use of multiple probes pointing from multiple vantage points around the object; for example, looking out from the probe's face, eight independent spheres could be observed as if eight probes around different types of objects were observing eight independent locations on the object from eight different directions. Both objects were constructed using a 3D-printed plastic structure with a 6.35 mm diameter cup-shaped support at the top to receive steel supports with a nominal diameter of 6.35 mm bonded in place with marine-grade epoxy resin.

[0324] The sphere object is modeled as a triangulated surface consisting of 5804 triangles and 2904 vertices, such as Figure 10 As shown. The average side length of the triangle is 0.2148 mm (standard deviation 0.0226 mm), which is less than the half-wavelength value of approximately 0.25 mm. The triangle is approximately equilateral. Similarly, complex objects are modeled as eight spheres using 23,232 vertices and 46,432 triangles, as shown. Figure 11 As shown, the object is stored on the GPU as an array containing vertices, vertex normals averaged from adjacent faces, and normalized effective vertex areas averaged from adjacent faces. Additionally, the positions of the 4×4 sparse array elements, modified by a 4×4 transformation matrix, are also stored on the GPU.

[0325] Figure 10 A schematic diagram of a sphere object model used in an example object beamformer is shown.

[0326] Figure 11 A schematic diagram of the complex object model used in the example object beamformer is shown.

[0327] These objects are mechanically fixed to the robotic arm and placed in a container. The effective center position of each object, determined by the coordinate system of each object model, is programmed to be at the origin in the robot tool reference frame. For example, the center position of the sphere object model is at (x,y,z) coordinates (0,0,0), so an appropriate offset is added to the robot's end effector position to center the tool position at the center of the sphere. The coordinates of the robot tool position are recorded along with RF ultrasonic echoes of 100 randomly selected independent positions of the object (e.g., 100 random XYZ positions for the sphere object and 100 random 6DoF positions (e.g., XYZ angles and three Euler angles) for the complex object). For the complex object, the randomly generated EAX and EAY angles are set to the same values ​​as additional consistency checks. Due to the nature of the robot's internal control system, both command position and actual position are generated. The actual position is a function of the position encoder of each of the six joints on the robotic arm, and thus a measurement of the object's spatial position. The robot’s actual position is used as the measurement position in the experiment, which may differ slightly from the commanded position, depending on many factors outside the scope of this demonstration.

[0328] Figure 12A and Figure 12B Example fully specific STA RF echoes of 4×4 sparse arrays recorded for complex objects of spheres and octagons are shown. For illustrative purposes, the monostation echoes are summed twice to maintain amplitudes similar to those of the bistation echoes summed twice. The first 300 samples are blanked to ignore transmit pulse interference, resulting in an array of 3072 samples × 136 echoes, as shown in the figure (e.g., 35.7143 MHz sampling rate). Grayscale indicates the echo amplitude ranging from -500 to 500 in units of the scaled ADC (e.g., the maximum range of the cell receive apodization is -16384 to +16382). In Figure 12(a), echoes from the surface of the steel spheres are observed in the sample range of 2100–2160, corresponding to a depth range of 43.8–45.1 mm with an estimated sound velocity of 1491 m / s at a tank temperature of 22.75 °C. Similarly, in Figure 12(b), the echo from the surface of the complex object is observed starting from around sample range 1740, corresponding to a depth of 36.1 mm at an estimated sound velocity of 1482 m / s at a tank temperature of 20.0 °C. Note that the echo from the complex object is much more complex than that from the spherical object.

[0329] The object beamformer function, coded for the GPU, uses an object model, an array model, and RF echoes. The objective function is established based on the object beamforming echo power integrated over a 23-sample time window at 35.7143 MHz (e.g., from -0.308 to 0.308 microseconds in 0.028 microsecond increments), resulting in a scalar value. Within the object beamformer, parameters affecting the output include the mirror index value 4 (e.g., p = 4), the transmit directivity index 1 (e.g., qt = 1), and the receive directivity index value 1 (e.g., qr = 1). For each trial location of the object, a Monte Carlo optimizer is used to solve for the object's position, e.g., the XYZ position of a sphere, and the 6DoF position of a complex object consisting of XYZ and three Euler angles. The well-known Kabsch algorithm is used to estimate the least root mean square error (LRMSE) transformation that associates the measured robot tool position in the robot reference frame with the estimated SAOI position in the array reference frame, and the coordinates of the object and robot are reported in the array reference frame for easy interpretation.

[0330] Figure 13 Example results from 100 position trials of a spherical object are shown, with the SAOI estimated position labeled "x" and the robot position labeled "o". Note that the test range for each dimension in the first trial was approximately ±5 mm. Error statistics for the complex object are shown in Table 1. Note that the position error is approximately zero-mean, and the RMS value is much smaller than the wavelength value of 0.494 mm at the speed of sound during data collection. The RMS error of the estimated transformation relating the robot reference frame to the array reference frame is 0.16 mm. Note that the robot coordinates are in a coordinate system at the internal reference robot base point, which is more than 1 meter from the tool center of the object in the tank. Very careful measurements using a reference measuring device (e.g., FaroArm) may have determined that the robot is likely a significant source of error, as it claims a position repeatability of ±0.1 mm.

[0331] Table 1 Error analysis at 100 positions of the sphere object

[0332] <![CDATA[ coordinate ]]> <![CDATA[ RMS error ]]> <![CDATA[ Mean error ]]> <![CDATA[ Median error ]]> X 0.072mm -0.001mm 0.009mm Y 0.126mm 0.020mm -0.001mm Z 0.070mm -0.006mm -0.011mm

[0333] Example results of 100 positional trials on the complex object are shown in Figure 14(a) for the XYZ positions and Figure 14(b) for the Euler angles EAX, EAY, and EAZ, where the SAOI position is labeled "x" and the robot position is labeled "o". Note that from the first trial, the inspection range for each dimension was approximately ±8 mm and ±5 degrees. The error statistical analysis of the complex object is shown in Table 2.

[0334] To examine the consistency between the robot measurements and the SAOI estimated position on two degrees of freedom, for example, the EAY and EAZ coordinates were compared for the robot and for the estimated position (set to be equal in the experiment). In other words, the RMS error between EAY and EAZ was compared for the robot and for the SAOI measured position, and the results are summarized in Table 3. Note that the robot's RMS error accounts for approximately 60% of the SAOI error, at least for EAY and EAZ.

[0335] Table 2 Error Analysis of 100 Positions in Complex Objects

[0336] <![CDATA[ coordinate ]]> <![CDATA[ RMS error ]]> <![CDATA[ Mean error ]]> <![CDATA[ Median error ]]> X 0.279mm 0.094mm 0.088mm Y 0.306mm -0.221mm -0.189mm Z 0.192mm 0.007mm -0.008mm EAX 0.101 degrees 0.138 degrees 0.138 degrees EAY 0.162 degrees -0.290 degrees -0.284 degrees EAZ 0.204 degrees -0.043 degrees -0.023 degrees

[0337] For this example implementation, the error, while more significant than that of a single spherical object, is reasonable given the significantly greater complexity of solving for six unknown degrees of freedom. Several factors may contribute to the error, including but not limited to: errors in defining the tool center position of the complex object, robot repeatability errors, the superposition of multiple echoes, the presence of numerous local maxima in the 6DoF parameter space, and inaccuracies in the placement of the sphere within the complex object. Nevertheless, for a wide range of positions and angles, complex objects spanning nearly 50 mm in size were successfully positioned at a distance of approximately 35 mm. The example results are particularly impressive considering the relatively small 4×4 sparse aperture, consisting of only 16 elements spanning less than 10 mm × 10 mm. Modifications to the implementation of the example SAOI method can be used to improve performance.

[0338] Table 3. Consistency analysis of EAY and EAZ between measured robot position and SAOI estimated position.

[0339] <![CDATA[ coordinate ]]> <![CDATA[ Robot RMS Error ]]> <![CDATA[ SAOI RMS error ]]> EAY-EAZ 0.168 degrees 0.273 degrees

[0340] Example

[0341] In some embodiments of the present technology (Example A1), a tomographic synthetic aperture acoustic imaging system includes: a transducer element array operable for transmitting, receiving, and / or transmitting and receiving acoustic signals to an object, which utilizes the object to form a synthetic aperture of the acoustic imaging system, wherein the acoustic signals include transmitted acoustic signals and received acoustic echoes returned from the object; an object beamforming unit including one or more processors and one or more memories, and configured to (i) coherently beamform one or more regions of the object based on the position, orientation, and / or geometry of the transducer element array relative to an object model (e.g., wherein the object model includes information representing the surface of the object), and (ii) generate one or more beamforming output signals in a digital format, the one or more beamforming output signals including spatial information about one or more regions of the object obtained from beamforming the acoustic echoes; and a data processing unit including a processor and a memory, connected to the object beamforming unit and The transducer element array communicates and is configured to (i) process one or more beamforming output signals to generate at least one scalar output associated with the one or more beamforming output signals, (ii) process at least one scalar output to generate optimized parameters associated with the transducer element array and / or object model, (iii) instruct an object beamformer to re-beamform an object using updated optimized parameters associated with the transducer element array and / or object model to generate updated one or more beamforming output signals, and (iv) detect an object by (a) comparing at least one scalar output with a threshold, (b) comparing a differential change of at least one scalar output with a threshold, and / or (c) comparing at least one optimized parameter and / or a differential change of at least one optimized parameter with a threshold; and a display unit operable to generate an image of the object based on a reproduction of the object's position, orientation, geometry, and / or surface properties relative to the array's coordinate system determined by the data processing unit.

[0342] Example A2 includes a system according to any one of Examples A1-A31, comprising transmitter and receiver circuitry coupled to an array of transducer elements and configured to (i) generate and / or process a transmitted acoustic waveform as a digital signal for transduction by one or more selected transmitting transducer elements of the array and for transmission as one or more acoustic waveforms, and (ii) convert acoustic echoes received at one or more selected receiving transducer elements of the array into digital signals representing acoustic return echo waveforms.

[0343] Example A3 includes a system according to any one of Examples A1-A31, wherein at least one scalar output generated by the data processing unit includes a quantitative value of the beamforming echo derived from one or more of integral power, peak power, peak amplitude, peak amplitude, root mean square amplitude, root mean square amplitude, average amplitude, average amplitude, peak-to-peak amplitude, peak autocorrelation amplitude, peak autocorrelation amplitude, spectral bandwidth, or spectral center frequency.

[0344] Example A4 includes a system according to any one of Examples A1-A31, wherein at least one scalar output generated by the data processing unit includes a quantitative value of the beamforming echo derived by: (i) matching a received acoustic echo returned from an object with a stored echo using one or more of the following: mean absolute difference, mean Euclidean distance, mean edit distance, peak cross-correlation amplitude or peak cross-correlation amplitude, peak convolution amplitude, peak convolution amplitude, peak coherence, and peak amplitude squared coherence; wherein the stored echo is derived from one or more of the following: a calibrated beamforming echo from an object, a measured echo from an object, a measured transmit impulse response, a measured receive impulse response, a transmit / receive impulse response, a system impulse response, a transmit waveform, a transmit waveform convolved with itself, a filtered transmit waveform, an analytical transmit waveform, a windowed transmit waveform, a demodulated transmit waveform, a mathematically defined transmit waveform, or a mathematically defined system impulse response.

[0345] Example A5 includes a system according to any one of Examples A1-A31, wherein the data processing unit is configured to detect an object by comparing at least one scalar output with a threshold and based on the convergence of one or more optimization parameters satisfying one or more thresholds.

[0346] Example A6 includes a system according to any one of Examples A1-A31, wherein the object beamformer unit is configured to (i) calculate delays and weights based on the geometry of the transducer element array and the object model, and (ii) generate a digital signal corresponding to one or more beamforming output signals obtained from beamforming acoustic echoes according to the calculated delays and weights.

[0347] Example A7 includes a system according to any one of Examples A1-A31, wherein the data processing unit or object beamformer unit is configured to generate an optimization comprising two or more iterations to produce two or more scalar outputs of a scalar output corresponding to two or more beamformed echoes of an object, such that a better one of the two or more scalar outputs of the scalar outputs can be selected, the two or more beamformed echoes of the object corresponding to two or more sets of parameters describing the array and / or the object.

[0348] Example A8 includes the system according to any one of Example A7 and / or Examples A1-A31, wherein an object is detected when optimization produces a non-significant change below a threshold in two or more scalar outputs of one scalar output corresponding to two or more rebeamforming echoes of an object, the two or more rebeamforming echoes of which correspond to two or more sets of parameters describing the array and / or the object.

[0349] Example A9 includes a system according to any one of Example A7 and / or Examples A1-A31, wherein an object is detected when optimization produces a non-significant change below a threshold in a function of two or more parameter sets and / or two or more parameter sets describing an array and / or an object, the two or more parameter sets describing the array and / or the object corresponding to two or more re-beamforming echoes of the object.

[0350] Example A10 includes a system according to any one of Examples A9 and / or Examples A1-A31, wherein the function applied to two or more sets of parameters includes one or more of the Euclidean norm, Manhattan norm, p-norm, maximal norm, composite norm, asymmetric norm, and Mahalanobis distance.

[0351] Example A11 includes a system according to any one of Examples A9 and / or Examples A1-A31, wherein the same function is applied to two or more parameter sets to produce two or more scalars representing the two or more parameter sets.

[0352] Example A12 includes a system according to any one of Examples A1-A31, wherein the object beamformer uses the same synthetic aperture echo set to re-beamform the object.

[0353] Example A13 includes a system according to any one of Examples A1-A31, wherein the object beamformer uses a partially updated synthetic aperture echo set to re-beamform the object.

[0354] Example A14 includes a system according to any one of Examples A1-A31, wherein the object beamformer uses a fully updated synthetic aperture echo set to re-beamform the object.

[0355] Example A15 includes a system according to any one of Examples A1-A31, wherein the object beamformer unit is configured to store an object model.

[0356] Example A16 includes a system according to any one of Examples A1-A31, wherein the object beamforming unit is configured to generate an object model and / or modify the object model.

[0357] Example A17 includes a system according to any one of Examples A1-A31, wherein the object beamforming unit is configured to store the geometry of an array of transducer elements.

[0358] Example A18 includes a system according to any one of Examples A1-A31, wherein the object beamforming unit is configured to generate and / or modify the geometry of the transducer element array.

[0359] Example A19 includes a system according to any one of Examples A1-A31, wherein the display unit includes a display screen and is configured to present a visual display of an object on the display screen, wherein the reproduction of the object in a reference frame of the transducer element array or in an external reference frame is useful, such that the visual display of the object is operable for updating and visualizing the relative motion between the object and the transducer element array.

[0360] Example A20 includes a system according to any one of Examples A1-A31, wherein the object beamforming unit is configured to (i) calculate a delay determined from each transmitter location to a point on the object model and back to each receiver location; (ii) calculate weights of one or more of specular scattering, acoustic field directivity, and complex reflectivity based on the incident vector, reflection vector, receiver vector, transducer normal vector, object surface normal vector, and / or prior information about the object related to complex reflectivity; and (iii) apply the calculated delays and calculated weights to a stored echo before summing the delayed and weighted echoes to generate a single beamforming echo.

[0361] Example A21 includes the system according to any one of Examples A1-A31, wherein the data processing unit is configured to integrate the beamforming echo power over a time window and optimize the transformations applied to the transducer element positions and normal vectors as inputs to the beamformer in order to maximize the integrated beamformer echo power over the time window.

[0362] Example A22 includes the system according to any one of Examples A1-A31, wherein the display unit is configured to visualize objects in the reference frame of the array according to the inverse transformation of the optimized transformation.

[0363] Example A23 includes a system according to any one of Examples A1-A31, wherein the transducer element array includes one or more transmitter transducer elements and one or more receiver transducer elements spatially separated from the object.

[0364] Example A24 includes a system according to any one of Examples A23 and / or Examples A1-A31, wherein one or more transmitter transducer elements and one or more receiver transducer elements completely or at least partially surround the object.

[0365] Example A25 includes a system according to any one of Examples A1-A31, wherein the transducer element array includes at least three transducer elements configured to generate at least three reflection samples, including a monostatic reflection sample and a bistatic reflection sample of the object, such that the reflection samples are significantly separated on the surface of the object.

[0366] Example A26 includes a system according to any one of Examples A1-A31, wherein the transducer element array includes at least three transducer elements configured to generate at least three monostatic and at least three bistatic reflection samples of an object, such that the reflection samples are significantly separated on the surface of the object.

[0367] Example A27 includes a system according to any one of Examples A1-A31, wherein the object model includes a plurality of vertices and a plurality of faces, the plurality of vertices and faces approximating the object with a face resolution of no more than one acoustic wavelength.

[0368] Example A28 includes a system according to any one of Examples A27 and / or Examples A1-A31, wherein the surfaces are half or less of the acoustic wavelength resolution.

[0369] Example A29 includes a system according to any one of Examples A1-A31, wherein the object model comprises a plurality of points and a plurality of surface normal vectors corresponding to each point, the plurality of points and the plurality of surface normal vectors approximating the object within at least one wavelength acoustic resolution.

[0370] Example A30 includes a system according to any one of Examples A29 and / or Examples A1-A31, wherein a plurality of points and a plurality of surface normals corresponding to each point approximate an object within less than half the acoustic wavelength resolution.

[0371] Example A31 includes a system according to any one of Examples A1-A30, wherein one or more acoustic waveforms include one or more composite waveforms, the one or more composite waveforms including two or more separate coded waveforms.

[0372] In some embodiments of the present technology (Example A32), a method for synthetic aperture acoustic imaging for tomography includes: transmitting and receiving acoustic signals at and from an object by transducer elements of a transducer array forming a synthetic aperture based on transmitting an acoustic waveform transduced to the object and receiving acoustic echoes returned from the object; performing beamforming on the object by coherently summing delayed and weighted echo samples of returned acoustic echoes returned from one or more regions of the object to generate one or more beamforming output signals, wherein the one or more beamforming output signals are a function of one or more inputs for beamforming; generating one or more scalar outputs based on the one or more beamforming output signals; optimizing the one or more scalar outputs according to at least some of the object's position, orientation, geometric attributes, or surface characteristics; detecting the object by using an optimizer to determine the degree of optimization of one or more objective functions based on the values ​​of the optimized inputs and outputs or variations of the optimized inputs and outputs, compared with a detection criterion; and generating an image of the object based on the reproduction of the object's position, orientation, and geometric attributes and / or surface characteristics.

[0373] Example A33 includes the method according to any one of Examples A32-A39, wherein beamforming an object includes calculating delays and weights corresponding to one or more regions of the object based on at least some of the position, orientation, and geometry of the transducer element array relative to the object model.

[0374] Example A34 includes the method according to any one of Examples A32-A39, wherein one or more scalar outputs are optimized based on at least some of the position, orientation, or geometry of the transducer element array.

[0375] Example A35 includes the method according to any one of Examples A32-A39, wherein the generated one or more beamforming output signals include spatial information about one or more regions of the object, which is derived from a coherent summation of delayed and weighted echo samples from one or more regions of the object.

[0376] Example A36 includes a method according to any one of Examples A32-A39, wherein determining at least some of the position, orientation, geometry, or surface properties of an object comprises: generating at least one scalar output associated with one or more beamforming output signals, and processing at least one scalar output to generate an optimized set of parameters that defines a transducer element array and / or defines an object model.

[0377] Example A37 includes a method according to any one of Examples A32-A39, comprising: generating delayed echo samples and weighting factors of the echo samples corresponding to transmitter and receiver locations of the transducer array, points of the object model, attributes of the transducer array, and attributes of the object model, wherein the generated delayed echo samples and weighting factors of the echo samples are used to coherently sum delayed and weighted echo samples from one or more regions of the object.

[0378] Example A38 includes the method according to any one of Examples A32-A39, wherein one or more beamforming output signals are repeatedly generated from the same set of received echo samples, a partially new set of received echo samples, or a completely new set of received echo samples, based on optimization of one or more scalar outputs.

[0379] Example A39 includes the method according to any one of Examples A32-A38, wherein the method is implemented by the system according to any one of Examples A1-A31.

[0380] In some embodiments of the present technology (Example A40), a method for synthetic aperture acoustic imaging for tomography includes: transmitting acoustic signals to and receiving acoustic signals from the object to form a synthetic aperture of an acoustic imaging system, wherein the acoustic signals include transmitted acoustic signals and received acoustic echoes returned from the object; performing coherent beamforming on one or more regions of the object based on the position, orientation, and / or geometry of a transducer element array relative to an object model; generating one or more beamforming output signals in a digital format, the one or more beamforming output signals including spatial information about one or more regions of the object obtained from beamforming the acoustic echoes; and processing the one or more beamforming output signals to generate a synthetic aperture related to the one or more beamforming output signals. At least one scalar output associated with the signal, processing at least one scalar output to generate optimized parameters associated with (i) the transducer element array of the acoustic imaging system and / or (ii) an object model; re-beamforming the object using the updated optimized parameters associated with the transducer element array and / or object model to generate updated one or more beamforming output signals; detecting the object by (i) comparing at least one scalar output with a threshold, (ii) comparing the differential change of at least one scalar output with a threshold, and / or (iii) comparing at least one optimized parameter and / or the differential change of at least one optimized parameter with a threshold; and generating an image of the object based on the reproduction of the object's position, orientation, geometry, and / or surface properties relative to the array's coordinate system.

[0381] Example A41 includes the method according to any one of Examples A40-A42, and also includes the method according to any one of Examples 32-38.

[0382] Example A42 includes the method according to any one of Examples A40-A41, wherein the method is implemented by the system according to any one of Examples A1-A31.

[0383] In some embodiments of the present technology (Example B1), a tomographic synthetic aperture acoustic imaging system includes: a transducer element array operable to transmit, receive, and / or transmit and receive acoustic signals to an object, which utilizes the object to form a synthetic aperture of the acoustic imaging system, wherein the acoustic signals include transmitted acoustic signals and received acoustic echoes returned from the object; an object beamforming unit including one or more processors and one or more memories, and configured to (i) beamform the object toward one or more regions of the object based on the position, orientation, and / or geometry of the transducer element array relative to an object model, the object model including information representing the object, and (ii) generate... The system comprises: one or more beamforming output signals in digital format, the beamforming output signals including spatial information about one or more regions of an object obtained from beamforming acoustic echoes; a data processing unit, including a processor and a memory, communicating with the object beamforming unit and the transducer element array, and configured to optimize the one or more beamforming output signals to determine one or more of a set of position, orientation, geometry, or physical properties; and a display unit operable to generate an image of the object based on a reproduction of one or more of the set of position, orientation, geometry, or physical properties relative to the transducer element array determined by the data processing unit. In some example embodiments, the object beamforming unit is a hardware unit independent of the data processing unit; while in some embodiments, the object beamforming unit is a software module, and one or more processors and one or more memories of the object beamforming unit are respectively the processor and memory of the data processing unit.

[0384] Example B2 includes a system according to any one of Examples B1-B35, wherein the system includes transmitter and receiver circuitry coupled to an array of transducer elements and configured to (i) generate and / or process a transmitted acoustic waveform as a digital signal for transduction by one or more selected transmitting transducer elements of the array and for transmission as one or more acoustic waveforms, and (ii) convert acoustic echoes received at one or more selected receiving transducer elements of the array into digital signals representing acoustic return echo waveforms.

[0385] Example B3 includes a system according to any one of Examples B1-B35, wherein the object beamformer unit is configured to (i) calculate delays and weights based on the geometry of the transducer element array and the object model, and (ii) generate a digital signal corresponding to one or more beamforming output signals obtained from beamforming acoustic echoes according to the calculated delays and weights.

[0386] Example B4 includes a system according to any one of Examples B1-B35, wherein the data processing unit is configured to optimize one or more beamforming output signals generated by the object beamforming unit by: (i) processing the one or more beamforming output signals to generate at least one scalar output associated with the one or more beamforming output signals; (ii) processing at least one scalar output to generate optimized parameters associated with the transducer element array and / or object model; (iii) instructing the object beamforming unit to re-beamform the object using the updated optimized parameters associated with the transducer element array and / or object model to generate updated one or more beamforming output signals; and (iv) detecting the object by (a) comparing at least one scalar output with a threshold, (b) comparing the differential change of at least one scalar output with a threshold, and / or (c) comparing at least one optimized parameter and / or the differential change of at least one optimized parameter with a threshold.

[0387] Example B5 includes a system according to any one of Examples B4 or B1-B35, wherein the data processing unit is configured to detect an object by comparing at least one scalar output with a threshold and based on the convergence of one or more optimization parameters satisfying one or more thresholds.

[0388] Example B6 includes a system according to any one of Example B4 or Examples B1-B35, wherein the optimization parameters include one or more of optimizer output, step size, optimality metric, or residual.

[0389] Example B7 includes a system according to any one of Example B4 or Examples B1-B35, wherein at least one scalar output generated by the data processing unit includes a quantitative value of the beamforming echo derived from one or more of integral power, peak power, peak amplitude, peak amplitude, root mean square amplitude, root mean square amplitude, average amplitude, average amplitude, peak-to-peak amplitude, peak autocorrelation amplitude, peak autocorrelation amplitude, spectral bandwidth, or spectral center frequency.

[0390] Example B8 includes a system according to any one of Example B4 or Examples B1-B35, wherein at least one scalar output generated by the data processing unit includes a quantitative value of the beamforming echo obtained by: (i) matching the received acoustic echo returned from the object with a stored echo using one or more of the following: mean absolute difference, mean Euclidean distance, mean edit distance, peak cross-correlation amplitude or peak cross-correlation amplitude, peak convolution amplitude, peak convolution amplitude, peak coherence, and peak amplitude squared coherence; wherein the stored echo is derived from one or more of the following: a calibrated beamforming echo from the object, a measured echo from the object, a measured transmit impulse response, a measured receive impulse response, a transmit / receive impulse response, a system impulse response, a transmit waveform, a transmit waveform convolved with itself, a filtered transmit waveform, an analytical transmit waveform, a windowed transmit waveform, a demodulated transmit waveform, a mathematically defined transmit waveform, or a mathematically defined system impulse response.

[0391] Example B9 includes a system according to any one of Examples B4 or B1-B35, wherein the data processing unit or object beamformer unit is configured to generate an optimization comprising two or more iterations to produce two or more scalar outputs of a scalar output corresponding to two or more beamformed echoes of an object, such that a better one of the two or more scalar outputs of the object can be selected, the two or more beamformed echoes of the object corresponding to two or more sets of parameters describing the transducer element array and / or the object.

[0392] Example B10 includes a system according to any one of Examples B9 or B1-B35, wherein an object is detected when optimization produces a non-significant change below a threshold in two or more scalar outputs of one scalar output corresponding to two or more rebeamforming echoes of an object, the two or more rebeamforming echoes of the object corresponding to two or more sets of parameters describing the transducer element array and / or the object.

[0393] Example B11 includes a system according to any one of Examples B9 or B1-B35, wherein an object is detected when the optimization produces a non-significant change below a threshold in a function of two or more re-beamforming echoes of a transducer element array and / or two or more parameter sets of the object.

[0394] Example B12 includes a system according to any one of Example B11 or Examples B1-B35, wherein the function applied to two or more sets of parameters includes one or more of the Euclidean norm, Manhattan norm, p-norm, maximal norm, composite norm, asymmetric norm, and Mahalanobis distance.

[0395] Example B13 includes a system according to any one of Example B11 or Examples B1-B35, wherein the same function is applied to two or more sets of parameters to produce two or more scalars representing the two or more sets of parameters.

[0396] Example B14 includes a system according to any one of Examples B1-B35, wherein the object beamformer unit is configured to coherently beamform an object for multiple regions of the object by individually performing coherent object beamforming on each region of a plurality of regions of the object model and then coherently combining the signals obtained from each region where coherent object beamforming was performed in an objective function.

[0397] Example B15 includes a system according to any one of Examples B1-B35, wherein the object beamformer unit is configured to coherently beamform an object for multiple regions of the object by individually performing coherent object beamforming on each region of a plurality of regions of the object model, and subsequently incoherently combining the signals obtained from each region where coherent object beamforming was performed in an objective function.

[0398] Example B16 includes a system according to any one of Examples B1-B35, wherein the set of physical properties of an object includes one or more surface properties, one or more volume properties, or both one or more surface properties and one or more volume properties of the object.

[0399] Example B17 includes a system according to any one of Example B16 or Examples B1-B35, wherein the set of physical properties of the object includes the object's density, bulk modulus, or acoustic properties.

[0400] Example B18 includes the system according to any one of Examples B1-B35, wherein the object beamforming unit is configured to re-beamform the object using the same synthetic aperture echo set.

[0401] Example B19 includes the system according to any one of Examples B1-B35, wherein the object beamforming unit is configured to re-beamform the object using a partially updated synthetic aperture echo set.

[0402] Example B20 includes the system according to any one of Examples B1-B35, wherein the object beamforming unit is configured to re-beamform the object using a fully updated synthetic aperture echo set.

[0403] Example B21 includes the system according to any one of Examples B1-B35, wherein the object beamformer unit is configured to store an object model.

[0404] Example B22 includes a system according to any one of Examples B1-B35, wherein the object beamformer unit is configured to generate an object model, modify an object model, or both generate and modify an object model.

[0405] Example B23 includes a system according to any one of Examples B1-B35, wherein the object beamforming unit is configured to store the geometry of an array of transducer elements.

[0406] Example B24 includes a system according to any one of Examples B1-B35, wherein the object beamforming unit is configured to generate information about the geometry of the transducer element array, modify information about the geometry of the transducer element array, or generate and modify information about the geometry of the transducer element array.

[0407] Example B25 includes a system according to any one of Examples B1-B35, wherein the display unit includes a display screen and is configured to present a visual display of an object on the display screen, wherein the reproduction of the object in a reference frame of the transducer element array or in an external reference frame is useful, such that the visual display of the object is operable for updating and visualizing the relative motion between the object and the transducer element array.

[0408] Example B26 includes a system according to any one of Examples B1-B35, wherein the object beamforming unit is configured to (i) calculate a delay determined from each transmitter location to a point on the object model and back to each receiver location; (ii) calculate weights for one or more of specular scattering, acoustic field directivity, attenuation, diffusion loss, and complex reflectivity based on the incident vector, reflection vector, receiver vector, transducer normal vector, object surface normal vector, and / or prior information about the object related to complex reflectivity; and (iii) apply the calculated delays and calculated weights to stored echoes before combining delayed and weighted echoes to generate one or more beamforming echoes.

[0409] Example B27 includes the system according to any one of Examples B1-B35, wherein the data processing unit is configured to integrate the beamforming echo power over a time window and optimize the transformations applied to the transducer element positions and normal vectors as inputs to the beamformer in order to maximize the beamformer echo power integrated over the time window.

[0410] Example B28 includes a system according to any one of Examples B1-B35, wherein the display unit is configured to visualize objects in a reference frame of the transducer element array according to the inverse transformation of the optimized transformation.

[0411] Example B29 includes a system according to any one of Examples B1-B35, wherein the transducer element array includes one or more transmitter transducer elements and one or more receiver transducer elements spatially separated from the object.

[0412] Example B30 includes a system according to any one of Example B29 or Examples B1-B35, wherein one or more transmitter transducer elements and one or more receiver transducer elements completely or at least partially surround the object.

[0413] Example B31 includes a system according to any one of Examples B1-B35, wherein the transducer element array includes at least three transducer elements configured to generate at least three reflection samples, including a monostatic reflection sample and a bistatic reflection sample of the object, such that the reflection samples are significantly separated on the surface of the object.

[0414] Example B32 includes a system according to any one of Examples B1-B35, wherein the transducer element array includes at least three transducer elements configured to generate at least three monostatic and at least three bistatic reflection samples of an object, such that the reflection samples are significantly separated on the surface of the object.

[0415] Example B33 includes a system according to any one of Examples B1-B35, wherein the object model comprises a plurality of vertices and a plurality of faces, the plurality of vertices and the plurality of faces approximating the object with a face resolution not greater than one acoustic wavelength.

[0416] Example B34 includes a system according to any one of Examples B1-B35, wherein the object model comprises a plurality of points and a plurality of surface normal vectors corresponding to each point, the plurality of points and the plurality of surface normal vectors approximating the object within at least one acoustic wavelength resolution.

[0417] Example B35 includes a system according to any one of Examples B1-B35, wherein one or more acoustic waveforms include one or more composite waveforms, the one or more composite waveforms including two or more separate coded waveforms.

[0418] In some embodiments of the present technology (Example B36), a method for synthetic aperture acoustic imaging for tomography includes: transmitting acoustic signals to and receiving acoustic signals from an object by transducer elements of a transducer element array forming a synthetic aperture based on transmitting an acoustic waveform transduced to the object and receiving acoustic echoes returned from the object; performing beamforming on the object using echo samples of returned acoustic echoes returned from one or more regions of the object to generate one or more beamforming output signals, wherein the one or more beamforming output signals are a function of one or more inputs for beamforming, wherein the one or more inputs for beamforming include information representing the object; optimizing the one or more beamforming output signals to determine one or more of a set of position, orientation, geometry, or physical properties of the object; and generating an image of the object based on the reproduction of one or more of the set of position, orientation, geometry, or physical properties of the object.

[0419] Example B37 includes the method according to any one of Examples B36-B45, wherein one or more of the set of position, orientation, geometry, or physical properties of an object are determined by the following steps: generating one or more scalar outputs based on one or more beamforming output signals; optimizing the one or more scalar outputs according to at least some of the position, orientation, geometry, or physical properties of the object; and detecting the object by using an optimizer to determine the degree of optimization of one or more objective functions based on the values ​​of the optimized inputs and outputs or changes in the values ​​of the optimized inputs and outputs, compared with a detection criterion.

[0420] Example B38 includes the method according to any one of Example 37 or Examples B36-B45, wherein one or more scalar outputs are optimized based on at least some of the position, orientation, or geometry of the transducer element array.

[0421] Example B39 includes a method according to any one of Examples B36-B45, wherein determining one or more of a set of object location, orientation, geometry, or physical properties includes: generating at least one scalar output associated with one or more beamforming output signals, and processing at least one scalar output to generate an optimized set of parameters defining a transducer element array and / or defining an object model.

[0422] Example B40 includes the method according to any one of Examples B36-B45, wherein the set of physical properties of the object includes one or more surface properties, one or more volume properties, or both one or more surface properties and one or more volume properties of the object.

[0423] Example B41 includes the method according to any one of Examples B36-B45, wherein beamforming the object includes calculating delays and weights corresponding to one or more regions of the object based on at least some of the position, orientation, and geometry of the transducer element array relative to the object model.

[0424] Example B42 includes the method according to any one of Example 41 or Examples B36-B45, wherein the generated one or more beamforming output signals include spatial information about one or more regions of the object, which is derived from either a coherent summation of delayed and weighted echo samples from one or more regions of the object or a coherent multiplication of delayed and weighted echo samples from one or more regions of the object.

[0425] Example B43 includes a method according to any one of Examples B36-B45, comprising: generating delayed echo samples and echo sample weighting factors corresponding to transmitter and receiver locations of transducer elements of an array, attributes of transducer elements of an array, points of an object model, and attributes of an object model, wherein the generated delayed echo samples and echo sample weighting factors are used to coherently combine (e.g., sum and / or multiply) delayed and weighted echo samples from one or more regions of the object.

[0426] Example B44 includes the method according to any one of Examples B36-B45, wherein one or more beamforming output signals are repeatedly generated from the same set of received echo samples, a partially new set of received echo samples, or a completely new set of received echo samples, based on optimization of one or more scalar outputs.

[0427] Example B45 includes the method according to any one of Examples B36-B45, wherein the method is implemented by the system according to any one of Examples B1-B35.

[0428] In some embodiments of the present technology (Example C1), a synthetic aperture acoustic imaging system includes: an array of different acoustic transducer devices, the different acoustic transducer devices being located at different positions around a target object and configured to transmit, receive, and / or transmit and receive acoustic signals to the object in order to achieve a synthetic aperture of the acoustic imaging system using the object, wherein the acoustic signals include transmitted acoustic signals and received acoustic echoes returned from the object; and a computing device communicating with the array of different acoustic transducer devices and including a processor and a memory, the computing device including an object beamforming unit configured to (i) combine at least one or more regions of the object according to the position, orientation, and / or geometry of the transducer element array relative to the object model. The device uses at least some received acoustic echoes from the domain to beamform an object, the object model including information representing the object, and (ii) generates one or more beamforming output signals, the one or more beamforming output signals including spatial information about one or more regions of the object obtained from beamforming the acoustic echoes, and the computing device includes a data processing unit that communicates with the object beamforming unit and is configured to (i) optimize one or more beamforming output signals to determine a set of position, orientation, geometry, or physical attributes, and (ii) generate an image of the object based on a reproduction of one or more of the set of position, orientation, geometry, or physical attributes determined by the data processing unit relative to the transducer element array.

[0429] Example C2 includes a system according to any one of Examples C1-C35, comprising: transmitter and receiver circuitry coupled to an array of transducer elements and configured to (i) generate and / or process a transmitted acoustic waveform as a digital signal for transduction by one or more selected transmitted transducer elements of the array and for transmission as one or more acoustic waveforms, and (ii) convert acoustic echoes received at one or more selected received transducer elements of the array into digital signals representing acoustic return echo waveforms.

[0430] Example C3 includes a system according to any one of Examples C1-C35, wherein the object beamformer unit is configured to (i) calculate delays and weights based on the geometry of the transducer element array and the object model, and (ii) generate a digital signal corresponding to one or more beamforming output signals obtained from beamforming acoustic echoes according to the calculated delays and weights.

[0431] Example C4 includes a system according to any one of Examples C1-C35, wherein the data processing unit is configured to optimize one or more beamforming output signals generated by the object beamforming unit by: (i) processing the one or more beamforming output signals to generate at least one scalar output associated with the one or more beamforming output signals; (ii) processing at least one scalar output to generate optimized parameters associated with the transducer element array and / or object model; (iii) instructing the object beamforming unit to re-beamform the object using updated optimized parameters associated with the transducer element array and / or object model to generate updated one or more beamforming output signals; and (iv) detecting the object by (a) comparing at least one scalar output with a threshold, (b) comparing a differential change of at least one scalar output with a threshold, and / or (c) comparing at least one optimized parameter and / or a differential change of at least one optimized parameter with a threshold.

[0432] Example C5 includes a system according to any one of Examples C4 or C1-C35, wherein the data processing unit is configured to detect an object by comparing at least one scalar output with a threshold and based on the convergence of one or more optimization parameters satisfying one or more thresholds.

[0433] Example C6 includes a system according to any one of Example C4 or Examples C1-C35, wherein the optimization parameters include one or more of optimizer output, step size, optimality metric, or residual.

[0434] Example C7 includes a system according to any one of Example C4 or Examples C1-C35, wherein at least one scalar output generated by the data processing unit includes a quantitative value of the beamforming echo derived from one or more of integral power, peak power, peak amplitude, peak amplitude, root mean square amplitude, root mean square amplitude, average amplitude, average amplitude, peak-to-peak amplitude, peak autocorrelation amplitude, peak autocorrelation amplitude, spectral bandwidth, or spectral center frequency.

[0435] Example C8 includes a system according to any one of Example C4 or Examples C1-C35, wherein at least one scalar output generated by the data processing unit includes a quantitative value of the beamforming echo obtained by: (i) matching the received acoustic echo returned from the object with a stored echo using one or more of the following: mean absolute difference, mean Euclidean distance, mean edit distance, peak cross-correlation amplitude or peak cross-correlation amplitude, peak convolution amplitude, peak convolution amplitude, peak coherence, and peak amplitude squared coherence; wherein the stored echo is derived from one or more of the following: a calibrated beamforming echo from the object, a measured echo from the object, a measured transmit impulse response, a measured receive impulse response, a transmit / receive impulse response, a system impulse response, a transmit waveform, a transmit waveform convolved with itself, a filtered transmit waveform, an analytical transmit waveform, a windowed transmit waveform, a demodulated transmit waveform, a mathematically defined transmit waveform, or a mathematically defined system impulse response.

[0436] Example C9 includes a system according to any one of Examples C4 or C1-C35, wherein the data processing unit or object beamformer unit is configured to generate an optimization comprising two or more iterations to produce two or more scalar outputs of a scalar output corresponding to two or more beamformed echoes of an object, such that a better one of the two or more scalar outputs of the object can be selected, the two or more beamformed echoes of the object corresponding to two or more sets of parameters describing the transducer element array and / or the object.

[0437] Example C10 includes a system according to any one of Examples C9 or C1-C35, wherein an object is detected when optimization produces a non-significant change below a threshold in two or more scalar outputs of one scalar output corresponding to two or more rebeamforming echoes of an object, the two or more rebeamforming echoes of the object corresponding to two or more sets of parameters describing the transducer element array and / or the object.

[0438] Example C11 includes a system according to any one of Example C9 or Examples C1-C35, wherein an object is detected when the optimization produces a non-significant change below a threshold in a function of two or more re-beamforming echoes of a transducer element array and / or two or more parameter sets of the object.

[0439] Example C12 includes a system according to any one of Example C11 or Examples C1-C35, wherein the function applied to two or more sets of parameters includes one or more of the Euclidean norm, Manhattan norm, p-norm, maximal norm, composite norm, asymmetric norm, and Mahalanobis distance.

[0440] Example C13 includes a system according to any one of Example C11 or Examples C1-C35, wherein the same function is applied to two or more sets of parameters to produce two or more scalars representing the two or more sets of parameters.

[0441] Example C14 includes a system according to any one of Examples C1-C35, wherein the object beamformer unit is configured to coherently beamform an object for multiple regions of the object by individually performing coherent object beamforming on each of multiple regions of the object model and then coherently combining the signals obtained from each region where coherent object beamforming was performed in an objective function.

[0442] Example C15 includes a system according to any one of Examples C1-C35, wherein the object beamformer unit is configured to coherently beamform an object for multiple regions of the object by performing coherent object beamforming individually on each region of a plurality of regions of the object model, and then incoherently combining the signals obtained from each region where coherent object beamforming was performed in an objective function.

[0443] Example C16 includes a system according to any one of Examples C1-C35, wherein the set of physical properties of an object includes one or more surface properties, one or more volume properties, or both one or more surface properties and one or more volume properties of the object.

[0444] Example C17 includes a system according to any one of Example C16 or Examples C1-C35, wherein the set of physical properties of the object includes the object's density, bulk modulus, or acoustic properties.

[0445] Example C18 includes the system according to any one of Examples C1-C35, wherein the object beamforming unit is configured to re-beamform the object using the same synthetic aperture echo set.

[0446] Example C19 includes the system according to any one of Examples C1-C35, wherein the object beamforming unit is configured to re-beamform the object using a partially updated synthetic aperture echo set.

[0447] Example C20 includes the system according to any one of Examples C1-C35, wherein the object beamforming unit is configured to re-beamform the object using a fully updated synthetic aperture echo set.

[0448] Example C21 includes the system according to any one of Examples C1-C35, wherein the object beamformer unit is configured to store an object model.

[0449] Example C22 includes a system according to any one of Examples C1-C35, wherein the object beamformer unit is configured to generate an object model, modify an object model, or both generate and modify an object model.

[0450] Example C23 includes a system according to any one of Examples C1-C35, wherein the object beamforming unit is configured to store the geometry of an array of transducer elements.

[0451] Example C24 includes the system according to any one of Examples C1-C35, wherein the object beamforming unit is configured to generate information about the geometry of the transducer element array, modify information about the geometry of the transducer element array, or generate and modify information about the geometry of the transducer element array.

[0452] Example C25 includes the system according to any one of Examples C1-C35, further comprising: a display unit including a display screen and configured to present a visual display of an image of an object on the display screen, wherein the image of the object is rendered in a reference frame of the transducer element array or an external reference frame, such that the visual display of the image of the object is operable for updating and visualizing the relative motion between the object and the transducer element array.

[0453] Example C26 includes a system according to any one of Examples C1-C35, wherein the object beamforming unit is configured to (i) calculate a delay determined from each transmitter location to a point on the object model and back to each receiver location; (ii) calculate weights for one or more of specular scattering, acoustic field directivity, attenuation, diffusion loss, and complex reflectivity based on the incident vector, reflection vector, receiver vector, transducer normal vector, object surface normal vector, and / or prior information about the object related to complex reflectivity; and (iii) apply the calculated delays and calculated weights to stored echoes before combining delayed and weighted echoes to generate one or more beamforming echoes.

[0454] Example C27 includes the system according to any one of Examples C1-C35, wherein the data processing unit is configured to integrate the beamforming echo power over a time window and optimize the transformations applied to the transducer element positions and normal vectors as inputs to the beamformer in order to maximize the beamformer echo power integrated over the time window.

[0455] Example C28 includes a system according to any one of Examples C1-C35, wherein the display unit is configured to visualize objects in a reference frame of the transducer element array according to the inverse transformation of the optimized transformation.

[0456] Example C29 includes a system according to any one of Examples C1-C35, wherein the transducer element array includes one or more transmitter transducer elements and one or more receiver transducer elements spatially separated from the object.

[0457] Example C30 includes a system according to any one of Example C29 or Examples C1-C35, wherein one or more transmitter transducer elements and one or more receiver transducer elements completely or at least partially surround the object.

[0458] Example C31 includes a system according to any one of Examples C1-C35, wherein the transducer element array includes at least three transducer elements configured to generate at least three reflection samples, including a monostatic reflection sample and a bistatic reflection sample of the object, such that the reflection samples are significantly separated on the surface of the object.

[0459] Example C32 includes a system according to any one of Examples C1-C35, wherein the transducer element array includes at least three transducer elements configured to generate at least three monostatic and at least three bistatic reflection samples of an object, such that the reflection samples are significantly separated on the surface of the object.

[0460] Example C33 includes a system according to any one of Examples C1-C35, wherein the object model comprises a plurality of vertices and a plurality of faces, the plurality of vertices and the plurality of faces approximating the object with a face resolution not greater than one acoustic wavelength.

[0461] Example C34 includes a system according to any one of Examples C1-C35, wherein the object model comprises a plurality of points and a plurality of surface normal vectors corresponding to each point, the plurality of points and the plurality of surface normal vectors approximating the object within at least one acoustic wavelength resolution.

[0462] Example C35 includes a system according to any one of Examples C1-C35, wherein one or more acoustic waveforms include one or more synthetic waveforms, the one or more synthetic waveforms including two or more separate coded waveforms.

[0463] The embodiments of the subject matter and functional operation described in this patent document can be implemented in various systems, digital electronic circuits, or computer software, firmware, or hardware (including the structures disclosed in this specification and their structural equivalents), or in combinations of one or more of them. Embodiments of the subject matter described in this specification can be implemented as one or more computer program products, i.e., one or more modules of computer program instructions encoded on a tangible and non-transitory computer-readable medium for execution by or control of the operation of a data processing apparatus. The computer-readable medium can be a machine-readable storage device, a machine-readable storage substrate, a memory device, a combination of materials that implement machine-readable propagation signals, or a combination of one or more of them. The terms "data processing unit" or "data processing apparatus" encompass all means, devices, and machines for processing data, including, for example, a programmable processor, a computer, or a plurality of processors or computers. In addition to hardware, the apparatus may also include code that generates an execution environment for the computer program in question, for example, code constituting processor firmware, a protocol stack, a database management system, an operating system, or a combination of one or more of them.

[0464] A computer program (also called a program, software, software application, script, or code) can be written in any programming language, including compiled or interpreted languages, and can be deployed in any form, including as a standalone program or as a module, component, subroutine, or other unit suitable for a computing environment. A computer program does not necessarily correspond to a file in a file system. A program can be stored as part of a file that holds other programs or data (e.g., one or more scripts stored as markup language documents), in a single file dedicated to the program in question, or in multiple collaborating files (e.g., a file storing one or more modules, subroutines, or code sections). A computer program can be deployed to execute on a single computer, or on multiple computers located in one place or distributed across multiple locations and interconnected through a communication network.

[0465] The processes and logic flows described in this specification can be executed by one or more programmable processors that execute one or more computer programs to perform functions by manipulating input data and generating outputs. The processes and logic flows can also be executed by dedicated logic circuitry, and the apparatus can be implemented as dedicated logic circuitry, such as FPGAs (Field-Programmable Gate Arrays) or ASICs (Application-Specific Integrated Circuits).

[0466] For example, processors suitable for executing computer programs include general-purpose and special-purpose microprocessors, as well as any one or more processors in any kind of digital computer. Typically, a processor receives instructions and data from read-only memory or random access memory, or both. The basic components of a computer are a processor for executing instructions and one or more memory devices for storing instructions and data. Typically, a computer will also include, or be operatively coupled to, one or more mass storage devices (e.g., magnetic disks, magneto-optical disks, or optical disks) for storing data, to receive data from, transfer data to, or both. However, a computer does not need to have such devices. Computer-readable media suitable for storing computer program instructions and data include all forms of non-volatile memory, media, and memory devices, including, for example, semiconductor memory devices such as EPROM, EEPROM, and flash memory devices. The processor and memory may be supplemented or incorporated into dedicated logic circuitry.

[0467] This specification, together with the accompanying drawings, is to be considered exemplary only, where exemplary means example. As used herein, the singular forms “a,” “an,” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Additionally, the use of “or” is intended to include “and / or”, unless the context clearly indicates otherwise.

[0468] While this patent document contains numerous details, these details should not be construed as limiting any invention or the scope of the claims, but rather as descriptions of features characteristic of particular embodiments of a particular invention. Certain features described in this patent document within the context of individual embodiments may also be implemented in combination in a single embodiment. Conversely, various features described in the context of a single embodiment may also be implemented individually or in any suitable sub-combination in multiple embodiments. Furthermore, although features may be described above as functioning in certain combinations, and even initially claimed in this way, one or more features from a claimed combination may be removed from that combination in some cases, and the claimed combination may be for sub-combinations or variations thereof.

[0469] Similarly, although operations are described in a specific order in the accompanying drawings, this should not be construed as requiring these operations to be performed in the specific order or sequence shown, or requiring all illustrated operations to be performed to obtain the desired result. Furthermore, the separation of various system components in the embodiments described in this patent document should not be construed as requiring such separation in all embodiments.

[0470] Only some implementation methods and examples are described, and other implementations, enhancements and variations may be made based on what is described and shown in this patent document.

Claims

1. A synthetic aperture acoustic imaging system, comprising: An array of different acoustic transducer devices located at different positions around a target object and configured to send, receive and / or send and receive acoustic signals to the object in order to achieve the synthetic aperture of the acoustic imaging system using the object, wherein the acoustic signals include transmitted acoustic signals and received acoustic echoes returned from the object. as well as A computing device, communicating with an array of different acoustic transducer devices and including a processor and a memory, the computing device including an object beamforming unit configured to (i) beamform an object by combining at least some received acoustic echoes from at least one region of the object, based on a function of the position, orientation, and / or geometry of an object model stored in the memory of the computing device relative to the array of transducer elements of the different transducer devices, the object model including information representing the object, and (ii) generate one or more beamforming output signals, the one or more... The beamforming output signal includes spatial information about one or more regions of the object obtained from beamforming the acoustic echo, and the computing device includes a data processing unit that communicates with the object beamforming unit and is configured to (i) optimize one or more beamforming output signals to determine one or more of a set of position, orientation, geometry, or physical properties, and (ii) generate an image of the object based on a reproduction of one or more of the set of position, orientation, geometry, or physical properties relative to the coordinate system of the transducer element array determined by the data processing unit. The object beamformer unit is configured to (i) calculate delay and weight based on the geometry of the transducer element array and the object model, and (ii) generate a digital signal corresponding to one or more beamforming output signals obtained from beamforming acoustic echoes according to the calculated delay and weight.

2. The system according to claim 1, comprising: Transmitter and receiver circuitry, coupled to the transducer element array, is configured to (i) generate and / or process transmitted acoustic waveforms as digital signals to be transduced by one or more selected transmitted transducer elements of the array and transmitted as one or more acoustic waveforms, and (ii) convert acoustic echoes received at one or more selected received transducer elements of the array into digital signals representing acoustic return echo waveforms.

3. The system according to claim 1, wherein, The data processing unit is configured to optimize one or more beamforming output signals generated by the object beamforming unit in the following manner: (i) processing the one or more beamforming output signals to generate at least one scalar output associated with the one or more beamforming output signals, (ii) processing the at least one scalar output to generate optimized parameters associated with the transducer element array and / or the object model, (iii) instructing the object beamforming unit to rebeamform the object using the updated optimized parameters associated with the transducer element array and / or the object model to generate updated one or more beamforming output signals, and (iv) detecting the object by (a) comparing the at least one scalar output with a threshold, (b) comparing the differential change of the at least one scalar output with a threshold, and / or (c) comparing at least one of the optimized parameters and / or the differential change of at least one of the optimized parameters with a threshold.

4. The system according to claim 3, wherein, The data processing unit is configured to detect the object by comparing the at least one scalar output with the threshold and based on the convergence of one or more optimization parameters satisfying one or more thresholds.

5. The system according to claim 3, wherein, The optimization parameters include one or more of the optimizer output, step size, optimality metric, or residual.

6. The system according to claim 3, wherein, The data processing unit generates at least one scalar output including a quantitative value of the beamforming echo derived from one or more of the following: integral power, peak power, peak amplitude, peak amplitude, root mean square amplitude, root mean square amplitude, average amplitude, average amplitude, peak-to-peak amplitude, peak autocorrelation amplitude, peak autocorrelation amplitude, spectral bandwidth, or spectral center frequency.

7. The system according to claim 3, wherein, At least one scalar output generated by the data processing unit includes a quantitative value of the beamforming echo obtained by: (i) matching the received acoustic echo returned from the object with a stored echo using one or more of the following: mean absolute difference, mean Euclidean distance, mean edit distance, peak cross-correlation amplitude or peak cross-correlation amplitude, peak convolution amplitude, peak convolution amplitude, peak coherence, and peak amplitude squared coherence; wherein the stored echo is derived from one or more of the following: a calibrated beamforming echo from the object, a measured echo from the object, a measured transmit impulse response, a measured receive impulse response, a transmit / receive impulse response, a system impulse response, a transmit waveform, a transmit waveform convolved with itself, a filtered transmit waveform, an analytical transmit waveform, a windowed transmit waveform, a demodulated transmit waveform, a mathematically defined transmit waveform, or a mathematically defined system impulse response.

8. The system according to claim 3, wherein, The data processing unit or the object beamformer unit is configured to generate an optimization comprising two or more iterations to produce two or more scalar outputs of a scalar output corresponding to two or more beamformed echoes of the object, such that a better one of the two or more scalar outputs of the scalar output can be selected, wherein the two or more beamformed echoes of the object correspond to two or more sets of parameters describing the transducer element array and / or the object.

9. The system according to claim 8, wherein, The object is detected when the optimization produces a non-significant change below a threshold in two or more scalar outputs of one of two or more rebeamforming echoes of the object, the two or more rebeamforming echoes of the object corresponding to two or more sets of parameters describing the transducer element array and / or the object.

10. The system according to claim 8, wherein, The object is detected when the optimization produces a non-significant change below a threshold in a function describing the transducer element array and / or two or more parameter sets corresponding to two or more re-beamforming echoes of the object.

11. The system according to claim 10, wherein, The functions applied to the two or more parameter sets include one or more of the following: Euclidean norm, Manhattan norm, p-norm, maximal norm, composite norm, asymmetric norm, and Mahalanobis distance.

12. The system according to claim 10, wherein, The same function is applied to two or more sets of parameters to produce two or more scalars representing the two or more sets of parameters.

13. The system according to claim 1, wherein, The object beamformer unit is configured to perform coherent beamforming on the object for multiple regions of the object by performing coherent object beamforming on each region of the multiple regions of the object model individually and then coherently combining the signals obtained from each region where coherent object beamforming was performed in an objective function.

14. The system according to claim 1, wherein, The object beamformer unit is configured to perform coherent beamforming on the object for multiple regions of the object by performing coherent object beamforming on each region of the multiple regions of the object model individually, and then performing incoherent combination of the signals obtained from each region of the coherent object beamforming in the objective function.

15. The system according to claim 1, wherein, The set of physical attributes of the object includes one or more surface attributes, one or more volume attributes, or both one or more surface attributes and one or more volume attributes.

16. The system according to claim 15, wherein, The set of physical properties of the object includes the object's density, bulk modulus, or acoustic properties.

17. The system according to claim 1, wherein, The object beamforming unit is configured to re-beamform the object using the same synthetic aperture echo set.

18. The system according to claim 1, wherein, The object beamformer unit is configured to re-beamform the object using a partially updated set of synthetic aperture echoes.

19. The system according to claim 1, wherein, The object beamformer unit is configured to re-beamform the object using a fully updated synthetic aperture echo set.

20. The system according to claim 1, wherein, The object beamformer unit is configured to store the object model.

21. The system according to claim 1, wherein, The object beamformer unit is configured to generate the object model, modify the object model, or both generate and modify the object model.

22. The system according to claim 1, wherein, The object beamformer unit is configured to store the geometry of the transducer element array.

23. The system according to claim 1, wherein, The object beamforming unit is configured to generate information about the geometry of the transducer element array, modify information about the geometry of the transducer element array, or generate and modify information about the geometry of the transducer element array.

24. The system according to claim 1, further comprising: The display unit includes a display screen and is configured to present a visual display of an image of the object on the display screen, wherein the image of the object is rendered in a reference frame of the transducer element array or in an external reference frame, such that the visual display of the image of the object is operable for updating and visualizing the relative motion between the object and the transducer element array.

25. The system according to claim 1, wherein, The object beamforming unit is configured to (i) calculate the delay determined from each transmitter position to a point on the object model and back to each receiver position; and (ii) calculate the weights of one or more of specular scattering, acoustic field directivity, attenuation, diffusion loss, and complex reflectivity based on the incident vector, reflection vector, receiver vector, transducer normal vector, object surface normal vector, and / or prior information about the object related to complex reflectivity. (iii) Before combining the delayed and weighted echoes to generate one or more beamforming echoes, the calculated delay and calculated weights are applied to the stored echoes.

26. The system according to claim 1, wherein, The data processing unit is configured to integrate the beamforming echo power over a time window and optimize the transformations applied to the transducer element positions and normal vectors, which are inputs to the beamformer, in order to maximize the beamformer echo power integrated over the time window.

27. The system according to claim 25, wherein, The display unit is configured to visualize the object in the reference frame of the transducer element array according to the inverse transformation of the optimized transformation.

28. The system according to claim 1, wherein, The transducer element array includes one or more transmitter transducer elements and one or more receiver transducer elements that are spatially separated from the object.

29. The system according to claim 28, wherein, The one or more transmitter transducer elements and the one or more receiver transducer elements completely or at least partially surround the object.

30. The system according to claim 1, wherein, The transducer element array includes at least three transducer elements configured to generate at least three reflection samples, including a monostatic reflection sample and a bistatic reflection sample of the object, such that the reflection samples are significantly separated on the surface of the object.

31. The system according to claim 1, wherein, The transducer element array includes at least three transducer elements configured to generate at least three monostatic and at least three bistatic reflection samples of the object, such that the reflection samples are significantly separated on the surface of the object.

32. The system according to claim 1, wherein, The object model includes multiple vertices and multiple faces, which approximate the object with a face resolution no greater than one acoustic wavelength.

33. The system according to claim 1, wherein, The object model includes multiple points and multiple surface normal vectors corresponding to each point, each point and the multiple surface normal vectors approximating the object within at least one acoustic wavelength resolution.

34. The system according to claim 1, wherein, The one or more acoustic signals include one or more composite waveforms, and the one or more composite waveforms include two or more separate coded waveforms.

35. A method for synthetic aperture acoustic imaging, comprising: The transducer elements of the transducer element array form a synthetic aperture by transmitting acoustic signals to the object and receiving acoustic signals from the object. Beamforming is performed on the object using echo samples of returned acoustic echoes received from one or more regions of the object to generate one or more beamforming output signals, wherein the one or more beamforming output signals are a function of one or more inputs for beamforming, wherein the one or more inputs for beamforming include information representing the object; Optimize the one or more beamforming output signals to determine one or more of the object's position, orientation, geometry, or set of physical properties; as well as An image of the object is generated based on a reproduction of one or more of the object's position, orientation, geometry, or physical properties. Beamforming the object includes calculating delays and weights corresponding to one or more regions of the object based on at least some of the position, orientation, and geometry of the transducer element array relative to the object model. The generated one or more beamforming output signals include spatial information about one or more regions of the object, which is obtained from coherent summation of delayed and weighted echo samples from one or more regions of the object, or from coherent multiplication of delayed and weighted echo samples from one or more regions of the object.

36. The method according to claim 35, wherein, One or more of the object's location, orientation, geometry, or physical properties are determined by the following methods: Generate one or more scalar outputs based on the one or more beamforming output signals; The one or more scalar outputs are optimized based on at least some of the object’s position, orientation, geometric properties, or physical properties. An optimizer is used to detect objects by determining the degree of optimization of one or more objective functions based on the optimized input and output values ​​or the changes in the optimized input and output compared to the detection criteria.

37. The method of claim 36, wherein, The one or more scalar outputs are optimized based on at least some of the position, orientation, or geometry of the transducer element array.

38. The method according to claim 35, wherein, Determining one or more of the following sets of properties of the object: position, orientation, geometry, or physical attributes: Generate at least one scalar output associated with the one or more beamforming output signals, and Process the at least one scalar output to generate an optimized set of parameters that defines the transducer element array and / or the object model.

39. The method according to claim 35, wherein, The set of physical attributes of the object includes one or more surface attributes, one or more volume attributes, or both one or more surface attributes and one or more volume attributes.

40. The method of claim 35, comprising: Generate delayed echo samples and echo sample weighting factors corresponding to the transmitter and receiver positions of the transducer elements of the array, the attributes of the transducer elements of the array, the points of the object model, and the attributes of the object model. The generated delayed echo samples and echo sample weighting factors are used for coherent combination of delayed and weighted echo samples from one or more regions of the object.

41. The method according to claim 35, wherein, The beamforming output signal is repeatedly generated from the same set of received echo samples, a partially new set of received echo samples, or a completely new set of received echo samples by optimizing one or more scalar outputs.

42. The method according to any one of claims 35-41, wherein, The method is implemented by the system according to any one of claims 1-34.

Images