Apparatus, system, method, and computer program for compounding signals of each received ultrasound frequency to generate an output ultrasound image.

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] The embodiments generally relate to the field of signal processing for imaging devices, and the field of signal processing for ultrasonic imaging devices or probes, including those incorporating micro-ultrasonic 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) used to emit and receive acoustic energy from a target being imaged. The reflected waveform is received by a transducer (e.g., a micro-ultrasound transducer), converted into an electrical signal, and further processed to create an image. Fluid velocity and direction of fluid flow (e.g., 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 movements can be utilized in various medical diagnostic applications and to support other medical procedures.

[0004] Depending on the image quality acquired through the use of ultrasound imaging devices, it may be difficult to distinguish anatomical features with sufficient certainty. Mechanisms to improve the image quality acquired by ultrasound imaging devices are needed. [Overview of the project]

[0005] In some embodiments, ultrasound imaging devices may operate according to one or more sets of instructions using algorithms to collectively or individually assist in acquiring ultrasound images of a higher quality and more reliable than those of the latest technology. [Brief explanation of the drawing]

[0006] Novel features of the present invention are described in detail in the appended claims. Features and advantages of several embodiments will be better understood by referring to the following detailed description of exemplary embodiments in which the principles of the present invention are utilized, and to the accompanying drawings (hereinafter also referred to as "Figures").

[0007] [Figure 1] This is a block diagram of an imaging device having selectively modifiable characteristics according to the disclosed embodiment.

[0008] [Figure 2] This is a diagram of an imaging system having selectively modifiable characteristics according to a disclosed embodiment.

[0009] [Figure 3] This is a schematic diagram of an imaging device having selectively modifiable characteristics according to the disclosed embodiments.

[0010] [Figure 4] An example of a receiving channel according to the principle described herein is shown.

[0011] [Figure 5] This is a series of graphs plotting the power (in decibels (dB)) versus frequency (in megahertz (MHz)) of the received ultrasound waveform at different frequencies and penetration depths of the imaging target.

[0012] [Figure 6] The four sets of ultrasound images of the target object are shown (output image D was obtained by simply averaging the pixel illumination of images A-C, with each of images A-C corresponding to a different reflected frequency).

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

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

[0015] [Figure 9A] The graph shows a voltage-to-time (in seconds) plot for multimodal pulses using fundamental frequencies for transmission at 1.75 MHz, 3.5 MHz, and 5.1 MHz.

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

[0017] [Figure 10] Similar to Figure 6, this is a set of images generated using the pulses in Figures 9A and 9B to produce images A-C, and further generating compound image D using simple compounding.

[0018] [Figure 11] Similar to Figure 6, but a set of images using alpha blending with DAC in multimodal pulses, as shown in Figures 9A and 9B.

[0019] [Figure 12] Two output images, D1 and D2, are shown (image D1 represents the result obtained using alpha blending in the DAC for the same three receiving frequencies as in Figure 6, and D2 represents the result obtained using DR compensation for the same three receiving frequencies).

[0020] [Figure 13] The image shows a set of three output images D1, D2, and D3 (image D1 represents the result obtained using simple compounding for the same three receiving frequencies as in Figure 6, D2 represents the result obtained using alpha blending in the DAC for the same three receiving frequencies, and D3 represents the result obtained using adaptive compounding).

[0021] [Figure 14] Flowcharts of the process according to several embodiments are shown. [Modes for carrying out the invention]

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

[0023] In some embodiments, each frequency is a harmonic of the 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 transmission frequency, i.e., the fundamental frequency (e.g., 1.75 MHz).

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

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

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

[0027] Advantageously, some embodiments provide algorithms that enable the generation of ultrasonic images that allow for a reconciliation of the benefit of better image resolution at deeper target object locations associated with lower frequencies of reflected ultrasonic waveforms and the benefit of better resolution at shallower penetrations associated with higher frequencies of reflected ultrasonic waveforms. The latter enables the generation of images that demonstrate improved image resolution at various depths of the target object.

[0028] Some existing solutions use tissue harmonic imaging (TiHI). In TiHI, 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, the transducer separately generates distinct transmitted ultrasonic waveforms, each at a given fundamental frequency (3.0 MHz and 3.5 MHz), and the device processes the two corresponding reflected ultrasonic waveforms at the second harmonic of each fundamental frequency. This processing involves compounding the two received signals, which may include some form of alpha blending, but the transmission includes multiple transmissions at different time points, and therefore the compound is not based on a reflected ultrasonic waveform based on a single transmission.

[0031] Referring now to Figures 1 to 4, these figures show devices and circuits that may be used to implement some of the embodiments described herein. Next, in the context of Figures 5 to 14, a further specific description of the embodiments is provided below.

[0032] Some embodiments relate to imaging devices, more specifically, electronically configurable ultrasound imaging devices. Ultrasound imaging devices may be used to non-invasively image internal tissues, bones, blood flow, or organs of the human or animal body. Images may then be displayed. To perform ultrasound imaging, an ultrasound imaging device transmits an ultrasound signal into the body and receives the reflected signal from the body part being imaged. Such an ultrasound imaging device includes a transducer and associated electronic equipment, 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 in medical imaging; flow measurement in pipe, speaker, and microphone arrays; lithotomy; localized tissue heating for therapeutic purposes; and high-intensity focused ultrasound (HIFU) surgery.

[0033] From the embodiments for carrying out the present invention, of which only exemplary embodiments are illustrated and described, additional aspects and advantages of several embodiments will be readily apparent to those skilled in the art. As will be understood, some embodiments can achieve other different objectives, and some of their details can be modified in various obvious ways without departing from the present disclosure. Accordingly, the drawings and description should be considered illustrative and not restrictive.

[0034] Conventionally, imaging devices such as ultrasound imagers used in medical imaging utilize piezoelectric (PZT) materials or other piezoelectric ceramic and polymer composite materials. Such imaging devices may include a housing that contains transducers having PZT material, and other electronic equipment that forms and displays the image on a display unit. To manufacture bulk PZT elements or transducers, thick slabs of piezoelectric material can be cut into large rectangular PZT elements. These rectangular PZT elements can be expensive to manufacture because the manufacturing process typically involves precisely cutting thick rectangular PZT or ceramic material and mounting them on a substrate at precise intervals. Furthermore, the impedance of the transducer is often much higher than the impedance of the transducer's transmitting / receiving electronic equipment, 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 the transmitted signal. Since transducer power dissipation is proportional to the square of the drive voltage, this high drive voltage results in high power dissipation. This high power dissipation generates heat within the imaging device, resulting in the need for a cooling system. 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 transmitter / receiver electronics of a transducer may be located far away from the transducer itself, thus requiring a very thin coaxial cable between the transducer and the transmitter / receiver electronics. Generally, the cable is of an appropriate length for delay and impedance matching, and additional impedance matching networks are quite often used to efficiently connect the transducer to the electronics through the cable.

[0037] Some embodiments may be used in conjunction with imaging devices that utilize either piezoelectric micro-ultrasonic transducer (pMUT) technology or capacitive micro-ultrasonic transducer (cMUT) technology, as will be described in more detail herein.

[0038] Generally, both cMUTs and pMUTs contain a diaphragm (a thin film attached to the end or at a point inside the probe), but "conventional" bulk PZT elements typically consist of solid material pieces.

[0039] Piezoelectric micro-ultrasonic transducers (pMUTs) can be efficiently formed on substrates by leveraging various semiconductor wafer manufacturing operations. Currently, semiconductor wafers can be 6-inch (15.24 cm), 8-inch (20.32 cm), and 12-inch (30.48 cm) in size, capable of accommodating hundreds of transducer arrays. These semiconductor wafers begin as silicon substrates on which various processing operations are performed. One example of such an operation is the formation of an SiO2 layer, also known as an insulating oxide. Various other operations are performed, such as the addition of a metal layer that acts as an interconnect and adhesive pad to enable connection to other electronic devices. Yet another example of a mechanical operation is cavity etching. Compared to conventional transducers with large piezoelectric materials, pMUT elements built on semiconductor substrates are smaller, have lower manufacturing costs, and offer simpler and higher-performance interconnects between electronic devices and transducers. Therefore, pMUTs offer greater flexibility in the operating frequency of imaging devices using 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 the received echo signal, and a control circuit to control various operations. The ASIC may be formed on a separate semiconductor wafer. This ASIC may be placed very close to the pMUT or cMUT elements to reduce parasitic losses. In a specific example, the ASIC may be located only 50 micrometers (μm) or less away from the transducer array. In a broader example, the distance between two wafers or two dies may be less than 100 μm, and each wafer contains many dies, with the dies containing transducers in the transducer wafer and ASICs in the ASIC wafer. In some embodiments, the ASIC has dimensions that match the pMUT or cMUT array, allowing the devices to be stacked to enable interconnection between wafers, or interconnection of transducer dies on the ASIC wafer or between transducer dies and ASIC dies. Alternatively, transducers can also be deployed on the ASIC wafer using low-temperature piezo sputtering and other low-temperature processes compatible with ASIC processing.

[0041] According to one embodiment, the ASIC and transducer may have similar footprints regardless of where they are interconnected. More specifically, according to the latter embodiment, the footprint of the ASIC may be an integer multiple or divisor of the footprint of the MUT.

[0042] Regardless of whether the imaging device is based on a pMUT or a cMUT, imaging devices according to some embodiments may include multiple transmit channels and multiple receive channels. The transmit channels drive transducer elements with voltage pulses at frequencies to which the transducer elements respond. This causes these elements to emit an ultrasonic waveform, which is directed toward the object being 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 a 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 is reflected back to the transducer elements in the form of received / reflected ultrasonic energy, which can 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 an electrical signal, and the electrical signal may be processed by other circuits to generate an image of the object for display based on the electrical signal.

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

[0044] An imaging device incorporating the features of the embodiment can advantageously reduce or solve the problem.

[0045] In one embodiment, the imaging device may include a transducer and a handheld case housing related electronic circuits such as a control circuit and optionally a computing device. The imaging device may also include a battery to power the electronic circuits.

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

[0047] The following description includes specific details to provide an understanding of the disclosure for illustrative purposes. However, it will be apparent to those skilled in the art that the disclosure can be implemented without these details. Furthermore, those skilled in the art will recognize that the examples of the disclosure described below can be implemented in various ways, such as processes, one or more processors (processing circuits) of control circuits, one or more processors (or processing circuits) of computing devices, systems, devices, or methods on tangible computer-readable media.

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

[0049] The elements / components shown in the figures are illustrative of exemplary embodiments and are intended to avoid obscuring the disclosure. Any reference in this specification to “one example,” “preferred example,” “one example,” “example,” “one embodiment,” “several embodiments,” or “embodiments” means that any particular feature, structure, characteristic, or function described in relation to an example is included in at least one example of this disclosure, and may be included in more than one example. In various parts of this specification, the phrases “in one example,” “in one example,” “in an example,” “in one embodiment,” “in several embodiments,” or “in embodiments” do not necessarily all refer to the same one or more examples. The terms “include,” “including,” “comprise,” and “comprising” are understood to be open terms, and any list below is illustrative and not intended to limit the items listed. Any titles used herein are for illustrative purposes only and should not be used to limit the scope of the description or the claims. Furthermore, any use of certain terms in various parts of this specification is illustrative and should not be construed as limiting.

[0050] Referring here to the drawings, Figure 1 is a block diagram of an imaging device 100 having a controller or control circuit 106 that controls selectively changeable channels (108, 110) and performs image calculations on a computing device 112 according to the principles described herein. As stated above, the imaging device 100 may be used to produce 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 an imaging device may include either a pMUT or cMUT, which may be called a transducer or imager, which may be based on photoacoustic or ultrasonic effects. The imaging device 100 may also be used to image other objects. For example, the imaging device may be used in medical imaging; flow measurement in pipe, speaker, and microphone arrays; lithotomy; localized tissue heating for treatment; and high-intensity focused ultrasound (HIFU) surgery.

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

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

[0053] To facilitate such imaging, the imaging device 100 includes one or more ultrasonic transducers 102, each transducer 102 including an array of ultrasonic transducer elements 104. Each ultrasonic transducer element 104 can 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 the 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 (abbreviated as pressure waves). For example, a control circuit 106 may be configured to control a particular transducer element 104 to transmit a pressure wave toward the target object being imaged, while other transducer elements 104 simultaneously receive pressure wave / ultrasonic energy reflected from the target object and generate charges based on the received waves / received ultrasonic energy / received energy in response to them.

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

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

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

[0057] The ultrasonic waveform travels toward the object being imaged, and a portion of the waveform is reflected back to the transducer 102, where the ultrasonic waveform is converted into electrical energy by the piezoelectric effect. The receiving channel 110 collects the electrical energy thus obtained, processes it, and transmits it to a computing device 112, which, for example, unfolds 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 transmit channels and receive channels to transducer elements may be controlled by a control circuit 106. In some examples, the control circuit may include transmit channels 108 and receive channels 110, for example, as shown in Figure 1. For example, the transducer elements 104 of 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., 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 by each transducer element 104 in each row. In another example, i.e., during different imaging modes, each transducer element 104 may be coupled to its own dedicated transmit channel 108 and its own dedicated receive channel 110. In some embodiments, the transducer element 104 may be coupled to both the transmit channel 108 and the receive channel 110. For example, the transducer element 104 may be adapted to generate and transmit an ultrasonic pulse and then detect the echo of that pulse in a form that converts the reflected ultrasonic energy into electrical energy.

[0059] The control circuit 106 may be embodied as any one or more circuits configured to perform the functions described herein. For example, the control circuit 106 may be embodied as 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., or otherwise may include them.

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

[0061] Each transducer element may have any suitable shape, such as a square, rectangle, ellipse, or circle. The transducer elements may be arranged in a two-dimensional array with orthogonal arrangements, such as N columns and M rows, as described herein, or in an asymmetric (or staggered) linear array.

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

[0063] Figure 2 is a diagram of an imaging environment including an imaging system having selectively configurable characteristics according to one embodiment. The imaging system in Figure 2 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 will be described in more detail below.

[0064] As shown in Figure 2, according to one embodiment, unlike the embodiment in Figure 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 separate devices (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 Figure 1, for example, the display device, computing device, and associated display may be part of the imaging device 202 (shown here). That is, the imaging device 100, computing device 216, and display device 220 may be located in a single housing.

[0065] In some embodiments, the “computing device” as referred to herein may be configured to generate signals to perform at least one of the following: display an image of an object on a display, or communicate information about this image to a user.

[0066] As illustrated, the imaging system includes an imaging device 202 configured to generate and transmit a pressure wave 210 toward an object such as a heart 214 via a transmit channel (Figure 1, 108) in transmit mode / process. The internal organ or other imaging object may reflect a portion of the pressure wave 210 toward the imaging device 202, which may receive the reflected pressure wave via a receive channel (Figure 1, 110) via a transducer (e.g., transducer 102 in Figure 1) and a control circuit (Figure 1, 106). The transducer may generate an electrical signal based on the received ultrasonic energy in receive mode / process. The transmit mode or receive mode may be applicable in the context of an imaging device that can be configured to transmit or receive, albeit at different times. However, as stated above, some imaging devices according to the embodiments may be adapted to perform 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 in the figure, although embodiments may also include 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 use the signals from the computing device to display the image of the object. The computing system may further provide the user with information about defective pixels, as described above.

[0067] In some embodiments, the imaging device may include a portable and / or handheld device adapted to communicate signals with a computing device wirelessly (using wireless communication protocols such as IEEE 802.11 or Wi-Fi® protocol, Bluetooth® protocol including Bluetooth® Low Energy, millimeter-wave communication protocol, or any other wireless communication protocol that would be within the knowledge of those skilled in the art) or via a wired connection such as a cable (USB2, USB3, USB3.1, USB-C, etc.) or an interconnection on a microelectronic device. In the case of tethering or a wired connection, the imaging device may include a port to receive a cable connection for a cable communicating with the computing device, as described in more detail in connection with Figure 3. 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 this disclosure may be implemented with 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 using voltage signals to control the ultrasonic waveforms generated in the transducer elements of the imaging device, and further, to process the received ultrasonic energy.

[0069] Figure 3 shows diagrams of imaging devices according to several embodiments, which will be described in more detail below.

[0070] As shown in Figure 3, the imaging device 300 may include a handheld case 331 housing a transducer 302 and associated electronics. The imaging device may also include a battery 338 for powering the electronics. Figure 3 thus shows one embodiment of a portable imaging device capable of 2D and 3D imaging using a 2D array pMUT, optionally constructed on a silicon wafer. Coupled with an application-specific integrated circuit (ASIC) 106 having an electronic configuration of specific parameters, such an array enables high-quality image processing at a lower cost than previously possible. Furthermore, power consumption may be changed and temperature may be varied by controlling specific parameters, such as the number of channels used.

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

[0072] Turning our attention to Figure 3 in more detail, Figure 3 is a schematic diagram of an imaging device 300 having selectively adjustable features according to several embodiments. The imaging device 300 may be similar to the imaging device 100 in Figure 1 or the imaging device 202 in Figure 2, just as an example. As mentioned above, the imaging device may include an ultrasound medical probe. Figure 3 shows the transducer 302 of the imaging device 300. As mentioned above, the transducer 302 may include an array of transducer elements (Figure 1, 104) adapted to transmit and receive pressure waves (Figure 2, 210). In some examples, the imaging device 300 may include a coating layer 322 that acts as an impedance matching interface between the transducer 302 and the human body or other mass or tissue to which the pressure waves (Figure 2, 210) are transmitted. In some cases, the coating layer 322 may function as a lens if designed with a curvature that matches a desired focal length.

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

[0074] In some embodiments, the user may apply the gel to the living skin before direct contact with the coating layer 322 to improve impedance matching at the interface between the coating layer 322 and the human body. Impedance matching reduces the loss of pressure waves at the interface (Figure 2, 210) and the loss of reflected waves 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, and vice versa. The thickness of the coating layer 322 may be one-quarter of the wavelength of the pressure wave (Figure 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 described above, the transmit channel 108 and receive channel 110 may be selectively modifiable or adjustable, meaning that the number of transmit channel 108 and receive channel 110 active at a given time can be changed, for example, so 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 channel 108 and receive channel 110 based on pixels being tested for defects and / or pixels determined to be defective.

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

[0078] The imaging device may also include one or more processors 326 for controlling the components of the imaging device 100. In addition to the control circuit 106, one or more processors 326 may be configured to perform at least one of the following: control the operation of transducer elements; process electrical signals based on ultrasonic waveforms reflected from transducer elements; or generate signals to cause the generation of an image of an object being imaged by one or more processors in a computing device, such as computing device 112 in Figure 1 or computing device 216 in Figure 2. One or more processors 326 may be further adapted to perform other processing functions associated with the imaging device. One or more processors 326 may be embodied as any type of processor 326. For example, 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 computing engine, an image processor, a microcontroller, a field-programmable gate array (FPGA), or other processor or processing / control circuit. The imaging device 100 may also include circuitry 328, such as an analog front-end (AFE) for processing / adjusting signals, and an acoustic absorption 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 absorption 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), otherwise they may be reflected and impair image quality. Figure 3 shows the acoustic absorption layer 330, but this component may be omitted if other components would interfere with the material transmission of ultrasonic waves in the reverse direction.

[0079] The analog front-end 328 can be embodied as one or more circuits configured to interface with the control circuit 106 and other components of the imaging device, such as 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, and so on.

[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 (Figure 2, 216), via, for example, 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 kind 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 the operation of the imaging device 100, such as operating systems, applications, programs, libraries, and drivers.

[0081] In some examples, the imaging device 100 may include a battery 338 for supplying power to the components of the imaging device 100. The battery 338 may also include a battery charging circuit, which may be a wireless or wired charging circuit (not shown). The imaging device may include a gauge indicating the remaining battery charge consumed, which is 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] Figure 4 shows a receiving channel 110 according to an example of the principle described herein. The receiving channel 110 is coupled to a transducer element (Figure 1, 104) to receive reflected pressure waves (Figure 2, 210). Figure 4 also shows the connection between the transducer element (Figure 1, 104) and the transmitting channel (Figure 1, 110). In one example, the transmitting channel (Figure 1, 108) is directed to a high impedance during the receiving operation at the node where the received pressure and transmitted pulse meet. Specifically, the reflected pressure waves are converted into charge within the transducer element 104, which is converted into a voltage by a low-noise amplifier (LNA) (456). The LNA (456) is a charge amplifier, and the charge is converted into 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 within the transducer into a voltage output and also amplifies the received echo signal. The switch (transmit / receive switch) connects the LNA(456) to the transducer element 104 in receive operation mode.

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

[0085] Therefore, a larger gain is used for compensation, and this larger gain 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 it 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 scan lines. In some implementations, the demodulation unit 464 may be implemented elsewhere, for example, within the FPGA. The demodulation unit frequency shifts the carrier signal to the baseband using two components (I and Q) in quadrature phase 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, it needs to have little to no latency so as not to delay signal processing after it is turned on.

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

[0087] Some embodiments further aim to effectively generate multimodal transmitted ultrasonic waveforms in multiple fundamental frequency pulses, including low and high frequency pulses, and to utilize the broad bandwidth of transducer elements in transducer arrays such as those described above by imaging in the same or similar frequency pulses as the fundamental frequency pulses. In multimodal imaging (MI) implementations, the frequencies of the processed reflected ultrasonic waveforms can be used, for example, to generate the respective electrical signals representing the reflected ultrasonic waveforms substantially simultaneously. Figure 11 is a representative diagram showing THI according to some embodiments. These figures are described in more detail below.

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

[0089] In some embodiments, it is possible to leverage the advantages of higher harmonics (when THI is used) or higher frequencies (when MI is used), thereby providing a higher overall spatial resolution compared to lower harmonics / lower frequencies, along with the advantages 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 the target object.

[0090] In some embodiments, advantageously, the wide bandwidth of the transducer element makes it possible to extract high-information content from the electrical signals generated from the reflected ultrasonic waveform at all imaging depths. In some embodiments, high-information content is extracted by (1) processing the reflected ultrasonic waveform at higher frequencies to obtain electrical signals corresponding to the near field of the image (the field corresponding to lower / shallower penetration) and a narrower lateral image angle (the lateral image angle refers to the angle θ, which will be described in more detail in the context of Figure 6); and (2) processing the reflected ultrasonic waveform at lower frequencies to obtain electrical signals corresponding to the far field of the image (the field corresponding to higher / deeper penetration) and / or a wider lateral image angle.

[0091] Figures 6, 7, 10, 11, 12, and 13 show ultrasound images corresponding to ultrasound frames. In this specification, “frame” or “image” refers to an image of a cross-sectional plane of an object, which may consist of individual scan lines. Scan lines may be seen as individual layers or slices of the image. Depending on the resolution, a particular image may contain a different number of scan lines, ranging from fewer than a hundred to several hundred.

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

[0093] Figure 6 illustrates a potential problem when imaging is performed based on a single receiving frequency (i.e., a single frequency at which the reflected ultrasonic waveform is processed or demodulated to produce the displayed output image). For example, in image A, better overall penetration resolution and better resolution at a wider image angle θ (where θ represents the angle on either side of line CL) are obtained. However, the resolution of image A at shallower depths can be improved compared to the resolution at shallower depths for images B and C (indicated by the "spotlight" region SL in images B and C). Therefore, for a given receiving frequency, there is no single image that can provide a suitable image with sufficient resolution at the desired depth and / or image angle. In addition, it is desirable to maintain resolution while moving away from the SL region to obtain a more uniform image.

[0094] Figure 6 highlights that image improvements can occur in multiple dimensions, such as along the depth of the indicated target object and across angles θ. At wider angles θ, higher frequencies do not offer the same fidelity or signal-to-noise ratio as lower frequencies.

[0095] Some embodiments envision leveraging the better resolution obtained from electrical signals corresponding to low-frequency processing of reflected ultrasonic waveforms for both deeper penetration and wider angles.

[0096] Figure 6, described above as a diagram illustrating some of the problems that can arise with ultrasound imaging, shows the centerline CL from which θ can be measured, and the spotlight region SL, which corresponds to a region with better resolution for higher frequency processing. Figures 7 and 10 through 12 show images of the same type as those in Figure 6, and as a result, the depiction of CL, SL, and θ is omitted in those images. However, 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 through 12, and can be described below in the context of those figures.

[0097] Figures 6, 7, 10, and 11 show an example of THI in which a single low-frequency transmission pulse (1.75 MHz) is applied to the transmitted ultrasonic waveform, and the resulting reflected ultrasonic waveform is demodulated in three imaging frequency bands (1.75 MHz, 3.5 MHz, and 5.25 MHz). In THI, a narrow baseband filter is applied to the electrical signals corresponding to the demodulation of these three imaging harmonics, and further processing may be performed to generate electrical signals corresponding to three images, namely images A, B, and C in Figures 6, 7, 10, and 11, respectively. These electrical signals corresponding to these three images A-C can then be compounded according to one 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-compensated 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 averaging]

[0099] Refer again to Figure 6, which has already been partially described above. In the example of Figure 6, the compound of electrical signals corresponding to the images of three received frequencies at A, B, and C (the fundamental and two harmonics in Figure 6, but the simple averaging according to the embodiment may also be used in embodiments of multimodal imaging) can be achieved by simple averaging to obtain the image D. Simple averaging in the context of Figure 6 may involve averaging the pixel illumination of each of the given pixel positions corresponding to each of the received frequencies for each of the given pixel positions defined by depth and image angle.

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

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

[0102] [Weighted Averaging]

[0103] In a compound with weighted averaging, the pixel illuminance at a given pixel position can be obtained through an equation such as Equation 2 below. I out =(α × I high + β × I mid + Υ × I low ) / 3 Equation (2) Here, I out , I high , I mid and I lowThe formula is as defined above for Equation 1, where α, β, and Υ are the weights used for each of the listed pixel illuminances. α, β, and Υ can be based on the needs of the application, such as whether depth resolution is required.

[0104] [Alpha blending using depth-adaptive compound]

[0105] Here, in the context of a compounding method according to one embodiment, involving alpha blending using a depth-adaptive compound (DAC), see Figures 7 and 8.

[0106] Figure 7 shows four sets of ultrasound images of the target object (image D, the output image, is acquired through alpha blending DAC of the pixel illumination of images A-C). Images A-C in Figure 7, which are the same as those in Figure 6, correspond to images acquired at a receiving frequency equal to the fundamental frequency (A), images acquired at a receiving frequency equal to the second harmonic of the fundamental frequency (B), and images acquired at a receiving 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 the pixel illuminance at each given pixel position, which corresponds to each receiving frequency, by the respective alpha multiplier, for each given pixel position defined by depth and image angle. Each multiplier for the pixel illuminance at a given pixel position may be a function of one or more alpha values, where each alpha value is a function of the penetration depth.

[0108] For an example of alpha blending using a DAC, see Equation 3 below. 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 This is defined for equation 1 above, α high This corresponds to the depth-dependent alpha value at high reception frequencies; and α mid This corresponds to the depth-dependent α value of the intermediate receiving frequency. Therefore, the multipliers for each pixel illumination from equation 3 above may be as follows. α high :I high multiplier; (1-α high ) × α mid :I mid multiplier; and (1-α high ) × (1-α mid ):I low multiplier

[0109] Figure 8 shows the α used in the above equation 4. high and α mid This is one example of a plot of α value versus depth (shown in mm) for α. high It is clear from plot 800 that the alpha value can drop to zero at a given depth, for example, approximately 92 mm in the case of plot 800. In contrast, α mid The alpha value is α high It drops to zero much more gradually, and at much deeper penetrations, at 200 mm in the case of plot 800, it drops to zero. Therefore, alpha blending with a DAC may aim to minimize the contribution of higher received frequencies to pixel illumination at deeper depths, taking into account lower resolution and fidelity. For deeper pixel positions, the contribution of pixel illumination at high and intermediate received frequencies may approach zero, in which case alpha blending with a DAC would use only pixel illumination at lower received frequencies (above 200 mm in the case of Figure 8).

[0110] Therefore, the multiplier for pixel illumination at various receiving frequencies can be based on the depth of the pixel position.

[0111] [Multimodal transmitted ultrasonic waveform]

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

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

[0114] Figure 10 is similar to that of Figure 6, but this time images A to C are generated using the pulses in Figures 9A and 9B, and in order to generate compound image D, the simple compound described above is used to further compound the electrical signals corresponding to images A to C, resulting in a set of 1000 images.

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

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

[0117] [Pre-compounding gain compensation]

[0118] According to one embodiment, the compounding method may include gain-compensated compounding, in which case individual frequency bands of the reflected ultrasonic waveform may be pre-processed before compounding. For example, the electrical signals corresponding to each of the received 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] Referring now to Figure 12, this figure shows two output images D1 and D2 (image D1 represents the acquisition using alpha blending with the DAC for three receiving frequencies similar to those in Figures 6, 7, 10, or 11, while D2 represents the acquisition using DR compensation for the same three receiving frequencies, with a DR of 60 and different gains). The comparison of D1 and D2 shows a clear advantage in that using DR compensation improves contrast compared to alpha blending with the DAC, all else being equal.

[0120] [Maximum and minimum adaptive compound]

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

[0122] Referring now to Figure 13, this figure shows a set of three output images D1, D2, and D3 (image D1 represents the acquisition using simple compound for three receiving frequencies similar to those in Figures 6, 7, 10, or 11; D2 represents the acquisition using alpha blending with DAC for the same three receiving frequencies; and D3 represents the acquisition using maximum and minimum adaptive compound according to Equation 5 below). A comparison of D1, D2, and D3 shows a clear advantage in that the adaptive compound is used to improve contrast and resolution compared to simple compound and alpha blending with DAC, while all else is equal.

[0123] The output image D3 in Figure 13 is the result of a simple nonlinear adaptive compounding method using blending of maximum, minimum, and average frames or pixel illumination according to Equation 4 below. I out =I max ·α max +(1-α max )·(αmin Base I min +(1-α min )·I depth_comp ) (Equation 4) Here, I max :MAX(I high ,I mid ,I low ): I min :MIN(I high ,I mid ,I low ): I depth_comp This corresponds to the pixel illumination at the pixel position 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 this purpose). For example, α max This is the illuminance (i.e., I) to a given value such as one between 0 and 1 and including 0 and 1. max , I min or I depth_comp It can be determined from a reference table that maps at least one of the following. Thus, according to one example, the selected illuminance (i.e., I max , I min or I depth_comp If at least one of the following is greater than the threshold X1, then α max may have a set first value (for example, between 0 and 1 and including 0 and 1); if the selected illuminance is less than X2, α max may have a second set value (between 0 and 1 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, α max This is determined based on a linear function which is a straight line between the first set value and the second set value; and α min : Minimum transmittance coefficient based on known alpha blending methods (I max (Transmittance coefficient used for this purpose). For example, αmax Similarly, α min This is the illuminance (i.e., I) to a given value such as one between 0 and 1 and including 0 and 1. max , I min or I depth_comp It can be determined from a reference table that maps at least one of the following. Thus, according to one example, the selected illuminance (i.e., I max , I min or I depth_comp If at least one of the following is greater than the threshold X1, then α min may have a set first value (for example, between 0 and 1 and including 0 and 1); if the selected illuminance is greater than X2, α min may have a second set value (between 0 and 1 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, α min This can be determined based on a linear function, which is a straight line between a first set value and a second set value.

[0124] [Predicted Compound]

[0125] Predictive compounds according to some embodiments involve determining the 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 Figure 6, a predictive compound may be used to determine the relationship between an electrical signal corresponding to region NF1 (region NF at a received frequency of 1.75 MHz) and an electrical signal corresponding to region A2 (region NF at a received frequency of 3.5 MHz). The predictive compound may then include using this relationship together with the electrical signal in region FF1 (region FF at a received frequency of 1.75 MHz) to predict the electrical signal in region FF2 (region FF at a received frequency of 3.5 MHz).

[0126] This relationship can be useful, for example, in enabling the prediction of an image at a higher reception frequency with a deeper penetration using a lower frequency with better penetration as an input signal. Thus, an image at a deeper depth of a target object at a higher frequency can be replaced with an image predicted from the same depth at a lower frequency.

[0127] Some examples of prediction compounds, including simple prediction compounds, prediction compounds using a point spread function (PSF), and machine learning (ML)-based prediction compounds, will be described later.

[0128] [Simple Prediction Compound]

[0129] A simple way of using a prediction compound can involve the use of Equation 5. I FF2 =I FF1 . I NF2 / I NF1 Equation (5) Here, I FF2 : Pixel illuminance in region FF2 - this is the predicted pixel illuminance; I FF1 : Pixel illuminance in region FF1 obtained from the reflected ultrasonic waveform; I NF2 : Pixel illuminance in region NF2 obtained from the reflected ultrasonic waveform; and I NF1 : Pixel illuminance in region NF1 obtained from the reflected ultrasonic waveform.

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

[0131] As an example, once the relationship as described above is found for a 5 MHz image with a prediction compound, for example, a penetration ten times better than that in a 5 MHz image without a prediction compound can occur.

[0132] [Prediction Compound Using Point Spread Function (PSF)]

[0133] The point spread function (PSF) of the reflected ultrasonic waveform depends on a plurality of factors including the received frequency, the received bandwidth, and the focus in the target object. The PSF can be considered to represent the scattering of the beam reflected from the target object. In order to obtain an image corresponding to the region of the target object being imaged, as a predicted measurement, the PSF can be convolved using the scattering distribution corresponding to the region of the target object being imaged, such as pixels. Therefore, the frequency-dependent PSF can be convolved using the target object-dependent scattering distribution. Therefore, when the received frequencies are different, for example, for received 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 described above in the general description of the prediction compound in the context of the image of FIG. 6, for example, Region NF1 corresponds to region NF in the 1.75 MHz received frequency image; Region FF1 corresponds to region FF in the 1.75 MHz received frequency image; Region NF2 is in region NF but corresponds to that of the 3.5 MHz reception frequency image; Region FF2 is in region FF but corresponds to that of the 3.5 MHz reception frequency image; PSF1 corresponds to the PSF of the 1.75 MHz reception frequency image; PSF2 corresponds to the PSF of the 3.5 MHz reception frequency image; and PSF inverse is assumed to be the reciprocal of PSF and assume.

[0135] Considering the above definition, it can be assumed that Equation 6 can be used when it is desired to predict the pixel illumination in B2, which represents the scattering distribution (correlated with the target object in region FF). I FF2 =I NF2 ×(I FF1 *PSF2 inverse ) / (I NF1 *PSF1 inverse ) Equation (6) Here, *PSF inverse represents deconvolution based on PSF.

[0136] [ML-based prediction compound]

[0137] Some embodiments propose an ML-based prediction compound that can provide higher-resolution images (e.g., 5 MHz) in the far-field by using ML-based deconvolution while maintaining penetration at a lower frequency (e.g., 1.75 MHz).

[0138] [Proposed method]

[0139] The basic idea is to a) identify the relationship between local regions of low-frequency and high-frequency images using near-field data, and b) apply the identified relationship to the low-frequency image to obtain the high-frequency image in the far-field. These steps will be detailed below for the proposed method.

[0140] [Training and validation datasets]

[0141] To perform ML-based compounding according to several embodiments, the following actions may be performed. 1) Select a 5.0MHz image as the desired output image, using a 1.75MHz image as the input; 2) Select the near-field noise figure (NF) (e.g., depth of 0-5 cm) for each of the 1.75 MHz and 5.0 MHz images; 3) Divide the near-field NF into many "speckle cells" to create a training dataset; that is, create many sub-regions (e.g., square sub-regions with measurements of 5mm x 5mm) within both near-field regions to create input (1.75MHz sub-region) and output (5.0MHz sub-region); 4) Creating a training dataset using multiple frames and / or views of individual sub-regions; for example, by using approximately 50 different imaging views for a pin target phantom and for each sub-region speckle phantom, it is possible to create more than 5000 images for use as a training dataset (which can be optionally expanded by transformation, scaling, etc., if necessary); 5) Create a validation dataset using intermediate distance field speckle cells (5-10 cm); 6) Use the central region MF (e.g., depth of 0-5 cm) in each of the 1.75 MHz and 5.0 MHz images as the validation dataset; 7) Develop 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 and validation datasets; and 8) Predict images in region B (where B is the far-field region) at receiving frequencies of 1.75 MHz and 5.0 MHz based on the trained model.

[0142] The training dataset can be used to identify relationships between regions A at lower and higher receiving frequencies. These relationships can also be used for other ML-related applications, such as speckle cell identification, differentiation between speckle and noise, gain equalization, and edge enhancement.

[0143] [Lateral frequency compound]

[0144] Refer again to Figure 6, which illustrates the problems that can arise when imaging is based on a single receiving frequency. For example, looking at image A, we see better overall penetration resolution and better resolution at a wider image angle θ (where θ represents the angle on either side of line CL). However, images B and C show a spotlight area SL in the near and central regions. However, SL is not present in image A at lower frequencies.

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

[0146] For an example of a lateral frequency compound, see Equation 7 below. 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) Here, I out (r,θ): Output pixel illumination at depth r and image angle θ; I high: Pixel illuminance of the high reception frequency image (image C) at depth r and image angle θ; r: Image depth; θ: Image angle; α high (r, θ): Transmittance coefficient α of the high reception frequency at depth r and image angle θ (α high (r, θ) is α max or α min may be determined in the same manner as described above for either one, the difference being that α high (r, θ) can have different ranges and different breakpoints X1 and X2 for different r and different θ); α mid (r, θ): Transmittance coefficient α of the intermediate reception 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 one, the difference being that α mid (r, θ) can have different ranges and different breakpoints X1 and X2 for different r and different θ); I mid : Pixel illuminance of the intermediate reception frequency image (image B) at depth r and image angle θ; I low : Pixel illuminance of the low reception frequency image (image A) at depth r and image angle θ. Therefore, each multiplier of the pixel illuminance from the above formula 4 may be as follows. 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 the image angle for reducing "spotlight" artifacts at higher frequencies and improving the signal-to-noise ratio (SNR) in the lateral direction. Equation 9 below illustrates the above relationship. Img out (r,p) = Σ 3 i-1 α(r,p,i)·Img in (r,p,i) Equation (8) Here, i: Frequency bandwidth of the imaged waveform; p: line index coordinates of the image; Img out (r,p): Output image illuminance at depth r and line index coordinate p; Img in (r,p,i): input image intensity; and α(r,p,i): The transmittance coefficient α(r,p,i) for depth r, line index coordinate p, and frequency band i is α max or α min The same method described above may be used to determine any of the following, 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] Lateral compounding improves directivity, potentially providing a larger field of view, such as a 150-degree field of view.

[0149] [Color Doppler / Flow Compound]

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

[0151] Equations 9 to 12 may be used to calculate the alpha-blended result of the output image produced using a color Doppler compound according to one embodiment. Maximum parameter: R0 out.max =full( 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 = full(Σ Nfreq i=1 R0 in (i) Equation (11) R1 out.mean = full(Σ Npfreq i=1 R1 in (i) Equation (12) Here, 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 : The average first lag output value of the autocorrelation; R0 in (i): Zero-lag input value corresponding to the frequency band i of the autocorrelation imaging waveform corresponding to the alpha blending used (see, for example, equations 14 and 15 below); and R1 in (i): The first lag input value corresponding to the frequency band i of the autocorrelation imaging waveform corresponding to the alpha blending used (see, for example, equations 14 and 15 below).

[0152] The maximum and mean 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) Here, R0 out : 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 mean first lag autocorrelation; α: Alpha value for alpha blending.

[0153] While the above description of exemplary embodiments may specifically refer to veins, embodiments are not limited in this respect and relate to the detection and tracking of any blood vessel having a body that can be targeted for ultrasound imaging for foreign objects accessing the blood vessel. Also, while certain colors are mentioned above to indicate suitability for access or other parameters relating to the blood vessel, embodiments are not limited in this respect and include, to the extent thereto, displaying blood vessel parameters to the user through a UI in any way, such as through text, visual images or codes, voice communication, etc.

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

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

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

[0157] While implementations have been described with reference to specific and exemplary embodiments, it is clear that various modifications and changes can be made to these embodiments without departing from the broader scope of this disclosure. Many of the configurations and processes described herein can be used in combination or in parallel implementations. Therefore, this specification and the drawings should be considered illustrative rather than restrictive. The accompanying drawings forming parts of this specification illustrate, not restrictive, specific embodiments in which the subject may be implemented. The embodiments shown are described in sufficient detail to enable a person skilled in the art to implement the teachings disclosed herein. Other embodiments may be used and derived therefrom, and structural and logical substitutions and modifications may be made without departing from the scope of this disclosure. Therefore, this detailed description should not be interpreted restrictively, and the scope of the various embodiments is defined solely by the appended claims, along with the entire scope of equivalents to which such claims are entitled.

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

[0159] While 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 merely as examples. Embodiments are not intended to be limited by the specific examples provided herein. Embodiments of the present disclosure are described with reference to the preceding specification, but the descriptions and examples of embodiments herein are not intended to be constrained. Those skilled in the art will readily conceive of numerous variations, modifications, and substitutions without departing from the concepts of the present disclosure. Furthermore, it should be understood that all aspects of the various embodiments are not limited to the specific descriptions, configurations, or relative proportions described herein, depending on various conditions and variables. It should be understood that various alternative means to the embodiments described herein may be used. Accordingly, it is assumed that the present disclosure also covers any such alternatives, modifications, variations, or equivalents.

[0160] [example]

[0161] Exemplary examples of the technologies disclosed herein are provided below. One embodiment of these technologies may include one or more of the following examples, and any combination thereof.

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

[0163] Example 2 includes the subject matter described in Example 1, wherein each of the reflected frequencies corresponds to each harmonic of the fundamental frequency of the transmitted ultrasonic waveform, and the fundamental frequency is a single frequency of the transmitted ultrasonic waveform.

[0164] Example 3 includes the subject matter described in Example 1, wherein the transmitted ultrasonic waveform is a multimodal waveform having fundamental frequencies corresponding to each of the reflected ultrasonic waveforms.

[0165] Example 4 includes the subject matter described in Example 1, wherein one or more processors implement a prediction algorithm in the electrical signals; 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 is different from the first image region; and compound the information using the predicted electrical signal to obtain the compounded electrical signal such that the output image in the second image region corresponds to the predicted electrical signal.

[0166] Example 5 includes the subject described in any one of Examples 1 to 4, wherein the reflected frequencies comprise N reflected frequencies; the electrical signals comprise 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 comprises a pixel at its respective pixel position, and each pixel position of the N input images is defined by depth and angle; compounding the information comprises compounding the 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 further includes the subject matter described in Example 5, wherein the one or more processors further cause the electrical signals to undergo gain compensation or dynamic range compensation before compounding.

[0168] Example 7 includes the subject matter described in Example 5, wherein the simple averaging involves performing either a simple average or a weighted average of the respective pixel illuminations across the N input images for each pixel position.

[0169] Example 8 includes the subject described in Example 5, wherein alpha blending with depth-adaptive compounding comprises multiplying each pixel illuminance, such as between the N input images, by a corresponding alpha multiplier for each pixel position, where each alpha multiplier is a function of one or more alpha values, and the one or more alpha values ​​are a function of at least one of the depth of each pixel position or the angle of each pixel position.

[0170] In Example 9, for each of the aforementioned pixel positions, 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 ) is given by, where I out This is the output pixel illuminance at each pixel position in the output image; high I is the pixel illuminance at each pixel position of the input image corresponding to the highest of the received frequencies; mid This is the pixel illuminance at each pixel position of the input image corresponding to the intermediate of the received frequencies; low α is the pixel illuminance at each pixel position of the input image corresponding to the lowest of the received frequencies; high α corresponds to the depth-dependent α value of the highest of the received frequencies; and α mid This includes the subject described in Example 8, which corresponds to the depth-dependent α value of the intermediate of the received frequencies.

[0171] In Example 10, for each of the aforementioned pixel positions, 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 ) is given by, where r: depth; θ: angle; I out (r,θ): Output pixel illumination at depth r and image angle θ; I high : Pixel illumination at depth r and image angle θ of the highest received frequency; I mid : Pixel illumination at depth r and image angle θ for the intermediate of the received frequencies; I low : Pixel illumination at depth r and image angle θ of the lowest of the received frequencies; α high α corresponds to the alpha value of the highest of the received frequencies at depth r and image angle θ; and α mid This includes the subject described in Example 8, which corresponds to the alpha value at the intermediate of the received frequencies at depth r and image angle θ.

[0172] Example 11 includes the subject described in Example 5, wherein the maximum and minimum adaptive compound uses a blend of the maximum, minimum, and average pixel illumination, such as between the N input images, for each pixel position.

[0173] Example 12 shows that for each of the pixel positions, 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 ) is given by, where I max :MAX(I high ,I mid ,I low );I min :MIN(I high ,I mid ,I low );I depth_compα corresponds to the pixel illumination at the pixel position after alpha blending with depth compensation; max is, I max , I min or I depth_comp Corresponding to the maximum transmittance alpha value coefficient based on at least one of the following, α max has a set first value between 0 and 1, and including 0 and 1; and α min is, I max , I min or I depth_comp Corresponding to the minimum transmittance alpha value coefficient based on at least one of the following, α min This includes the subject described in Example 11, which has a second set value that is between 0 and 1 and different from the first set value which includes 0 and 1.

[0174] Example 13 includes the subject matter described in Example 5, wherein the predictive compound 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 a second region in the second input image based on the relationship.

[0175] Example 14 shows that the pixel illuminance of the pixels in the second region is I FF2 =I FF1 . I NF2 / I NF1 Given by, 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 This is the pixel illuminance in FF2; FF1 This is the pixel illuminance in region FF1; NF2 is the pixel illuminance in region NF2; and I NF1 This includes the subject described in Example 13, which is the pixel illuminance in region NF1.

[0176] Example 15 shows that the pixel illuminance of the pixels in the second region is I FF2 =I NF2 × (I FF1 *PSF2 inverse ) / (I NF1 *PSF1 inverse ) is given by, 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 This is the pixel illuminance in FF2; FF1 This is the pixel illuminance in region FF1; NF2 is the pixel illuminance 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 reciprocal of PSF1 corresponding to deconvolution; and PSF1 inverse This includes the subject described in Example 13, which is the reciprocal of PSF1 corresponding to deconvolution.

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

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

[0179] Example 18 includes the subject described in Example 5, wherein the color Doppler compound combines the respective pixel illuminations, such as between the N input images, based on at least one of the following information for each pixel position: depth, angle, signal-to-noise ratio, flow velocity, or force.

[0180] Example 19 shows the R0 of the output image. out and R1 out R0 out =α·R0 out.max +(1-α)·R0 out.mean and R1 out =α·R1 out.max +(1-α)·R1 out.mean This is given by, where R0 out : Zero-lag output corresponding to the alpha-blended values ​​of the maximum and mean zero-lag autocorrelation; R1 out : First lag output corresponding to the alpha-blended values ​​of the maximum and mean first lag autocorrelation; α: Alpha value of the alpha blending; R0 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 mean 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 corresponding to the alpha blending used, including the subject as described in Example 18.

[0181] Example 20 includes the subject described in 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 fundamental frequency of each of the transmitted ultrasonic waveforms includes one of 1.75 MHz, 3.5 MHz, and 5.0 MHz.

[0182] Example 21 includes a user interface device having a display device; and a computing device communicatively coupled to the user interface device, the computing device having memory and one or more processors coupled to the memory, wherein the one or more processors simultaneously receive electrical signals based on the respective reflected frequencies of reflected ultrasonic waveforms reflected from a target object as a result of a transmitted ultrasonic waveform; compound the information from the electrical signals to generate a compounded electrical signal; and, based on the compounded electrical signal, cause the generation of an output image on a display device.

[0183] Example 22 includes the subject matter described in Example 21, wherein each of the reflected frequencies corresponds to each harmonic of the fundamental frequency of the transmitted ultrasonic waveform, and the fundamental frequency is a single frequency of the transmitted ultrasonic waveform.

[0184] Example 23 includes the subject matter described in Example 21, wherein the transmitted ultrasonic waveform is a multimodal waveform having fundamental frequencies corresponding to each of the reflected ultrasonic waveforms.

[0185] Example 24 includes the subject matter described in Example 21, wherein one or more processors implement a prediction algorithm in the electrical signals; 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 is different 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 in the second image region corresponds to the predicted electrical signal.

[0186] Example 25 includes the subject described in any one of Examples 21 to 24, wherein the reflected frequencies comprise N reflected frequencies; the electrical signals comprise 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 comprises a pixel at its respective pixel position, and each pixel position of the N input images is defined by depth and angle; compounding the information comprises compounding the 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 further includes the subject matter described in Example 25, wherein the one or more processors further cause the electrical signals to undergo gain compensation or dynamic range compensation before compounding.

[0188] Example 27 includes the subject matter described in Example 25, wherein the simple averaging involves performing either a simple average or a weighted average of the respective pixel illuminations across the N input images for each pixel position.

[0189] Example 28 describes alpha blending with depth-adaptive compound, which for each pixel position multiplies the respective pixel illumination, 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 position or the angle of each pixel position, and the subject as described in Example 25.

[0190] Example 29 shows that for each of the pixel positions, 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 ) is given by, where I out This is the output pixel illuminance at each pixel position in the output image; high I is the pixel illuminance at each pixel position of the input image corresponding to the highest of the received frequencies; mid This is the pixel illuminance at each pixel position of the input image corresponding to the intermediate of the received frequencies; low α is the pixel illuminance at each pixel position of the input image corresponding to the lowest of the received frequencies; high α corresponds to the depth-dependent α value of the highest of the received frequencies; and α mid This includes the subject described in Example 28, which corresponds to the depth-dependent α value of the intermediate of the received frequencies.

[0191] In Example 30, for each of the aforementioned pixel positions, the output illuminance in the output image is 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 ) is given by, where r: depth; θ: angle; I out(r,θ): Output pixel illumination at depth r and image angle θ; I high : Pixel illumination at depth r and image angle θ of the highest received frequency; I mid : Pixel illumination at depth r and image angle θ for the intermediate of the received frequencies; I low : Pixel illumination at depth r and image angle θ of the lowest of the received frequencies; α high α corresponds to the alpha value of the highest of the received frequencies at depth r and image angle θ; and α mid This includes the subject described in Example 28, which corresponds to the alpha value at the intermediate of the received frequencies at depth r and image angle θ.

[0192] Example 31 includes the subject described in Example 25, wherein the maximum and minimum adaptive compound uses a blend of the maximum, minimum, and average pixel illumination, such as between the N input images, for each pixel position.

[0193] Example 32 shows that for each of the pixel positions, 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 ) is given by, where I max :MAX(I high ,I mid ,I low );I min :MIN(I high ,I mid ,I low );I depth_comp α corresponds to the pixel illumination at the pixel position after alpha blending with depth compensation; max is, I max , I min or I depth_comp Corresponding to the maximum transmittance alpha value coefficient based on at least one of the following, α maxhas a set first value between 0 and 1, and including 0 and 1; and α min is, I max , I min or I depth_comp Corresponding to the minimum transmittance alpha value coefficient based on at least one of the following, α min This includes the subject described in Example 31, which has a second set value that is between 0 and 1 and different from the first set value which includes 0 and 1.

[0194] Example 33 includes the subject matter described in Example 25, wherein the predictive compound 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 a second region in the second input image based on the relationship.

[0195] Example 34 shows that the pixel illuminance of the pixels in the second region is I FF2 =I FF1 . I NF2 / I NF1 Given by, 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 This is the pixel illuminance in FF2; FF1 This is the pixel illuminance in region FF1; NF2 is the pixel illuminance in region NF2; and I NF1 This includes the subject described in Example 33, which is the pixel illuminance in region NF1.

[0196] Example 35 shows that the pixel illuminance of the pixels in the second region is I FF2 =I NF2 × (I FF1 *PSF2 inverse ) / (I NF1 *PSF1 inverse) is given by, 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 This is the pixel illuminance in FF2; FF1 This is the pixel illuminance in region FF1; NF2 is the pixel illuminance 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 reciprocal of PSF1 corresponding to deconvolution; and PSF1 inverse This includes the subject described in Example 33, which is the reciprocal of PSF1 corresponding to deconvolution.

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

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

[0199] Example 38 includes the subject described in Example 25, wherein the color Doppler compound includes combining the respective pixel illuminations, such as between the N input images, based on at least one of the following information for each pixel position: depth, angle, signal-to-noise ratio, flow velocity, or force.

[0200] Example 39 shows the R0 of the output image. out and R1 out R0 out =α·R0 out.max +(1-α)·R0 out.mean and R1 out =α·R1 out.max +(1-α)·R1 out.mean This is given by, where R0 out : Zero-lag output corresponding to the alpha-blended values ​​of the maximum and mean zero-lag autocorrelation; R1 out : First lag output corresponding to the alpha-blended values ​​of the maximum and mean first lag autocorrelation; α: Alpha value of the alpha blending; R0 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 mean 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 corresponding to the alpha blending used, including the subject as described in Example 38.

[0201] Example 40 includes the subject matter described in any one of Examples 21 to 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 fundamental frequency of each of the transmitted ultrasonic waveforms includes one of 1.75 MHz, 3.5 MHz, and 5.0 MHz.

[0202] Example 41 is a method performed in a computing device including a memory and one or more processors coupled to the memory, the method comprising: receiving electrical signals based on the respective reflected frequencies 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 the generation of an output image on a display device based on the compounded electrical signal.

[0203] Example 42 includes the subject matter described in Example 41, wherein each of the reflected frequencies corresponds to each harmonic of the fundamental frequency of the transmitted ultrasonic waveform, and the fundamental frequency is a single frequency of the transmitted ultrasonic waveform.

[0204] Example 43 includes the subject matter described in Example 41, wherein the transmitted ultrasonic waveform is a multimodal waveform having fundamental frequencies corresponding to each of the reflected ultrasonic waveforms.

[0205] Example 44 includes the subject matter of Example 41, wherein the step of compounding the information is a step of implementing a prediction algorithm on the electrical signal; 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 prediction electrical signal for a second image region of the target object that is different from the first image region; and the step of using the prediction electrical signal to obtain the compounded electrical signal such that the output image in the second image region corresponds to the prediction electrical signal.

[0206] Example 45 includes the subject of Example 41, wherein the reflected frequencies comprise N reflected frequencies; the electrical signals comprise 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 comprises a pixel at its respective pixel position, and each pixel position of the N input images is defined by depth and angle; the step of compounding information comprises compounding information from the N sets of electrical signals; and the method further comprises the step of 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 described in Example 45, further comprising the step of subjecting the electrical signal to gain compensation or dynamic range compensation before compounding.

[0208] Example 47 includes the subject matter described in Example 45, wherein the simple averaging involves performing either a simple average or a weighted average of the respective pixel illuminations across the N input images for each pixel position.

[0209] Example 48 includes the subject described in Example 45, wherein alpha blending with depth-adaptive compounding comprises multiplying each pixel illuminance, such as between the N input images, by a corresponding alpha multiplier for each pixel position, where each alpha multiplier is a function of one or more alpha values, and the one or more alpha values ​​are a function of at least one of the depth of each pixel position or the angle of each pixel position.

[0210] Example 49 shows that for each of the pixel positions, the output illuminance in the output image is I out =Ihigh ·α high +(1-α high )·(α mid ·I mid +(1-α mid )·I low ) is given by, where I out This is the output pixel illuminance at each pixel position in the output image; high I is the pixel illuminance at each pixel position of the input image corresponding to the highest of the received frequencies; mid This is the pixel illuminance at each pixel position of the input image corresponding to the intermediate of the received frequencies; low α is the pixel illuminance at each pixel position of the input image corresponding to the lowest of the received frequencies; high α corresponds to the depth-dependent α value of the highest of the received frequencies; and α mid This includes the subject described in Example 48, which corresponds to the depth-dependent α value of the intermediate of the received frequencies.

[0211] In Example 50, for each of the aforementioned pixel positions, the output illuminance in the output image is 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 ) is given by, where r represents depth; θ represents angle; I out (r,θ) is the output pixel illuminance at depth r and image angle θ; high is the pixel illumination at depth r and image angle θ of the highest received frequency; mid This is the pixel illuminance at depth r and image angle θ for the intermediate of the received frequencies; low α is the pixel illumination at depth r and image angle θ of the lowest of the received frequencies; highα corresponds to the alpha value of the highest of the received frequencies at depth r and image angle θ; and α mid This includes the subject described in Example 48, which corresponds to the alpha value at the intermediate of the received frequencies at depth r and image angle θ.

[0212] Example 51 includes the subject described in Example 45, wherein the maximum and minimum adaptive compound uses a blend of the maximum, minimum, and average pixel illuminations, such as between the N input images, for each pixel position.

[0213] Example 52 shows that for each of the pixel positions, the output illuminance in the output image is I out =I max ·α max +(1-α max )·(α min Base I min +(1-α min )·I depth_comp ) is given by, where I max :MAX(I high ,I mid ,I low );I min :MIN(I high ,I mid ,I low );I depth_comp α corresponds to the pixel illumination at the pixel position after alpha blending with depth compensation; max is, I max , I min or I depth_comp Corresponding to the maximum transmittance alpha value coefficient based on at least one of the following, α max has a set first value between 0 and 1, and including 0 and 1; and α min is, I max , I min or I depth_comp Corresponding to the minimum transmittance alpha value coefficient based on at least one of the following, α min This includes the subject described in Example 51, which has a second set value that is between 0 and 1 and different from the first set value that includes 0 and 1.

[0214] Example 53 includes the subject matter described in Example 45, wherein the predictive compound 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 a second region in the second input image based on the relationship.

[0215] Example 54 shows that the pixel illuminance of the pixels in the second region is I FF2 =I FF1 . I NF2 / I NF1 Given by, 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 This is the pixel illuminance in FF2; FF1 This is the pixel illuminance in region FF1; NF2 is the pixel illuminance in region NF2; and I NF1 This includes the subject described in Example 43, which is the pixel illuminance in region NF1.

[0216] Example 55 shows that the pixel illuminance of the pixels in the second region is I FF2 =I NF2 × (I FF1 *PSF2 inverse ) / (I NF1 *PSF1 inverse ) is given by, 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 This is the pixel illuminance in FF2; FF1 This is the pixel illuminance in region FF1; NF2is the pixel illuminance 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 reciprocal of PSF1 corresponding to deconvolution; and PSF1 inverse This includes the subject described in Example 53, which is the reciprocal of PSF1 corresponding to deconvolution.

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

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

[0219] Example 58 includes the subject described in Example 45, wherein the color Doppler compound includes combining the respective pixel illuminations, such as between the N input images, based on at least one of the following pieces of information: depth, angle, signal-to-noise ratio, flow velocity, or force, for each pixel position.

[0220] Example 59 shows the R0 of the output image. out and R1 outR0 out =α·R0 out.max +(1-α)·R0 out.mean and R1 out =α·R1 out.max +(1-α)·R1 out.mean This is given by, where R0 out : Zero-lag output corresponding to the alpha-blended values ​​of the maximum and mean zero-lag autocorrelation; R1 out : First lag output corresponding to the alpha-blended values ​​of the maximum and mean first lag autocorrelation; α: Alpha value of the alpha blending; R0 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 mean 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 corresponding to the alpha blending used, including the subject as described in Example 58.

[0221] Example 60 includes the subject described in 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 fundamental frequency of each of the transmitted ultrasonic waveforms includes one of 1.75 MHz, 3.5 MHz, and 5.0 MHz.

[0222] Example 61 includes an apparatus comprising means for performing a method described in any one of Examples 41 to 60.

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

[0224] Example 63 includes an imaging device comprising the apparatus described in any one of Examples 1 to 20, and further comprising the user interface device.

[0225] Example 64 includes a product comprising one or more tangible computer-readable non-temporary storage media containing computer-executable instructions that, when executed by at least one computer processor, enable at least one processor to perform the method described in any one of Examples 21 to 40.

Claims

1. A computing device comprising memory and one or more processors coupled to the memory, wherein the one or more processors are A plurality of electrical signals are simultaneously received based on the respective reflected frequencies of the reflected ultrasonic waveforms reflected from the target object as a result of the transmitted ultrasonic waveform, wherein the plurality of reflected frequencies comprises N reflected frequencies, and the plurality of electrical signals comprises N sets of the plurality of electrical signals, each set corresponding to one of the plurality of reflected frequencies, and each set of the plurality of electrical signals corresponds to N input images of the target object, with each individual input image comprising pixels at their respective pixel positions; The information from the aforementioned multiple electrical signals is compounded to generate a compounded set of multiple electrical signals, and here compounding is performed as follows: The relationship is determined such that one is a first set of multiple electrical signals corresponding to a first image region of the target object at a first frequency among the multiple reflected frequencies, and the other is a first set of multiple electrical signals corresponding to the first image region of the target object at a second frequency among the multiple reflected frequencies. Using the above relationship, predict the second set of multiple electrical signals corresponding to the second image region of the target object at the second frequency among the multiple reflected frequencies, based on the second set of multiple electrical signals corresponding to the second image region of the target object at the first frequency among the multiple reflected frequencies. including; and Based on the compounded multiple electrical signals, the generation of an output image on the display is triggered. Device.

2. The apparatus according to claim 1, wherein the transmitted ultrasonic waveform is a multimodal waveform having fundamental frequencies corresponding to each of the reflected frequencies of the reflected ultrasonic waveform.

3. The position of each pixel in the N input images is defined by depth and angle; and The one or more processors compound the information from N sets of the plurality of electrical signals by using maximum and minimum adaptive compounds. The apparatus according to claim 2.

4. The one or more processors described above are Implementing a prediction algorithm in the plurality of electrical signals; one or more processors using information from the first plurality of electrical signals corresponding to the first image region of the target object to generate a plurality of predicted electrical signals for a second image region of the target object that is different from the first image region; and Using the plurality of predicted electrical signals, the compounded plurality of electrical signals are acquired such that the output image in the second image region corresponds to the plurality of predicted electrical signals. The information is compounded by The apparatus according to claim 1.

5. The position of each pixel in the N input images is defined by depth and angle; and Compounding information includes compounding information from N sets of the plurality of electrical signals. The apparatus according to any one of claims 1 to 4.

6. The apparatus according to claim 5, wherein one or more processors compound the information from N sets of the plurality 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.

7. The apparatus according to claim 6, wherein the one or more processors further cause the plurality of electrical signals to undergo gain compensation or dynamic range compensation before compounding.

8. The apparatus according to claim 6, wherein one or more processors compound the information from the N sets of electrical signals by using simple averaging, which includes performing either simple averaging or weighted averaging of the respective pixel illuminations across the N input images for each pixel position.

9. The apparatus according to claim 6, wherein the one or more processors compound the information from the N sets of electrical signals by using alpha blending with depth-adaptive compounding, which includes multiplying each pixel illuminance, such as between the N input images, by a corresponding alpha multiplier for each pixel position, each alpha multiplier being a function of one or more alpha values, and each of the one or more alpha values ​​being a function of at least one of the depth of each pixel position or the angle of each pixel position.

10. The apparatus according to claim 6, wherein one or more processors compound the information from the N sets of electrical signals by using a maximum and minimum adaptive compound, which includes using a blend of the maximum, minimum and average pixel illuminations, such as between the N input images, for each pixel position.

11. The one or more processors described above are Determining the relationship between a first plurality of electrical signals corresponding to a first region in a first input image corresponding to a first reflected frequency and a second plurality of electrical signals corresponding to the first region in a second input image corresponding to a second reflected frequency; and Predicting a third set of electrical signals corresponding to a second region of the second input image based on the aforementioned relationship. The information from the N sets of the plurality of electrical signals is compounded by using a predictive compound, including The apparatus according to claim 6.

12. The apparatus according to claim 6, wherein the one or more processors compound the information from the N sets of electrical signals by using a color Doppler compound, which includes combining the respective pixel illuminations, such as between the N input images, based on at least one of the following information: depth, angle, signal-to-noise ratio, flow velocity, or force, for each pixel position.

13. The apparatus according to any one of claims 1 to 4, wherein each of the reflected frequencies includes 1.75 MHz, 3.5 MHz, and 5.0 MHz, and each of the fundamental frequencies of the transmitted ultrasonic waveform includes 1.75 MHz, 3.5 MHz, and 5.0 MHz.

14. A user interface device having a display device; and A computing device is communicably coupled to the user interface device, and the computing device has a memory and one or more processors coupled to the memory. Equipped with, The one or more processors described above are A plurality of electrical signals are simultaneously received based on the respective reflected frequencies of the reflected ultrasonic waveforms reflected from the target object as a result of the transmitted ultrasonic waveform, wherein the plurality of reflected frequencies comprises N reflected frequencies, and the plurality of electrical signals comprises N sets of the plurality of electrical signals, each set of which corresponds to one of the plurality of reflected frequencies, and each set of the plurality of electrical signals corresponds to N input images of the target object, each input image comprising pixels at their respective pixel positions; and The information from the aforementioned multiple electrical signals is compounded to generate a compounded set of multiple electrical signals, and here compounding is performed as follows: The relationship is determined such that one is a first set of multiple electrical signals corresponding to a first image region of the target object at a first frequency among the multiple reflected frequencies, and the other is a first set of multiple electrical signals corresponding to the first image region of the target object at a second frequency among the multiple reflected frequencies. Using the above relationship, predict the second set of multiple electrical signals corresponding to the second image region of the target object at the second frequency among the multiple reflected frequencies, based on the second set of multiple electrical signals corresponding to the second image region of the target object at the first frequency among the multiple reflected frequencies. including; and Based on the compounded multiple electrical signals, the generation of an output image is triggered on the display device. system.

15. The transmitted ultrasonic waveform is a multimodal waveform having fundamental frequencies corresponding to each of the reflected ultrasonic waveforms; The position of each pixel in the N input images is defined by depth and angle; and The one or more processors compound the information from N sets of the plurality of electrical signals by using maximum and minimum adaptive compounds. The system according to claim 14.

16. The system according to claim 15, wherein one or more processors compound the information from the N sets of electrical signals by using the maximum and minimum adaptive compound, which includes using a blend of the maximum, minimum and average pixel illuminations, such as between the N input images, for each pixel position.

17. The system according to claim 14, wherein the one or more processors compound the information from the N sets of electrical signals by using alpha blending with depth-adaptive compounding, which includes multiplying each pixel illuminance, such as between the N input images, by a corresponding alpha multiplier for each pixel position, each alpha multiplier being a function of one or more alpha values, and the one or more alpha values ​​being a function of at least one of the depth of each pixel position or the angle of each pixel position.

18. The system according to any one of claims 14 to 17, wherein the one or more processors further cause the plurality of electrical signals to undergo gain compensation or dynamic range compensation before compounding.

19. A method performed by a computing device including memory and one or more processors coupled to the memory, The step of one or more processors simultaneously receiving a plurality of electrical signals based on the respective reflected frequencies of reflected ultrasonic waveforms reflected from a target object as a result of an ultrasonic waveform transmitted by an imaging device physically separated from the computing device, wherein the plurality of reflected frequencies comprises N reflected frequencies, the plurality of electrical signals comprises N sets of a plurality of electrical signals, each set of which corresponds to one of the plurality of reflected frequencies, the N sets of the plurality of electrical signals each correspond to N input images of the target object, and each input image comprises pixels at their respective pixel positions; The step of one or more processors compounding information from the plurality of electrical signals to generate a plurality of compounded electrical signals, wherein the step of compounding the information is: The step of determining a relationship in which one is a first set of multiple electrical signals corresponding to a first image region of the target object at a first frequency among the multiple reflected frequencies, and the other is a first set of multiple electrical signals corresponding to the first image region of the target object at a second frequency among the multiple reflected frequencies, A step of using the above relationship to predict a second set of multiple electrical signals corresponding to a second image region of the target object at a second frequency among the multiple reflected frequencies, based on a second set of multiple electrical signals corresponding to a second image region of the target object at a first frequency among the multiple reflected frequencies. It has; and The step in which one or more processors cause the generation of an output image on a display device based on the compounded plurality of electrical signals. A method that includes [a certain feature].

20. The step of compounding the aforementioned information is: In the step of implementing a prediction algorithm for the plurality of electrical signals, one or more processors use information from the first plurality of electrical signals corresponding to the first image region of the target object to generate a plurality of predicted electrical signals for a second image region of the target object that is different from the first image region; and The step of acquiring the compounded electrical signals using the plurality of predicted electrical signals such that the output image in the second image region corresponds to the plurality of predicted electrical signals. Having, The method according to claim 19.

21. The transmitted ultrasonic waveform is a multimodal waveform having fundamental frequencies corresponding to each of the reflected ultrasonic waveforms; The position of each pixel in the N input images is defined by depth and angle; and The step of compounding the information includes compounding information from N sets of the plurality of electrical signals by using maximum and minimum adaptive compounds. The method according to claim 19.

22. The method according to claim 19, wherein the step of compounding the information comprises compounding the information from N sets of the plurality 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.

23. The method according to claim 22, further comprising the step of causing one or more processors to subject the plurality of electrical signals to gain compensation or dynamic range compensation before the step of compounding the information.

24. The method according to claim 22, wherein the step of compounding the information from the N sets of the plurality of electrical signals is to compound the information from the N sets of the plurality of electrical signals by using simple averaging, which includes performing either simple averaging or weighted averaging of the respective pixel illuminations across the N input images for each pixel position.

25. The method according to claim 22, wherein the step of compounding the information from the N sets of the plurality of electrical signals comprises compounding the information from the N sets of the plurality of electrical signals by using alpha blending with depth adaptive compounding, which includes multiplying each pixel illuminance, such as between the N input images, by a corresponding alpha multiplier for each pixel position, where each alpha multiplier is a function of one or more alpha values, and the one or more alpha values ​​are a function of at least one of the depth of each pixel position or the angle of each pixel position.

26. The method according to claim 22, wherein the step of compounding the information from the N sets of the plurality of electrical signals is to compound the information from the N sets of the plurality of electrical signals by using a maximum and minimum adaptive compound, which includes using a blend of the maximum, minimum and average pixel illumination, such as between the N input images, for each pixel position.

27. The step of compounding the information from N sets of the plurality of electrical signals is: Determining the relationship between a first plurality of electrical signals corresponding to a first region in a first input image corresponding to a first reflected frequency and a second plurality of electrical signals corresponding to the first region in a second input image corresponding to a second reflected frequency; and Predicting a third set of electrical signals corresponding to a second region of the second input image based on the aforementioned relationship. The step includes compounding the information from N sets of the plurality of electrical signals by using a predictive compound, The method according to claim 22.

28. To predict, Determining the first region to correspond to the short-range region; Determining the second region to correspond to the long-range region; Dividing the aforementioned short-range region into multiple sub-regions; To generate a training dataset based on a plurality of first input images and a plurality of first output images in the aforementioned near-field region; Developing an ML-based model for the relationship between the pixel illuminance at the first reflected frequency in the first region and the pixel illuminance at the second reflected frequency in the first region, based on the training dataset; and Predicting the pixel illuminance at the second reflected frequency in the second region based on the model. Includes machine learning (ML) based compounds, The method according to claim 27.

29. The method according to claim 22, wherein the step of compounding the information from the N sets of the plurality of electrical signals is to compound the information from the N sets of the plurality of electrical signals by using a color Doppler compound, which includes combining the respective pixel illuminations, such as between the N input images, based on at least one of the following information: depth, angle, signal-to-noise ratio, flow velocity, or force, for each pixel position.

30. An apparatus comprising means for performing the method described in any one of claims 19 to 29.

31. A computer program comprising a plurality of stored instructions that, when executed, cause one or more processors to perform the method according to any one of claims 19 to 29.

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