KR20240135413A - Device, system and method for generating an output ultrasound image by synthesizing signals of each received ultrasound frequency - Google Patents

Abstract

A device, a method and a computer-implemented medium. The device simultaneously receives electrical signals based on respective reflection frequencies of reflected ultrasonic waves reflected from a target object as a result of a transmitted ultrasonic wave; generates synthesized electrical signals by synthesizing information from the electrical signals; and generates an output image on a display based on the synthesized electrical signals.

Description

Device, system and method for generating an output ultrasound image by synthesizing signals of each received ultrasound frequency

The embodiments relate generally to the field of signal processing for imaging devices, and signal processing for ultrasound imaging devices or probes, such as those including micromachined ultrasound transducers (MUTs).

Ultrasound imaging is widely used in medicine and non-destructive testing.

An ultrasound imaging probe or ultrasound imaging device typically comprises an array of many individual ultrasound transducers (pixels) that are used to emit and receive acoustic energy toward a target to be imaged. The reflected waveform is received by the transducer (e.g., a micromachined ultrasound transducer), converted into an electrical signal, and further signal processed to produce an image. The fluid velocity and direction of fluid flow (e.g., relative to blood flow) may also be measured or detected by ultrasound and presented visually to an ultrasound imaging device operator. Such quantification and visualization of anatomical structures and movements may be used in a variety of medical diagnostic applications and in support of other medical procedures.

The image quality of images acquired using ultrasound imaging devices can sometimes make it difficult to distinguish anatomical features with sufficient certainty. Mechanisms are needed to improve the image quality acquired by ultrasound imaging devices.

The ultrasound imaging device of some embodiments may operate in accordance with one or more sets of instructions, collectively or individually, utilizing algorithms to assist in obtaining ultrasound images whose quality is more reliable than those of the prior art.

The novel features of the present invention are set forth with particularity in the appended claims. A better understanding of the features and advantages of some embodiments will be obtained by reference to the following detailed description, which sets forth illustrative embodiments in which the principles of the present invention are utilized, and the accompanying drawings (also referred to as “drawings” and “drawings” herein).
FIG. 1 is a block diagram of an imaging device having optionally changeable characteristics according to disclosed embodiments.
FIG. 2 is a diagram of an imaging system having optionally changeable characteristics according to disclosed embodiments.
FIG. 3 is a schematic diagram of an imaging device having optionally changeable characteristics according to disclosed embodiments.
Figure 4 illustrates a receiving channel according to an example of the principles described herein.
Figure 5 is a series of graphs plotting power in decibels (DB) versus frequency in megahertz (MHz) for received ultrasound waveforms at different frequencies and different penetration depths of the imaging target.
Figure 6 illustrates a set of four individual ultrasound images of a target object, where the output image (D) is obtained by simple averaging of the pixel irradiances of the images (AC), each of which corresponds to a different reflection frequency.
Figure 7 illustrates a set of four individual ultrasound images of the target object, where the output image (D) is obtained through alpha blending using depth adaptive compounding (DAC) of the pixel irradiances of the images (AC) corresponding to the three individual reflection frequencies.
Figure 8 is an example of a plot of α value versus depth for alpha blending using a DAC.
Figure 9a shows a plot of voltage versus time in seconds for multimode pulses using fundamental frequencies to transmit pulses of 1.75 MHz, 3.5 MHz, and 5.1 MHz.
Figure 9b shows a plot of the frequency distribution for the transmitted ultrasonic waveform of Figure 9a, plotting power in DB units versus frequency in Hz.
Figure 10 is a set of images similar to those of Figure 6, but using the pulses of Figures 9a and 9b to generate images (AC), and also using simple compositing to generate a composite image (D).
Figure 11 is a set of images similar to those in Figure 6, but utilizing alpha blending using a DAC of multi-mode pulses as shown in Figures 9a and 9b.
Figure 12 shows two output images D1 and D2, where image D1 is obtained using alpha blending with a DAC on three receive frequencies similar to those in Figure 6, and D2 is obtained using DR compensation on the same three receive frequencies.
Figure 13 shows a set of three output images D1, D2 and D3, where image D1 is obtained using simple synthesis on three receive frequencies similar to those in Figure 6, D2 is obtained using alpha blending with a DAC on the same three receive frequencies and D3 is obtained using adaptive synthesis.
Figure 14 illustrates a flow chart of a process according to some embodiments.

Some embodiments relate to a computing device, a computer-readable storage medium, a method and a system. The computing device simultaneously receives respective electrical signals based on respective frequencies corresponding to reflected ultrasonic waves reflected from a target object that has experienced a transmitted ultrasonic wave; synthesizes information from the respective electrical signals to generate synthesized electrical signals; and generates an image on a display based on the synthesized electrical signals.

In some embodiments, each frequency is a harmonic of the fundamental frequency, the harmonic corresponding to the reflected ultrasonic waveform, and the transmitted ultrasonic waveform is at the fundamental frequency. In such embodiments, there is a single transmitting frequency, the fundamental frequency, for example, 1.75 MHz.

In some embodiments, each frequency is a respective frequency of the reflected ultrasound waveform, and the transmitted ultrasound waveform is a multi-mode waveform having a fundamental frequency corresponding to each frequency of the reflected ultrasound waveform. In this embodiment, the transducer elements of the transducer array of the ultrasound imaging device can be multi-mode, as described in more detail below. Thus, the transducer elements generate the transmitted ultrasound waveform based on a plurality of different fundamental frequencies, such as, for example, 1.75 MHz fundamental, 3.5 MHz fundamental, and 5.0 MHz fundamental.

In some embodiments, the device further implements a prediction algorithm that generates predictive electrical signals for a second region of the target object, the second region being different from the first region, using information from the electrical signals corresponding to the first region of the target object; and generates an image on the display based on combined electrical signals that are a combination of the synthesized electrical signals and the predictive electrical signals.

The synthesis according to some embodiments may include one or more of simple averaging, weighted averaging, alpha blending using depth adaptive synthesis, gain-compensated synthesis, adaptive synthesis, predictive synthesis, lateral frequency synthesis and Doppler synthesis, including any combination thereof. These synthesis methods are described in more detail below.

Advantageously, some embodiments provide an algorithm that allows for the generation of ultrasound images that blend the benefit of better image resolution at deeper target object locations associated with lower frequencies of the reflected ultrasound waveforms with the benefit of better resolution at shallower penetrations associated with higher frequencies of the reflected ultrasound waveforms. The latter allows for the generation of images exhibiting improved image resolution at various depths of the target object.

Some existing solutions utilize tissue harmonic imaging (TiHI). In TiHI, a transducer generates a transmitted ultrasound waveform at a single fundamental frequency (1.5 MHz), and the device processes a reflected ultrasound waveform at one harmonic of the fundamental frequency of the transmitted ultrasound waveform, typically at twice the fundamental frequency (3.0 MHz).

Some existing solutions use synthetic harmonic imaging (CHI).

In CHI, the transducer generates separate transmitted ultrasonic waveforms, each at a given fundamental frequency (3.0 MHz and 3.5 MHz), at separate times, and the device processes two corresponding reflected ultrasonic waveforms at the second harmonic of each of the fundamental frequencies. The processing includes synthesizing the two received signals, and may include some form of alpha blending, but since the transmissions include multiple transmissions at different times, the synthesis is not based on a single reflected ultrasonic waveform.

Reference is now made to FIGS. 1-4 which illustrate devices and circuits that may be utilized to implement some embodiments as described herein. A specific discussion of the embodiments is further provided below in the context of FIGS. 5-14.

Some embodiments relate to imaging devices, and more particularly to electronically configurable ultrasound imaging devices. Ultrasonic imaging devices can be used to noninvasively image internal tissues, bones, blood flow, or organs of human or animal bodies. The images can then be displayed. To perform ultrasound imaging, an ultrasound imaging device transmits an ultrasound signal into the body and receives a reflected signal from the body part being imaged. Such an ultrasound imaging device may include a transducer and associated electronics, which may be referred to as a transceiver or an imager and may be based on the opto-acoustic or ultrasonic effect. Such transducers may be used for imaging, and may also be used in other applications. For example, the transducers may be used in medical imaging; flow measurement in pipes, speakers, and microphone arrays; lithotripsy; localized tissue heating for treatment; and high-intensity focused ultrasound (HIFU) surgery.

Additional aspects and advantages of some embodiments will become readily apparent to those skilled in the art from the present detailed description, wherein only exemplary embodiments are shown and described. As will be appreciated, some embodiments may achieve other and different ends, and their several details may be modified in various obvious respects, all without departing from the teachings of the present disclosure. Accordingly, the drawings and description are to be regarded in an illustrative rather than a restrictive sense.

Traditionally, imaging devices, such as ultrasound imagers used in medical imaging, utilize piezoelectric (PZT) materials or other piezoelectric ceramic and polymer composites. These imaging devices may include a housing for housing the transducers from the PZT material, as well as other electronics for forming the image and displaying it on a display unit. To manufacture bulk PZT elements or transducers, a thick slab of piezoelectric material may be cut into large rectangular PZT elements. These rectangular PZT elements can be expensive to manufacture because the manufacturing process typically involves precisely cutting rectangular thick PZT or ceramic pieces and mounting them on substrates at precise spacings. Additionally, the impedance of the transducers is much higher than the impedance of the transmit/receive electronics for the transducers, which can affect performance.

Additionally, these thick bulk PZT elements may require very high voltage pulses, for example, more than 100 volts (V), to generate the transmitted signals. These high drive voltages result in high power consumption, since power consumption in the transducers is proportional to the square of the drive voltage. This high power consumption generates heat within the imaging device, thus requiring cooling arrangements. These cooling systems increase the manufacturing cost and weight of the imaging devices, making them more burdensome to operate.

Moreover, the transmit/receive electronics for the transducers may be located remotely from the transducers themselves, thus requiring micro-coaxial cables between the transducers and the transmit/receive electronics. Typically, the cables are of precise length for delay and impedance matching, and quite often, additional impedance matching networks are utilized for efficient connection of the transducers to the electronics via the cables.

Some embodiments may be utilized in the context of imaging devices utilizing piezoelectric micromachined ultrasound transducer (pMUT) or capacitive micromachined ultrasound transducer (cMUT) technologies, as described in more detail herein.

Typically, MUTs, such as both cMUTs and pMUTs, contain a diaphragm (a thin membrane attached to its edges or at some point inside the probe), whereas a “traditional” bulk PZT element is typically constructed as a solid piece of material.

Piezoelectric micromachined ultrasound transducers (pMUTs) can be efficiently formed on a substrate utilizing a variety of semiconductor wafer fabrication operations. Semiconductor wafers can currently be 6-inch, 8-inch, and 12-inch in size and can house arrays of hundreds of transducers. These semiconductor wafers begin as a silicon substrate on which various processing operations are performed. An example of such an operation is the formation of SiO2 layers, also known as insulating oxides. Various other operations, such as the addition of metal layers to act as interconnects and bond pads, are performed to allow connection to other electronic devices. Another example of a mechanical operation is the etching of cavities. Compared to conventional transducers having bulky piezoelectric materials, pMUT elements built on semiconductor substrates are smaller, less expensive to manufacture, and have simpler and higher performance interconnects between the electronics and the transducers. Thus, they offer greater flexibility in the operating frequency of the imaging device utilizing the same and the potential for producing higher quality images.

In some embodiments, the imaging device is coupled to an application-specific integrated circuit (ASIC) that includes a transmit driver, a sensing circuit for the received echo signal, and control circuitry for controlling various operations. The ASIC may be formed on another semiconductor wafer. The ASIC may be placed in close proximity to the pMUT or cMUT elements to reduce parasitic losses. As a specific example, the ASIC may be less than 50 micrometers (μm) from the transducer array. In a broader example, there may be less than 100 μm of separation between two wafers or two dies, where each wafer includes a number of dies, the dies including transducers within the transducer wafer and ASICs within the ASIC wafer. In some embodiments, the ASIC has dimensions that match the pMUT or cMUT array, allowing the devices to be stacked for wafer-to-wafer interconnection or transducer die-to-ASIC die interconnection on the ASIC wafer. Alternatively, the transducer can also be developed on top of the ASIC wafer using low temperature piezoelectric material sputtering and other low temperature processes that are compatible with ASIC processing.

In one embodiment, anywhere in the ASIC and transducer interconnect, the two may have similar footprints. More specifically, in the latter embodiment, the footprint of the ASIC may be an integer multiple or divisor of the footprint of the MUT.

Regardless of whether the imaging device is based on a pMUT or a cMUT, the imaging device according to some embodiments may include multiple transmit channels and multiple receive channels. The transmit channels drive the transducer elements with voltage pulses at a frequency to which the transducer elements respond. This causes an ultrasound wave to be emitted from the elements, which wave will be directed toward a subject to be imaged (a target subject), such as an organ or other tissue within the body. In some examples, the imaging device having an array of transducer elements may be in mechanical contact with the body using a gel between the imaging device and the body. The ultrasound wave travels toward the subject, i.e., the organ, and a portion of the wave is reflected back to the transducer elements in the form of received/reflected ultrasound energy, which may be converted into electrical energy within the imaging device. The received ultrasound energy may then be further processed by the multiple receive channels to convert the received ultrasound energy into electrical signals, which may be processed by other circuitry to develop an image of the subject for display based on the electrical signals.

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

An imaging device incorporating the features of the embodiments may advantageously reduce or resolve the problems.

In an embodiment, the imaging device may include a handheld casing housing the transducers and associated electronic circuits, such as control circuitry and optionally a computing device. The imaging device may also include a battery for powering the electronic circuits.

Accordingly, some embodiments relate to a portable imaging device utilizing 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.

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

One of ordinary skill in the art will recognize that (1) certain manufacturing operations may be selectively performed; (2) the operations may not be limited to the specific order presented herein; and (3) certain operations may be performed in different orders, including concurrently.

The elements/components depicted in the drawings illustrate exemplary embodiments and are intended to avoid obscuring the present disclosure. Reference herein to “one example,” “a preferred example,” “an example,” “examples,” “an embodiment,” “some embodiments,” or “embodiments” means that a particular feature, structure, characteristic, or function described in connection with the example is included in at least one example of the present disclosure and may be present in more than one example. The appearances of the phrases “in one example,” “in an example,” “in examples,” “in an embodiment,” “in some embodiments,” or “in embodiments” in various places throughout the specification are not necessarily all referring to the same example or examples. The terms “include,” “including,” “comprise,” and “comprising” are to be understood as open-ended terms, and that any list that follows is an example and is not intended to be limited to the recited items. Any headings used herein are for organizational purposes only and should not be used to limit the scope of the description or claims. Additionally, the use of specific terms in various places in this specification is for illustrative purposes only and should not be construed as limiting.

Referring now to the drawings, FIG. 1 is a block diagram of an imaging device (100) having a controller or control circuit (106) for controlling selectively changeable channels (108, 110) according to the principles described herein and having imaging computations performed on a computing device (112). As described above, the imaging device (100) may be used to generate images of internal tissues, bones, blood flow, or organs of human or animal bodies. Accordingly, the imaging device (100) may transmit signals into the body and receive reflected signals from the body part being imaged. Such imaging devices may include pMUTs or cMUTs, which may be referred to as transducers or imagers, which may be based on opto-acoustic or ultrasonic effects. The imaging device (100) may be used to image other objects as well. For example, the imaging device may be used for: medical imaging; flow measurement in pipes, speakers, and microphone arrays; lithotripsy; localized tissue heating for treatment; and can be used in high intensity focused ultrasound (HIFU) surgery.

In addition to use in human patients, the imaging device (100) can also be used to obtain images of internal organs of animals. In addition to imaging internal organs, 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.

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-scan, two-dimensional imaging, also known as B-scan, three-dimensional imaging, also known as C-scan, and Doppler imaging (i.e., determining motion, such as fluid flow, within a blood vessel using Doppler ultrasound). The imaging device (100) can be switched between different imaging modes, including but not limited to linear mode and sector mode, and can be electronically configured under program control.

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

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

To generate the 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 an array of transducers (102), i.e., transducer elements (104), with voltage pulses at a frequency to which they respond. This causes an ultrasound waveform to be emitted from the transducer elements (104) toward the object to be imaged.

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

The ultrasound wave travels toward the object to be imaged, and a portion of the wave is reflected back to the transducer (102), which converts it into electrical energy via the piezoelectric effect. The receiving channels (110) collect the electrical energy thus obtained, process it, and transmit it to a computing device (112) that, for example, develops or generates an image that can be displayed.

In some examples, the number of transmit channels (108) and receive channels (110) within the imaging device (100) may remain constant, while the number of transducer elements (104) to which they are coupled may vary. In one embodiment, the coupling of the transmit and receive channels to the transducer elements may be controlled by the control circuitry (106). In some examples, for example, as illustrated in FIG. 1 , the control circuitry may include the transmit channels (108) and receive channels (110). For example, the transducer elements (104) of the transducer (102) may be formed as a two-dimensional spatial array having N columns and M rows. In a particular example, the two-dimensional array of transducer elements (104) may have 128 columns and 32 rows. In this example, the imaging device (100) can 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) can be coupled to multiple or a single pixel (104). For example, depending on the imaging mode (e.g., linear mode where multiple transducers transmit ultrasound in the same spatial direction, or sector mode where multiple transducers transmit ultrasound in different spatial directions), each row of transducer elements (104) can be coupled to a single transmit channel (108) and a single receive channel (110). In this example, the transmit channel (108) and receive channel (110) can receive composite signals that combine the signals received from each transducer element (104) within 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, a transducer element (104) may be coupled to both the transmit channel (108) and the receive channel (110). For example, a transducer element (104) may be adapted to generate and transmit an ultrasound pulse and then detect an echo of that pulse in the form of converting the reflected ultrasound energy into electrical energy.

The control circuit (106) may be implemented as any circuit or circuits configured to perform the functions described herein. For example, the control circuit (106) may be implemented as, or may include in any other manner, an application-specific integrated circuit (ASIC), a field programmable gate array (FPGA), a system-on-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, and the like.

The exemplary computing device (112) may be implemented as any suitable computing device including any suitable components, such as one or more processors, memory circuits, communication circuits, a battery, a display, and the like. In one embodiment, the computing device (112) may be integrated into a single package or single chip, or a single system on a chip (SoC), along with the control circuitry (106), the transducers (102), and the like, for example, as suggested in the embodiment of FIG. 1. In other embodiments, some or all of the computing devices may be within a separate package from the control circuitry and the transducers, and the like, for example, as suggested in the embodiment of FIG. 2, as described in more detail below.

Each transducer element may have any suitable shape, such as square, rectangular, oval, or circular. The transducer elements may be arranged in a two-dimensional array with orthogonal directions, such as N columns and M rows, as described herein, or may be arranged in an asymmetric (or staggered) rectilinear array.

The transducer elements (104) may have associated transmit driver circuits for associated transmit channels, and low noise amplifiers for associated receive channels. Thus, a transmit channel may include transmit drivers, and a receive channel may include one or more low noise amplifiers. For example, although not explicitly shown, each of the transmit and receive channels may include multiplexing and address control circuitry that allows particular transducer elements or sets of transducer elements to be activated, deactivated, or placed into a low power mode. It is understood that the transducers may be arranged in patterns other than orthogonal rows and columns, such as in a circular fashion, or in other patterns based on the range of ultrasound waveforms to be generated therefrom.

FIG. 2 is a diagram of an imaging environment including an imaging system having optionally configurable features according to an embodiment. The imaging system of FIG. 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 described in more detail below.

As illustrated in FIG. 2, the computing device (216) may, according to one embodiment, and unlike the embodiment of FIG. 1, be physically separate from the imaging device (220). For example, the computing device (216) and the display device (220) may be disposed within a separate device (in this context, the computing system (222) depicted is physically separate from the imaging device (202) during operation) relative to the components of the imaging device (202). The computing system (222) may include a mobile device, such as a cell phone or tablet, or a stationary computing device capable of displaying images to a user. In another example, as illustrated in FIG. 1 , the display device, the computing device, and the associated display may be part of the imaging device (202) (now illustrated). That is, the imaging device (100), the computing device (216), and the display device (220) may be disposed within a single housing.

The "computing device" referred to herein may, in some embodiments, be configured to generate signals to cause at least one of: causing an image of a subject to be displayed on a display, or causing information about the image to be communicated to a user.

As illustrated, the imaging system includes an imaging device (202) configured to generate and transmit pressure waves (210) toward a subject, such as a heart (214), via transmit channels (FIG. 1, 108) in a transmit mode/process. The internal organ or other subject to be imaged may reflect a portion of the pressure waves (210) toward the imaging device (202), and the imaging device may receive the reflected pressure waves via a transducer (such as the transducer (102) of FIG. 1), receive channels (FIG. 1, 110), and control circuitry (FIG. 1, 106). The transducer may generate an electrical signal based on the received ultrasound energy in the receive mode/process. The transmit mode or receive mode may be applicable in the context of imaging devices that may be configured to either transmit or receive, but at different times. However, as previously noted, some imaging devices according to embodiments may be adapted to be in both the transmit mode and the receive mode simultaneously. The system also includes a computing device (216) that communicates with the imaging device (100) via a communications channel, such as the illustrated wireless communications channel (218), although embodiments also encompass wired communications between the computing system and the imaging device. The imaging device (100) can communicate signals to the computing device (216), which can have one or more processors that process the received signals to complete the formation of an image of the subject. A display device (220) of the computing system (222) can then display the images of the subject using the signals from the computing device. The computing system can additionally convey information to the user regarding the defective pixel, as noted above.

An imaging device according to some embodiments may include a handheld device adapted to communicate signals with a computing device, either wirelessly (using a wireless communication protocol such as IEEE 802.11 or Wi-Fi protocols, Bluetooth protocols including Bluetooth Low Energy, mmWave communication protocols, or any other wireless communication protocol within the knowledge of one of ordinary skill in the art), or via a wired connection such as a cable (such as USB2, USB 3, USB 3.1, and USB-C) or interconnections on a microelectronic device. For a tethered or wired connection, the imaging device may include a port, as described in more detail with respect to FIG. 3 , for receiving a cable connection of a cable in communication with the computing device. For a wireless connection, the imaging device (100) may include a wireless transceiver for communicating with the computing device (216).

It should be appreciated that in various embodiments, different aspects of the present disclosure may be performed in different components. For example, in one embodiment, the imaging device may include circuitry (e.g., channels) that cause ultrasound waveforms to be transmitted and received through its transducers, while the computing device may be adapted to generate ultrasound waveforms in the transducer elements of the imaging device using voltage signals, and further control such circuitry for processing the received ultrasound energy.

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

As illustrated in FIG. 3, the imaging device (300) may include a handheld casing (331) housing the transducers (302) and associated electronics. The imaging device may also include a battery (338) for powering the electronics. Thus, FIG. 3 illustrates an embodiment of a portable imaging device capable of 2D and 3D imaging using a 2D array of pMUTs, optionally built on a silicon wafer. Such an array coupled to an application-specific integrated circuit (ASIC) (106) having an electronic configuration of specific parameters enables higher quality image processing at a lower cost than previously possible. Furthermore, by controlling specific parameters, such as the number of channels utilized, power consumption can be varied and temperature can be varied.

The imaging device (300) according to some embodiments is configured to allow for 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 data set of one or more pixels of a transducer array of the imaging device to a baseline pixel performance data set of the same pixels, as described in more detail below.

Now looking at FIG. 3 in more detail, FIG. 3 is a schematic diagram of an imaging device (300) having optionally adjustable features, according to some embodiments. The imaging device (300) may be similar to the imaging device (100) of FIG. 1 or the imaging device (202) of FIG. 2, for example only. As described above, the imaging device may comprise an ultrasound medical probe. FIG. 3 illustrates transducer(s) (302) of the imaging device (300). As described above, the transducer(s) (302) may comprise arrays of transducer elements (FIG. 1, 104) adapted to transmit and receive pressure waves (FIG. 2, 210). In some examples, the imaging device (300) may include a coating layer (322) that acts as an impedance matching interface between the transducers (302) and the human body or other mass or tissue through which the pressure waves (FIG. 2, 210) are transmitted. In some cases, the coating layer (322) may act as a lens when designed with a curvature that matches a desired focal length.

The imaging device (300) may be implemented in any suitable form factor. In some embodiments, a portion of the imaging device (300), including the transducers (302), may extend outward from the remainder of the imaging device (100). The imaging device (300) may be implemented as any suitable ultrasound medical probe, such as a convex array probe, a micro-convex array probe, a linear array probe, an endovaginal probe, an endorectal probe, a surgical probe, an intraoperative probe, or the like.

In some embodiments, a user can apply a gel on the skin of a living body prior to direct contact with the coating layer (322) so that impedance matching at the interface between the coating layer (322) and the body can be improved. Impedance matching reduces loss of pressure waves (FIG. 2, 210) at the interface and loss of reflected waves traveling toward the imaging device (300) at the interface.

In some examples, the coating layer (322) may be a flat layer to maximize transmission of acoustic signals from the transducer(s) (102) to the body and vice versa. The thickness of the coating layer (322) may be 1/4 wavelength of the pressure wave (FIG. 2, 210) to be generated by the transducer(s) (102).

The imaging device (300) also includes control circuitry (106), optionally in the form of an application-specific integrated circuit (ASIC chip or ASIC), for controlling the transducers (102). The control circuitry (106) may be coupled to the transducers (102) via, for example, bumps. As described above, the transmit channels (108) and receive channels (110) may be optionally changeable or adjustable, meaning that the number of transmit channels (108) and receive channels (110) that are active at a given time may be varied, such that, for example, one or more pixels that are determined to be defective are not utilized. For example, the control circuitry (106) may be adapted to selectively adjust the transmit channels (108) and receive channels (110) based on which pixels are to be tested for defects and/or based on which pixels are determined to be defective.

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

The imaging device may also include one or more processors (326) for controlling components of the imaging device (100). The one or more processors (326), in addition to the control circuitry (106), may be configured to perform at least one of: controlling activation of the transducer elements, processing electrical signals based on reflected ultrasonic waves from the transducer elements, or generating signals that cause generation of an image of an object being imaged by one or more processors of a computing device, such as the computing device (112) of FIG. 1 or the computing device (216) of FIG. 2. The one or more processors (326) may be further adapted to perform other processing functions associated with the imaging device. The one or more processors (326) may be implemented as any type of processors (326). For example, the one or more of the processors (326) may be implemented as a single or multi-core processor(s), a single or multi-socket processor, a digital signal processor, a graphics processor, a neural network computation engine, an image processor, a microcontroller, a field programmable gate array (FPGA), or other processor or processing/control circuitry. The imaging device (100) may also include circuit(s) (328), such as an Analog Front End (AFE) for processing/conditioning the signals, and an acoustic absorbent layer (330) for absorbing waves generated by the transducers (102) and propagating toward the circuits (328). That is, the transducer(s) (102) may be mounted on the substrate and attached to the acoustic absorbent layer (330). This layer absorbs any ultrasound signals that are emitted in the reverse direction (i.e., away from the coating layer (322) toward the port (334), which may otherwise be reflected and interfere with the quality of the image. Although FIG. 3 illustrates an acoustic absorbing layer (330), this component may be omitted in cases where other components prevent the material from transmitting ultrasound in the reverse direction.

The analog front end (328) may be implemented as any circuit or circuits configured to interface with other components of the imaging device, such as the control circuit (106) and the processor (326). For example, the analog front end (328) may include one or more digital-to-analog converters, one or more analog-to-digital converters, one or more amplifiers, and the like.

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

In some examples, the imaging device (100) may include a battery (338) to power components of the imaging device (100). The battery (338) may also include battery charging circuits, which may be wireless or wired charging circuits (not shown). The imaging device may include a gauge that is used to indicate consumed battery charge and to configure the imaging device to optimize power management for improved battery life. Additionally or alternatively, in some embodiments, the imaging device may be powered by an external power source, such as by plugging the imaging device into a wall outlet.

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

The LNA (456) converts the charge within the transducer into a voltage output and also amplifies the received echo signal. A switch (transmit/receive switch) connects the LNA (456) to the transducer element (104) in receive operation mode.

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

Therefore, a larger gain is utilized for compensation, which is implemented by the TGA. The bandpass filter (460) operates to filter out noise and out-of-band signals. An analog-to-digital converter (ADC) (462) digitizes the analog signal to convert the signal into the digital domain so that further processing can be done digitally. Data from the ADC (462) is then digitally processed in a 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 in the FPGA. The demodulation unit frequency-shifts the carrier signal to baseband with two orthogonal components (I and Q) for further digital processing. In some examples, 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, as the ADC (462) is repeatedly turned off and on, there is little or no need to have latency so as not to delay signal processing after turn-on.

Some embodiments aim to exploit the wide bandwidth of the transducer elements of a transducer array such as those described above by effectively generating an ultrasonic waveform transmitted at a low fundamental frequency of a low frequency pulse and imaging at harmonics of a single fundamental frequency. In a triple harmonic imaging (THI) implementation, the first through third harmonics may be utilized to substantially simultaneously generate respective electrical signals representing, for example, the reflected ultrasonic waveform. FIGS. 6, 7 and 10 are representative drawings illustrating THI according to some embodiments. These drawings are described in more detail below.

Some embodiments also aim to utilize the wide bandwidth of the transducer elements of the transducer array, such as those described above, by effectively generating a multi-mode transmitted ultrasound waveform in a plurality of fundamental frequency pulses, including low frequency and high frequency pulses, and imaging in frequency pulses that are the same or similar to the fundamental frequency pulse. In a multi-mode imaging (MI) implementation, the frequencies of the reflected ultrasound waveforms, which are processes, can be utilized to substantially simultaneously generate respective electrical signals representing the reflected ultrasound waveforms, for example. FIG. 11 is a representative diagram illustrating a THI according to some embodiments. These diagrams are described in more detail below.

FIG. 5 is a plot (500) illustrating a series of graphs plotting power in decibels (DB) versus frequency in megahertz (MHz) for received ultrasound waveforms at different frequencies and different penetration depths of an imaging target. As illustrated in FIG. 5 , the reflected ultrasound waveform may exhibit higher power at lower frequencies (1.5 MHz) and thus better penetration (note that even depths of 140-160 mm are associated with higher DBs), whereas the reflected ultrasound waveform may exhibit increasingly lower power as frequency increases. Even at shallower penetrations (e.g., 20-40 mm), lower frequencies exhibit higher power. FIG. 5 demonstrates the advantage of lower frequencies in terms of resolution at greater penetrations, as discussed above. The figure further suggests that for shallower penetrations or depths, higher frequencies may lead to better resolution (see, e.g., plot for depths of 20-40 mm).

Some embodiments allow for leveraging the benefits of higher harmonics (when THI is utilized) or higher frequencies (when MI is utilized), which provides overall higher spatial resolution (compared to lower harmonics/lower frequencies) and the benefits of lower harmonics/lower frequencies. Lower harmonics/lower frequencies provide better penetration resolution (compared to higher harmonics/higher frequencies). As used herein, “penetration” refers to the imaging depth of a target object.

Some embodiments advantageously allow for the use of wideband transducer elements to extract high information content at all imaging depths from the electrical signals generated from the reflected ultrasound waveforms. Some embodiments extract high information content by (1) processing the reflected ultrasound waveforms at higher frequencies to obtain electrical signals corresponding to the near field of the image (fields corresponding to lower/shallower penetration) and corresponding to tighter lateral image angles (lateral image angles, referred to as angles θ, as described in more detail in the context of FIG. 6 ); and (2) processing the reflected ultrasound waveforms at lower frequencies to obtain electrical signals corresponding to the far field of the image (fields corresponding to higher/deeper penetration) and/or for wider lateral image angles.

Figures 6, 7, 10, 11, 12 and 13 illustrate ultrasound images corresponding to ultrasound frames. The term "frame" or "image" as used herein refers to an image of a cross-sectional plane through an object, which may be composed of individual scan lines. The scan lines may be viewed as individual layers or slices of the image. Depending on the resolution, a particular image may include different numbers of scan lines, from less than 100 to several hundred.

Now, referring to FIG. 6 which illustrates a set (600) of four respective ultrasound images of a target object, the depth being plotted on the left axis (cm), and the output image (image D) being obtained by simple averaging of the pixel irradiances of the images (A-C), each of the images (A-C) corresponding to a given reflection frequency. In FIG. 6, images A, B, C and D respectively represent: A. an image generated from electrical signals corresponding to a first frequency of a reflected ultrasound waveform, the first frequency corresponding to a fundamental frequency of transmission of the transmitted ultrasound waveform; B. an image generated from electrical signals corresponding to a second frequency of the reflected ultrasound waveform, the second frequency corresponding to a second harmonic of the fundamental frequency; C. an image generated from electrical signals corresponding to a third frequency of the reflected ultrasound waveform, the third frequency corresponding to a third harmonic of the fundamental frequency; and D. an image generated from simple synthesis of the electrical signals of the respective images of images A to C. Lines 13 and 9 in Fig. 6 represent penetration depths of 13 cm and 9 cm, respectively.

Figure 6 illustrates problems that may arise when imaging based on a single receive frequency (i.e., a single frequency at which the reflected ultrasound waveform is processed or demodulated to produce an output image to be displayed). For example, looking at image A, it has better overall penetration resolution, and better resolution at wider image angles θ (where θ represents the angle on either side of line CL). However, the resolution of image A at shallower depths may be improved compared to the resolution at shallower depths for images B and C (as indicated in images B and C by the "spotlight" regions SL). Thus, no single image for a given receive frequency may provide an adequate image with sufficient resolution at the desired depths and/or image angles. Furthermore, it is desirable to maintain resolution while eliminating the SL region in order to have a more uniform image.

Figure 6 highlights that image enhancement can occur in multiple dimensions, for example along the depth of the target object as shown and also over angles θ. For wider angles θ, higher frequencies will not have as good fidelity or signal-to-noise ratio as lower frequencies.

Some embodiments contemplate utilizing better resolution obtained from the electrical signal corresponding to lower frequency processing of the reflected ultrasonic waveform for both deeper penetration and wider angles.

As a drawing illustrating some of the problems that can occur in ultrasound imaging, FIG. 6, mentioned above, shows a center line (CL) from which θ can be measured and a spotlight area (SL) corresponding to areas of better resolution for higher frequency processing. FIGS. 7 and 10-12 illustrate similar types of images to those of FIG. 6, and consequently the illustration of CL, SL and θ has been omitted from these pictures, but it should be understood that the concepts of CL, SL and θ discussed in connection with FIG. 6 are equally applicable to FIGS. 7 and 10-12, and may be discussed hereinafter in connection with these drawings.

FIGS. 6, 7, 10 and 11 illustrate examples of THI, where a single low frequency transmit pulse (1.75 MHz) is applied to the transmitted ultrasonic waveform, and the resulting reflected ultrasonic waveform is demodulated at three imaging frequency bands (1.75 MHz, 3.5 MHz and 5.25 MHz). For THI, a narrow baseband filter may be applied to the electrical signals corresponding to the demodulation of the three imaging harmonics, followed by further processing to generate electrical signals corresponding to three images, namely images A, B and C, for each of FIGS. 6, 7, 10 and 11. The electrical signals corresponding to the three images (A-C) may then be composited according to any of the composite processes listed below to improve contrast resolution, spatial resolution or penetration resolution. Some of the synthesis methods according to the embodiments may include simple averaging, weighted averaging, alpha blending with depth adaptive synthesis, gain-compensated synthesis, maximum and minimum adaptive synthesis, predictive synthesis, lateral frequency adaptive synthesis and Doppler synthesis. These synthesis methods are described in more detail below.

Simple averaging

Referring again to FIG. 6 already partially described above. According to the example of FIG. 6 , a synthesis of electrical signals corresponding to images at three reception frequencies at A, B and C (in the case of FIG. 6 the fundamental and two harmonics, although in multi-mode imaging embodiments a simple averaging according to embodiments may also be used) can be achieved via simple averaging to obtain an image at D. Simple averaging in the context of FIG. 6 may comprise averaging, for each given pixel location defined by the depth and the image angle, the respective pixel irradiances at said given pixel location corresponding to the respective frequencies of the reception frequencies.

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

For example, the pixel irradiances at each of the receive frequencies for a given pixel location, say at a depth of 8 cm and an image angle of 5 degrees, can be used to compute a simple average of the pixel irradiance at that location, which can be used to produce the pixel irradiance in image D. The latter would be a linear average. For simple averaging, see Equation 1 below:

Here,

is the output pixel irradiance at a given pixel location for the output image (image D);

is the pixel irradiance at a given pixel location for the high reception frequency image (image C);

is the pixel irradiance at a given pixel location for the intermediate reception frequency image (image B);

is the pixel irradiance at a given pixel location for the low-frequency image (image A).

Weighted averaging

In synthesis involving weighted averaging, the pixel irradiance at a given pixel location can be obtained through a mathematical expression such as Equation 2 below:

Here, , , and is as defined for the above mathematical expression 1, and α, β and γ are weights to be used for each of the mentioned pixel irradiances. α, β and γ can be based on application requirements, such as whether depth resolution is required.

Alpha Blending with Depth-Adaptive Compositing

Now, reference is made to FIGS. 7 and 8 in the context of a compositing method according to one embodiment involving alpha blending using depth-adaptive compositing (DAC).

Fig. 7 illustrates a set (700) of four respective ultrasound images of a target object, where the output image (image D) is acquired via an alpha blending DAC of the pixel irradiances of the images (A-C). The images (A-C) of Fig. 7, similar to those of Fig. 6, correspond to image (A) acquired at a receive frequency equal to the fundamental frequency, image (B) acquired at a receive frequency equal to the second harmonic of the fundamental frequency, and image (C) acquired at a receive frequency equal to the third harmonic of the fundamental frequency. Line 13 represents the penetration depth at 13 cm, and line 9 represents the penetration depth at 9 cm.

Alpha blending using a DAC in the context of FIG. 7 may include, for each given pixel location defined by depth and image angle, multiplying each of the pixel irradiances at the given pixel location corresponding to each of the received frequencies by a respective alpha multiplier. Each of the multipliers for each of the pixel irradiances at the given pixel location may be a function of one or more alpha values, wherein the individual alpha values are a function of penetration depth.

For an example of alpha blending using a DAC, see Equation 3 below:

Here, , , and is defined as in the mathematical expression 1 above,

corresponds to depth-dependent α values at high reception frequencies;

corresponds to the depth-dependent α value of the intermediate receiving frequency.

Therefore, the multiplier for each of the pixel irradiances from Equation 3 above can be:

: It's a multiplier;

: It's a multiplier;

: It's a multiplier.

Figure 8 is used in the mathematical expression 4 above. and An example of a plot (800) of α value versus depth (mm). From the plot (800), It is clear that the α value for can drop to zero at a given depth, for example at a depth of about 92 mm in the case of the example plot (800). In contrast, The α value of and falls to zero much more gradually, in the case of the example plot (800), at much deeper penetration, at 200 mm. Thus, alpha blending using the DAC may aim to minimize the contribution of higher receive frequencies to the pixel irradiance at deeper depths, given the lower resolution and fidelity. For deeper pixel locations, the contribution of pixel irradiances at high and intermediate receive frequencies may tend to zero, in which case alpha blending using the DAC will only utilize pixel irradiances of lower receive frequencies (in the case of Figure 8, beyond 200 mm).

Therefore, multipliers of pixel irradiances for different receiving frequencies can be based on the depth of the pixel location.

Multimode transmitted ultrasound waves

As previously mentioned, the embodiments, and the synthesis methods described herein, do not require that the transmitted ultrasound waveform have a single fundamental frequency as the transmit frequency, and the receive frequencies of the reflected ultrasound waveform include harmonics of that fundamental frequency (THI). Some other embodiments include within their scope the use of a transmitted ultrasound waveform characterized by three fundamental frequencies as the transmit frequency, and the receive frequencies corresponding to the fundamental frequency (MI). As previously mentioned, some transducer elements may have multi-mode capability, and thus may correspond to multi-mode elements, which would enable multi-mode imaging modalities. Embodiments include within their scope a computing device capable of processing electrical signals based on both the THI modality and the MI modality.

FIGS. 9A, 9B, 10, and 11 illustrate exemplary multi-mode pulses that may be utilized in the MI embodiments described herein for better penetration resolution. In particular, FIG. 9A illustrates a plot (900A) showing voltage plotted against time (in seconds) for multi-mode pulses utilizing fundamental frequencies to transmit the pulses (1.75 MHz, 3.5 MHz, and 5.1 MHz). FIG. 9B illustrates a plot (900B) of the frequency distribution for the transmitted ultrasound waveform of FIG. 9A, plotting power in DB versus frequency in Hz.

FIG. 10 is a set (1000) of images similar to those of FIG. 6, but this time using the pulses of FIGS. 9A and 9B to generate images (A-C), and also using simple synthesis as described above to synthesize electrical signals corresponding to images (A-C) to generate a composite image (D).

FIG. 11 is a set (1000) of images similar to those of FIG. 6, but utilizing alpha blending with a DAC as described above for multi-mode pulses such as those shown in FIGS. 9a and 9b.

As can be seen from the comparison of FIGS. 11 and 10 , alpha blending using DAC in MI in the illustrated example yields an output image (D) having better overall resolution compared to simple compositing in MI. As can be seen from the comparison of FIGS. 6 and 10 , simple compositing in THI in the illustrated example yields an output image (D) having better overall resolution compared to simple compositing in MI. As can be seen from the comparison of FIGS. 7 and 11 , alpha blending using DAC in THI in the illustrated example yields an output image (D) having better overall resolution compared to adaptive compositing in MI. As can be seen from the comparison of FIGS. 11 and 6 , alpha blending using DAC in MI in the illustrated example yields an output image (D) having better overall resolution compared to adaptive compositing in THI.

Gain-compensation before synthesis

In one embodiment, the synthesis method may include gain-compensated synthesis, whereby individual frequency bands of the reflected ultrasound waveform may be preprocessed prior to synthesis. For example, electrical signals corresponding to each of the received frequencies processed for image generation may be gain compensated or dynamic range (DR) compensated to improve the quality of the output image.

Now, referring to Fig. 12 which shows two output images D1 and D2, image D1 is represented as obtained using alpha blending with a DAC on three receive frequencies similar to those of Fig. 6, Fig. 7, Fig. 10 or Fig. 11, and D2 is represented as obtained using DR compensation on the same three receive frequencies with a DR of 60 and different gains. A comparison of D1 and D2 shows a clear advantage of improved contrast using DR compensation as compared to alpha blending with a DAC, otherwise all else being equal.

Adaptive synthesis of maximum and minimum

Adaptive synthesis typically involves several nonlinear methods of combining or synthesizing electrical signals corresponding to the received frequencies of reflected ultrasonic waveforms. Exemplary adaptive synthesis techniques are described below.

Now, referring to Fig. 13 which illustrates a set of three output images D1, D2 and D3, where image D1 is represented as obtained using simple synthesis on three receive frequencies similar to those of Fig. 6, Fig. 7, Fig. 10 or Fig. 11, D2 is represented as obtained using alpha blending using a DAC on the same three receive frequencies and D3 is represented as obtained using maximum and minimum adaptive synthesis according to equation (5) below. A comparison of D1, D2 and D3 shows a clear advantage of improved contrast and better resolution using adaptive synthesis as compared to simple synthesis and alpha blending using a DAC, all else being equal.

The output image (D3) of Fig. 13 is the result of a simple nonlinear adaptive synthesis method using blending of maximum, minimum and average frames or pixel irradiances according to the following mathematical expression 4:

Here,

;

;

corresponds to the pixel irradiance at the pixel location after alpha blending with depth compensation as described above in the context of mathematical expression 3;

: The maximum transparency factor (the "alpha value", or is the transparency coefficient to be used for. For example, is the irradiance (i.e., , , or can be determined from a lookup table that maps at least one of the following values: 0 and 1, for example, to a given value, such as between 0 and 1 and including 0 and 1. Thus, according to one example, the selected irradiance (i.e., , , or If at least one of them) is greater than the threshold X1, can have a set first value (e.g., between 0 and 1 and inclusive); if the selected irradiance is less than X2, can have a second setpoint value (between 0 and 1 and including 0 and 1, wherein the second setpoint value is different from the first setpoint value); if the selected irradiance is between X1 and X2, is determined based on a linear function which is a straight line between the first set value and the second set value;

: Minimum transparency factor based on known alpha blending methods ( is the transparency coefficient to be used for. For example, Similarly, is the irradiance (i.e., , , or can be determined from a lookup table that maps at least one of the following values: 0 and 1, for example, to a given value, such as between 0 and 1 and including 0 and 1. Thus, according to one example, the selected irradiance (i.e., , , or If at least one of them) is greater than the threshold X1, can have a set first value (e.g., between 0 and 1 and inclusive); if the selected irradiance is greater than X2, can have a second setpoint value (between 0 and 1 and including 0 and 1, wherein the second setpoint value is different from the first setpoint value); if the selected irradiance is between X1 and X2, can be determined based on a linear function which is a straight line between the first set value and the second set value.

Predictive synthesis

In some embodiments, the predictive synthesis includes determining a relationship between first electrical signals corresponding to a first region (region NF) of an image based on a first reception frequency, electrical signals corresponding to the first region (region NF) of the image based on a second reception frequency, and predicting third electrical signals corresponding to a second region (region FF) of the image based on the second reception frequency. For example, referring to FIG. 6 , the predictive synthesis may be used to determine a relationship between an electrical signal corresponding to region NF1 (region NF of 1.75 MHz reception frequency) and an electrical signal corresponding to region A2 (region NF of 3.5 MHz reception frequency). The predictive synthesis may then include predicting electrical signals in region FF2 (region FF of 3.5 MHz reception frequency) using the relationship together with the electrical signals in region FF1 (region FF of 1.75 MHz reception frequency).

This relationship can be helpful, for example, in allowing better penetration of lower frequencies as input signals to predict images at deeper penetrations at higher reception frequencies. Thus, images at deeper depths of the target object at higher frequencies can be replaced with images predicted from the same depths at lower frequencies.

Some examples of predictive synthesis, including simple predictive synthesis, predictive synthesis using point spread functions (PSFs), and machine learning (ML)-based predictive synthesis, are described below.

Simple predictive synthesis

A simple way to use predictive synthesis may involve the use of Equation 5:

Here,

I _FF2 : Pixel irradiance in area FF2, which is the predicted pixel irradiance;

I _FF1 : Pixel irradiance in area FF1 obtained from the reflected ultrasonic waveform;

I _NF2 : Pixel irradiance in area NF2 obtained from the reflected ultrasonic waveform;

I _NF1 : Pixel irradiance in area NF1 obtained from the reflected ultrasonic waveform.

The reverse can also be done, where the higher resolution region from the higher receive frequencies can be used to predict the same region for the lower receive frequencies. The lower receive frequencies do not have as good a resolution as the higher receive frequencies. The use of adaptive predictive synthesis will take advantage of the lower penetration of the lower frequency image and the better resolution of the higher frequencies, thus resulting in an image with some predicted parts that exhibit better penetration at the higher frequencies, for example.

For example, if a relationship is found as described above, there may be, for example, 10x better penetration for a 5 MHz image using predictive synthesis than for a 5 MHz image not using predictive synthesis.

Prediction synthesis using point spread function (PSF)

The point spread function (PSF) of a reflected ultrasound waveform depends on several factors, including the receiver frequency, the receiver bandwidth, and the focus at the target object. The PSF can be thought of as representing the scattering of the reflected beam from the target object. To obtain an image corresponding to the region of the target object being imaged, as a predictive measure, the PSF can be convolved with a scattering distribution corresponding to the region of the target object being imaged, such as pixels. Thus, a frequency dependent PSF can be convolved with a target dependent scattering distribution. Thus, when the receiver frequency is different, say, from a receiver frequency of 1.75 MHz to 3.5 MHz, the PSF will change, but for the same region being imaged, the scattering distribution will be the same.

For example, as discussed above in the general discussion of predictive synthesis with respect to the images in Fig. 6, it is assumed that:

Area NF1 corresponds to area NF of the 1.75 MHz receive frequency image;

Area FF1 corresponds to area FF of the 1.75 MHz receive frequency image;

Area NF2 corresponds to area NF, but corresponds to area NF of the 3.5 MHz receive frequency image;

Area FF2 corresponds to area FF, but corresponds to area FF of the 3.5 MHz receiving frequency image;

PSF1 corresponds to the PSF for the 1.75 MHz receive frequency image;

PSF2 corresponds to the PSF for the 3.5 MHz receive frequency image;

PSFinverse is the inverse of PSF.

Given the above definitions, if we want to predict the pixel irradiance at B2, which represents the scatter distribution (which is correlated to the target object in region FF), we can assume that we can use Equation 6:

Here, *PSF _inverse represents deconvolution based on PSF.

ML-based predictive synthesis

Some embodiments propose ML-based predictive synthesis that can provide higher resolution images (e.g., 5 MHz) in the far field while maintaining penetration at lower frequencies (e.g., 1.75 MHz) using ML-based deconvolution.

Proposed method:

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

Training and validation datasets:

The following operations may be performed to perform ML-based synthesis according to some embodiments:

1) Select 1.75 MHz image as input and 5.0 MHz as desired output image;

2) Selecting the near-field region NF (e.g., at a depth of 0-5 cm) in each of the 1.75 MHz and 5.0 MHz images;

3) Generating a training data set by segmenting the near-field region NF into many “speckle cells”, i.e., generating many sub-regions within the near-field region, for example, square sub-regions measuring 5 mm x 5 mm, to generate input (1.75 MHz sub-region) and output (5.0 MHz sub-region);

4) Using multiple frames and/or multiple views of individual sub-regions to generate a training data set; for example, using about 50 different imaging views on the pin target phantom and the speckle phantom of each sub-region can optionally generate more than about 5000 images to use as a training data set, which may also be augmented by transformation, scaling, etc., if desired;

5) Generating a validation data set using mid-field speckle cells at (5-10 cm);

6) Using the mid-field region MF (e.g., at a depth of 0-5 cm) from each of the 1.75 MHz and 5.0 MHz images as a validation data set;

7) Developing an ML-based training model for the relationship between regions A of 1.75 MHz and 5 MHz receiving frequencies based on the training data set and the validation data set; and

8) Predict images in the regions B of 1.75 MHz and 5 MHz receiving frequencies based on the training model, where B is the far-field region.

The training data set can be used to identify relationships between regions A, such as between lower and higher reception frequencies. These relationships can also be used for other ML-related applications, such as identifying speckle cells, distinguishing speckle from noise, gain equalization, edge enhancement, etc.

Lateral frequency synthesis

Referring again to Figure 6, which illustrates problems that can arise when imaging based on a single receiving frequency. For example, looking at image A, we see that it has better overall penetration resolution, and better resolution over wider image angles θ (where θ represents the angle on either side of line CL). However, images B and C show the spotlight region SL within the near-field and mid-field regions. However, the SL is missing from the lower frequency image A.

Lateral frequency synthesis may include, for each given pixel location defined by depth and image angle, multiplying each of the pixel irradiances at the given pixel location corresponding to each of the received frequencies by a respective alpha multiplier. Each of the multipliers for each of the pixel irradiances at the given pixel location may be a function of one or more alpha values, wherein the individual alpha values may be a function of both the image angle and the penetration depth.

For an example of lateral frequency synthesis, see Equation 7 below:

Here,

: is the output pixel irradiance at depth r and image angle θ;

: is the pixel irradiance at depth r and image angle θ for the high reception frequency image (image C);

r: image depth;

θ: image angle;

: is the transparency coefficient α at depth r and image angle θ for high reception frequency, where Is or can be determined in the same way as described above for either one, and the difference is that can have different ranges and different breakpoints X1 and X2 for different r's and different θ's;

: is the transparency coefficient α at depth r and image angle θ for the intermediate reception frequency, where Is or can be determined in the same way as described above for either one, and the difference is that can have different ranges and different breakpoints X1 and X2 for different r's and different θ's;

: is the pixel irradiance at depth r and image angle θ for the intermediate received frequency image (image B);

: is the pixel irradiance at depth r and image angle θ for the low-frequency image (image A).

Therefore, the multiplier for each of the pixel irradiances from Equation 4 above can be:

: It's a multiplier;

: It's a multiplier;

: It's a multiplier.

Therefore, the coefficient α (transparency coefficient) is a function of the image angle to improve the signal-to-noise ratio (SNR) in the lateral directions as well as reduce the "spotlight" artifacts at higher frequencies. Equation 9 below shows the above relationship:

Here,

i : is the frequency band of the imaging waveforms;

p : line index coordinates for the image;

: is the output image irradiance at depth r and line index coordinate p;

: are the input image intensities respectively;

: is the transparency coefficient for depth r, line index coordinate p, and frequency band I, where Is or can be determined in the same way as described above for either one, and the difference is That is, it can have different ranges and different breakpoints X1 and X2 for different r's, different p's and different i's.

Lateral synthesis can improve directivity and provide a larger field of view, such as a 150-degree field of view.

Color Doppler/Flow Synthesis

Color Doppler synthesis according to some embodiments utilizes additional parameters associated with Doppler imaging, including velocity (flow and direction), in addition to irradiance. Color Doppler synthesis according to some embodiments may include combining flow velocity or power from multiple frequency data. Power or velocity from multiple frequency bands is combined based on a) depth, b) angle, c) SNR, d) flow velocity, etc.

According to one embodiment, alpha blending results for an output image generated using color Doppler synthesis can be calculated using Equations 9 to 12.

Maximum parameters:

Average parameters:

Here,

: is the maximum zero-lag output value of the autocorrelation corresponding to the alpha blending to be used;

: is the maximum first lag output value of autocorrelation;

: is the average zero-lag output value of the autocorrelation corresponding to the alpha blending to be used;

: is the average first lag output value of the autocorrelation;

: is a zero-lag input value corresponding to the frequency band i of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used (see, for example, Equations 14 and 15 below);

: The first lag input value corresponding to the frequency band i of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used (see, for example, Equations 14 and 15 below).

The maximum and average of R0 and R1 are calculated from Equations 9 to 12 above, and then alpha blended as shown in Equations 14 and 15 below:

Here,

: Zero-lag output corresponding to the alpha blending value of the maximum and average zero-lag autocorrelations;

: The first lag output corresponding to the alpha blending value of the maximum and average first lag autocorrelations;

α: Alpha value for alpha blending.

While the above description of exemplary embodiments may specifically refer to veins, the embodiments are not limited thereto, and relate to detection and tracking of any blood vessels in the body that may be the subject of ultrasound imaging where the blood vessel may be accessed by a foreign body. Additionally, while specific colors have been mentioned above to indicate suitability for access or other parameters related to the blood vessel, the embodiments are not limited thereto, and include, within their scope, displaying the blood vessel parameters to the user via the UI in any manner, such as via text, a visual image or code, or voice communication.

In an example, the instructions implemented by the processor (326) may be provided via the memory (336) or any other memory or storage device of the imaging device, or the processor (326) or any other processor of the imaging device may be implemented as a tangible, non-transitory, machine-readable medium comprising code that instructs the processor (326) to perform electronic operations within the casing, such as operations corresponding to any of the methods/processes herein. The processor (326) can access the non-transitory machine-readable medium via an interconnection between the memory (336) and the processor (326). For example, the non-transitory machine-readable medium may be implemented by a separate memory within the memory (336) or the processor (326), or may include specific storage units such as optical disks, flash drives, or any number of other hardware devices that may be plugged into the casing. A non-transitory machine-readable medium may contain instructions that direct the processor (326) to perform a particular sequence or flow of actions, for example, as described with respect to the flowchart(s) and block diagram(s) of the operations and functionality depicted herein. As used herein, the terms "machine-readable medium" and "computer-readable medium" are interchangeable.

FIG. 14 illustrates a method (1400) performed on a computing device including a memory and one or more processors coupled to the memory. The method (1400) includes, at operation (1402), simultaneously receiving electrical signals based on respective reflection frequencies of reflected ultrasonic waveforms reflected from a target object as a result of a transmitted ultrasonic waveform. The method includes, at operation (1404), synthesizing information from the electrical signals to generate synthesized electrical signals. The method includes, at operation (1406), generating an output image on a display device based on the synthesized electrical signals.

Unless explicitly stated otherwise, any of the examples described below may be combined with any other example (or combination of examples). The aspects described herein may also implement hierarchical application of the method, for example by introducing hierarchical prioritization of utilization for different functions (e.g., low/medium/high priority, etc.).

While the implementations have been described with reference to specific exemplary embodiments, it will be apparent that various modifications and changes may be made to these embodiments without departing from the broader scope of the present disclosure. Many of the arrangements and processes described herein may be utilized in combination or in parallel implementations. Accordingly, the present specification and drawings are to be regarded in an illustrative rather than a restrictive sense. The accompanying drawings, which form a part hereof, illustrate, by way of example, and not limitation, specific embodiments in which the subject matter may be practiced. The illustrated embodiments are described in sufficient detail to enable those skilled in the art to practice the teachings disclosed herein. Other embodiments may be utilized and derived therefrom, and structural and logical substitutions and changes may be made without departing from the scope of the present disclosure. This detailed description is therefore not to be taken in a limiting sense, and the scope of the various embodiments is defined only by the appended claims, along with the full scope of equivalents to which such claims are entitled.

These aspects of the subject matter of the present invention may be mentioned herein individually and/or collectively merely for convenience and without intending to voluntarily limit the scope of the present application to any single aspect or inventive concept if more than one is actually disclosed.

While preferred embodiments of the present disclosure have been illustrated and described herein, it will be apparent to those skilled in the art that such embodiments are provided by way of example only. It is not intended that the embodiments be limited to the specific examples provided herein. While embodiments of the present disclosure have been described with reference to the foregoing specification, the descriptions and illustrations of the embodiments herein are not intended to be construed in a limiting sense. It will now occur to those skilled in the art that numerous modifications, variations, and substitutions may occur without departing from the concepts of the present disclosure. Moreover, it will be appreciated that all aspects of the various embodiments are not limited to the specific depictions, configurations, or relative proportions set forth herein, which depend upon various conditions and variables. It should be appreciated that various alternatives to the embodiments described herein may be utilized. Accordingly, the present disclosure is also contemplated to cover any such alternatives, modifications, variations, or equivalents.

Examples

Illustrative examples of the technologies disclosed herein are provided below. Embodiments of these technologies may include any one or more and any combination of the examples described below.

Example 1 includes a device of a computing device including a memory, and one or more processors coupled to the memory, wherein the one or more processors are configured to simultaneously receive electrical signals based on respective reflection frequencies of reflected ultrasonic waveforms reflected from a target object as a result of a transmitted ultrasonic waveform; synthesize information from the electrical signals to generate synthesized electrical signals; and generate an output image on a display based on the synthesized electrical signals.

Example 2 includes the subject matter of Example 1, wherein each of the reflection frequencies corresponds to a respective harmonic of the fundamental frequency of the transmitted ultrasonic waveform, where the fundamental frequency is a single frequency of the transmitted ultrasonic waveform.

Example 3 includes the subject matter of Example 1, wherein the transmitted ultrasonic waveform is a multimode waveform having fundamental frequencies corresponding to respective frequencies of the reflected ultrasonic waveform.

Example 4 includes the subject matter of Example 1, wherein one or more processors implement a prediction algorithm for the electrical signals, wherein the one or more processors generate prediction electrical signals for a second image area of the target object, the second image area being different from the first image area, using information from the first electrical signals corresponding to the first image area of the target object; and synthesize information by obtaining electrical signals synthesized using the prediction electrical signals such that an output image in the second image area corresponds to the prediction electrical signals.

Example 5 includes the subject matter of any one of Examples 1 to 4, wherein the reflection frequencies include N reflection frequencies; the electrical signals include N sets of electrical signals, each set of electrical signals corresponding to one of the reflection frequencies, the N sets of electrical signals corresponding to N input images of a target object; the individual input images include pixels at respective pixel locations, each pixel location of the N input images being defined by a depth and an angle; and synthesizing information includes synthesizing information from the N sets of electrical signals; and the one or more processors synthesize information from the N sets of electrical signals using at least one of simple averaging, weighted averaging, alpha blending using depth-adaptive synthesis, max and min adaptive synthesis, predictive synthesis, lateral frequency synthesis and color Doppler synthesis.

Example 6 includes the subject matter of Example 5, and further comprises one or more processors subjecting the electrical signals to gain compensation or dynamic range compensation prior to synthesis.

Example 7 includes the subject matter of Example 5, wherein simple averaging comprises performing, for each pixel location, either a simple averaging or a weighted averaging of the individual pixel irradiances across the N input images.

Example 8 includes the subject matter of Example 5, and wherein alpha blending using depth-adaptive synthesis comprises multiplying, for each pixel location, each of the pixel irradiances between the N input images by a corresponding alpha multiplier, wherein each alpha multiplier is a function of one or more alpha values, and wherein the one or more alpha values are a function of at least one of a depth of the respective pixel location or an angle of the respective pixel location.

Example 9 includes the subject matter of Example 8, wherein for each pixel location, the output irradiance in the output image is is given by , where, is the output pixel irradiance for each pixel location in the output image; is the pixel irradiance for each pixel location in the input image corresponding to the highest frequency among the received frequencies; is the pixel irradiance for each pixel location in the input image corresponding to the intermediate frequency among the received frequencies; is the pixel irradiance for each pixel location in the input image corresponding to the lowest frequency among the received frequencies; corresponds to the depth-dependent α value of the highest frequency among the received frequencies; corresponds to the depth-dependent α value of the intermediate frequency among the received frequencies.

Example 10 includes the subject matter of Example 8, wherein for each pixel location, the output irradiance in the output image is is given by , where, r: depth; θ: angle; : Output pixel irradiance at depth r and image angle θ; : Pixel irradiance at depth r and image angle θ for the highest of the received frequencies; : Pixel irradiance at depth r and image angle θ for the intermediate frequency among the received frequencies; : Pixel irradiance at depth r and image angle θ for the lowest of the received frequencies; corresponds to the alpha value of the highest frequency among the received frequencies at depth r and image angle θ; corresponds to the alpha value at the middle frequency among the received frequencies at depth r and image angle θ.

Example 11 includes the subject matter of Example 5, wherein the maximum and minimum adaptive synthesis involves using blending of the maximum, minimum and average pixel irradiances among the N input images for each pixel location.

Example 12 includes the subject matter of Example 11, such that for each pixel location, the output irradiance in the output image is is given by , where, ; ; corresponds to pixel irradiance at pixel location after alpha blending using depth compensation; Is , , or Corresponding to the maximum transparency alpha value coefficient based on at least one of has a set first value between 0 and 1 and including 0 and 1; silver , , or Corresponding to a minimum transparency alpha value coefficient based on at least one of has a set second value that is different from the set first value and is between 0 and 1 and includes 0 and 1.

Example 13 includes the subject matter of Example 5, wherein the predictive synthesis comprises determining a relationship between first electrical signals corresponding to a first area in a first input image corresponding to a first reflection frequency, and second electrical signals corresponding to a first area in a second input image corresponding to a second reflection frequency; and predicting third electrical signals corresponding to a second area in the second input image based on the relationship.

Example 14 includes the subject matter of Example 13, wherein the pixel irradiance of a pixel in the second region is , where, FF1 is a second region in the first input image; FF2 is a second region in the second input image; NF1 is a first region in the first input image; NF2 is a first region in the second input image; I _FF2 is a pixel irradiance in FF2; I _FF1 is a pixel irradiance in the region FF1; I _NF2 is a pixel irradiance in the region NF2; I _NF1 is a pixel irradiance in the region NF1.

Example 15 includes the subject matter of Example 13, wherein the pixel irradiance of a pixel in the second region is , where, FF1 is a second region in the first input image; FF2 is a second region in the second input image; NF2 is a first region in the second input image; I _FF2 is pixel irradiance in FF2; I _FF1 is pixel irradiance in the region FF1; I _NF2 is pixel irradiance in the region NF2; PSF1 corresponds to a point spread function (PSF) for the first receive frequency; PSF2 corresponds to a PSF for the second receive frequency; PSF1 _inverse is an inverse of PSF1 corresponding to the deconvolution; and PSF1 _inverse is an inverse of PSF1 corresponding to the deconvolution.

Example 16 includes the subject matter of Example 13, wherein the ML-based synthesis comprises: determining that a first region corresponds to a near-field region; determining that a second region corresponds to a far-field region; segmenting the near-field region into a plurality of sub-regions, for example, square sub-regions; generating a training data set based on a plurality of first input images and a plurality of first output images in the near-field region; developing an ML-based model for a relationship between pixel irradiances at a first reflection frequency within the first region and pixel irradiances at a second reflection frequency within the first region based on the training data set; and predicting pixel irradiances at the second reflection frequency within the second region based on the model.

Example 17 includes the subject matter of Example 16, and further includes generating a validation data set based on a plurality of first input images and a plurality of first output images in a mid-field region of the input images, and developing an ML-based model based on the training data set and the validation data set.

Example 18 includes the subject matter of Example 5, wherein the color Doppler synthesis comprises combining, for each pixel location, individual pixel irradiances between the N input images based on at least one of information about depth, angle, signal-to-noise ratio, flow velocity or power.

Example 19 includes the subject of Example 18, and R0 _out and R1 _out of the output image are and is given by , where, : Zero-lag output corresponding to the alpha blending value of the maximum and average zero-lag autocorrelations; : First lag output corresponding to the alpha blending value of the maximum and average first lag autocorrelations; α: alpha value for alpha blending; : The maximum zero-lag output value of the autocorrelation corresponding to the alpha blending to be used; : Maximum first lag output value of autocorrelation; : The average zero-lag output value of the autocorrelation corresponding to the alpha blending to be used; : Average first lag output value of autocorrelation; : a zero-lag input value corresponding to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used; and : The first lag input value corresponding to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used.

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

Example 21 includes a system comprising a user interface device including a display device; and a computing device communicatively coupled to the user interface device, the computing device including a memory, and one or more processors coupled to the memory, the one or more processors configured to simultaneously receive electrical signals based on respective reflection frequencies of reflected ultrasonic waveforms reflected from a target object as a result of a transmitted ultrasonic waveform; synthesize information from the electrical signals to generate synthesized electrical signals; and generate an output image on the display device based on the synthesized electrical signals.

Example 22 includes the subject matter of Example 21, wherein each of the reflection frequencies corresponds to a respective harmonic of the fundamental frequency of the transmitted ultrasonic waveform, the fundamental frequency being a single frequency of the transmitted ultrasonic waveform.

Example 23 includes the subject matter of Example 21, wherein the transmitted ultrasonic waveform is a multimode waveform having fundamental frequencies corresponding to respective frequencies of the reflected ultrasonic waveform.

Example 24 includes the subject matter of Example 21, wherein one or more processors implement a prediction algorithm for electrical signals, wherein the one or more processors generate prediction electrical signals for a second image area of the target object, the second image area being different from the first image area, using information from the first electrical signals corresponding to the first image area of the target object; and synthesize information by obtaining electrical signals synthesized using the prediction electrical signals such that an output image in the second image area corresponds to the prediction electrical signals.

Example 25 includes the subject matter of any one of Examples 21 to 24, wherein the reflection frequencies comprise N reflection frequencies; the electrical signals comprise N sets of electrical signals, each set of electrical signals corresponding to one of the reflection frequencies, the N sets of electrical signals corresponding to N input images of a target object; the individual input images comprise pixels at respective pixel locations, each pixel location of the N input images being defined by a depth and an angle; and synthesizing information comprises synthesizing information from the N sets of electrical signals; and the one or more processors synthesize information from the N sets of electrical signals using at least one of simple averaging, weighted averaging, alpha blending using depth-adaptive synthesis, max and min adaptive synthesis, predictive synthesis, lateral frequency synthesis and color Doppler synthesis.

Example 26 includes the subject matter of Example 25, and further comprises causing the one or more processors to subject the electrical signals to gain compensation or dynamic range compensation prior to synthesis.

Example 27 includes the subject matter of Example 25, wherein the simple averaging comprises performing, for each pixel location, either a simple averaging or a weighted averaging of the individual pixel irradiances across the N input images.

Example 28 includes the subject matter of Example 25, and wherein alpha blending using depth-adaptive synthesis comprises multiplying, for each pixel location, each pixel irradiance between the N input images by a corresponding alpha multiplier, wherein each alpha multiplier is a function of one or more alpha values, and wherein the one or more alpha values are a function of at least one of a depth of the respective pixel location or an angle of the respective pixel location.

Example 29 includes the subject matter of Example 28, wherein for each pixel location, the output irradiance in the output image is is given by , where, is the output pixel irradiance for each pixel location in the output image; is the pixel irradiance for each pixel location in the input image corresponding to the highest frequency among the received frequencies; is the pixel irradiance for each pixel location in the input image corresponding to the intermediate frequency among the received frequencies; is the pixel irradiance for each pixel location in the input image corresponding to the lowest frequency among the received frequencies; corresponds to the depth-dependent α value of the highest frequency among the received frequencies; corresponds to the depth-dependent α value of the intermediate frequency among the received frequencies.

Example 30 includes the subject matter of Example 28, wherein for each pixel location, the output irradiance in the output image is is given by , where, r: depth; θ: angle; : Output pixel irradiance at depth r and image angle θ; : Pixel irradiance at depth r and image angle θ for the highest of the received frequencies; : Pixel irradiance at depth r and image angle θ for the intermediate frequency among the received frequencies; : Pixel irradiance at depth r and image angle θ for the lowest of the received frequencies; : the alpha value of the highest frequency among the received frequencies at depth r and image angle θ; and : Alpha value at the middle frequency among the received frequencies at depth r and image angle θ.

Example 31 includes the subject matter of Example 25, wherein the max and min adaptive synthesis involves using, for each pixel location, blending of the maximum, minimum and average pixel irradiances between the N input images.

Example 32 includes the subject matter of Example 31, wherein for each pixel location, the output irradiance in the output image is is given by , where, ; ; corresponds to pixel irradiance at pixel location after alpha blending using depth compensation; Is , , or Corresponding to the maximum transparency alpha value coefficient based on at least one of has a set first value between 0 and 1 and including 0 and 1; silver , , or Corresponding to a minimum transparency alpha value coefficient based on at least one of has a set second value that is different from the set first value and is between 0 and 1 and includes 0 and 1.

Example 33 includes the subject matter of Example 25, wherein the predictive synthesis comprises determining a relationship between first electrical signals corresponding to a first area in a first input image corresponding to a first reflection frequency, and second electrical signals corresponding to a first area in a second input image corresponding to a second reflection frequency; and predicting third electrical signals corresponding to the second area in the second input image based on the relationship.

Example 34 includes the subject matter of Example 33, wherein the pixel irradiance of a pixel in the second region is , where, FF1 is a second region in the first input image; FF2 is a second region in the second input image; NF1 is a first region in the first input image; NF2 is a first region in the second input image; I _FF2 is a pixel irradiance in FF2; I _FF1 is a pixel irradiance in the region FF1; I _NF2 is a pixel irradiance in the region NF2; I _NF1 is a pixel irradiance in the region NF1.

Example 35 includes the subject matter of Example 33, wherein the pixel irradiance of a pixel in a second region is , where, FF1 is a second region in the first input image; FF2 is a second region in the second input image; NF2 is a first region in the second input image; I _FF2 is pixel irradiance in FF2; I _FF1 is pixel irradiance in the region FF1; I _NF2 is pixel irradiance in the region NF2; PSF1 corresponds to a point spread function (PSF) for the first receive frequency; PSF2 corresponds to a PSF for the second receive frequency; PSF1 _inverse is an inverse of PSF1 corresponding to the deconvolution; and PSF1 _inverse is an inverse of PSF1 corresponding to the deconvolution.

Example 36 includes the subject matter of Example 33, wherein the ML-based synthesis comprises: determining that a first region corresponds to a near-field region; determining that a second region corresponds to a far-field region; segmenting the near-field region into a plurality of sub-regions, for example, square sub-regions; generating a training data set based on a plurality of first input images and a plurality of first output images in the near-field region; developing an ML-based model for a relationship between pixel irradiances at a first reflection frequency within the first region and pixel irradiances at a second reflection frequency within the first region based on the training data set; and predicting pixel irradiances at the second reflection frequency within the second region based on the model.

Example 37 includes the subject matter of Example 36, and further includes generating a validation data set based on a plurality of first input images and a plurality of first output images in a mid-field region of the input images, and developing an ML-based model based on the training data set and the validation data set.

Example 38 includes the subject matter of Example 25, wherein the color Doppler synthesis comprises combining, for each pixel location, individual pixel irradiances between the N input images based on at least one of information about depth, angle, signal-to-noise ratio, flow velocity or power.

Example 39 includes the subject of Example 38, and R0 _out and R1 _out of the output image are and is given by , where, : Zero-lag output corresponding to the alpha blending value of the maximum and average zero-lag autocorrelations; : First lag output corresponding to the alpha blending value of the maximum and average first lag autocorrelations; α: alpha value for alpha blending; : The maximum zero-lag output value of the autocorrelation corresponding to the alpha blending to be used; : Maximum first lag output value of autocorrelation; : The average zero-lag output value of the autocorrelation corresponding to the alpha blending to be used; : Average first lag output value of autocorrelation; : a zero-lag input value corresponding to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used; and : The first lag input value corresponding to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used.

Example 40 includes the subject matter of any one of Examples 21 to 24, wherein each of the reflected frequencies comprises 1.75 MHz, 3.5 MHz and 5.0 MHz, and wherein the fundamental frequency of the transmitted ultrasonic waveform is a single frequency of 1.75 MHz; or wherein each of the fundamental frequencies of the transmitted ultrasonic waveform comprises 1.75 MHz, 3.5 MHz and 5.0 MHz.

Example 41 includes a method performed on a computing device including a memory and one or more processors coupled to the memory, the method comprising: simultaneously receiving electrical signals based on respective reflection frequencies of reflected ultrasonic waveforms reflected from a target object as a result of a transmitted ultrasonic waveform; generating synthesized electrical signals by synthesizing information from the electrical signals; and generating an output image on a display device based on the synthesized electrical signals.

Example 42 includes the subject matter of Example 41, wherein each of the reflection frequencies corresponds to a respective harmonic of the fundamental frequency of the transmitted ultrasonic waveform, the fundamental frequency being a single frequency of the transmitted ultrasonic waveform.

Example 43 includes the subject matter of Example 41, wherein the transmitted ultrasonic waveform is a multimode waveform having fundamental frequencies corresponding to respective frequencies of the reflected ultrasonic waveform.

Example 44 includes the subject matter of Example 41, wherein synthesizing information comprises implementing a prediction algorithm on the electrical signals, wherein the one or more processors generate prediction electrical signals for a second image area of the target object, the second image area being different from the first image area, using information from the first electrical signals corresponding to the first image area of the target object; and obtaining electrical signals synthesized using the prediction electrical signals such that an output image in the second image area corresponds to the prediction electrical signals.

Example 45 includes the subject matter of Example 41, wherein the reflection frequencies include N reflection frequencies; the electrical signals include N sets of electrical signals, each set of electrical signals corresponding to one of the reflection frequencies, the N sets of electrical signals corresponding to N input images of a target object; the individual input images include pixels at respective pixel locations, each pixel location of the N input images being defined by a depth and an angle; and synthesizing information includes synthesizing information from the N sets of electrical signals; the method further includes synthesizing information from the N sets of electrical signals using at least one of simple averaging, weighted averaging, alpha blending using depth-adaptive synthesis, max and min adaptive synthesis, predictive synthesis, lateral frequency synthesis and color Doppler synthesis.

Example 46 includes the subject matter of Example 45, and further comprises the step of subjecting the electrical signals to gain compensation or dynamic range compensation prior to synthesis.

Example 47 includes the subject matter of Example 45, wherein the simple averaging comprises performing, for each pixel location, either a simple averaging or a weighted averaging of the respective pixel irradiances across the N input images.

Example 48 includes the subject matter of Example 45, wherein alpha blending using depth-adaptive synthesis comprises multiplying, for each pixel location, each of the pixel irradiances between the N input images by a corresponding alpha multiplier, wherein each alpha multiplier is a function of one or more alpha values, and wherein the one or more alpha values are a function of at least one of a depth of the respective pixel location or an angle of the respective pixel location.

Example 49 includes the subject matter of Example 48, wherein for each pixel location, the output irradiance in the output image is is given by , where, is the output pixel irradiance for each pixel location in the output image; is the pixel irradiance for each pixel location in the input image corresponding to the highest frequency among the received frequencies; is the pixel irradiance for each pixel location in the input image corresponding to the intermediate frequency among the received frequencies; is the pixel irradiance for each pixel location in the input image corresponding to the lowest frequency among the received frequencies; corresponds to the depth-dependent α value of the highest frequency among the received frequencies; corresponds to the depth-dependent α value of the intermediate frequency among the received frequencies.

Example 50 includes the subject matter of Example 48, wherein for each pixel location, the output irradiance in the output image is is given by , where r represents depth; θ represents angle; is the output pixel irradiance at depth r and image angle θ; is the pixel irradiance at depth r and image angle θ for the highest of the received frequencies; is the pixel irradiance at depth r and image angle θ for the intermediate frequency among the received frequencies; is the pixel irradiance at depth r and image angle θ for the lowest of the received frequencies; corresponds to the alpha value of the highest frequency among the received frequencies at depth r and image angle θ; corresponds to the alpha value at the middle frequency among the received frequencies at depth r and image angle θ.

Example 51 includes the subject matter of Example 45, wherein the max and min adaptive synthesis involves using, for each pixel location, blending of the maximum, minimum and average pixel irradiances among the N input images.

Example 52 includes the subject matter of Example 51, wherein for each pixel location, the output irradiance in the output image is is given by , where, ; ; corresponds to pixel irradiance at pixel location after alpha blending using depth compensation; Is , , or Corresponding to the maximum transparency alpha value coefficient based on at least one of has a set first value between 0 and 1 and including 0 and 1; silver , , or Corresponding to a minimum transparency alpha value coefficient based on at least one of has a set second value that is different from the set first value and is between 0 and 1 and includes 0 and 1.

Example 53 includes the subject matter of Example 45, wherein the predictive synthesis comprises determining a relationship between first electrical signals corresponding to a first area in a first input image corresponding to a first reflection frequency, and second electrical signals corresponding to a first area in a second input image corresponding to a second reflection frequency; and predicting third electrical signals corresponding to the second area in the second input image based on the relationship.

Example 54 includes the subject matter of Example 43, wherein the pixel irradiance of a pixel in the second region is , where, FF1 is a second region in the first input image; FF2 is a second region in the second input image; NF1 is a first region in the first input image; NF2 is a first region in the second input image; I _FF2 is a pixel irradiance in FF2; I _FF1 is a pixel irradiance in the region FF1; I _NF2 is a pixel irradiance in the region NF2; I _NF1 is a pixel irradiance in the region NF1.

Example 55 includes the subject matter of Example 53, wherein the pixel irradiance of a pixel in a second region is , where, FF1 is a second region in the first input image; FF2 is a second region in the second input image; NF2 is a first region in the second input image; I _FF2 is pixel irradiance in FF2; I _FF1 is pixel irradiance in the region FF1; I _NF2 is pixel irradiance in the region NF2; PSF1 corresponds to a point spread function (PSF) for the first receive frequency; PSF2 corresponds to a PSF for the second receive frequency; PSF1 _inverse is an inverse of PSF1 corresponding to the deconvolution; and PSF1 _inverse is an inverse of PSF1 corresponding to the deconvolution.

Example 56 includes the subject matter of Example 53, wherein the ML-based synthesis comprises: determining that a first region corresponds to a near-field region; determining that a second region corresponds to a far-field region; segmenting the near-field region into a plurality of sub-regions, for example, square sub-regions; generating a training data set based on a plurality of first input images and a plurality of first output images in the near-field region; developing an ML-based model for a relationship between pixel irradiances at a first reflection frequency within the first region and pixel irradiances at a second reflection frequency within the first region based on the training data set; and predicting pixel irradiances at the second reflection frequency within the second region based on the model.

Example 57 includes the subject matter of Example 56, and further comprises generating a validation data set based on a plurality of first input images and a plurality of first output images in a mid-field region of the input images, and developing an ML-based model based on the training data set and the validation data set.

Example 58 includes the subject matter of Example 45, wherein the color Doppler synthesis comprises combining, for each pixel location, individual pixel irradiances between the N input images based on at least one of information about depth, angle, signal-to-noise ratio, flow velocity or power.

Example 59 includes the subject of Example 58, and R0 _out and R1 _out of the output image are and is given by , where, : Zero-lag output corresponding to the alpha blending value of the maximum and average zero-lag autocorrelations; : First lag output corresponding to the alpha blending value of the maximum and average first lag autocorrelations; α: alpha value for alpha blending; : The maximum zero-lag output value of the autocorrelation corresponding to the alpha blending to be used; : Maximum first lag output value of autocorrelation; : The average zero-lag output value of the autocorrelation corresponding to the alpha blending to be used; : Average first lag output value of autocorrelation; : a zero-lag input value corresponding to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used; and : The first lag input value corresponding to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used.

Example 60 includes the subject matter of Example 41, wherein each of the reflected frequencies comprises 1.75 MHz, 3.5 MHz and 5.0 MHz, and wherein the fundamental frequency of the transmitted ultrasonic waveform is a single frequency of 1.75 MHz; or wherein each of the fundamental frequencies of the transmitted ultrasonic waveform comprises 1.75 MHz, 3.5 MHz and 5.0 MHz.

Example 61 includes a device comprising means for performing the method of any one of claims 41 to 60.

Example 62 includes one or more computer-readable media having stored thereon a plurality of instructions that, when executed, cause one or more processors to perform a method of any one of claims 41 to 60.

Example 63 includes an imaging device comprising any one of the devices of claims 1 to 20, and further comprising a user interface device.

Example 64 includes an article comprising one or more tangible computer-readable non-transitory storage media comprising computer-executable instructions that, when executed by at least one computer processor, cause the at least one processor to perform any of the methods of Examples 21 to 40.

Claims (62)

As a device of a computing device,
A memory, and one or more processors coupled to the memory, wherein the one or more processors are:
Simultaneously receiving electrical signals based on respective reflection frequencies of reflected ultrasonic waves reflected from a target object as a result of the transmitted ultrasonic waves;
Generating synthesized electrical signals by compounding information from the above electrical signals;
A device that generates an output image on a display based on the synthesized electrical signals. In the first paragraph,
A device wherein each of the above reflected frequencies corresponds to respective harmonics of the fundamental frequency of the transmitted ultrasonic waveform, wherein the fundamental frequency is a single frequency of the transmitted ultrasonic waveform. In the first paragraph,
A device wherein the transmitted ultrasonic waveform is a multi-mode waveform having fundamental frequencies corresponding to the respective frequencies of the reflected ultrasonic waveform. In the first paragraph,
One or more of the above processors,
Implementing a prediction algorithm for said electrical signals, wherein said one or more processors generate prediction electrical signals for a second image area of said target object, said second image area being different from said first image area, using information from said first electrical signals corresponding to said first image area of said target object; and
Obtaining the synthesized electric signals using the predicted electric signals so that the output image in the second image area corresponds to the predicted electric signals.
A device for synthesizing said information. In any one of claims 1 to 4,
The above reflection frequencies include N reflection frequencies;
The above electrical signals include N sets of electrical signals, each set of electrical signals corresponding to one of the reflection frequencies, and the N sets of electrical signals corresponding to N input images of the target object;
The individual input images contain pixels at respective pixel locations, and each pixel location of the N input images is defined by depth and angle;
Synthesizing information comprises synthesizing information from said N sets of electrical signals;
A device wherein said one or more processors synthesize said information from said sets of N electrical signals using at least one of simple averaging, weighted averaging, alpha blending using depth adaptive synthesis, max and min adaptive synthesis, predictive synthesis, lateral frequency synthesis and color Doppler synthesis. In paragraph 5,
The device further comprises one or more processors, wherein the electrical signals are subjected to gain compensation or dynamic range compensation prior to synthesis. In paragraph 5,
A device wherein simple averaging comprises performing, for each pixel location, either a simple averaging or a weighted averaging of the pixel irradiances across the N input images. In paragraph 5,
A device wherein alpha blending using depth-adaptive synthesis comprises multiplying, for each pixel location, each pixel irradiance between the N input images by a corresponding alpha multiplier, wherein each alpha multiplier is a function of one or more alpha values, the one or more alpha values being a function of at least one of a depth of the respective pixel location or an angle of the respective pixel location. In Article 8,
For each pixel location above, the output irradiance in the output image is,
is given by , where,
is the output pixel irradiance for each pixel location for the output image;
is the pixel irradiance for each pixel location in the input image corresponding to the highest frequency among the received frequencies;
is the pixel irradiance for each pixel location in the input image corresponding to the intermediate frequency among the above receiving frequencies;
is the pixel irradiance for each pixel location in the input image corresponding to the lowest frequency among the above receiving frequencies;
corresponds to the depth-dependent α value of the highest frequency among the above receiving frequencies;
A device corresponding to a depth-dependent α value of an intermediate frequency among the above receiving frequencies. In Article 8,
For each pixel location above, the output irradiance in the output image is,
is given by , where,
r: depth;
θ: angle;
: Output pixel irradiance at depth r and image angle θ;
: Pixel irradiance at depth r and image angle θ for the highest of the received frequencies;
: Pixel irradiance at depth r and image angle θ for the intermediate frequency among the above receiving frequencies;
: Pixel irradiance at depth r and image angle θ for the lowest of the above receiving frequencies;
corresponds to the alpha value of the highest frequency among the received frequencies at depth r and image angle θ;
A device corresponding to an alpha value at an intermediate frequency among the reception frequencies at depth r and image angle θ. In paragraph 5,
A device wherein the maximum and minimum adaptive synthesis comprises blending, for each pixel location, of the maximum, minimum and average pixel irradiances among the N input images. In Article 11,
For each pixel location above, the output irradiance in the output image is,
is given by , where,
;
;
corresponds to pixel irradiance at pixel location after alpha blending using depth compensation;
Is , , or Corresponding to the maximum transparency alpha value coefficient based on at least one of has a set first value between 0 and 1 and including 0 and 1;
silver , , or Corresponding to a minimum transparency alpha value coefficient based on at least one of A device having a set second value which is different from the set first value and is between 0 and 1 and including 0 and 1. In paragraph 5,
Predictive synthesis is,
Determining a relationship between first electrical signals corresponding to a first region in a first input image corresponding to a first reflection frequency and second electrical signals corresponding to the first region in a second input image corresponding to a second reflection frequency; and
Based on the above relationship, predicting third electrical signals corresponding to the second region of the second input image.
A device comprising: In Article 13,
The pixel irradiance of the pixel in the above second region is,
is 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 is the pixel irradiance at FF2;
I _FF1 is the pixel irradiance in area FF1;
I _NF2 is the pixel irradiance in area NF2;
I _NF1 is the pixel irradiance in area NF1, device. In Article 13,
The pixel irradiance of the pixel in the above second region is,
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 is the pixel irradiance at FF2;
I _FF1 is the pixel irradiance in area FF1;
I _NF2 is the pixel irradiance in area NF2;
PSF1 corresponds to the point spread function (PSF) for the first receiving frequency;
PSF2 corresponds to the PSF for the second receiving frequency;
PSF1 _inverse is the inverse of PSF1 corresponding to deconvolution;
PSF1 _inverse is the device that is the inverse of PSF1 corresponding to deconvolution. In Article 13,
ML-based synthesis is,
Determining that the above first region corresponds to the near-field region;
Determining that the above second region corresponds to a long-range region;
Segmenting the above near-field region into a plurality of sub-regions, for example, square sub-regions;
Generating a training data set based on a plurality of first input images and a plurality of first output images in the near-field region;
Developing an ML-based model for the relationship between pixel irradiances at the first reflection frequency within the first region and pixel irradiances at the second reflection frequency within the first region based on the training data set; and
Predicting pixel irradiances at the second reflection frequency within the second region based on the above model.
A device comprising: In Article 16,
A device wherein the ML-based synthesis further comprises generating a validation data set based on a plurality of first input images and a plurality of first output images in a mid-range region of the input images, and developing the ML-based model based on the training data set and the validation data set. In paragraph 5,
A device wherein color Doppler synthesis comprises combining, for each pixel location, the respective pixel irradiances between said N input images based on at least one of information about depth, angle, signal-to-noise ratio, flow velocity or power. In Article 18,
R0 _out and R1 _out of the above output image are and is given by , where,
: Zero-lag output corresponding to the alpha blending value of the maximum and average zero-lag autocorrelations;
: First lag output corresponding to the alpha blending value of the maximum and average first lag autocorrelations;
α: Alpha value for alpha blending;
: The maximum zero-lag output value of the autocorrelation corresponding to the alpha blending to be used;
: Maximum first lag output value of autocorrelation;
: The average zero-lag output value of the autocorrelation corresponding to the alpha blending to be used;
: Average first lag output value of autocorrelation;
: a zero-lag input value corresponding to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used; and
: A device, wherein the first lag input value corresponds to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used. In any one of claims 1 to 4,
The above respective reflection frequencies include 1.75 MHz, 3.5 MHz and 5.0 MHz,
The fundamental frequency of the transmitted ultrasonic wave is a single frequency of 1.75 MHz; or
A device, wherein each of the fundamental frequencies of the transmitted ultrasonic waveforms is one of 1.75 MHz, 3.5 MHz and 5.0 MHz. As a system,
A user interface device including a display device; and
A computing device communicatively coupled to the user interface device
, wherein the computing device comprises a memory and one or more processors coupled to the memory, wherein the one or more processors include:
Simultaneously receiving electrical signals based on respective reflection frequencies of reflected ultrasonic waves reflected from a target object as a result of the transmitted ultrasonic waves;
Generating synthesized electrical signals by synthesizing information from the above electrical signals;
A system for generating an output image on a display device based on the synthesized electrical signals. In Article 21,
A system wherein each of the above reflected frequencies corresponds to respective harmonics of the fundamental frequency of the transmitted ultrasonic waveform, wherein the fundamental frequency is a single frequency of the transmitted ultrasonic waveform. In Article 21,
A system wherein the transmitted ultrasonic waveform is a multi-mode waveform having fundamental frequencies corresponding to the respective frequencies of the reflected ultrasonic waveform. In Article 21,
One or more of the above processors,
Implementing a prediction algorithm for said electrical signals, wherein said one or more processors generate prediction electrical signals for a second image area of said target object, said second image area being different from said first image area, using information from said first electrical signals corresponding to said first image area of said target object; and
Obtaining the synthesized electric signals using the predicted electric signals so that the output image in the second image area corresponds to the predicted electric signals.
A system for synthesizing said information. In any one of Articles 21 to 24,
The above reflection frequencies include N reflection frequencies;
The above electrical signals include N sets of electrical signals, each set of electrical signals corresponding to one of the reflection frequencies, and the N sets of electrical signals corresponding to N input images of the target object;
The individual input images contain pixels at respective pixel locations, and each pixel location of the N input images is defined by depth and angle;
Synthesizing information comprises synthesizing information from said N sets of electrical signals;
A system wherein said one or more processors synthesize said information from said sets of N electrical signals using at least one of simple averaging, weighted averaging, alpha blending using depth adaptive synthesis, max and min adaptive synthesis, predictive synthesis, lateral frequency synthesis and color Doppler synthesis. In Article 25,
The system further comprises one or more processors that subject the electrical signals to gain compensation or dynamic range compensation prior to synthesis. In Article 25,
A system wherein simple averaging comprises performing, for each pixel location, either a simple averaging or a weighted averaging of the respective pixel irradiances across the N input images. In Article 25,
Alpha blending using depth-adaptive synthesis comprises, for each pixel location, multiplying each pixel irradiance between said N input images by a corresponding alpha multiplier, wherein each alpha multiplier is a function of one or more alpha values, wherein the one or more alpha values are a function of at least one of a depth of said each pixel location or an angle of said each pixel location. In Article 28,
For each pixel location above, the output irradiance in the output image is,
is given by , where,
is the output pixel irradiance for each pixel location for the output image;
is the pixel irradiance for each pixel location in the input image corresponding to the highest frequency among the received frequencies;
is the pixel irradiance for each pixel location in the input image corresponding to the intermediate frequency among the above receiving frequencies;
is the pixel irradiance for each pixel location in the input image corresponding to the lowest frequency among the above receiving frequencies;
corresponds to the depth-dependent α value of the highest frequency among the above receiving frequencies;
A system corresponding to a depth-dependent α value of an intermediate frequency among the above receiving frequencies. In Article 28,
For each pixel location above, the output irradiance in the output image is,
is given by , where,
r: depth;
θ: angle;
: Output pixel irradiance at depth r and image angle θ;
: Pixel irradiance at depth r and image angle θ for the highest of the received frequencies;
: Pixel irradiance at depth r and image angle θ for the intermediate frequency among the above receiving frequencies;
: Pixel irradiance at depth r and image angle θ for the lowest of the above receiving frequencies;
: the alpha value of the highest frequency among the received frequencies at depth r and image angle θ; and
: System, where the alpha value at the intermediate frequency among the above receiving frequencies at depth r and image angle θ. In Article 25,
A system wherein the maximum and minimum adaptive synthesis comprises, for each pixel location, blending of the maximum, minimum and average pixel irradiances among the N input images. In Article 31,
For each pixel location above, the output irradiance in the output image is,
is given by , where,
;
;
corresponds to pixel irradiance at pixel location after alpha blending using depth compensation;
Is , , or Corresponding to the maximum transparency alpha value coefficient based on at least one of has a set first value between 0 and 1 and including 0 and 1;
silver , , or Corresponding to a minimum transparency alpha value coefficient based on at least one of A system having a set second value which is different from the set first value and is between 0 and 1 and including 0 and 1. In Article 25,
Predictive synthesis is,
Determining a relationship between first electrical signals corresponding to a first region in a first input image corresponding to a first reflection frequency and second electrical signals corresponding to the first region in a second input image corresponding to a second reflection frequency; and
Based on the above relationship, predicting third electrical signals corresponding to the second region of the second input image.
A system comprising: In Article 33,
The pixel irradiance of the pixel in the above second region is,
is 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 is the pixel irradiance at FF2;
I _FF1 is the pixel irradiance in area FF1;
I _NF2 is the pixel irradiance in area NF2;
I _NF1 is the pixel irradiance in area NF1, system. In Article 33,
The pixel irradiance of the pixel in the above second region is,
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 is the pixel irradiance at FF2;
I _FF1 is the pixel irradiance in area FF1;
I _NF2 is the pixel irradiance in area NF2;
PSF1 corresponds to the point spread function (PSF) for the first receiving frequency;
PSF2 corresponds to the PSF for the second receiving frequency;
PSF1 _inverse is the inverse of PSF1 corresponding to deconvolution;
PSF1 _inverse is the inverse of PSF1 corresponding to deconvolution, system. In Article 33,
ML-based synthesis is,
Determining that the above first region corresponds to the near-field region;
Determining that the above second region corresponds to the long-range region;
Segmenting the above near-field region into a plurality of sub-regions, for example, square sub-regions;
Generating a training data set based on a plurality of first input images and a plurality of first output images in the near-field region;
Developing an ML-based model for the relationship between pixel irradiances at the first reflection frequency within the first region and pixel irradiances at the second reflection frequency within the first region based on the training data set; and
Predicting pixel irradiances at the second reflection frequency within the second region based on the above model.
A system comprising: In Article 36,
A system wherein the ML-based synthesis further comprises generating a validation data set based on a plurality of first input images and a plurality of first output images in the mid-field region of the input images, and developing the ML-based model based on the training data set and the validation data set. In Article 25,
Color Doppler synthesis is a system comprising combining, for each pixel location, the respective pixel irradiances between the N input images based on at least one of information about depth, angle, signal-to-noise ratio, flow velocity or power. In Article 38,
R0 _out and R1 _out of the above output image are and is given by , where,
: Zero-lag output corresponding to the alpha blending value of the maximum and average zero-lag autocorrelations;
: First lag output corresponding to the alpha blending value of the maximum and average first lag autocorrelations;
α: Alpha value for alpha blending;
: The maximum zero-lag output value of the autocorrelation corresponding to the alpha blending to be used;
: Maximum first lag output value of autocorrelation;
: The average zero-lag output value of the autocorrelation corresponding to the alpha blending to be used;
: Average first lag output value of autocorrelation;
: a zero-lag input value corresponding to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used; and
: A system, wherein the first lag input value corresponds to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used. In any one of Articles 21 to 24,
The above respective reflection frequencies include 1.75 MHz, 3.5 MHz and 5.0 MHz,
The fundamental frequency of the transmitted ultrasonic wave is a single frequency of 1.75 MHz; or
A system, wherein each of the fundamental frequencies of the transmitted ultrasonic waveforms is one of 1.75 MHz, 3.5 MHz and 5.0 MHz. A method performed in a computing device comprising a memory and one or more processors coupled to the memory,
A step of simultaneously receiving electrical signals based on respective reflection frequencies of a reflected ultrasonic waveform reflected from a target object as a result of a transmitted ultrasonic waveform;
A step of generating synthesized electrical signals by synthesizing information from the above electrical signals; and
A step of generating an output image on a display device based on the above synthesized electrical signals.
A method comprising: In Article 41,
A method wherein each of the above reflected frequencies corresponds to respective harmonics of the fundamental frequency of the transmitted ultrasonic waveform, wherein the fundamental frequency is a single frequency of the transmitted ultrasonic waveform. In Article 41,
A method wherein the transmitted ultrasonic waveform is a multi-mode waveform having fundamental frequencies corresponding to the respective frequencies of the reflected ultrasonic waveform. In Article 41,
Synthesizing the above information is:
Implementing a prediction algorithm for said electrical signals, wherein said one or more processors generate prediction electrical signals for a second image area of said target object, said second image area being different from said first image area, using information from said first electrical signals corresponding to said first image area of said target object; and
Obtaining the synthesized electric signals using the predicted electric signals so that the output image in the second image area corresponds to the predicted electric signals.
A method comprising: In Article 41,
The above reflection frequencies include N reflection frequencies;
The above electrical signals include N sets of electrical signals, each set of electrical signals corresponding to one of the reflection frequencies, and the N sets of electrical signals corresponding to N input images of the target object;
The individual input images contain pixels at respective pixel locations, and each pixel location of the N input images is defined by depth and angle;
Synthesizing information comprises synthesizing information from said N sets of electrical signals;
The method further comprises a step of synthesizing the information from the N sets of electrical signals using at least one of simple averaging, weighted averaging, alpha blending using depth adaptive synthesis, max and min adaptive synthesis, predictive synthesis, lateral frequency synthesis and color Doppler synthesis. In Article 45,
A method further comprising the step of subjecting said electrical signals to gain compensation or dynamic range compensation prior to synthesis. In Article 45,
A method wherein simple averaging comprises performing, for each pixel location, either a simple averaging or a weighted averaging of the respective pixel irradiances across the N input images. In Article 45,
A method using depth-adaptive synthesis for alpha blending comprises multiplying, for each pixel location, each pixel irradiance between said N input images by a corresponding alpha multiplier, wherein each alpha multiplier is a function of one or more alpha values, wherein the one or more alpha values are a function of at least one of a depth of said each pixel location or an angle of said each pixel location. In Article 48,
For each pixel location above, the output irradiance in the output image is,
is given by , where,
is the output pixel irradiance for each pixel location for the output image;
is the pixel irradiance for each pixel location in the input image corresponding to the highest frequency among the received frequencies;
is the pixel irradiance for each pixel location in the input image corresponding to the intermediate frequency among the above receiving frequencies;
is the pixel irradiance for each pixel location in the input image corresponding to the lowest frequency among the above receiving frequencies;
corresponds to the depth-dependent α value of the highest frequency among the above receiving frequencies;
A method corresponding to a depth-dependent α value of an intermediate frequency among the above receiving frequencies. In Article 48,
For each pixel location above, the output irradiance in the output image is,
is given by , where,
r represents depth;
θ represents the angle;
is the output pixel irradiance at depth r and image angle θ;
is the pixel irradiance at depth r and image angle θ for the highest of the received frequencies;
is the pixel irradiance at depth r and image angle θ for the intermediate frequency among the above receiving frequencies;
is the pixel irradiance at depth r and image angle θ for the lowest of the above receiving frequencies;
corresponds to the alpha value of the highest frequency among the received frequencies at depth r and image angle θ;
A method in which the alpha value at the intermediate frequency among the reception frequencies at depth r and image angle θ corresponds to the alpha value. In Article 45,
A method wherein the maximum and minimum adaptive synthesis comprises blending, for each pixel location, of the maximum, minimum and average pixel irradiances among the N input images. In Article 51,
For each pixel location above, the output irradiance in the output image is,
is given by , where,
;
;
corresponds to pixel irradiance at pixel location after alpha blending using depth compensation;
Is , , or Corresponding to the maximum transparency alpha value coefficient based on at least one of has a set first value between 0 and 1 and including 0 and 1;
silver , , or Corresponding to a minimum transparency alpha value coefficient based on at least one of A method wherein the first set value is different from the first set value and has a second set value between 0 and 1 and including 0 and 1. In Article 45,
Predictive synthesis is,
Determining a relationship between first electrical signals corresponding to a first region in a first input image corresponding to a first reflection frequency and second electrical signals corresponding to the first region in a second input image corresponding to a second reflection frequency; and
Based on the above relationship, predicting third electrical signals corresponding to the second region of the second input image.
A method comprising: In Article 43,
The pixel irradiance of the pixel in the above second region is,
is 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 is the pixel irradiance at FF2;
I _FF1 is the pixel irradiance in area FF1;
I _NF2 is the pixel irradiance in area NF2;
I _NF1 is the pixel irradiance in area NF1, method. In Article 53,
The pixel irradiance of the pixel in the above second region is,
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 is the pixel irradiance at FF2;
I _FF1 is the pixel irradiance in area FF1;
I _NF2 is the pixel irradiance in area NF2;
PSF1 corresponds to the point spread function (PSF) for the first receiving frequency;
PSF2 corresponds to the PSF for the second receiving frequency;
PSF1 _inverse is the inverse of PSF1 corresponding to deconvolution;
PSF1 _inverse is the inverse of PSF1 corresponding to deconvolution. In Article 53,
ML-based synthesis is,
Determining that the above first region corresponds to the near-field region;
Determining that the above second region corresponds to a long-range region;
Segmenting the above near-field region into a plurality of sub-regions, for example, square sub-regions;
Generating a training data set based on a plurality of first input images and a plurality of first output images in the near-field region;
Developing an ML-based model for the relationship between pixel irradiances at the first reflection frequency within the first region and pixel irradiances at the second reflection frequency within the first region based on the training data set; and
Predicting pixel irradiances at the second reflection frequency within the second region based on the above model.
A method comprising: In Article 56,
A method wherein the ML-based synthesis further comprises generating a validation data set based on a plurality of first input images and a plurality of first output images in the mid-field region of the input images, and developing the ML-based model based on the training data set and the validation data set. In Article 45,
Color Doppler synthesis is a method comprising combining, for each pixel location, the respective pixel irradiances between the N input images based on at least one of information about depth, angle, signal-to-noise ratio, flow velocity or power. In Article 58,
R0 _out and R1 _out of the above output image are and is given by , where,
: Zero-lag output corresponding to the alpha blending value of the maximum and average zero-lag autocorrelations;
: First lag output corresponding to the alpha blending value of the maximum and average first lag autocorrelations;
α: Alpha value for alpha blending;
: The maximum zero-lag output value of the autocorrelation corresponding to the alpha blending to be used;
: Maximum first lag output value of autocorrelation;
: The average zero-lag output value of the autocorrelation corresponding to the alpha blending to be used;
: Average first lag output value of autocorrelation;
: a zero-lag input value corresponding to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used; and
: A method, wherein the first lag input value corresponds to the frequency band of the imaging waveforms for the autocorrelation corresponding to the alpha blending to be used. In Article 41,
The above respective reflection frequencies include 1.75 MHz, 3.5 MHz and 5.0 MHz,
The fundamental frequency of the transmitted ultrasonic wave is a single frequency of 1.75 MHz; or
A method, wherein each of the fundamental frequencies of the transmitted ultrasonic waveforms is one of 1.75 MHz, 3.5 MHz and 5.0 MHz. As a device,
A device comprising means for performing the method of any one of claims 41 to 60. As one or more computer-readable media,
One or more computer readable media comprising a plurality of instructions stored thereon, which, when executed, cause one or more processors to perform the method of any one of claims 41 to 60.