Apparatus, system and method for compounding received ultrasound frequency signals to generate an output ultrasound image - Patents.com

Abstract

An apparatus, method, and computer-implemented medium that simultaneously receives electrical signals based on the reflected frequencies of each of the reflected ultrasonic waveforms that are reflected from a target object as a result of a transmitted ultrasonic waveform; compounds information from the electrical signals to generate a compounded electrical signal; and causes generation of an output image on a display based on the compounded electrical signal.

Description

[Technical field]

[0001] FIELD OF THE DISCLOSURE Embodiments relate generally to the field of signal processing for imaging devices and for ultrasound imaging devices or probes, such as those that include miniature ultrasound transducers (MUTs). [Background technology]

[0002] Ultrasound imaging is widely used in the fields of medicine and non-destructive testing.

[0003] An ultrasound imaging probe or device typically includes an array of many individual ultrasound transducers (pixels) that are used to emit and receive acoustic energy to and from a target being imaged. The reflected waveforms are received by the transducers (e.g., fine ultrasound transducers), converted to electrical signals, and further signal processed to produce an image. Fluid velocity and direction of fluid flow (e.g., for blood flow) can also be measured or detected by ultrasound and visually presented to the operator of the ultrasound imaging device. This quantification and visualization of anatomical structures and motion can be utilized in a variety of medical diagnostic applications and in support of other medical procedures.

[0004] The quality of images acquired through the use of ultrasound imaging devices can make it difficult to distinguish anatomical features with sufficient certainty. A mechanism is needed to improve the image quality acquired by ultrasound imaging devices. Summary of the Invention

[0005] The ultrasound imaging device of some embodiments may operate according to one or more sets of instructions using algorithms to collectively or individually assist in obtaining ultrasound images whose quality is more reliable than that of the state of the art. [Brief description of the drawings]

[0006] The novel features of the invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of several embodiments will be obtained by reference to the following detailed description that sets forth illustrative embodiments, in which the principles of the invention are utilized, and the accompanying drawings (also referred to herein as "figures").

[0007] [Figure 1] FIG. 1 is a block diagram of an imaging device having selectively alterable characteristics in accordance with disclosed embodiments.

[0008] [Diagram 2] FIG. 1 is a diagram of an imaging system having selectively alterable characteristics in accordance with disclosed embodiments.

[0009] [Diagram 3] 1 is a schematic diagram of an imaging device having selectively alterable characteristics in accordance with disclosed embodiments;

[0010] [Figure 4] 1 illustrates a receiving channel according to an example of principles described herein.

[0011] [Diagram 5] 1 is a series of graphs plotting power (shown in decibels (dB)) versus frequency (shown in megahertz (MHz)) of a received ultrasonic waveform at different frequencies and different penetration depths of an imaging target.

[0012] [Figure 6] A set of four respective ultrasound images of a target object is shown (output image D is obtained through simple averaging of the pixel illumination of images AC, each of which corresponds to a different reflected frequency).

[0013] [Figure 7]A set of four respective ultrasound images of a target object is shown (output image D is obtained through alpha blending using depth-adaptive compounding (DAC) of pixel illumination of images A-C corresponding to the three respective reflected frequencies).

[0014] [Figure 8] 1 is an example of a plot of alpha value versus depth for alpha blending using a DAC.

[0015] [Figure 9A] 1 shows plots of voltage versus time (shown in seconds) for multimodal pulses using fundamental frequencies for transmission in pulses of 1.75 MHz, 3.5 MHz, and 5.1 MHz.

[0016] [Figure 9B] 9B shows a plot of the frequency distribution of the transmitted ultrasound waveform of FIG. 9A, plotting power (shown in dB) versus frequency (shown in Hz).

[0017] [Figure 10] A set of images similar to that of FIG. 6, but using the pulses of FIGS. 9A and 9B to generate images A-C, and further using simple compounding to generate compounded image D.

[0018] [Figure 11] A set of images similar to those of FIG. 6, but using alpha blending with DAC on a multimodal pulse such as those shown in FIGS. 9A and 9B.

[0019] [Figure 12] Two output images D1 and D2 are shown (image D1 represents the one obtained using alpha blending in the DAC for three receive frequencies similar to those in FIG. 6, and image D2 represents the one obtained using DR compensation for the same three receive frequencies).

[0020] [Figure 13] A set of three output images D1, D2 and D3 are shown (image D1 represents that obtained using simple compounding for three receive frequencies similar to those in FIG. 6, D2 represents that obtained using alpha blending at the DAC for the same three receive frequencies, and D3 represents that obtained using adaptive compounding).

[0021] [Figure 14] 1 illustrates a flowchart of a process according to some embodiments. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0022] Some embodiments relate to a computing device apparatus, a computer readable storage medium, a method and a system, in which the computing device apparatus simultaneously receives respective electrical signals based on respective frequencies corresponding to reflected ultrasonic waveforms reflected from a target object exposed to the transmitted ultrasonic waveforms; combines information from the respective electrical signals to generate a compounded electrical signal; and causes generation of an image on a display based on the compounded electrical signal.

[0023] In some embodiments, the respective frequencies are respective harmonics of a fundamental frequency, corresponding to the reflected ultrasonic waveform, and the transmitted ultrasonic waveform is at the fundamental frequency, In such embodiments, there is a single transmit frequency, i.e., the fundamental frequency (e.g., 1.75 MHz).

[0024] In some embodiments, the respective frequencies are respective frequencies of the reflected ultrasonic waveform, and the transmitted ultrasonic waveform is a multi-modal waveform having a fundamental frequency corresponding to each of the reflected ultrasonic waveforms. In this embodiment, the transducer elements of the transducer array of the ultrasonic imaging device may be multi-modal, as described in more detail below. As such, the transducer elements generate transmitted ultrasonic waveforms based on a number of different fundamental frequencies, such as, for example, a fundamental of 1.75 MHz, a fundamental of 3.5 MHz, and a fundamental of 5.0 MHz.

[0025] In some embodiments, the device further implements a predictive algorithm to use information from the electrical signal corresponding to a first region of the target object to generate a predicted electrical signal of a second region of the target object that is different from the first region; and causes generation of an image on the display based on a combined electrical signal that is a combination of the compounded electrical signal and the predicted electrical signal.

[0026] Compounding according to some embodiments may include one or more of (including any combination of) simple averaging, weighted averaging, alpha blending with depth adaptive compounding, gain compensation compounding, adaptive compounding, predictive compounding, lateral frequency compounding, and Doppler compounding. Such compounding methods are described in further detail below.

[0027] Advantageously, some embodiments provide algorithms that enable the generation of ultrasound images that allow for a compromise between the benefits of better image resolution at deeper target object locations associated with lower frequencies of the reflected ultrasonic waveforms and the benefits of better resolution at shallower penetration associated with higher frequencies of the reflected ultrasonic waveforms, the latter enabling the generation of images that exhibit improved image resolution at various depths of the target object.

[0028] Some existing solutions use tissue harmonic imaging (TiHI), in which a transducer generates a transmitted ultrasonic waveform at a single fundamental frequency (1.5 MHz) and the device processes the reflected ultrasonic waveform at one harmonic of the fundamental frequency of the transmitted ultrasonic waveform, typically at a harmonic based on twice the fundamental frequency (3.0 MHz).

[0029] Some existing solutions use Compound Harmonic Imaging (CHI).

[0030] In CHI, a transducer separately generates separate transmitted ultrasound waveforms, each at a given fundamental frequency (3.0 MHz and 3.5 MHz), and the device processes two corresponding reflected ultrasound waveforms at the second harmonic of each of the fundamental frequencies. This processing involves compounding the two received signals, which may include some form of alpha blending, but the transmission involves multiple transmissions at different times, and therefore the compounding is not based on a reflected ultrasound waveform that is based on a single transmission.

[0031] Reference is now made to Figures 1 to 4, which illustrate devices and circuits that may be used to implement some embodiments described herein. Specific descriptions of the embodiments are now provided further below in the context of Figures 5 to 14.

[0032] Some embodiments relate to imaging devices, more specifically, electronically configurable ultrasound imaging devices. Ultrasound imaging devices can be used to non-invasively image internal tissues, bones, blood flow, or organs of a human or animal body. The image can then be displayed. To perform ultrasound imaging, ultrasound imaging devices transmit ultrasound signals into the body and receive reflected signals from the body part being imaged. Such ultrasound imaging devices include a transducer and associated electronics, which may be referred to as a transceiver or imager, and may be based on photoacoustic or ultrasonic effects. Such transducers may be used for imaging and may also be used for other applications. For example, transducers may be used for medical imaging; flow measurement in pipes, speakers, and microphone arrays; lithotripsy; localized tissue heating for therapy; and high intensity focused ultrasound (HIFU) surgery.

[0033] Additional aspects and advantages of some embodiments will be readily apparent to those skilled in the art from the detailed description of the invention, in which only exemplary embodiments are shown and described. As will be understood, some embodiments can achieve other different goals, and their several details can be modified in various obvious respects, all without departing from the present disclosure. Thus, the drawings and description should be regarded as illustrative in nature, and not as restrictive.

[0034] Traditionally, imaging devices such as ultrasound imagers used in medical imaging use piezoelectric (PZT) materials or other composites of piezoelectric ceramics and polymers. Such imaging devices may include a housing that contains a transducer having PZT material, and other electronics that form and display an image on a display unit. To manufacture bulk PZT elements or transducers, a slab of thick piezoelectric material may be cut into large rectangular PZT elements. These rectangular PZT elements can be expensive to construct because the manufacturing process typically involves precisely cutting rectangular pieces of thick PZT or ceramic material and mounting them on a substrate at precise intervals. Additionally, the impedance of the transducer is much higher than the impedance of the transducer's transmit / receive electronics, which can affect performance.

[0035] Furthermore, such thick bulk PZT elements may require very high voltage pulses, for example, 100 volts (V) or more, to generate a transmit signal. This high drive voltage results in high power dissipation, since the power dissipation of the transducer is proportional to the square of the drive voltage. This high power dissipation generates heat within the imaging device, which in turn requires cooling devices. These cooling systems increase the manufacturing cost and weight of the imaging device, which further burdens the operation of the imaging device.

[0036] Furthermore, the transmit / receive electronics of the transducer may be located far away from the transducer itself, thus necessitating a micro-coaxial cable between the transducer and the transmit / receive electronics. Typically the cable is the correct length for delay and impedance matching, and quite often an additional impedance matching network is used to efficiently connect the transducer to the electronics through the cable.

[0037] Some embodiments may be utilized in conjunction with imaging devices utilizing either piezoelectric micro-ultrasound transducer (pMUT) technology or capacitive micro-ultrasound transducer (cMUT) technology, as described in further detail herein.

[0038] Generally, both MUTs, such as cMUTs and pMUTs, contain a diaphragm (a thin membrane attached to the end or at some point inside the probe), whereas "traditional" bulk PZT elements usually consist of a solid piece of material.

[0039] Piezoelectric micro-ultrasonic transducers (pMUTs) can be efficiently formed on substrates utilizing various semiconductor wafer fabrication operations. Currently, semiconductor wafers can be 6-inch (15.24 centimeters), 8-inch (20.32 centimeters), and 12-inch (30.48 centimeters) in size and can accommodate hundreds of transducer arrays. These semiconductor wafers start as silicon substrates on which various processing operations are performed. One example of such an operation is the formation of a SiO2 layer, also known as an insulating oxide. Various other operations are performed, such as the addition of metal layers that act as interconnects and bond pads to allow connection to other electronics. Yet another example of a mechanical operation is the etching of cavities. Compared to conventional transducers with large piezoelectric materials, pMUT elements built on semiconductor substrates are less large, less expensive to manufacture, and the interconnects between electronics and transducers are simpler and more capable. Thus, pMUTs offer greater flexibility in the operating frequency of imaging devices that use pMUTs, and the potential to produce higher quality images.

[0040] In some embodiments, the imaging device is coupled to an application specific integrated circuit (ASIC) including a transmit driver, a sensing circuit for received echo signals, and a control circuit for controlling various operations. The ASIC may be formed on a separate semiconductor wafer. The ASIC may be placed in close proximity to the pMUT or cMUT elements to reduce parasitic losses. As a specific example, the ASIC may be 50 micrometers (μm) or less away from the transducer array. In a broader example, the spacing between two wafers or two dies may be less than 100 μm, with each wafer including many dies, including transducers in a transducer wafer and ASICs in an ASIC wafer. In some embodiments, the ASIC has matching dimensions for the pMUT or cMUT array, allowing devices to be stacked to make interconnections between wafers, or between transducer dies or transducer dies and ASIC dies on the ASIC wafer. Alternatively, transducers can be deployed on the ASIC wafer using low temperature piezo material sputtering and other low temperature processes compatible with ASIC processing.

[0041] According to one embodiment, wherever the ASIC and transducer interconnect, the two may have similar footprints. More specifically, according to the latter embodiment, the footprint of the ASIC may be an integer multiple or submultiple of the footprint of the MUT.

[0042] Regardless of whether the imaging device is based on a pMUT or a cMUT, an imaging device according to some embodiments may include multiple transmit channels and multiple receive channels. The transmit channels drive the transducer elements with voltage pulses at a frequency to which the transducer elements respond. This causes the elements to emit ultrasonic waveforms that are directed toward the object to be imaged (target object), for example, an organ or other tissue within the body. In some examples, an imaging device with an array of transducer elements may be in mechanical contact with the body using gel between the imaging device and the body. The ultrasonic waveform travels toward the object, i.e., the organ, and a portion of the waveform reflects back to the transducer elements in the form of received / reflected ultrasonic energy, and the received ultrasonic energy may be converted into electrical energy within the imaging device. The received ultrasonic energy may then be further processed by multiple receive channels to convert the received ultrasonic energy into electrical signals, which may be processed by other circuitry to generate an image of the object for display based on the electrical signals.

[0043] One embodiment of an ultrasound imaging device includes a transducer array and control circuitry including, for example, an application specific integrated circuit (ASIC), transmit and receive beamforming circuitry, and optionally additional control electronics.

[0044] Imaging devices incorporating features of the embodiments may advantageously reduce or eliminate the problem.

[0045] In one embodiment, the imaging device may include a handheld case in which the transducer and associated electronic circuitry, such as control circuitry and optionally a computing device, are housed. The imaging device may also include a battery that provides power to the electronic circuitry.

[0046] Accordingly, some embodiments relate to portable imaging devices that utilize either pMUT elements or cMUT elements in a 2D array, in some embodiments, an array of such transducer elements is coupled to an application specific integrated circuit (ASIC) of the imaging device.

[0047] In the following description, for the purpose of explanation, specific details are described to provide an understanding of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure can be practiced without these details. Furthermore, those skilled in the art will recognize that the examples of the present disclosure described below can be implemented in various ways, such as a process, one or more processors (processing circuits) of a control circuit, one or more processors (or processing circuits) of a computing device, a system, a device, or a method on a tangible computer-readable medium.

[0048] Those skilled in the art will recognize that (1) certain manufacturing operations may be performed optionally; (2) operations may not be limited to the particular order described herein; and (3) certain operations may be performed in different orders, including being performed simultaneously.

[0049] The elements / components shown in the figures are illustrative of exemplary embodiments and are intended to avoid obscuring the disclosure. Reference herein to "one example", "preferred example", "one example", "example", "one embodiment", "some embodiments", or "embodiments" means that a particular feature, structure, characteristic, or function described in connection with the example is included in at least one example of the disclosure and may be included in more than one example. The appearance of the phrases "in one example", "in one example", "in an example", "in one embodiment", "some embodiments", or "in an embodiment" in various places in this specification do not necessarily all refer to the same example or examples. The terms "include", "including", "comprise", and "comprising" are understood to be open-ended terms, and any listings below are examples and are not meant to be limited to the listed items. Any headings used herein are for organizational purposes only and should not be used to limit the scope of the description or the claims. Furthermore, the use of certain terms in various places in this specification is for illustrative purposes and should not be construed as limiting.

[0050] Referring now to the drawings, FIG. 1 is a block diagram of an imaging device 100 having a controller or control circuit 106 that controls the selectively alterable channels (108, 110) and performs image calculations on a computing device 112 according to the principles described herein. As described above, the imaging device 100 may be used to generate images of internal tissues, bones, blood flow, or organs of a human or animal body. Thus, the imaging device 100 may transmit signals into the body and receive reflected signals from the body part being imaged. Such imaging devices may include either pMUTs or cMUTs, which may be referred to as transducers or imagers, which may be based on photoacoustic or ultrasound effects. The imaging device 100 may also be used to image other objects. For example, the imaging device may be used for medical imaging; flow measurements in pipes, speakers, and microphone arrays; lithotripsy; localized tissue heating for therapy; and high intensity focused ultrasound (HIFU) surgery.

[0051] In addition to use on human patients, imaging device 100 may also be used to obtain images of the internal organs of animals. Furthermore, in addition to imaging internal organs, imaging device 100 may also be used to determine the direction and velocity of blood flow in arteries and veins, such as in Doppler mode imaging, and may also be used to measure tissue stiffness.

[0052] The imaging device 100 may be used to perform different types of imaging. For example, the imaging device 100 may be used to perform one-dimensional imaging, also known as A-scan, two-dimensional imaging, also known as B-scan, three-dimensional imaging, also known as C-scan, and Doppler imaging (i.e., determining motion, such as fluid flow in blood vessels, using Doppler ultrasound). The imaging device 100 may be switched into different imaging modes, including, but not limited to, linear and sector modes, and may be electronically configured under program control.

[0053] To facilitate such imaging, the imaging device 100 includes one or more ultrasonic transducers 102, each of which includes an array of ultrasonic transducer elements 104. Each ultrasonic transducer element 104 may be embodied as any suitable transducer element, such as a pMUT or cMUT element. The transducer elements 104 operate to 1) generate ultrasonic pressure waves that pass through a body or other mass, and 2) receive reflected waves (received ultrasonic energy) from an object or other mass within the body being imaged. In some examples, the imaging device 100 may be configured to simultaneously transmit and receive ultrasonic waveforms or ultrasonic pressure waves (pressure waves, for short). For example, the control circuitry 106 may control certain transducer elements 104 to transmit pressure waves toward a target object being imaged, while other transducer elements 104 may be configured to simultaneously receive the pressure waves / ultrasonic energy reflected from the target object and generate charges based thereon in response to the received waves / received ultrasonic energy / received energy.

[0054] In some examples, each transducer element 104 may be configured to transmit or receive signals at a particular frequency and bandwidth associated with a center frequency, and optionally, additional center frequencies and bandwidths. Such multi-frequency transducer elements 104 may be referred to as multimodal elements 104 and may extend the bandwidth of the imaging device 100. The transducer elements 104 may be capable of emitting or receiving signals at any suitable center frequency, such as from about 0.1 to about 100 megahertz. The transducer elements 104 may be configured to emit or receive signals at one or more center frequencies in a range from about 1.75 megahertz to about 5 megahertz.

[0055] To generate pressure waves, the imaging device 100 may include multiple transmit (Tx) channels 108 and multiple receive (Rx) channels 110. The transmit channels 108 may include multiple components that drive the transducer 102, i.e., the array of transducer elements 104, with voltage pulses at frequencies to which they respond. This causes ultrasonic waveforms to radiate from the transducer elements 104 toward the imaging object.

[0056] According to some embodiments, the ultrasound waveform may include one or more ultrasound pressure waves transmitted substantially simultaneously from one or more corresponding transducer elements of the imaging device.

[0057] The ultrasonic waveform travels towards the imaging object and a portion of the waveform is reflected back to the transducer 102, where the piezoelectric effect converts the ultrasonic waveform into electrical energy. The receive channel 110 collects the resulting electrical energy and transmits it to a computing device 112, which processes it and, for example, develops or generates an image that can be displayed.

[0058] In some examples, the number of transmit channels 108 and receive channels 110 in the imaging device 100 may remain constant, but the number of transducer elements 104 to which they are coupled may vary. In one embodiment, the coupling of the transmit channels and receive channels to the transducer elements may be controlled by the control circuit 106. In some examples, the control circuit may include the transmit channels 108 and receive channels 110, for example, as shown in FIG. 1 . For example, the transducer elements 104 of the transducer 102 may be formed in a two-dimensional spatial array having N columns and M rows. In a specific example, the two-dimensional array of transducer elements 104 may have 128 columns and 32 rows. In this example, the imaging device 100 may have up to 128 transmit channels 108 and up to 128 receive channels 110. In this example, each transmit channel 108 and receive channel 110 may be coupled to multiple or a single pixel 104. For example, depending on the imaging mode (e.g., either a linear mode in which multiple transducers transmit ultrasound in the same spatial direction, or a sector mode in which multiple transducers transmit ultrasound in different spatial directions), each row of transducer elements 104 may be coupled to a single transmit channel 108 and a single receive channel (110). In this example, the transmit channel 108 and the receive channel 110 may receive a composite signal, which combines the signals received at each transducer element 104 in the respective row. In another example, i.e., during different imaging modes, each transducer element 104 may be coupled to its dedicated transmit channel 108 and its dedicated receive channel 110. In some embodiments, a transducer element 104 may be coupled to both a transmit channel 108 and a receive channel 110. For example, the transducer elements 104 may be adapted to generate and transmit an ultrasound pulse and then detect echoes of the pulse in a form that converts the reflected ultrasound energy into electrical energy.

[0059] The control circuitry 106 may be embodied as any one or more circuits configured to perform the functions described herein. For example, the control circuitry 106 may be embodied as or otherwise include an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), a system on a chip, a processor and memory, a voltage source, a current source, one or more amplifiers, one or more digital-to-analog converters, one or more analog-to-digital converters, etc.

[0060] The exemplary computing device 112 may be embodied as any suitable computing device including any suitable components, such as, for example, one or more processors, memory circuitry, communication circuitry, a battery, a display, etc. In one embodiment, the computing device 112 may be integrated with the control circuitry 106, transducer 102, etc. in a single package or chip, or in a single system-on-chip (SoC), as suggested in the embodiment of FIG. 1. In other embodiments, as described in more detail below, some or all of the computing device may be in a separate package from the control circuitry and transducer, etc., as suggested in the embodiment of FIG. 2.

[0061] Each transducer element may have any suitable shape, such as square, rectangular, elliptical, or circular, etc. The transducer elements may be arranged in an orthogonally arranged two-dimensional array, such as N columns and M rows, as described herein, or may be arranged in an asymmetric (or staggered) linear array.

[0062] The transducer elements 104 may have associated transmit driver circuits for associated transmit channels and low noise amplifiers for associated receive channels. Thus, the transmit channels may include transmit drivers and the receive channels may include one or more low noise amplifiers. For example, although not explicitly shown, the transmit and receive channels may each include multiplexing and address control circuitry to enable activation, deactivation, or low power modes of particular transducer elements and sets of transducer elements. It will be appreciated that the transducers may be arranged in patterns other than orthogonal rows and columns, such as in a circular shape, or other patterns based on the range of ultrasonic waveforms generated therefrom.

[0063] 2 is a diagram of an imaging environment including an imaging system having selectively configurable characteristics, according to one embodiment, which may include an imaging device 202 and a computing system 222 including a computing device 216 and a display 220 coupled to the computing device, as described in further detail below.

[0064] As shown in FIG. 2, according to one embodiment, unlike the embodiment of FIG. 1, the computing device 216 may be physically separated from the imaging device 220. For example, the computing device 216 and the display device 220 may be located in a separate device (in this context, the illustrated computing system 222 is physically separated from the imaging device 202 during operation) compared to the components of the imaging device 202. The computing system 222 may include a mobile device, such as a mobile phone or tablet, or a stationary computing device that can display images to a user. In another example, as shown in FIG. 1, for example, the display device, the computing device, and the associated display may be part of the imaging device 202 (shown here). That is, the imaging device 100, the computing device 216, and the display device 220 may be located in a single housing.

[0065] A "computing device" as referred to in this specification may, in some embodiments, be configured to generate signals to cause an image of an object to be displayed on a display or to communicate information regarding the image to a user.

[0066] As shown, the imaging system includes an imaging device 202 configured to generate and transmit pressure waves 210 toward an object, such as a heart 214, via a transmit channel (FIG. 1, 108) in a transmit mode / process. An internal organ or other imaging object may reflect a portion of the pressure waves 210 toward the imaging device 202, which may receive the reflected pressure waves via a transducer (such as transducer 102 in FIG. 1) via a receive channel (FIG. 1, 110) and via a control circuit (FIG. 1, 106). The transducer may generate an electrical signal based on the received ultrasonic energy in a receive mode / process. The transmit mode or receive mode may be applicable in the context of an imaging device that may be configured to either transmit or receive, albeit at different times. However, as previously mentioned, some imaging devices according to embodiments may be adapted to be in both transmit and receive modes simultaneously. The system also includes a computing device 216 that communicates with the imaging device 100 via a communication channel, such as a wireless communication channel 218 as shown, although embodiments also encompass within their scope wired communication between the computing system and the imaging device. The imaging device 100 may transmit signals to the computing device 216, which may have one or more processors for processing the received signals to complete the formation of an image of the object. The display device 220 of the computing system 222 may then display the image of the object using the signals from the computing device. The computing system may further convey information regarding the defective pixels to a user, as described above.

[0067] An imaging device according to some embodiments may include a portable and / or handheld device adapted to communicate signals with a computing device over a communication channel, wirelessly (using wireless communication protocols such as IEEE 802.11 or Wi-Fi protocols, Bluetooth protocols including Bluetooth low energy, millimeter wave communication protocols, or any other wireless communication protocols that would be within the knowledge of one of ordinary skill in the art) or via a wired connection such as a cable (USB2, USB3, USB3.1, USB-C, etc.) or an interconnect on a microelectronic device. In the case of tethering or a wired connection, the imaging device may include a port, as described in further detail in connection with FIG. 3, to receive a cable connection of a cable that communicates with the computing device. In the case of a wireless connection, the imaging device 100 may include a wireless transceiver for communicating with the computing device 216.

[0068] It should be understood that in various embodiments, different aspects of the disclosure may be implemented in different components. For example, in one embodiment, an imaging device may include circuitry (such as channels) for transmitting and receiving ultrasonic waveforms through its transducer, while a computing device may be adapted to control such circuitry for ultrasonic waveforms generated at transducer elements of the imaging device using voltage signals, and further for processing of received ultrasonic energy.

[0069] FIG. 3 depicts a diagram of an imaging device according to some embodiments, as described in more detail below.

[0070] As can be seen in FIG. 3, the imaging device 300 may include a handheld case 331, housing the transducer 302 and associated electronics. The imaging device may also include a battery 338 for powering the electronics. FIG. 3 thus illustrates an embodiment of a portable imaging device capable of 2D and 3D imaging using a 2D array of pMUTs, optionally built on a silicon wafer. Such an array, coupled to an application specific integrated circuit (ASIC) 106 with an electronic configuration of specific parameters, allows for higher quality image processing at lower cost than previously possible. Furthermore, by controlling certain parameters, e.g., the number of channels used, power consumption may be altered and temperature may be changed.

[0071] The imaging device 300 according to some embodiments is configured to enable real-time system configurability and adaptability based on information about one or more defective pixels (defective pixel data), for example, by comparing a current pixel performance dataset of one or more pixels of a transducer array of the imaging device to a reference pixel performance dataset of the same pixels, as described in further detail below.

[0072] Attention now turns in more detail to FIG. 3, which is a schematic diagram of an imaging device 300 having selectively adjustable features, according to some embodiments. The imaging device 300 may be similar to the imaging device 100 of FIG. 1 or the imaging device 202 of FIG. 2, by way of example only. As mentioned above, the imaging device may include an ultrasound medical probe. FIG. 3 illustrates a transducer 302 of the imaging device 300. As mentioned above, the transducer 302 may include an array of transducer elements (FIG. 1, 104) adapted to transmit and receive pressure waves (FIG. 2, 210). In some examples, the imaging device 300 may include a coating layer 322 that serves as an impedance matching interface between the transducer 302 and the human body, or other mass or tissue, through which the pressure waves (FIG. 2, 210) are transmitted. In some cases, the coating layer 322 may function as a lens when designed with a curvature consistent with a desired focal length.

[0073] The imaging device 300 may be embodied in any suitable form factor. In some embodiments, a portion of the imaging device 300 including the transducer 302 may extend outwardly from the remainder of the imaging device 100. The imaging device 300 may be embodied as any suitable ultrasound medical probe, such as a convex array probe, a micro-convex array probe, a linear array probe, an intravaginal probe, a rectal probe, a surgical probe, an intraoperative probe, etc.

[0074] In some embodiments, a user may apply gel to the skin of the living body before direct contact with the coating layer 322, which may improve impedance matching at the interface between the coating layer 322 and the human body. Impedance matching reduces losses of the pressure wave (FIG. 2, 210) at the interface and the reflected wave traveling toward the imaging device 300 at the interface.

[0075] In some examples, the coating layer 322 may be a flat layer to maximize the transmission of acoustic signals from the transducer 102 to the body, or vice versa. The thickness of the coating layer 322 may be a quarter wavelength of the pressure waves (FIG. 2, 210) generated by the transducer 102.

[0076] The imaging device 300 also includes a control circuit 106, such as one or more processors, optionally in the form of an application specific integrated circuit (ASIC chip or ASIC), for controlling the transducer 102. The control circuit 106 may be coupled to the transducer 102 via bumps or the like. As mentioned above, the transmit channels 108 and receive channels 110 may be selectively alterable or adjustable, meaning that the number of transmit channels 108 and receive channels 110 that are active at a given time may be changed, e.g., such that one or more pixels determined to be defective are not used. For example, the control circuit 106 may be adapted to selectively adjust the transmit channels 108 and receive channels 110 based on pixels being tested for defects and / or based on pixels determined to be defective.

[0077] In some examples, the basis for changing channels may be a mode of operation, which may be selected based on which pixels are determined to be defective and, optionally, based on the type of defect for each defective pixel.

[0078] The imaging device may also include one or more processors 326 for controlling components of the imaging device 100. The one or more processors 326, in addition to the control circuitry 106, may be configured to at least one of control the operation of the transducer elements, process electrical signals based on ultrasonic waveforms reflected from the transducer elements, or generate signals to cause generation of an image of an object being imaged by one or more processors of a computing device, such as the computing device 112 of FIG. 1 or the computing device 216 of FIG. 2. The one or more processors 326 may be further adapted to perform other processing functions associated with the imaging device. The one or more processors 326 may be embodied as any type of processor 326. For example, the one or more processors 326 may be embodied as a single or multi-core processor, a single or multi-socket processor, a digital signal processor, a graphics processor, a neural network computation engine, an image processor, a microcontroller, a field programmable gate array (FPGA), or other processor or processing / control circuitry. The imaging device 100 may also include circuitry 328, such as an analog front end (AFE) for processing / conditioning the signal, and an acoustic absorbing layer 330 for absorbing waves generated by the transducer 102 and propagating toward the circuitry 328. That is, the transducer 102 may be mounted on a substrate and attached to the acoustic absorbing layer 330. This layer absorbs any ultrasonic signals radiating in the reverse direction (i.e., away from the coating layer 322 and toward the port 334) that may otherwise reflect and interfere with the quality of the image. Although FIG. 3 shows the acoustic absorbing layer 330, this component may be omitted if other components impede the material transmission of ultrasonic waves in the reverse direction.

[0079] The analog front end 328 may be embodied as any one or more circuits configured to interface with other components of the imaging device, such as the control circuit 106 and the processor 326. For example, the analog front end 328 may include, for example, one or more digital-to-analog converters, one or more analog-to-digital converters, one or more amplifiers, etc.

[0080] The imaging device may include a communication unit 332 for communicating data, including control signals, with an external device, such as a computing device (FIG. 2, 216), for example, via a port 334 or a wireless transceiver. The imaging device 100 may include a memory 336 for storing data. The memory 336 may be embodied as any type of volatile or non-volatile memory or data storage capable of performing the functions described herein. During operation, the memory 336 may store various data and software used during operation of the imaging device 100, such as an operating system, applications, programs, libraries, and drivers.

[0081] In some examples, imaging device 100 may include a battery 338 for powering components of imaging device 100. Battery 338 may also include battery charging circuitry, which may be a wireless or wired charging circuit (not shown). The imaging device may include a gauge indicating remaining battery power consumed, used to configure the imaging device to optimize power management to improve battery life. Additionally or alternatively, in some embodiments, the imaging device may be powered by an external power source, for example, by plugging the imaging device into a wall outlet.

[0082] FIG. 4 shows a receive channel 110 according to one example of the principles described herein. The receive channel 110 is coupled to a transducer element (FIG. 1, 104) to receive a reflected pressure wave (FIG. 2, 210). FIG. 4 also shows the connection between the transducer element (FIG. 1, 104) and the transmit channel (FIG. 1, 110). In one example, the transmit channel (FIG. 1, 108) faces high impedance during receive operation at the node where the receive pressure and transmit pulse meet. Specifically, the reflected pressure wave is converted to a charge in the transducer element 104, which is converted to a voltage by a low noise amplifier (LNA) (456). The LNA (456) is a charge amplifier, and the charge is converted to an output voltage. In some examples, the LNA (456) has a programmable gain, and the gain can be changed in real time.

[0083] The LNA (456) converts the charge in the transducer to a voltage output and also amplifies the received echo signal. A switch (transmit / receive switch) connects the LNA (456) to the transducer elements 104 in the receive mode of operation.

[0084] The output of this LNA (456) is then connected to other components to condition the signal. For example, a programmable gain amplifier (PGA) (458) adjusts the voltage magnitude and provides a way to change the gain as a function of time, and may be known as a time gain amplifier (TGA). As the signal travels deeper into the tissue, it attenuates.

[0085] Therefore, a larger gain is used for compensation, which is implemented by the TGA. The bandpass filter 460 operates to filter out noise and out-of-band signals. The analog-to-digital converter (ADC) 462 digitizes the analog signal and converts the signal to the digital domain so that further processing can be performed digitally. The data from the ADC 462 is then digitally processed in the demodulation unit 464 and passed to the FPGA 326 to generate the scan lines. In some implementations, the demodulation unit 464 may be implemented elsewhere, such as in the FPGA. The demodulation unit frequency shifts the carrier signal to baseband with two components (I and Q) in quadrature for further digital processing in some examples, and the analog-to-digital converter (ADC) 462 may implement a successive approximation register (SAP) architecture to reduce the latency of the ADC 462. That is, since the ADC 462 is repeatedly turned off and on, there should be little or no latency to not delay the signal processing after turning on.

[0086] Some embodiments aim to take advantage of the wide bandwidth of transducer elements of transducer arrays such as those described above by effectively generating a transmitted ultrasound waveform at a low frequency pulse, a low fundamental frequency, and imaging at harmonics of this single fundamental frequency. In a triharmonic imaging (THI) implementation, the first through third harmonic frequencies may be used, for example, to substantially simultaneously generate respective electrical signals representing reflected ultrasound waveforms. Figures 6, 7, and 10 are representative diagrams illustrating THI according to some embodiments. These diagrams are described in more detail below.

[0087] Some embodiments further aim to take advantage of the wide bandwidth of transducer elements of a transducer array such as those described above by effectively generating a multimodal transmit ultrasound waveform at multiple fundamental frequency pulses, including low and high frequency pulses, and imaging at the same or similar frequency pulses as the fundamental frequency pulses. In a multimodal imaging (MI) implementation, the frequencies of the processed reflected ultrasound waveforms may be used, for example, to substantially simultaneously generate respective electrical signals representing the reflected ultrasound waveforms. Figure 11 is a representative diagram illustrating a THI according to some embodiments. These diagrams are described in more detail below.

[0088] FIG. 5 is a plot 500 showing a series of graphs plotting power (shown in decibels (dB)) versus frequency (shown in megahertz (MHz)) of a received ultrasonic waveform at different frequencies and different penetration depths of an imaging target. As shown in FIG. 5, the reflected ultrasonic waveform may show higher power and therefore better penetration at lower frequencies (1.5 MHz) (note that even a depth of 140-160 mm is associated with higher dB), but the reflected ultrasonic waveform may show lower power at increasing frequencies. Even at shallower penetrations (e.g., 20-40 mm), lower frequencies show higher power. FIG. 5 shows the advantage of lower frequencies in terms of resolution at greater penetrations, as discussed above. This figure further suggests that at shallower penetrations or depths, higher frequencies may lead to better resolution (see, e.g., plot for depths of 20-40 mm).

[0089] Some embodiments allow for the advantage of higher harmonics (when THI is used) or higher frequencies (when MI is used), which provide overall higher spatial resolution compared to lower harmonics / lower frequencies, along with the advantage of lower harmonics / lower frequencies. These lower harmonics / lower frequencies provide better penetration resolution (compared to higher harmonics / higher frequencies). As used herein, "penetration" refers to the imaging depth of a target object.

[0090] Some embodiments advantageously allow for the use of a wide bandwidth of the transducer elements to extract high information content from electrical signals generated from reflected ultrasonic waveforms at all imaging depths. In some embodiments, high information content is extracted by (1) processing the reflected ultrasonic waveforms at higher frequencies to obtain electrical signals corresponding to the near field of the image (fields corresponding to lower / shallower penetration) and corresponding to narrower lateral image angles (lateral image angle refers to angle θ, which will be explained in more detail in the context of FIG. 6); and (2) processing the reflected ultrasonic waveforms at lower frequencies to obtain electrical signals corresponding to the far field of the image (fields corresponding to higher / deeper penetration) and / or for wider lateral image angles.

[0091] 6, 7, 10, 11, 12 and 13 show ultrasound images corresponding to ultrasound frames. A "frame" or "image" as referred to herein refers to an image of a cross-sectional plane of an object and may be composed of individual scanlines. Scanlines may be viewed as individual layers, or slices of an image. Depending on the resolution, a particular image may contain different numbers of scanlines, ranging from less than one hundred to several hundred.

[0092] Reference is now made to FIG. 6, which shows a set 600 of four respective ultrasound images of a target object, with depth plotted in cm on the left axis, and an output image, image D, obtained through simple averaging of pixel illumination of images A-C, each of which corresponds to a given reflected frequency. In FIG. 6, images A, B, C and D respectively represent: A is an image generated from an electrical signal corresponding to a first frequency of the reflected ultrasonic waveform (corresponding to the fundamental frequency of transmission of the transmitted ultrasonic waveform); B is an image generated from an electrical signal corresponding to a second frequency of the reflected ultrasonic waveform (corresponding to the second harmonic of the fundamental frequency); C is an image generated from an electrical signal corresponding to a third frequency of the reflected ultrasonic waveform (corresponding to the third harmonic of the fundamental frequency); and D is an image generated from a simple compounding of the electrical signals of each of images A-C. Lines 13 and 9 in FIG. 6 indicate penetration depths of 13 cm and 9 cm, respectively.

[0093] FIG. 6 illustrates a problem that can occur when imaging based on a single receive frequency (i.e., a single frequency at which reflected ultrasound waveforms are processed or demodulated to generate a displayed output image). For example, looking at image A, there is an overall better penetration resolution and better resolution at wider image angles θ (θ represents the angle on either side of line CL). However, the resolution of image A at shallower depths could be improved when compared to the resolution at shallower depths for images B and C (indicated by the "spotlight" region SL in images B and C). Thus, for a given receive frequency, no single image may provide a suitable image with sufficient resolution at the desired depth and / or image angle. Additionally, it is desirable to maintain resolution while moving away from the SL region to obtain a more uniform image.

[0094] 6 highlights that image improvement can occur in multiple dimensions, such as along the depth of the depicted target object and across the angle θ. At wider angles θ, higher frequencies do not have as good fidelity or signal-to-noise ratio compared to lower frequencies.

[0095] Some embodiments contemplate taking advantage of the better resolution obtained from electrical signals corresponding to low frequency processing of the reflected ultrasonic waveform, both for deeper penetration and wider angles.

[0096] Figure 6, described above as an illustration of some of the problems that may arise with ultrasound imaging, shows a centerline CL from which θ can be measured, and a spotlight area SL which corresponds to an area of ​​better resolution for higher frequency processing. Figures 7 and 10-12 show images comparable in type to those of Figure 6, and as a result, depictions of CL, SL and θ are omitted in those pictures, but it should be understood that the concepts of CL, SL and θ described in the context of Figure 6 are equally applicable to Figures 7 and 10-12 and may be described in the context of those figures below.

[0097] 6, 7, 10 and 11 show an example of a THI in which a single low frequency transmit pulse (1.75 Mhz) is applied to the transmitted ultrasound waveform and the resulting reflected ultrasound waveform is demodulated in three imaging frequency bands (1.75 Mhz, 3.5 Mhz, 5.25 Mhz). In the THI, narrow baseband filters are applied to the electrical signals corresponding to the demodulation of these three imaging harmonics, followed by further processing to generate electrical signals corresponding to three images, namely images A, B and C in each of FIGS. 6, 7, 10 and 11. The electrical signals corresponding to these three images A-C may then be compounded according to any of the compounding processes listed below to improve contrast resolution, spatial resolution or penetration resolution. Some compounding methods according to embodiments may include simple averaging, weighted averaging, alpha blending with depth adaptive compounding, gain compensation compounding, maximum and minimum adaptive compounding, adaptive compounding, predictive compounding, lateral frequency adaptive compounding, and Doppler compounding. Such compounding methods are described in more detail below.

[0098] [Simple Average]

[0099] Reference is again made to Figure 6, already partially described above. According to the example of Figure 6, compounding of electrical signals corresponding to images of three receive frequencies at A, B and C (fundamental and two harmonics in the case of Figure 6, but simple averaging according to embodiments may also be used in multimodal imaging embodiments) may be achieved by simple averaging to obtain an image at D. Simple averaging in the context of Figure 6 may involve averaging, for each given pixel location defined by depth and image angle, the respective pixel illuminations of said given pixel locations corresponding to each of the receive frequencies.

[0100] For a given frequency of the reflected ultrasonic waveform, the "pixel illuminance" I at a given pixel location refers to the radiant energy flux per unit area (perpendicular to the direction of flow of the radiant energy) at the given pixel location that would be generated based on the electrical signal corresponding to the given received frequency.

[0101] For example, the pixel illuminance at each of the receive frequencies for a given pixel location, say a depth of 8 cm and an image angle of 5 degrees, may be used to calculate a simple average of the pixel illuminance at that location, which may be used to generate the pixel illuminance in image D. The latter would be a linear average. See Equation 1 below for simple averaging. I out =(I high +I mid +I low ) / 3 Equation (1) Where: I out is the output pixel illuminance at a given pixel location in the output image (image D); I high is the pixel illuminance at a given pixel location in the high receive frequency image (image C); I mid is the pixel illumination at a given pixel location in the intermediate receive frequency image (image B); and I low is the pixel illumination at a given pixel location in the low receive frequency image (image A).

[0102] [Weighted averaging]

[0103] In compounding with weighted averaging, the pixel illumination at a given pixel location may be obtained through an equation such as Equation 2 below. I out =(α×I high +β×I mid +Υ×I low ) / 3 formula (2) Here, I out , I high , I mid and I lowwhere α, β, and Υ are weights used for each of the listed pixel illuminances. α, β, and Υ may be based on the needs of the application, such as whether depth resolution is required.

[0104] [Alpha blending with depth adaptive compounding]

[0105] Reference is now made to Figures 7 and 8 in the context of a compounding method according to one embodiment, which involves alpha blending using depth adaptive compounding (DAC).

[0106] Figure 7 shows a set 700 of four respective ultrasound images of a target object (the output image, Image D, is acquired through an alpha blending DAC of the pixel illumination of Images A-C). Images A-C in Figure 7, which are similar to those in Figure 6, correspond to an image acquired at a receive frequency equal to the fundamental frequency (A), an image acquired at a receive frequency equal to the second harmonic of the fundamental frequency (B), and an image acquired at a receive frequency equal to the third harmonic of the fundamental frequency (C). Line 13 indicates a penetration depth of 13 cm and line 9 indicates a penetration depth of 9 cm.

[0107] Alpha blending using a DAC in the context of Figure 7 may involve multiplying, for each given pixel location defined by depth and image angle, each pixel intensity at said given pixel location corresponding to each of the received frequencies by a respective alpha multiplier, where each of the multipliers for the pixel intensity at a given pixel location may be a function of one or more alpha values, with each alpha value being a function of penetration depth.

[0108] See Equation 3 below for an example of alpha blending using a DAC. I out =I high α high +(1-α high )·(α mid Imid +(1-α mid )·I low ) Formula (3) Here, I out , I high , I mid and I low is defined above for Equation 1, α high corresponds to the depth-dependent α value for high receive frequencies; and α mid corresponds to the depth-dependent α value of the intermediate receive frequency. Thus, the multiplier for each of the pixel illuminations from Equation 3 above may be: α high :I high multiplier; (1-α high )×α mid :I mid multiplier; and (1-α high )×(1-α mid ):I low multiplier

[0109] FIG. 8 shows the α high and α mid 8 is an example of a plot 800 of α values ​​versus depth (shown in mm) for α high It is clear from plot 800 that the α value of can drop to zero at a given depth, e.g., at a depth of about 92 mm in the example case of plot 800. In contrast, mid The α value of high 8, falls to zero much more slowly than at much deeper penetrations, at 200 mm in the example of plot 800. Thus, alpha blending with a DAC may aim to minimize the contribution of higher receive frequencies to pixel illumination at deeper depths, taking into account lower resolution and fidelity. For deeper pixel locations, the contribution of pixel illumination at high and mid receive frequencies may tend to zero, in which case alpha blending with a DAC would use only the pixel illumination of the lower receive frequencies (above 200 mm in the case of FIG. 8).

[0110] Thus, multipliers of pixel illumination for various received frequencies may be based on the depth of the pixel location.

[0111] [Multimodal transmitted ultrasound waveform]

[0112] As mentioned above, the embodiments, and thus the compounding methods described herein, do not require that the transmitted ultrasound waveform have a single fundamental frequency as the transmit frequency, and the received frequency of the reflected ultrasound waveform includes harmonics of that fundamental frequency (THI). Some other embodiments include, within their scope, the use of transmitted ultrasound waveforms characterized by three fundamental frequencies as the transmit frequencies, and the received frequencies correspond to the fundamental frequency (MI). As mentioned above, some transducer elements may have multimodal capabilities and thus correspond to multimodal elements that would enable multimodal imaging modalities. The embodiments include, within their scope, computing devices capable of processing electrical signals based on both the THI and MI modalities.

[0113] 9A, 9B, 10, and 11 show exemplary multimodal pulses that may be used in the MI embodiments described herein that are related to the use of MI for better penetration resolution. In particular, FIG. 9A shows a plot 900A showing voltage plotted against time (shown in seconds) for a multimodal pulse using fundamental frequencies for transmission at pulses of 1.75 MHz, 3.5 MHz, and 5.1 MHz. FIG. 9B shows a plot 900B of the frequency distribution of the transmitted ultrasound waveform of FIG. 9A plotting power (shown in dB) versus frequency (shown in Hz).

[0114] FIG. 10 is a set of images 1000 similar to that of FIG. 6, but this time using the pulses of FIGS. 9A and 9B to generate images A-C, and further using the simple compounding described above to compound the electrical signals corresponding to images A-C to generate compound image D.

[0115] FIG. 11 is a set of images 1000 similar to that of FIG. 6, but using alpha blending with the DAC described above for a multimodal pulse such as that shown in FIGS. 9A and 9B.

[0116] As can be seen from a comparison of Figures 11 and 10, alpha blending with DAC at MI in the illustrated example results in an output image D having better overall resolution compared to simple compounding at MI. As can be seen from a comparison of Figures 6 and 10, simple compounding at THI in the illustrated example results in an output image D having better overall resolution compared to simple compounding at MI. As can be seen from a comparison of Figures 7 and 11, alpha blending with DAC at THI in the illustrated example results in an output image D having better overall resolution compared to adaptive compounding at MI. As can be seen from a comparison of Figures 11 and 6, alpha blending with DAC at MI in the illustrated example results in an output image D having better overall resolution compared to adaptive compounding at THI.

[0117] [Compensation before compounding]

[0118] According to one embodiment, the compounding method may include gain compensation compounding, according to which individual frequency bands of the reflected ultrasonic waveform may be pre-processed prior to compounding. For example, the electrical signals corresponding to each of the receive frequencies being processed for image generation may be subject to gain compensation or dynamic range (DR) compensation to improve the quality of the output image.

[0119] Reference is now made to FIG. 12, which shows two output images D1 and D2 (image D1 represents the one obtained using alpha blending with DAC for three receive frequencies similar to those of FIG. 6, 7, 10 or 11, and D2 represents the one obtained using DR compensation for the same three receive frequencies, with a DR of 60 and different gains). A comparison of D1 and D2 shows the clear advantage of improved contrast using DR compensation compared to alpha blending with DAC, all else being equal.

[0120] [Maximum and minimum adaptive compounding]

[0121] Adaptive compounding generally involves some nonlinear method of combining or compounding electrical signals that correspond to the received frequencies of the reflected ultrasonic waveform. Exemplary adaptive compounding techniques are described below.

[0122] Reference is now made to FIG. 13, which shows a set of three output images D1, D2 and D3 (image D1 represents that obtained using simple compounding for three receive frequencies similar to those of FIG. 6, FIG. 7, FIG. 10 or FIG. 11, D2 represents that obtained using alpha blending with a DAC for the same three receive frequencies, and D3 represents that obtained using max and min adaptive compounding according to Equation 5 below). A comparison of D1, D2 and D3 shows the clear advantage of improved contrast and better resolution using adaptive compounding compared to simple compounding and alpha blending with a DAC, all else being equal.

[0123] Output image D3 of FIG. 13 is the result of a simple nonlinear adaptive compounding method using blending of maximum, minimum and average frame or pixel illumination according to Equation 4 below. I out =I max α max +(1-α max )·(αmin *I min +(1-α min )·I depth_comp ) (Equation 4) Where: I max :MAX(I high ,I mid ,I low ): I min :MIN(I high ,I mid ,I low ): I depth_comp corresponds to the pixel illuminance at the pixel location after alpha blending with depth compensation as described above in the context of Equation 3; α max : Maximum transmittance coefficient based on known alpha blending methods ("alpha value", or I max (Transmittance coefficient used for For example, α max converts the illuminance (i.e., I max , I min or I depth_comp Thus, according to one example, the selected illuminance (i.e., I max , I min or I depth_comp If at least one of the thresholds X1 and X2 is greater than the threshold X1, then max may have a set first value (e.g., between and including 0 and 1); if the selected illuminance is less than X2, then α max may have a second set value (between and including 0 and 1), the second set value being different from the first set value; if the selected illuminance is between X1 and X2, then α max is determined based on a linear function that is a straight line between the first set value and the second set value; and α min : Minimum transmittance coefficient (I max (Transmittance coefficient used for For example, αmax Similarly, α min converts the illuminance (i.e., I max , I min or I depth_comp Thus, according to one example, the selected illuminance (i.e., I max , I min or I depth_comp If at least one of the thresholds X1 and X2 is greater than the threshold X1, then min may have a set first value (e.g., between and including 0 and 1); if the selected illuminance is greater than X2, then α min may have a second set value (between and including 0 and 1), the second set value being different from the first set value; if the selected illuminance is between X1 and X2, then α min may be determined based on a linear function that is a straight line between the first set value and the second set value.

[0124] [Predictive Compound]

[0125] Predictive compounding according to some embodiments involves determining a relationship between a first electrical signal corresponding to a first region (region NF) of an image based on a first received frequency and an electrical signal corresponding to a first region (region NF) of an image based on a second received frequency, and predicting a third electrical signal corresponding to a second region (region FF) of an image based on a second received frequency. For example, referring to FIG. 6, predictive compounding may be used to determine a relationship between an electrical signal corresponding to region NF1 (region NF of a 1.75 MHz receive frequency) and an electrical signal corresponding to region A2 (region NF of a 3.5 MHz receive frequency). The predictive compounding may then include using this relationship with the electrical signal in region FF1 (region FF of a 1.75 MHz receive frequency) to predict an electrical signal in region FF2 (region FF of a 3.5 MHz receive frequency).

[0126] This relationship can be useful, for example, in allowing the better penetration of lower frequencies as input signals to be used to predict images at deeper penetration at higher received frequencies, so that images at deeper depths of a target object at higher frequencies can be replaced with images predicted from the same depths at lower frequencies.

[0127] Several examples of predictive compounds are described below, including simple predictive compounds, predictive compounds using point spread functions (PSFs), and machine learning (ML) based predictive compounds.

[0128] [Simple Predictive Compound]

[0129] A simple method of using a predictive compound may involve the use of Equation 5. I FF2 =I FF1 .I NF2 / I NF1 Formula (5) Where: I FF2 : pixel illuminance in region FF2 - this is the predicted pixel illuminance; I FF1 : pixel illuminance in region FF1 obtained from the reflected ultrasound waveform; I NF2 : pixel illumination in region NF2 obtained from the reflected ultrasound waveform; and I NF1 : Pixel illumination in region NF1 obtained from the reflected ultrasound waveform.

[0130] The reverse may also be done, where a higher resolution region from a higher receive frequency may be used to predict the same region for a lower receive frequency, which does not have as good a resolution as the higher receive frequency. Using an adaptive prediction compound will result in using the better resolution of the higher frequencies, which has the advantage that the penetration of the image at the lower frequencies is lower, resulting in an image with some predicted parts that show better penetration at the higher frequencies, for example.

[0131] As an example, once a relationship as described above is found for a 5 MHz image with a predictive compound, it may result in, for example, a 10 times better penetration than that for a 5 MHz image without a predictive compound.

[0132] [Predictive compounding using point spread function (PSF)]

[0133] The point spread function (PSF) of the reflected ultrasonic waveform depends on several factors, including the receive frequency, the receive bandwidth, and the focal point at the target object. The PSF can be thought of as representing the scattering of the reflected beam from the target object. As a predicted measurement, the PSF can be convolved with a scattering distribution corresponding to the region of the target object being imaged, such as a pixel, to obtain an image corresponding to the region of the target object being imaged. Thus, the frequency-dependent PSF can be convolved with a scattering distribution that is target object-dependent. Thus, for different received frequencies, e.g., for receive frequencies from 1.75 MHz to 3.5 MHz, the PSF will change, but the scattering distribution will be the same for the same region being imaged.

[0134] As mentioned above in the general description of predictive compounds, for example in the context of the image in FIG. Area NF1 corresponds to area NF of the 1.75 MHz receiving frequency image; Region FF1 corresponds to region FF of the 1.75 MHz receiving frequency image; Area NF2 is in area NF but corresponds to that of the 3.5 MHz receive frequency image; Area FF2 is in area FF but corresponds to that of the 3.5 MHz receive frequency image; PSF1 corresponds to the PSF of the 1.75MHz receiving frequency image; PSF2 corresponds to the PSF of the 3.5 MHz receive frequency image; and PSF inverse is the inverse of the PSF Assume that.

[0135] Given the above definitions, one may assume that if one wishes to predict pixel illuminance in B2, which represents a scattering distribution (correlated with a target object in region FF), one can use Equation 6. I FF2 =I NF2 ×(I FF1 *PSF2 inverse ) / (I NF1 *PSF1 inverse ) Formula (6) where *PSF inverse shows the PSF-based deconvolution.

[0136] [ML-based predictive compounding]

[0137] Some embodiments propose an ML-based predictive compounding that can provide higher resolution images (e.g., 5 MHz) in the far field using ML-based deconvolution while retaining the penetration of lower frequencies (e.g., 1.75 MHz).

[0138] [Proposed method]

[0139] The basic idea is to a) identify relationships between local regions of low-frequency and high-frequency images using near-field data, and b) obtain a high-frequency image in the far-field by applying the identified relationships to the low-frequency image. With these steps, the proposed method is detailed below.

[0140] [Training and validation datasets]

[0141] To perform ML-based compounding according to some embodiments, the following operations may be performed. 1) Selecting the 1.75MHz image as the input and the 5.0MHz image as the desired output image; 2) selecting a near-field region NF (e.g., a depth of 0–5 cm) in each of the 1.75 MHz and 5.0 MHz images; 3) Creating a training data set by splitting the near-field region NF into many "speckle cells", i.e., creating many sub-regions (e.g., square sub-regions measuring 5mm x 5mm) within both near-field regions to create an input (1.75MHz sub-region) and an output (5.0MHz sub-region); 4) Creating a training data set using multiple frames and / or multiple views of each sub-region; for example, using about 50 different imaging views for a pin target phantom and for a speckle phantom for each sub-region may create more than about 5000 images for use as a training data set (which may optionally also be expanded by transformation, scaling, etc., if necessary); 5) creating a validation dataset using a mid-distance field speckle cell (5-10 cm); 6) using the central region MF (e.g., 0-5 cm depth) in each of the 1.75 MHz and 5.0 MHz images as the validation dataset; 7) developing an ML-based training model for the relationship between region A of 1.75 MHz and 5.0 MHz receiving frequencies based on the training data set and the validation data set; and 8) Based on the training model, predict the image in region B (B is the far-field region) of 1.75 MHz and 5.0 MHz receiving frequencies.

[0142] The training data set can be used to identify the relationship between the region A between the lower and higher received frequencies. This relationship can also be used for other ML related applications, such as identifying speckle cells, distinguishing between speckle and noise, gain equalization, edge enhancement, etc.

[0143] [Lateral Frequency Compounding]

[0144] Referring again to Figure 6, which illustrates the problems that can occur when imaging based on a single receive frequency, looking at image A for example, there is better penetration resolution overall, and better resolution at wider image angles θ (θ represents the angle on either side of line CL). However, images B and C show spotlight regions SL in the near and central regions. However, SL is absent from the lower frequency image A.

[0145] Lateral frequency compounding may involve multiplying, for each given pixel location defined by depth and image angle, each pixel intensity at said given pixel location corresponding to each of the received frequencies by a respective alpha multiplier, where each multiplier of the pixel intensity at a given pixel location may be a function of one or more alpha values, and each alpha value may be a function of both image angle and penetration depth.

[0146] See Equation 7 below for an example of lateral frequency compounding. I out (r,θ)=I high α high (r,θ)+(1-α high (r,θ))·(α mid (r,θ)*I mid +(1-α mid (r,θ)) I low ) Formula (7) Where: I out (r,θ): output pixel illuminance at depth r and image angle θ; I high: pixel illuminance of the high receive frequency image (image C) at depth r and image angle θ; r:image depth; θ: image angle; α high (r,θ): The transmittance coefficient α(α high (r,θ) is α max or α min may be determined in the same manner as described above for either α high (r,θ) may have different ranges and different breakpoints X1 and X2 for different r and different θ); α mid (r, θ): The transmittance coefficient α of the intermediate receiving frequency at depth r and image angle θ (α mid (r,θ) is α max or α min may be determined in the same manner as described above for either α mid (r,θ) may have different ranges and different breakpoints X1 and X2 for different r and different θ); I mid : pixel illuminance of the intermediate received frequency image (image B) at depth r and image angle θ; I low : pixel illuminance of the low receive frequency image (image A) at depth r and image angle θ. Thus, the multipliers for each of the pixel illuminances from Equation 4 above may be: α high (r,θ): multiplier; (1-α high (r,θ))·α mid (r,θ):I mid multiplier; and (1-α high (r,θ))·(1-α mid (r,θ)):I low multiplier.

[0147] Therefore, the coefficient α (transmittance coefficient) is a function of image angle for reducing the "spotlight" artifact at higher frequencies and improving the signal-to-noise ratio (SNR) in the lateral direction. Equation 9 below shows the above relationship. Img out (r,p)=Σ 3 i-1 α(r,p,i)·Img in (r,p,i) Equation (8) Where: i: frequency band of the imaging waveform; p: image line index coordinate; Img out (r,p): Output image illuminance at depth r and line index coordinate p; Img in (r, p, i): input image intensity, respectively; and α(r,p,i): The transmittance coefficient α(r,p,i) for depth r, line index coordinate p, and frequency band i is max or α min (r, p, i) may be determined in the same manner as described above for any of the above, with the difference being that α(r, p, i) may have different ranges and different breakpoints X1 and X2 for different r, different p, and different i).

[0148] The lateral compound may improve directionality and provide a larger field of view, such as a 150 degree field of view.

[0149] [Color Doppler / Flow Compound]

[0150] Compounding using color Doppler according to some embodiments utilizes additional parameters associated with Doppler imaging, including velocity (flow and direction) in addition to illumination. Color Doppler compounding according to some embodiments may involve combining flow velocity or force from multiple frequency data. Forces or velocities from multiple frequency bands are combined based on a) depth, b) angle, c) SNR, d) flow velocity, etc.

[0151] Equations 9 through 12 may be used to calculate the alpha-blended result of an output image generated using color Doppler compounding according to one embodiment. Maximum parameters: R0 out.max =max( Nfreq i=1 R0 in (i)) Equation (9) R1 out.max =maxfreq( Nfreq i=1 tan -1 (R1 in (i))) Equation (10) Average parameters: R0 out.mean =max(Σ Nfreq i=1 R0 in (i)) Equation (11) R1 out.mean =max(Σ Npfreq i=1 R1 in (i)) Equation (12) Where: R0 out.max :The maximum zero-lag output value of the autocorrelation corresponding to the alpha blending used; R1 out.max :Maximum first lag output value of autocorrelation; R0 out.mean : the average zero-lag output value of the autocorrelation corresponding to the alpha blending used; R1 out.mean :Average first lag output value of the autocorrelation; R0 in (i): the zero-lag input value corresponding to frequency band i of the autocorrelation imaging waveform that corresponds to the alpha blending used (see, for example, Equations 14 and 15 below); and R1 in (i): The first lag input value corresponding to frequency band i of the autocorrelation imaging waveform that corresponds to the alpha blending used (see, for example, Equations 14 and 15 below).

[0152] The maximum and average values ​​of R0 and R1 are calculated from equations 9-12 above and then alpha blended as shown in equations 14 and 15 below. R0 out =α R0 out.max +(1-α)·R0 out.mean Formula (13) R1 out =α R1 out.max +(1-α)·R1 out.mean Formula (14) Where: R0 out : the zero-lag output corresponding to the alpha-blended values ​​of the maximum and mean zero-lag autocorrelation; R1 out : the first lag output corresponding to the alpha-blended values ​​of the maximum and average first lag autocorrelation; alpha: Alpha value for alpha blending.

[0153] While the above description of exemplary embodiments may specifically refer to veins, the embodiments are not so limited, but rather relate to the detection and tracking of any blood vessel having a body that may be a target for ultrasound imaging where a foreign object accesses the vessel. Also, while particular colors are mentioned above to indicate suitability for access or other parameters related to the vessel, the embodiments are not so limited, but include within their scope the display of blood vessel parameters to a user through a UI in any manner, such as through text, visual images or codes, voice communication, etc.

[0154] In one example, instructions implemented by the processor 326 may be provided via the memory 336, or any other memory or storage device of the imaging device, or may be embodied by the processor 326, or any other processor of the imaging device, as a tangible non-transitory machine-readable medium including code for directing the processor 326 to perform electronic operations, e.g., operations corresponding to any one of the methods / processes herein. The processor 326 may access the non-transitory machine-readable medium via an interconnection between the memory 336 and the processor 326. For example, the non-transitory machine-readable medium may be embodied by a separate memory within the memory 336 or the processor 326, or may include a tangible storage unit, such as an optical disk, a flash drive, or any number of other hardware devices that may be inserted into a casing. The non-transitory machine-readable medium may include instructions for directing the processor 326 to perform a particular sequence or flow of actions, e.g., as described with respect to the flowcharts and block diagrams of the operations and functionality shown herein. As used herein, the terms "machine-readable medium" and "computer-readable medium" are interchangeable.

[0155] 14 illustrates a method 1400 performed on a computing device including a memory and one or more processors coupled to the memory. The method 1400 includes, at Operation 1402, simultaneously receiving electrical signals based on reflected frequencies of respective reflected ultrasonic waveforms reflected from a target object as a result of a transmitted ultrasonic waveform. The method includes, at Operation 1404, compounding information from the electrical signals to generate a compounded electrical signal. The method includes, at Operation 1406, causing generation of an output image on a display device based on the compounded electrical signal.

[0156] Any of the examples described below may be combined with any other example (or combination of examples) unless expressly specified otherwise. Aspects described herein may also implement hierarchical application of the scheme, for example, by introducing hierarchical prioritization for different functions (e.g., low / medium / high priority, etc.).

[0157] Although the implementations have been described with reference to specific exemplary embodiments, it is apparent that various modifications and changes may be made to these embodiments without departing from the broader scope of the present disclosure. Many of the configurations and processes described herein may be used in combination or parallel implementations. Thus, the specification and drawings should be considered in an illustrative and not a limiting sense. The accompanying drawings, which form a part of this specification, show, by way of example and not by way of limitation, specific embodiments in which the subject matter may be practiced. The illustrated embodiments are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and obtained therefrom, and structural and logical substitutions and changes may be made without departing from the scope of the present disclosure. Thus, this detailed description should not be construed in a limiting sense, and the scope of the various embodiments is defined solely by the appended claims, along with the full scope of equivalents to which such claims are entitled.

[0158] Such aspects of the inventive subject matter may be referenced herein individually and / or collectively for convenience only, without any intention of intentionally limiting the scope of the application to any single aspect or inventive concept, even if more than one is actually disclosed.

[0159] Although preferred embodiments of the present disclosure have been shown and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the embodiments be limited by the specific examples provided herein. Although the embodiments of the present disclosure have been described with reference to the above specification, the description and illustration of the embodiments herein are not intended to be construed in a limiting sense. Numerous variations, changes, and substitutions will readily occur to those skilled in the art without departing from the concept of the present disclosure. Furthermore, it should be understood that all aspects of the various embodiments are not limited to the specific depictions, configurations, or relative proportions described herein, which depend upon various conditions and variables. It should be understood that various alternatives to the embodiments described herein may be used. It is therefore contemplated that the present disclosure covers any such alternatives, modifications, variations, or equivalents.

[0160] [example]

[0161] Illustrative examples of the techniques disclosed herein are provided below. An embodiment of these techniques may include any one or more of the examples described below, and any combination of these examples.

[0162] Example 1 includes an apparatus of a computing device comprising a memory and one or more processors coupled to the memory, the one or more processors simultaneously receiving electrical signals based on reflected frequencies of respective reflected ultrasonic waveforms reflected from a target object as a result of a transmitted ultrasonic waveform; compounding information from the electrical signals to generate a compounded electrical signal; and causing generation of an output image on a display based on the compounded electrical signal.

[0163] Example 2 includes the subject matter of example 1, wherein the respective reflected frequencies correspond to respective harmonics of a fundamental frequency of the transmitted ultrasonic waveform, the fundamental frequency being a single frequency of the transmitted ultrasonic waveform.

[0164] Example 3 includes the subject matter of example 1, wherein the transmitted ultrasonic waveform is a multi-modal waveform having a fundamental frequency corresponding to the respective frequency of the reflected ultrasonic waveform.

[0165] Example 4 includes the subject matter of Example 1, wherein the one or more processors implement a predictive algorithm on the electrical signals; the one or more processors use information from a first electrical signal corresponding to a first image region of the target object to generate a predicted electrical signal for a second image region of the target object that differs from the first image region; and compound the information by using the predicted electrical signal to obtain the compounded electrical signal such that the output image at the second image region corresponds to the predicted electrical signal.

[0166] Example 5 includes the subject matter of any one of Examples 1 to 4, wherein the reflected frequencies include N reflected frequencies; the electrical signals include N sets of electrical signals, each set corresponding to one of the reflected frequencies, and the N sets of electrical signals correspond to N input images of the target object; each input image includes a pixel at a respective pixel location, each pixel location of the N input images defined by a depth and an angle; compounding information includes compounding information from the N sets of electrical signals; and the one or more processors compound the information from the N sets of electrical signals by using at least one of simple averaging, weighted averaging, alpha blending with depth adaptive compounding, maximum and minimum adaptive compounding, predictive compounding, lateral frequency compounding, and color Doppler compounding.

[0167] Example 6 includes the subject matter of example 5, wherein the one or more processors further subject the electrical signals to gain compensation or dynamic range compensation prior to compounding.

[0168] Example 7 includes the subject matter of example 5, wherein simple averaging includes performing, for each pixel location, one of a simple averaging or a weighted averaging of respective pixel illuminance across the N input images.

[0169] Example 8 includes the subject matter described in Example 5, in which the alpha blending with depth adaptive compounding includes, for each pixel location, multiplying each pixel intensity, such as between the N input images, by a corresponding alpha multiplier, each alpha multiplier being a function of one or more alpha values, the one or more alpha values ​​being a function of at least one of a depth of the respective pixel location or an angle of the respective pixel location.

[0170] Example 9: For each pixel location, the output illuminance in the output image is I out =I high α high +(1-α high )·(α mid I mid +(1-α mid )·I low ), where I out is the output pixel illuminance at each pixel location of the output image; I high is the pixel intensity at each pixel location in the input image corresponding to the highest received frequency; I mid is the pixel intensity at each pixel location of the input image corresponding to the middle one of the received frequencies; I low is the pixel intensity at each pixel location of the input image corresponding to the lowest of the received frequencies; α high corresponds to a depth-dependent α value of the highest one of the received frequencies; and α mid corresponds to a depth-dependent α value for the intermediate ones of the received frequencies.

[0171] Example 10: For each pixel location, the output illuminance in the output image is I out (r,θ)=I high α high (r,θ)+(1-αhigh (r,θ))·(α mid (r,θ) I mid +(1-α mid (r,θ)) I low ) where r is the depth; θ is the angle; I out (r,θ): Output pixel illuminance at depth r and image angle θ; I high : pixel illuminance at depth r and image angle θ of the highest received frequency; I mid : pixel illuminance at depth r and image angle θ of the middle of the received frequencies; I low : pixel illuminance at depth r and image angle θ of the lowest of the received frequencies; α high corresponds to the alpha value of the highest of the received frequencies at depth r and image angle θ; and α mid corresponds to an alpha value at the middle one of the received frequencies at depth r and image angle θ.

[0172] Example 11 includes the subject matter described in example 5, wherein the maximum and minimum adaptive compounding includes using a blending of maximum, minimum and average pixel illumination intensities among the N input images for each pixel location.

[0173] Example 12: For each pixel location, the output illuminance in the output image is I out =I max α max +(1-α max )·(α min I min +(1-α min )·I depth_comp ), where I max :MAX(I high ,I mid ,I low );I min :MIN(I high ,I mid ,I low );I depth_compcorresponds to the pixel illuminance at the pixel location after alpha blending with depth compensation; α max I max , I min or I depth_comp α corresponds to a maximum transmittance alpha value factor based on at least one of max has a set first value between 0 and 1, inclusive; and α min I max , I min or I depth_comp α corresponds to a minimum transmittance alpha value factor based on at least one of min has a configured second value different from the configured first value, the configured second value being between 0 and 1, inclusive.

[0174] Example 13 includes the subject matter of example 5, wherein the predictive compounding includes determining a relationship between a first electrical signal corresponding to a first region in a first input image corresponding to a first reflected frequency and a second electrical signal corresponding to the first region in a second input image corresponding to a second reflected frequency; and predicting a third electrical signal corresponding to the second region of the second input image based on the relationship.

[0175] Example 14 is a graph showing the pixel illuminance of the pixel in the second region. FF2 =I FF1 .I NF2 / I NF1 where FF1 is the second region in the first input image; FF2 is the second region in the second input image; NF1 is the first region in the first input image; NF2 is the first region in the second input image; I FF2 is the pixel illuminance at FF2; I FF1 is the pixel illuminance in region FF1; I NF2 is the pixel illuminance in region NF2; and I NF1 is the pixel illuminance in region NF1.

[0176] In Example 15, the pixel illuminance of the pixel in the second region is I FF2 =I NF2 ×(I FF1 *PSF2 inverse ) / (I NF1 *PSF1 inverse ), where FF1 is the second region in the first input image; FF2 is the second region in the second input image; NF2 is the first region in the second input image; I FF2 is the pixel illuminance at FF2; I FF1 is the pixel illuminance in region FF1; I NF2 is the pixel illumination in region NF2; PSF1 corresponds to the point spread function (PSF) of the first received frequency; PSF2 corresponds to the PSF of the second received frequency; PSF1 inverse is the inverse of PSF1 corresponding to the deconvolution; and PSF1 inverse is the inverse of PSF1 corresponding to the deconvolution, including the subject matter described in Example 13.

[0177] Example 16 includes the subject matter of Example 13, wherein the ML-based compounding includes determining the first region to correspond to a near-distance region; determining the second region to correspond to a far-distance region; dividing the near-distance region into a plurality of sub-regions, e.g., square sub-regions; generating a training data set based on a plurality of first input images and a plurality of first output images in the near-distance region; developing an ML-based model of a relationship between pixel illuminance at the first reflected frequency in the first region and pixel illuminance at the second reflected frequency in the first region based on the training data set; and predicting pixel illuminance at the second reflected frequency in the second region based on the model.

[0178] Example 17 includes the subject matter of Example 16, wherein the ML-based compounding further includes generating a validation dataset based on a plurality of first input images and a plurality of first output images in a central region of the input images, and developing the ML-based model based on the training dataset and the validation dataset.

[0179] Example 18 includes the subject matter of example 5, wherein the color Doppler compounding includes combining, for each pixel location, respective pixel illumination intensities as between the N input images based on at least one of information regarding depth, angle, signal-to-noise ratio, flow velocity, or force.

[0180] Example 19 shows the R0 of the output image. out and R1 out is R0 out =α R0 out.max +(1-α)·R0 out.mean and R1 out =α R1 out.max +(1-α)·R1 out.mean where R0 out : Zero-lag output corresponding to the alpha-blended values ​​of maximum and average zero-lag autocorrelation; R1 out : the first lag output corresponding to the alpha blended values ​​of the maximum and average first lag autocorrelation; α: the alpha value of the alpha blending; R out.max : the maximum zero lag output value of the autocorrelation corresponding to the alpha blending used; R1 out.max : the maximum first lag output value of the autocorrelation; R0 out.mean : the average zero lag output value of the autocorrelation corresponding to the alpha blending used; R1 out.mean : the average first lag output value of the autocorrelation; R0 in (i): the zero lag input value corresponding to the frequency band of the autocorrelation imaging waveform corresponding to the alpha blending used; and R1 in(i): The first lag input value corresponding to the frequency band of the autocorrelation imaging waveform that corresponds to the alpha blending used, including the subject matter described in Example 18.

[0181] Example 20 includes the subject matter of any one of Examples 1 to 4, wherein the respective reflected frequencies include 1.75 MHz, 3.5 MHz, and 5.0 MHz, and the fundamental frequency of the transmitted ultrasonic waveform is a single frequency of 1.75 MHz; or the respective fundamental frequency of the transmitted ultrasonic waveform includes 1.75 MHz, 3.5 MHz, and 5.0 MHz.

[0182] Example 21 includes a system comprising: a user interface device having a display device; and a computing device communicatively coupled to the user interface device, the computing device having a memory and one or more processors coupled to the memory, the one or more processors simultaneously receiving electrical signals based on a reflected frequency of each of reflected ultrasonic waveforms reflected from a target object as a result of a transmitted ultrasonic waveform; compounding information from the electrical signals to generate a compounded electrical signal; and causing generation of an output image on a display device based on the compounded electrical signal.

[0183] Example 22 includes the subject matter of example 21, wherein the respective reflected frequencies correspond to respective harmonics of a fundamental frequency of the transmitted ultrasonic waveform, the fundamental frequency being a single frequency of the transmitted ultrasonic waveform.

[0184] Example 23 includes the subject matter of example 21, wherein the transmitted ultrasonic waveform is a multi-modal waveform having a fundamental frequency corresponding to the respective frequency of the reflected ultrasonic waveform.

[0185] Example 24 includes the subject matter of Example 21, wherein the one or more processors implement a predictive algorithm on the electrical signals; the one or more processors use information from a first electrical signal corresponding to a first image region of the target object to generate a predicted electrical signal for a second image region of the target object that differs from the first image region; and compound the information by using the predicted electrical signal to obtain the compounded electrical signal such that the output image at the second image region corresponds to the predicted electrical signal.

[0186] Example 25 includes the subject matter of any one of Examples 21 to 24, wherein the reflected frequencies include N reflected frequencies; the electrical signals include N sets of electrical signals, each set corresponding to one of the reflected frequencies, and the N sets of electrical signals correspond to N input images of the target object; each input image includes a pixel at a respective pixel location, each pixel location of the N input images defined by a depth and an angle; compounding information includes compounding information from the N sets of electrical signals; and the one or more processors compound the information from the N sets of electrical signals by using at least one of simple averaging, weighted averaging, alpha blending with depth adaptive compounding, maximum and minimum adaptive compounding, predictive compounding, lateral frequency compounding, and color Doppler compounding.

[0187] Example 26 includes the subject matter of example 25, wherein the one or more processors further subject the electrical signals to gain compensation or dynamic range compensation prior to compounding.

[0188] Example 27 includes the subject matter of example 25, wherein simple averaging includes performing, for each pixel location, one of a simple averaging or a weighted averaging of respective pixel illuminance across the N input images.

[0189] Example 28 includes alpha blending with depth adaptive compounding, which includes, for each pixel location, multiplying each pixel intensity, such as between the N input images, by a corresponding alpha multiplier, each alpha multiplier being a function of one or more alpha values, the one or more alpha values ​​being a function of at least one of the depth of each pixel location or the angle of each pixel location, and includes the subject matter described in Example 25.

[0190] Example 29 is a method for determining, for each pixel location, the output illuminance in the output image, I out =I high α high +(1-α high )·(α mid I mid +(1-α mid )·I low ), where I out is the output pixel illuminance at each pixel location of the output image; I high is the pixel intensity at each pixel location in the input image corresponding to the highest received frequency; I mid is the pixel intensity at each pixel location of the input image corresponding to the middle one of the received frequencies; I low is the pixel intensity at each pixel location of the input image corresponding to the lowest of the received frequencies; α high corresponds to a depth-dependent α value of the highest one of the received frequencies; and α mid corresponds to a depth-dependent α value for the intermediate one of the received frequencies.

[0191] Example 30 is a method for determining, for each pixel location, an output illuminance in the output image, I out (r,θ)=I high α high (r,θ)+(1-α high (r,θ))·(α mid (r,θ) I mid +(1-α mid (r,θ)) I low ) where r is the depth; θ is the angle; I out(r,θ): Output pixel illuminance at depth r and image angle θ; I high : pixel illuminance at depth r and image angle θ of the highest received frequency; I mid : pixel illuminance at depth r and image angle θ of the middle of the received frequencies; I low : pixel illuminance at depth r and image angle θ of the lowest of the received frequencies; α high corresponds to the alpha value of the highest of the received frequencies at depth r and image angle θ; and α mid corresponds to an alpha value at the middle one of the received frequencies at depth r and image angle θ.

[0192] Example 31 includes the subject matter of example 25, wherein the maximum and minimum adaptive compounding includes using a blending of maximum, minimum and average pixel illuminances between the N input images for each pixel location.

[0193] Example 32 is a method for determining, for each pixel location, the output illuminance in the output image, I out =I max α max +(1-α max )·(α min I min +(1-α min )·I depth_comp ), where I max :MAX(I high ,I mid ,I low );I min :MIN(I high ,I mid ,I low );I depth_comp corresponds to the pixel illuminance at the pixel location after alpha blending with depth compensation; α max I max , I min or I depth_comp α corresponds to a maximum transmittance alpha value factor based on at least one of maxhas a set first value between 0 and 1, inclusive; and α min I max , I min or I depth_comp α corresponds to a minimum transmittance alpha value factor based on at least one of min has a configured second value different from the configured first value, the configured second value being between 0 and 1, inclusive.

[0194] Example 33 includes the subject matter of example 25, where the predictive compounding includes determining a relationship between a first electrical signal corresponding to a first region in a first input image corresponding to a first reflected frequency and a second electrical signal corresponding to the first region in a second input image corresponding to a second reflected frequency; and predicting a third electrical signal corresponding to the second region of the second input image based on the relationship.

[0195] Example 34 is a method for determining whether a pixel illuminance of a pixel in the second region is I FF2 =I FF1 .I NF2 / I NF1 where FF1 is the second region in the first input image; FF2 is the second region in the second input image; NF1 is the first region in the first input image; NF2 is the first region in the second input image; I FF2 is the pixel illuminance at FF2; I FF1 is the pixel illuminance in region FF1; I NF2 is the pixel illuminance in region NF2; and I NF1 is the pixel illuminance in region NF1.

[0196] In Example 35, the pixel illuminance of the pixel in the second region is I FF2 =I NF2 ×(I FF1 *PSF2 inverse ) / (I NF1 *PSF1 inverse), where FF1 is the second region in the first input image; FF2 is the second region in the second input image; NF2 is the first region in the second input image; I FF2 is the pixel illuminance at FF2; I FF1 is the pixel illuminance in region FF1; I NF2 is the pixel illumination in region NF2; PSF1 corresponds to the point spread function (PSF) of the first received frequency; PSF2 corresponds to the PSF of the second received frequency; PSF1 inverse is the inverse of PSF1 corresponding to the deconvolution; and PSF1 inverse is the inverse of PSF1 corresponding to the deconvolution,

[0197] Example 36 includes the subject matter of Example 33, wherein the ML-based compounding includes determining the first region to correspond to a near-distance region; determining the second region to correspond to a far-distance region; dividing the near-distance region into a plurality of sub-regions, e.g., square sub-regions; generating a training data set based on a plurality of first input images and a plurality of first output images in the near-distance region; developing an ML-based model of a relationship between pixel illuminance at the first reflected frequency in the first region and pixel illuminance at the second reflected frequency in the first region based on the training data set; and predicting pixel illuminance at the second reflected frequency in the second region based on the model.

[0198] Example 37 includes the subject matter of Example 36, wherein the ML-based compounding further includes generating a validation dataset based on a plurality of first input images and a plurality of first output images in a central region of the input images, and developing the ML-based model based on the training dataset and the validation dataset.

[0199] Example 38 includes the subject matter of example 25, wherein the color Doppler compounding includes combining, for each pixel location, respective pixel illumination intensities as between the N input images based on at least one of information regarding depth, angle, signal-to-noise ratio, flow velocity, or force.

[0200] Example 39 shows the R0 of the output image. out and R1 out is R0 out =α R0 out.max +(1-α)·R0 out.mean and R1 out =α R1 out.max +(1-α)·R1 out.mean where R0 out : Zero-lag output corresponding to the alpha-blended values ​​of maximum and average zero-lag autocorrelation; R1 out : the first lag output corresponding to the alpha blended values ​​of the maximum and average first lag autocorrelation; α: the alpha value of the alpha blending; R out.max : the maximum zero lag output value of the autocorrelation corresponding to the alpha blending used; R1 out.max : the maximum first lag output value of the autocorrelation; R0 out.mean : the average zero lag output value of the autocorrelation corresponding to the alpha blending used; R1 out.mean : the average first lag output value of the autocorrelation; R0 in (i): the zero lag input value corresponding to the frequency band of the autocorrelation imaging waveform corresponding to the alpha blending used; and R1 in (i): The first lag input value corresponding to the frequency band of the autocorrelation imaging waveform that corresponds to the alpha blending used, including the subject matter described in Example 38.

[0201] Example 40 includes the subject matter of any one of Examples 21-24, wherein the respective reflected frequencies include 1.75 MHz, 3.5 MHz, and 5.0 MHz, and the fundamental frequency of the transmitted ultrasonic waveform is a single frequency of 1.75 MHz; or the respective fundamental frequency of the transmitted ultrasonic waveform includes 1.75 MHz, 3.5 MHz, and 5.0 MHz.

[0202] Example 41 includes a method executed on a computing device including a memory and one or more processors coupled to the memory, the method comprising: simultaneously receiving electrical signals based on reflected frequencies of respective reflected ultrasonic waveforms reflected from a target object as a result of a transmitted ultrasonic waveform; compounding information from the electrical signals to generate a compounded electrical signal; and causing generation of an output image on a display device based on the compounded electrical signal.

[0203] Example 42 includes the subject matter of example 41, wherein the respective reflected frequencies correspond to respective harmonics of a fundamental frequency of the transmitted ultrasonic waveform, the fundamental frequency being a single frequency of the transmitted ultrasonic waveform.

[0204] Example 43 includes the subject matter of example 41, wherein the transmitted ultrasonic waveform is a multi-modal waveform having a fundamental frequency corresponding to the respective frequency of the reflected ultrasonic waveform.

[0205] Example 44 includes the subject matter of Example 41, wherein compounding the information includes implementing a predictive algorithm on the electrical signals, the one or more processors using information from a first electrical signal corresponding to a first image region of the target object to generate a predicted electrical signal for a second image region of the target object that differs from the first image region; and using the predicted electrical signal to obtain the compounded electrical signal such that the output image at the second image region corresponds to the predicted electrical signal.

[0206] Example 45 includes the subject matter of Example 41, in which the reflected frequencies include N reflected frequencies; the electrical signals include N sets of electrical signals, each set corresponding to one of the reflected frequencies, and the N sets of electrical signals correspond to N input images of the target object; each input image includes a pixel at a respective pixel location, and each pixel location of the N input images is defined by a depth and an angle; compounding information includes compounding information from the N sets of electrical signals; and the method further includes compounding the information from the N sets of electrical signals by using at least one of simple averaging, weighted averaging, alpha blending with depth adaptive compounding, maximum and minimum adaptive compounding, predictive compounding, lateral frequency compounding, and color Doppler compounding.

[0207] Example 46 includes the subject matter of example 45, further comprising subjecting the electrical signal to gain compensation or dynamic range compensation prior to compounding.

[0208] Example 47 includes the subject matter of example 45, wherein simple averaging includes performing, for each pixel location, one of a simple averaging or a weighted averaging of respective pixel illuminance across the N input images.

[0209] Example 48 includes the subject matter of example 45, in which the alpha blending with depth adaptive compounding includes, for each pixel location, multiplying each pixel intensity, such as between the N input images, by a corresponding alpha multiplier, each alpha multiplier being a function of one or more alpha values, the one or more alpha values ​​being a function of at least one of a depth of each pixel location or an angle of each pixel location.

[0210] Example 49 is a method for determining, for each pixel location, the output illuminance in the output image, I out =Ihigh α high +(1-α high )·(α mid I mid +(1-α mid )·I low ), where I out is the output pixel illuminance at each pixel location of the output image; I high is the pixel intensity at each pixel location in the input image corresponding to the highest received frequency; I mid is the pixel intensity at each pixel location of the input image corresponding to the middle one of the received frequencies; I low is the pixel intensity at each pixel location of the input image corresponding to the lowest of the received frequencies; α high corresponds to a depth-dependent α value of the highest one of the received frequencies; and α mid corresponds to a depth-dependent α value for the intermediate one of the received frequencies.

[0211] Example 50 is a method for determining, for each pixel location, the output illuminance in the output image, I out (r,θ)=I high α high (r,θ)+(1-α high (r,θ))·(α mid (r,θ) I mid +(1-α mid (r,θ)) I low ) where r denotes depth; θ denotes angle; I out (r,θ) is the output pixel illuminance at depth r and image angle θ; I high is the pixel illuminance at depth r and image angle θ of the highest received frequency; I mid is the pixel illuminance at depth r and image angle θ of the middle one of the received frequencies; I low is the pixel illuminance at depth r and image angle θ of the lowest of the received frequencies; α highcorresponds to the alpha value of the highest of the received frequencies at depth r and image angle θ; and α mid corresponds to an alpha value at the intermediate one of the received frequencies at depth r and image angle θ.

[0212] Example 51 includes the subject matter of example 45, wherein the maximum and minimum adaptive compounding includes using a blending of maximum, minimum and average pixel illuminances among the N input images for each pixel location.

[0213] Example 52 is a method for determining, for each pixel location, the output illuminance in the output image, I out =I max α max +(1-α max )·(α min *I min +(1-α min )·I depth_comp ), where I max :MAX(I high ,I mid ,I low );I min :MIN(I high ,I mid ,I low );I depth_comp corresponds to the pixel illuminance at the pixel location after alpha blending with depth compensation; α max I max , I min or I depth_comp α corresponds to a maximum transmittance alpha value factor based on at least one of max has a set first value between 0 and 1, inclusive; and α min I max , I min or I depth_comp α corresponds to a minimum transmittance alpha value factor based on at least one of min has a configured second value different from the configured first value, the configured second value being between 0 and 1, inclusive.

[0214] Example 53 includes the subject matter of example 45, wherein the predictive compounding includes determining a relationship between a first electrical signal corresponding to a first region in a first input image corresponding to a first reflected frequency and a second electrical signal corresponding to the first region in a second input image corresponding to a second reflected frequency; and predicting a third electrical signal corresponding to the second region of the second input image based on the relationship.

[0215] Example 54 is a method for determining whether a pixel illuminance of a pixel in the second region is I FF2 =I FF1 .I NF2 / I NF1 where FF1 is the second region in the first input image; FF2 is the second region in the second input image; NF1 is the first region in the first input image; NF2 is the first region in the second input image; I FF2 is the pixel illuminance at FF2; I FF1 is the pixel illuminance in region FF1; I NF2 is the pixel illuminance in region NF2; and I NF1 is the pixel illuminance in region NF1.

[0216] In Example 55, the pixel illuminance of the pixel in the second region is I FF2 =I NF2 ×(I FF1 *PSF2 inverse ) / (I NF1 *PSF1 inverse ), where FF1 is the second region in the first input image; FF2 is the second region in the second input image; NF2 is the first region in the second input image; I FF2 is the pixel illuminance at FF2; I FF1 is the pixel illuminance in region FF1; I NF2is the pixel illumination in region NF2; PSF1 corresponds to the point spread function (PSF) of the first received frequency; PSF2 corresponds to the PSF of the second received frequency; PSF1 inverse is the inverse of PSF1 corresponding to the deconvolution; and PSF1 inverse is the inverse of PSF1 corresponding to the deconvolution.

[0217] Example 56 includes the subject matter of Example 53, wherein the ML-based compounding includes determining the first region to correspond to a near-distance region; determining the second region to correspond to a far-distance region; dividing the near-distance region into a plurality of sub-regions, e.g., square sub-regions; generating a training data set based on a plurality of first input images and a plurality of first output images in the near-distance region; developing an ML-based model of a relationship between pixel illuminance at the first reflected frequency in the first region and pixel illuminance at the second reflected frequency in the first region based on the training data set; and predicting pixel illuminance at the second reflected frequency in the second region based on the model.

[0218] Example 57 includes the subject matter of Example 56, wherein the ML-based compounding further includes generating a validation dataset based on a plurality of first input images and a plurality of first output images in a central region of the input images, and developing the ML-based model based on the training dataset and the validation dataset.

[0219] Example 58 includes the subject matter of example 45, wherein the color Doppler compounding includes combining, for each pixel location, respective pixel illumination intensities as between the N input images based on at least one of information regarding depth, angle, signal-to-noise ratio, flow velocity, or force.

[0220] Example 59 shows the R0 of the output image. out and R1 outis R0 out =α R0 out.max +(1-α)·R0 out.mean and R1 out =α R1 out.max +(1-α)·R1 out.mean where R0 out : Zero-lag output corresponding to the alpha-blended values ​​of maximum and average zero-lag autocorrelation; R1 out : the first lag output corresponding to the alpha blended values ​​of the maximum and average first lag autocorrelation; α: the alpha value of the alpha blending; R out.max : the maximum zero lag output value of the autocorrelation corresponding to the alpha blending used; R1 out.max : the maximum first lag output value of the autocorrelation; R0 out.mean : the average zero lag output value of the autocorrelation corresponding to the alpha blending used; R1 out.mean : the average first lag output value of the autocorrelation; R0 in (i): the zero lag input value corresponding to the frequency band of the autocorrelation imaging waveform corresponding to the alpha blending used; and R1 in (i): The first lag input value corresponding to the frequency band of the autocorrelation imaging waveform that corresponds to the alpha blending used, including the subject matter described in Example 58.

[0221] Example 60 includes the subject matter of example 41, wherein the respective reflected frequencies include 1.75 MHz, 3.5 MHz, and 5.0 MHz, and the fundamental frequency of the transmitted ultrasonic waveform is a single frequency of 1.75 MHz; or the respective fundamental frequency of the transmitted ultrasonic waveform includes 1.75 MHz, 3.5 MHz, and 5.0 MHz.

[0222] Example 61 includes an apparatus comprising means for carrying out the method of any one of examples 41 to 60.

[0223] Example 62 includes one or more computer-readable media comprising a plurality of instructions stored thereon that, when executed, cause one or more processors to perform the method of any one of Examples 41 to 60.

[0224] Example 63 comprises the apparatus of any one of Examples 1 to 20, including an imaging device further comprising the user interface device.

[0225] Example 64 includes an article of manufacture that includes one or more tangible, computer-readable, non-transitory storage media that include computer-executable instructions that, when executed by at least one computer processor, are operable to enable the at least one processor to perform a method according to any one of Examples 21 to 40.

Claims

1. 1. An apparatus of a computing device comprising a memory and one or more processors coupled to the memory, the one or more processors comprising: simultaneously receiving electrical signals based on the reflected frequencies of each of the reflected ultrasonic waveforms reflected from the target object as a result of the transmitted ultrasonic waveform; compounding information from the electrical signals to produce a compounded electrical signal; and causing generation of an output image on a display based on the compounded electrical signals. wherein each of the reflected frequencies corresponds to a respective harmonic of a fundamental frequency of the transmitted ultrasonic waveform, the fundamental frequency being a single frequency of the transmitted ultrasonic waveform. Device.

2. The apparatus of claim 1 , wherein the transmitted ultrasonic waveform is a multi-modal waveform having a fundamental frequency corresponding to the respective reflected frequency of the reflected ultrasonic waveform.

3. The one or more processors: implementing a predictive algorithm on the electrical signals, the one or more processors using information from a first electrical signal corresponding to a first image region of the target object to generate a predicted electrical signal for a second image region of the target object that differs from the first image region; and using the predicted electrical signal to obtain the compounded electrical signal such that the output image at the second image region corresponds to the predicted electrical signal. compounding the information by 2. The apparatus of claim 1.

4. The each reflected frequency includes N reflected frequencies; the electrical signals include N sets of electrical signals, each set corresponding to one of the respective reflected frequencies, the N sets of electrical signals corresponding to N input images of the target object; Each input image includes a pixel at a respective pixel location, each pixel location of the N input images being defined by a depth and an angle; and Compounding information includes compounding information from the N sets of electrical signals.

4. Apparatus according to any one of claims 1 to 3.

5. 5. The apparatus of claim 4, wherein the one or more processors compound the information from the N sets of electrical signals by using at least one of simple averaging, weighted averaging, alpha blending with depth adaptive compounding, maximum and minimum adaptive compounding, predictive compounding, lateral frequency compounding, and color Doppler compounding.

6. The apparatus of claim 5 , wherein the one or more processors further subject the electrical signals to gain compensation or dynamic range compensation prior to compounding.

7. The apparatus of claim 5 , wherein simple averaging comprises performing, for each pixel location, one of a simple averaging or a weighted averaging of respective pixel illuminance across the N input images.

8. 6. The apparatus of claim 5, wherein alpha blending with depth adaptive compounding includes, for each pixel location, multiplying a respective pixel intensity, such as among the N input images, by a corresponding alpha multiplier, each alpha multiplier being a function of one or more alpha values, the one or more alpha values ​​being a function of at least one of a depth of the respective pixel location or an angle of the respective pixel location.

9. The apparatus of claim 5 , wherein maximum and minimum adaptive compounding comprises using, for each pixel location, a blending of maximum, minimum and average pixel illumination among the N input images.

10. The predictive compound is determining a relationship between a first electrical signal corresponding to a first region in a first input image corresponding to a first reflected frequency and a second electrical signal corresponding to the first region in a second input image corresponding to a second reflected frequency; and predicting a third electrical signal corresponding to a second region of the second input image based on the relationship; Including, 6. The apparatus of claim 5.

11. 6. The apparatus of claim 5, wherein color Doppler compounding includes combining, for each pixel location, respective pixel illumination intensities as between the N input images based on at least one of information regarding depth, angle, signal-to-noise ratio, flow velocity or force.

12. the respective reflected frequencies include 1.75 MHz, 3.5 MHz, and 5.0 MHz; and The fundamental frequency of the transmitted ultrasonic waveform is a single frequency of 1.75 MHz; or The fundamental frequencies of each of the transmitted ultrasonic waveforms include 1.75 MHz, 3.5 MHz, and 5.0 MHz. One of the 4. Apparatus according to any one of claims 1 to 3.

13. A user interface device having a display device; and A computing device communicatively coupled to the user interface device, the computing device having a memory and one or more processors coupled to the memory. Equipped with The one or more processors: simultaneously receiving electrical signals based on the reflected frequencies of each of the reflected ultrasonic waveforms that are reflected from the target object as a result of the transmitted ultrasonic waveform; and compounding information from the electrical signals to produce a compounded electrical signal; and causing generation of an output image on a display device based on the compounded electrical signals. wherein each of the reflected frequencies corresponds to a respective harmonic of a fundamental frequency of the transmitted ultrasonic waveform, the fundamental frequency being a single frequency of the transmitted ultrasonic waveform. system.

14. 14. The system of claim 13, wherein the respective reflected frequencies correspond to respective harmonics of a fundamental frequency of the transmitted ultrasonic waveform, the fundamental frequency being a single frequency of the transmitted ultrasonic waveform.

15. 1. A method executed on a computing device including a memory and one or more processors coupled to the memory, comprising: simultaneously receiving electrical signals based on reflected frequencies of each of the reflected ultrasonic waveforms reflected from the target object as a result of the transmitted ultrasonic waveform; compounding information from the electrical signals to produce a compounded electrical signal; and causing generation of an output image on a display device based on the compounded electrical signals. Where: The each reflected frequency includes N reflected frequencies; the electrical signals include N sets of electrical signals, each set corresponding to one of the respective reflected frequencies, the N sets of electrical signals corresponding to N input images of the target object; Each input image includes a pixel at a respective pixel location, each pixel location of the N input images being defined by a depth and an angle; and compounding the respective reflected frequencies which correspond to respective harmonics of a fundamental frequency of the transmitted ultrasonic waveform, the fundamental frequency being a single frequency of the transmitted ultrasonic waveform. A method comprising:

16. The step of compounding the information comprises: implementing a predictive algorithm on the electrical signals, the one or more processors using information from a first electrical signal corresponding to a first image region of the target object to generate a predicted electrical signal for a second image region of the target object that differs from the first image region; and using said predicted electrical signal to obtain said compounded electrical signal such that said output image at said second image region corresponds to said predicted electrical signal. having The method of claim 15.

17. 16. The method of claim 15, further comprising compounding the information from the N sets of electrical signals by using at least one of simple averaging, weighted averaging, alpha blending with depth adaptive compounding, maximum and minimum adaptive compounding, predictive compounding, lateral frequency compounding, and color Doppler compounding.

18. The method of claim 17 , wherein simple averaging comprises performing, for each pixel location, one of a simple averaging or a weighted averaging of respective pixel illuminance across the N input images.

19. 20. The method of claim 17, wherein alpha blending with depth adaptive compounding includes, for each pixel location, multiplying a respective pixel intensity, such as among the N input images, by a corresponding alpha multiplier, each alpha multiplier being a function of one or more alpha values, the one or more alpha values ​​being a function of at least one of a depth of the respective pixel location or an angle of the respective pixel location.

20. 20. The method of claim 17, wherein max and min adaptive compounding comprises using, for each pixel location, a blending of maximum, minimum and average pixel illumination such as among the N input images.

21. The predictive compound is determining a relationship between a first electrical signal corresponding to a first region in a first input image corresponding to a first reflected frequency and a second electrical signal corresponding to the first region in a second input image corresponding to a second reflected frequency; and predicting a third electrical signal corresponding to a second region of the second input image based on the relationship; Including, 20. The method of claim 17.

22. To predict, determining the first region to correspond to a near field region; determining the second region to correspond to a far-field region; Dividing the near field into a plurality of subfields; generating a training data set based on a plurality of first input images and a plurality of first output images in the near field; developing an ML-based model of a relationship between pixel intensity at the first reflected frequency in the first region and pixel intensity at the second reflected frequency in the first region based on the training data set; and predicting pixel illuminance at the second reflected frequency in the second region based on the model. Machine learning (ML) based compounds including 22. The method of claim 21.

23. 20. The method of claim 17, wherein color Doppler compounding includes combining, for each pixel location, respective pixel illumination intensities as between the N input images based on at least one of information regarding depth, angle, signal-to-noise ratio, flow velocity, or force.

24. Apparatus comprising means for carrying out the method according to any one of claims 15 to 23.

25. A computer program comprising a plurality of stored instructions which, when executed, cause one or more processors to perform a method according to any one of claims 15 to 23.

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