Systems and methods for beamformed sound velocity selection

Abstract

Methods and systems for beamforming of received ultrasound signals to generate ultrasound images with increased resolution are provided. In one example, a method includes calculating, for each of a plurality of beamforming sound velocities, a respective beamforming quality metric, each beamforming quality metric calculated from an ultrasound receive channel signal that is signal delayed based on a respective beamformed sound signal; identifying a target beamforming sound velocity based on the beamforming quality metrics; and generating an ultrasound image using the target beamforming sound velocity.

Description

Technical Field

[0001] The embodiments of the subject matter disclosed herein relate to ultrasound imaging, and more specifically, to improving image quality for ultrasound imaging. Background Technology

[0002] Medical ultrasound is an imaging modality that uses ultrasound waves to probe the internal structures of a patient's body and generate corresponding images. For example, an ultrasound probe, comprising multiple transducer elements, emits ultrasound pulses that are reflected, refracted, or absorbed by structures within the body. The ultrasound probe then receives the reflected echoes, which are processed into images. Ultrasound images of internal structures can be saved for later analysis by clinicians to aid in diagnosis and / or can be displayed in real-time or near real-time on a display device. Summary of the Invention

[0003] In one implementation, a method includes: calculating a corresponding beamforming quality metric for each of a plurality of beamforming sound velocities, each beamforming quality metric being calculated using an ultrasonic receive channel signal with signal delay based on the corresponding beamforming sound velocity; identifying a target beamforming sound velocity based on the beamforming quality metric; and generating an ultrasonic image using the target beamforming sound velocity. In some examples, the target beamforming sound velocity may be used to calculate a receive beamforming time delay, a transmit beamforming time delay, or both the receive beamforming time delay and the transmit beamforming time delay when generating the ultrasonic image.

[0004] The above-described advantages, as well as other advantages and features, of this specification will become apparent, either alone or in connection with the accompanying drawings, from the following detailed description. It should be understood that the above summary is provided to present a simplified version of the selected concepts further described in the detailed description. This is not intended to identify key or essential features of the claimed subject matter, the scope of which is uniquely defined by the claims following the detailed description. Furthermore, the claimed subject matter is not limited to embodiments that address any of the disadvantages mentioned above or in any part of this disclosure. Attached Figure Description

[0005] A better understanding of the various aspects of this disclosure can be achieved by reading the following detailed description and referring to the accompanying drawings, in which:

[0006] Figure 1 A block diagram of an exemplary embodiment of an ultrasound system is shown;

[0007] Figure 2 It shows the formation Figure 1 A block diagram of the receiver, which is part of the system;

[0008] Figure 3It is a graph showing the calculation of the optimal beamforming sound velocity based on the best fit of the generated polynomial to multiple data values ​​representing beamforming quality at multiple beamforming sound velocities;

[0009] Figures 4A to 4B Ultrasonic images generated using suboptimal beamforming sound speed and optimal beamforming sound speed are shown respectively;

[0010] Figure 5 This is a graph showing multiple calculations of the optimal beamforming sound velocity for multiple scanning regions during ultrasound scanning;

[0011] Figure 6 The image shows an ultrasound image generated using the beamforming velocity closest to the calculated optimal beamforming velocity for multiple regions represented by region of interest markers;

[0012] Figure 7 This is a flowchart illustrating an exemplary method for generating ultrasound images based on optimal beamforming sound velocity calculations; and

[0013] Figure 8 This is a flowchart illustrating an exemplary method for calculating beamforming quality metrics from coherence factors. Detailed Implementation

[0014] Medical ultrasound imaging typically involves placing an ultrasound probe, comprising one or more transducer elements, onto an imaging subject (such as a patient) at the location of a target anatomical feature (e.g., abdomen, chest, etc.). Images are acquired by the ultrasound probe and displayed on a display device in real-time or near real-time (e.g., images are displayed as soon as they are generated without intentional delay). During the image acquisition process, transmit beamforming and receive beamforming can be used. Time delays can be implemented during transmit and receive beamforming to change the beamforming angle and focus range as ultrasound images are acquired. The beamforming time delay can be based on the speed at which ultrasound echoes travel through the imaging medium (e.g., tissue). This assumed speed is often the default speed set by the system. Different imaging media (e.g., adipose tissue, liver tissue) exhibit different sound propagation speeds, meaning that the speed at which ultrasound waves travel through a first medium may differ from the speed at which ultrasound waves travel through a second medium. Therefore, a predetermined or default assumed speed set by the system may result in a loss of image resolution, depending on the imaging medium covered during the ultrasound scan or variations in the imaging medium. Conventional ultrasound systems may allow operators to manually adjust the assumed velocity of sound used to calculate beamforming time delay, but various factors can hinder the correct selection of the velocity of sound (e.g., user inexperience, time constraints), potentially resulting in lower resolution ultrasound images. Furthermore, a single assumed velocity of sound may not be optimal when the velocity of sound varies within the medium. Additionally, ultrasound system manufacturers have found that most operators dislike manually adjusting the ultrasound system to optimize image quality.

[0015] Therefore, according to the embodiments disclosed herein, the optimal beamforming velocity can be automatically selected, such that the ultrasound image generated during a scan using the optimal beamforming velocity exhibits improved image resolution. Ultrasound data received by the transducer of the ultrasound probe (referred to herein as channel data) before being processed into an image can be analyzed to calculate and identify the beamforming velocity, allowing the optimal beamforming velocity to be selected before generating the ultrasound image displayed to the operator, and then the ultrasound image to be generated after the optimal beamforming velocity is selected. The channel data can be used to calculate beamforming quality metrics, thereby allowing the automatic selection of ultrasound imaging parameters, such as the beamforming velocities used for transmit beamforming calculations, receive beamforming calculations, or both transmit and receive beamforming calculations, and these parameters can be adjusted to improve ultrasound image resolution. The beamforming quality metric can be calculated from a coherence factor, which can be the ratio of the amplitude of the coherently summed received signal to the amplitude of the incoherently summed received signal, calculated from the channel data and serving as an indicator of possible ultrasound image resolution. Ultrasound images generated from automatically selected ultrasound imaging parameters can exhibit higher image resolution without operator intervention.

[0016] Figure 1 An exemplary ultrasound system is illustrated, comprising an ultrasound probe, a display device, and an imaging processing system. Ultrasound data can be acquired via the ultrasound probe, and ultrasound images can be calculated and displayed on the display device. Figure 2 It shows Figure 1 The receiver of the system has multiple receive channels for receiving beamforming. Figure 3 The figure shows a graph of a method for calculating the optimal beamforming sound velocity using data obtained via multiple receiving channels. Figure 4A The image shown is an ultrasonic image generated from the sound velocity of a suboptimal beamforming system. Figure 4B The text shows information from... Figure 3 The curve is used to select the optimal beam to form the ultrasonic image generated by the sound speed. Figure 5 The diagram shows several graphs illustrating a method for calculating the optimal beamforming sound velocity over a subdivided region of an ultrasound scan area. Figure 6 The image shown is an ultrasound image generated using the optimal beamforming sound velocity for each subdivision of the scan area. According to... Figure 7 A method can be used to calculate the optimal beamforming sound velocity and apply it to generate ultrasound images, where based on... Figure 8 The method calculates the coherence factor to select the optimal beamforming sound speed.

[0017] See Figure 1 A schematic diagram of an ultrasound imaging system 100 according to an embodiment of the present disclosure is shown. The ultrasound imaging system 100 includes a transmit beamformer 101 and a transmitter 102 that drives elements (e.g., transducer elements) 104 within a transducer array (referred to herein as probe 106) to transmit pulsed ultrasound signals (referred herein as transmit pulses) into a body (not shown). According to one embodiment, probe 106 may be a one-dimensional transducer array probe. However, in some embodiments, probe 106 may be a two-dimensional matrix transducer array probe. As further explained below, transducer element 104 may be made of a piezoelectric material. When a voltage is applied to a piezoelectric crystal, the piezoelectric crystal physically expands and contracts, thereby emitting ultrasound waves. In this way, transducer element 104 can convert an electronic emission signal into an acoustic emission beam.

[0018] After element 104 of probe 106 transmits a pulsed ultrasound signal into the patient's body, the pulsed ultrasound signal is reflected from internal structures (such as blood cells or muscle tissue) to produce an echo returning to element 104. The echo is converted into an electrical signal or ultrasound data by element 104, and the electrical signal is received by receiver 108. The electrical signal representing the received echo passes through receiver beamformer 110, which outputs ultrasound data.

[0019] The echo signals generated by the transmission operation are reflected from structures located at continuous distances along the emitted ultrasonic beam. The echo signals are sensed individually by each transducer element, and samples of the echo signal amplitude at specific time points represent the amount of reflection occurring at that specific distance. However, due to the difference in the propagation path between the reflection point P and each element, these echo signals are not detected simultaneously. Receiver 108 amplifies the individual echo signals, assigns a calculated reception time delay to each echo signal, and sums them to provide a single echo signal that approximately indicates the total ultrasonic energy reflected from point P located at a distance R along an ultrasonic beam oriented at angle θ.

[0020] During the reception of the echo, the time delay of each receiving channel is continuously varied to provide dynamic focusing of the received beam at a distance R, based on the assumed sound speed of the medium assuming that the echo signal is emitted from a distance R.

[0021] As instructed by processor 116, receiver 108 provides a time delay during scanning, such that the orientation of receiver 108 tracks the direction θ of the beam oriented by the transmitter, and samples the echo signal at consecutive distances R to provide time delay and phase shift for dynamic focusing along the beam at point P. Therefore, each transmission of the ultrasonic pulse waveform results in the acquisition of a series of data points representing the amount of sound reflected from a series of corresponding points P located along the ultrasonic beam.

[0022] According to some embodiments, probe 106 may include electronic circuitry to perform all or part of transmit beamforming and / or receive beamforming. For example, all or part of transmit beamformer 101, transmitter 102, receiver 108, and receive beamformer 110 may be located within probe 106. In this disclosure, the terms “scanning” or “under scanning” may also be used to refer to the process of acquiring data by transmitting and receiving ultrasound signals. In this disclosure, the term “data” may be used to refer to one or more datasets acquired using an ultrasound imaging system. User interface 115 may be used to control the operation of ultrasound imaging system 100, including for controlling the input of patient data (e.g., patient history), for changing scan or display parameters, for initiating probe repolarization sequences, etc. User interface 115 may include one or more of the following: a rotary element, a mouse, a keyboard, a trackball, hard keys linked to specific actions, soft keys configurable to control different functions, and a graphical user interface displayed on display device 118.

[0023] The ultrasound imaging system 100 also includes a processor 116 for controlling the transmitting beamformer 101, the transmitter 102, the receiver 108, and the receiving beamformer 110. The processor 116 communicates electronically (e.g., is communicatively connected) with the probe 106. For the purposes of this disclosure, the term "electronic communication" may be defined to include both wired and wireless communication. The processor 116 can control the probe 106 to acquire data according to instructions stored in the processor's memory and / or memory 120. The processor 116 controls which of the elements 104 are active and the shape of the beam emitted from the probe 106. The processor 116 also communicates electronically with a display device 118 and can process data (e.g., ultrasound data) into images for display on the display device 118. The processor 116 may include a central processing unit (CPU) according to one embodiment. According to other embodiments, the processor 116 may include other electronic components capable of performing processing functions, such as a digital signal processor, a field-programmable gate array (FPGA), or a graphics board. According to other embodiments, processor 116 may include multiple electronic components capable of performing processing functions. For example, processor 116 may include two or more electronic components selected from a list of electronic components, including: a central processing unit, a digital signal processor, a field-programmable gate array (FPGA), and a graphics board. According to another embodiment, processor 116 may also include a composite demodulator (not shown) that demodulates real RF (radio frequency) data and generates composite data. In another embodiment, demodulation may be performed earlier in the processing chain. Processor 116 is adapted to perform one or more processing operations based on multiple selectable ultrasound modalities on the data. In one example, data may be processed in real time during a scanning session because echo signals are received by receiver 108 and transmitted to processor 116. For the purposes of this disclosure, the term "real time" is defined as including processes performed without any intentional delay. For example, embodiments may acquire images at a real-time rate of 7 frames / second to 20 frames / second. Ultrasonic imaging system 100 is capable of acquiring 2D data of one or more planes at significantly faster rates. However, it should be understood that the real-time frame rate may depend on the length of time spent acquiring each frame of data used for display. Therefore, when acquiring relatively large amounts of data, the real-time frame rate may be slow. Consequently, some embodiments may have a real-time frame rate significantly faster than 20 frames per second, while others may have a real-time frame rate lower than 7 frames per second. Data may be temporarily stored in a buffer (not shown) during a scanning session and processed in a less real-time manner during real-time or offline operation. Some embodiments of the invention may include multiple processors (not shown) to handle the processing tasks handled by processor 116 according to the exemplary embodiments described above.For example, before displaying an image, a first processor can be used to demodulate and extract the RF signal, while a second processor can be used to further process the data (e.g., by augmenting the data as further described herein). It should be understood that other embodiments may use different processor arrangements.

[0024] The ultrasound imaging system 100 can continuously acquire data at frame rates, for example, from 10 Hz to 30 Hz (e.g., 10 to 30 frames per second). Images generated from the data can be refreshed on a display device 118 at a similar frame rate. Other embodiments are capable of acquiring and displaying data at different rates. For example, depending on the frame size and the intended application, some embodiments may acquire data at frame rates less than 10 Hz or greater than 30 Hz. A memory 120 is included for storing frames of processed acquired data. In an exemplary embodiment, the memory 120 has sufficient capacity to store at least several seconds of ultrasound data frames. The data frames are stored in a manner that facilitates retrieval based on their acquisition order or time. The memory 120 may include any known data storage medium.

[0025] In various embodiments of the invention, processor 116 can process data through different mode-related modules (e.g., B-mode, color Doppler, M-mode, color M-mode, spectral Doppler, elastography, TVI, strain, strain rate, etc.) to form 2D or 3D data. For example, one or more modules can generate B-mode, color Doppler, M-mode, color M-mode, spectral Doppler, elastography, TVI, strain, strain rate, and combinations thereof. As an example, one or more modules can process color Doppler data, which may include conventional color flow Doppler, power Doppler, HD flow, etc. Image lines and / or frames are stored in memory and may include timing information indicating the time when image lines and / or frames are stored in memory. These modules may include, for example, a scan conversion module for performing scan conversion operations to convert the acquired images from beam space coordinates to display space coordinates. A video processor module may be provided that reads the acquired images from memory and displays the images in real time while performing procedures (e.g., ultrasound imaging) on ​​a patient. The video processor module may include a separate image memory, and ultrasound images may be written to the image memory for reading and display by the display device 118.

[0026] In various embodiments of this disclosure, one or more components of the ultrasound imaging system 100 may be included in a portable handheld ultrasound imaging device. For example, a display device 118 and a user interface 115 may be integrated into the external surface of the handheld ultrasound imaging device, which may also include a processor 116 and a memory 120. A probe 106 may include a handheld probe that electronically communicates with the handheld ultrasound imaging device to collect raw ultrasound data. Transmit beamformer 101, transmitter 102, receiver 108, and receive beamformer 110 may be included in the same or different parts of the ultrasound imaging system 100. For example, transmit beamformer 101, transmitter 102, receiver 108, and receive beamformer 110 may be included in the handheld ultrasound imaging device, the probe, and combinations thereof.

[0027] After performing a two-dimensional ultrasound scan, a data block containing scan lines and their samples is generated. Following the application of a back-end filter, a process called scan transformation is performed to convert the two-dimensional data block into a displayable bitmap image with additional scan information, such as depth, the angle of each scan line, etc. During scan transformation, interpolation techniques are applied to fill in any missing holes (i.e., pixels) in the resulting image. These missing pixels occur because each element of the two-dimensional block should typically cover many pixels in the resulting image. For example, in current ultrasound imaging systems, bicubic interpolation is used, which utilizes the adjacent elements of the two-dimensional block. Therefore, if the two-dimensional block is relatively small compared to the size of the bitmap image, the scan-transformed image will include areas with lower or no resolution than optimal, especially for deeper regions.

[0028] Turn now Figure 2 , Figure 2 An embodiment 200 of receiver 214 is shown, which includes three sections: a time gain control section 226, a receive beamforming section 228, and an intermediate processor 230. In one example, receiver 214 may be... Figure 1 A non-limiting example of receiver 108. Time gain control (TGC) section 226 includes a corresponding amplifier 232 for each of the receive channels 234, and provides time gain control circuitry 236 for controlling the gain of the amplifier 232. The input of each amplifier 232 is coupled to a corresponding transducer element in transducer element 104 to amplify its received echo signal. The amplification provided by amplifier 232 is controlled via a control line 238 driven by TGC circuitry 236, which is set by manual operation of potentiometer 240.

[0029] The receive beamforming section 228 of receiver 214 includes a plurality of receive channels 234, each of which receives an analog echo signal from a corresponding amplifier 232 at a corresponding input 242. The analog signal is digitized and generated as a signed digitized sample stream. These samples are delayed in the receive channels such that when they are summed with samples from each of the other receive channels, the amplitude of the summed signal is a measure of the intensity of the echo signal reflected from a point P located at a distance R on the turning beam θ.

[0030] To properly sum the electrical signals generated by the echoes striking each transducer element 104, a time delay is introduced into each individual channel 234 of the receiver 214. The time delay added to each receiving channel may be based on the assumed speed of sound through the imaging tissue. In one example, the imaging tissue may contain more fat than muscle in certain areas, which would affect the time delay that might be applied when receiving beamforming in that area, because ultrasound waves travel at different speeds through fat than through muscle. In addition to the delayed banded sample, each receiving channel 234 also provides the amplitude or absolute value of the delayed banded sample. The delayed banded sample is provided to the coherent summing bus 244, while the amplitude of the delayed banded sample is provided to the incoherent summing bus 246. The coherent summing bus 244 uses a pipelined summer 248 to sum the delayed banded samples from each receiving channel 234 to produce a coherent sum (e.g., Figure 2 The incoherent summation bus 246 uses a pipelined summator 250 to sum the magnitudes of the signed samples of the delay from each received channel 234 to produce an incoherent sum (e.g., A). Figure 2 Incoherence and B in the middle.

[0031] The receiver intermediate processor section 230 receives coherently summed beam samples from the pipelined summer 248 and incoherently summed beam samples from the pipelined summer 250. The intermediate processor section 230 includes a detection processor 252.

[0032] The detection processor 252 calculates and applies a coherence factor according to this disclosure. The coherence factor is calculated for each data sample and can be (at least in one example) defined as the ratio of two quantities: the amplitude of the sum of the received signals; and the sum of the amplitudes of the received signals. In the detection processor 252, this ratio is calculated by calculating the absolute value of the coherent sum from pipeline summer 248, and then calculating the ratio of the absolute value of the coherent sum from pipeline summer 248 to the incoherent sum from pipeline summer 250. If the incoherent sum is zero, the ratio can be set to zero. In one example, the ratio is calculated by dividing the absolute value of the coherent sum by the incoherent sum and adding a small positive value, which avoids illegal division by zero when the incoherent sum is zero. In the example, the detection processor 252 is... Figure 1 A non-limiting example of processor 116. In another example, the detection processor 252 and... Figure 1 The processor 116 is discrete but operably coupled to the processor.

[0033] In the case of RF beamformers, the signals from each channel are real, signed quantities, and the coherent sum is the sum of the signals. The incoherent sum is the sum of the absolute values ​​of each signal (e.g., the sum of non-negative numbers). In the case of baseband beamformers, the real signals from each channel can be demodulated to form complex numbers, such that the coherent sum is also a complex number. The absolute value of the coherent sum can be a non-negative real number. The incoherent sum can be the sum of the absolute values ​​of the channel signals; it can also be a non-negative real number. In both cases, the ratio of the absolute values ​​of the coherent sum to the incoherent sum can be a non-negative real number.

[0034] During an ultrasound scan, the transmitter drives the probe so that the generated ultrasonic energy can be directed into a beam. To achieve this, the transmitter can apply a time delay to a pulse waveform that can be applied to the continuous transducer elements in the probe. By adjusting the time delay in a conventional manner, the ultrasonic energy beam can be directed at an angle away from an axis perpendicular to the probe surface, thereby focusing the beam within a fixed range. By continuously changing the time delay, an ultrasound scan can be performed as the angle at which the beam is directed gradually changes.

[0035] A receiving ultrasound beamformer can be used as part of an ultrasound imaging system. The ultrasound imaging system uses transmission events from the transmitting beamformer and reception events from the receiving beamformer to generate a line of ultrasound images. The transmitting beamformer can focus on one location within the scanning area, while the receiving beamformer can focus on the same location. During receiving beamforming, based on the assumed speed of sound propagation when the receiving beamformer receives the ultrasound waves, the time delay may be associated with the propagation of the ultrasound waves relative to angle and time.

[0036] Due to the varying sound propagation speeds of the anatomical media within the anatomical region, ultrasound waves may travel at different speeds during an ultrasound scan. Depending on the anatomical region the probe may be transmitting through, a default propagation speed can be considered for the ultrasound scan, which can be error-prone, resulting in lower image resolution. Because ultrasound waves propagate at different speeds in all media, errors may occur in scanning areas that include muscle, fat, and bone when using the default propagation speed during an ultrasound scan. When the receiving beamformer operates at a default propagation speed different from the actual propagation speed, the received data may be miscalculated, leading to inaccurate imaging during ultrasound image generation. Additionally, when the transmitting beamformer operates at a default propagation speed different from the actual propagation speed, the transmitting focus may degrade. In one example, an ultrasound scan may occur over an area including tissues with potentially different associated sound propagation speeds, and the ultrasound image generated from an ultrasound scan using the default sound speed may have suboptimal image quality. Automatically selecting the optimal beamforming sound speed can lead to improved image quality.

[0037] Turn now Figure 3 , Figure 3 An example is shown in graph 300 illustrating the calculated optimal beamforming sound velocity. Graph 300 plots a beamforming quality metric as a function of multiple beamforming sound velocities. In one example, it can be seen from... Figure 2 The beamforming quality is calculated in the intermediate processor 230 using a coherence metric (such as coherence ratio) of the received signal, as the ratio of the amplitude of the coherently summed received signal to the amplitude of the incoherently summed received signal. In one example, six instances of the beamforming quality metric can be calculated (e.g., at six different speeds of sound). Figure 3 In an alternative embodiment, fewer or more than six instances of beamforming quality metrics may be calculated. The beamforming quality metric at each beamforming sound velocity may be represented by multiple data points 304 as shown in graph 300.

[0038] In the example shown, an ultrasound scan is performed such that an echo is received from the subject after an ultrasound signal is emitted via an ultrasound probe. The echo is received by the ultrasound transducers of the ultrasound probe, and the output of each transducer is transmitted along the channel for processing, as described above. The channel signal is then processed in different time delay sequences based on several different sound velocities (1400 m / s, 1440 m / s, 1480 m / s, 1520 m / s, 1560 m / s, and 1600 m / s in this document), and a beamforming quality metric is calculated based on the received channel data for each sound velocity. Thus, each data point represents a beamforming quality metric for a given beamforming sound velocity. In this example, the beamforming sound velocity used for the transmit time-delay calculation is set to the default beamforming sound velocity. The same channel data can be time-delayed using each desired receive beamforming sound velocity. In an alternative example, the beamforming sound velocity used for the transmit time-delay calculation is set to the receive beamforming sound velocity. In this scenario, separate transmissions are required for each desired sound speed, and different channel data for each sound speed are used to calculate beamforming quality metrics.

[0039] The best-fit curve 306, shown on graph 302, represents an approximate relationship between beamforming quality and beamforming sound velocity as a function. The best-fit curve 306 can be a polynomial with fewer coefficients than the number of beamforming sound velocities. In one example, the best-fit curve 306 for six beamforming sound velocities could be a fourth-order polynomial, with coefficients determined using standard methods. When fitting a polynomial, the beamforming sound velocity can be represented using the formula x = (2... * c–c max -c min ) / (c max -c min ) Scale to the range [-1, 1], where x is the scaled speed of sound, c max For the maximum beamforming speed of sound, and c min To form the speed of sound for the smallest beam.

[0040] Using the best-fit curve 306, the optimal sound velocity 308 and the uncertainty range 310 can be calculated and determined from the equations associated with the best-fit curve 306. The optimal sound velocity 308 can represent the beamforming sound velocity from which the highest beamforming quality value is identified from the best-fit curve 306. The uncertainty range 310 can represent the range of beamforming sound velocities (including the optimal sound velocity 308) starting from the optimal sound velocity 308 in both increasing and decreasing directions. In one example, the uncertainty range 310 can be a defined range ending on either side of the optimal sound velocity 308 when the maximum absolute value of the difference between the optimal sound velocity 308 and another beamforming sound velocity is reached, where the maximum absolute difference Δf is equal to the standard deviation of the beamforming quality value from the best-fit curve. In another example, the uncertainty range 310 can be determined based on the second derivative of the best-fit curve 306 evaluated at the optimal sound velocity 308. Near the optimal scaled sound velocity x0 = 308, the best-fit curve f(x) for the scaled sound velocity can be obtained by the equation f(x) = (1 / 2)a(x – x0). 2 An approximation is made, where a is the second derivative of the best-fit curve evaluated at the optimal beamforming sound velocity x0. Through this approximation, the lower and upper bounds of the uncertainty range of the beamforming sound velocity are given by the expression x0 ± (2Δf / a). 1 / 2 Provided.

[0041] Once the optimal sound velocity 308 and the uncertainty range 310 are calculated and determined, the beamforming sound velocity can be automatically selected to generate an ultrasound image. The uncertainty range 310 can be used to automatically determine whether the optimal or default sound velocity should be used to generate the image presented to the operator. In one example, if the uncertainty range 310 is a large portion of the tested beamforming sound velocity range (50% in one example), the default beamforming sound velocity should be used. The uncertainty range 310 can be large (e.g., 50% of the velocity range) when the best-fit curve is not the best approximation of the beamforming quality value. In this case, the optimal sound velocity calculated from the best-fit curve may not produce a better image than the one calculated using the default sound velocity. The uncertainty range 310 can also be large when the beamforming quality value varies little with the sound velocity or when the fitted curve does not have a well-defined local maximum. In this case, the image calculated using the default beamforming sound velocity should be presented to the operator. If the uncertainty range 310 is not large, in one example, the optimal sound velocity 308 can be selected to generate the ultrasound image. In another example, the beamforming velocity of beamforming data point 312 that is closest to the optimal velocity of sound 308 can be selected to generate an ultrasound image.

[0042] Figure 4A An example of a first generated ultrasound image 400 is shown. In one example, the first generated ultrasound image 400 may be obtained using... Figure 3The beamforming of the ultrasonic image is generated from data points further away from the calculated optimal sound speed. Figure 4B An example of the second generated ultrasound image 450 is shown. Figure 4A and Figure 4B The images shown are generated from the same ultrasound receiving channel signal but have different time delays determined by different sound speeds. In one example, the second generated ultrasound image 450 could be an ultrasound image generated using the beamforming sound speed closest to the calculated optimal sound speed. Therefore, the first generated ultrasound image 400 could have a lower beamforming quality metric than the second generated ultrasound image 450, indicating lower image resolution and contrast.

[0043] Therefore, by selecting a target beamforming sound velocity that is closest to the beamforming sound velocity (which results in the highest beamforming quality metric for all ultrasound data used to generate the image), image quality can be improved compared to conventional methods that may rely on a default beamforming sound velocity. However, to further improve image quality, the ultrasound data obtained for image generation can be subdivided into multiple regions, and a target beamforming sound velocity can be selected for each region, as described in more detail below. This could allow for more targeted beamforming sound velocities, especially when using different sound propagation speeds for anatomical imaging.

[0044] Turn now Figure 5 , Figure 5 Several graphs 500 are shown illustrating the optimal beamforming velocity calculated for the maximum beamforming quality over a subset of regions in an ultrasound scan. Similar to... Figure 3 The curve 302 can be generated by calculating the best-fit curve, calculating the optimal sound velocity, calculating the uncertainty range, and selecting the beamforming sound velocity for the region represented by each curve, thereby generating each of the multiple curves 500 (such as the first curve 504). In one example, the ultrasound scan area (which may include ultrasound data used to generate the image) can be divided into nine regions, and a curve can be generated for each region. Figure 5 In an alternative implementation, the ultrasound scan area may be divided into fewer or more than nine regions. Each of the multiple graphs 500 may show multiple beamforming data points, where the beamforming sound velocity is on the x-axis and the beamforming quality is on the y-axis. In one example, ten sound velocities may be used to calculate ten corresponding beamforming quality metrics and plotted for each region in the ultrasound scan at each beamforming sound velocity. Figure 5In an alternative implementation, fewer or more than ten instances of beamforming can be recorded and plotted. During each instance of beamforming, a beamforming quality metric can be calculated from channel data associated with each subset of regions, allowing each instance to be plotted as a data point based on the quantifiable beamforming quality metric calculated from the channel data.

[0045] Dividing the ultrasound scan area into multiple regions may result in different optimal sound velocities being calculated based on the anatomical features within each region. In one example, the curves in multiple plots 504 may represent regions containing more adipose tissue, while another curve in multiple plots 504 may represent different regions containing less adipose tissue. Since the sound velocities differ in each region, different optimal beamforming sound velocities are generated. Furthermore, because the sound propagation time between the desired focus location and the transducer element is a function of the sound velocity along the acoustic path between the desired focus location and the element location, the optimal beamforming sound velocity at the desired focus location may not be the actual sound velocity at that focus location. Dividing the ultrasound scan area into multiple regions may result in ultrasound images with higher beamforming quality than ultrasound images generated using a single beamforming sound velocity for the entire ultrasound scan area.

[0046] Figure 6 An example of a generated ultrasound image 600 is shown, which can be generated using multiple calculated optimal beamforming sound velocities in various regions, as described above regarding Figure 5 As explained. These regions can be selected automatically or by the operator. Multiple partition markers (such as partition marker 612) can be visually presented to indicate the location of each region in the ultrasound image 600. Dividing an ultrasound image (such as ultrasound image 610) into multiple regions during image generation results in higher image resolution because optimal beamforming data is selected in each region.

[0047] Turn now Figure 7 , Figure 7 A flowchart illustrating an exemplary method 700 for generating an ultrasound image using a target beamforming sound velocity selected based on a beamforming quality metric to generate an ultrasound image with enhanced image resolution and / or contrast is shown. Reference Figures 1 to 2The system and components described herein constitute method 700, but it should be understood that other systems and components may be used to implement method 700 without departing from the scope of this disclosure. Method 700 may be executed according to instructions stored in a non-transitory memory (such as memory 120) of a computing device and by a processor (such as processor 116) of the computing device. In some examples, method 700 may be executed each time an ultrasound probe acquires ultrasound data and forms an image from the ultrasound data, such that the frequency of execution of method 700 matches the frame rate of ultrasound imaging. In other examples, method 700 may be executed at a predetermined frequency less than the frame rate (e.g., once per second) during ultrasound imaging, and / or in response to determining that the ultrasound probe has moved or that the anatomical structure being imaged has changed.

[0048] At 702, method 700 includes specific treatments for each element in the ultrasound probe (such as receiving channel 234, as per...). Figure 2 (Shown and described) Acquire the undelayed channel signal. Channel data can be acquired via receive beamforming before generating ultrasound images. As mentioned above regarding Figure 1 and Figure 2 As explained, the channel signal includes echoes detected by the ultrasonic transducer elements, which are received after ultrasonic signals are emitted from the ultrasonic transducer elements of the ultrasonic probe. In some examples, sufficient channel signals to generate a complete ultrasound image can be acquired and stored in memory.

[0049] At 704, method 700 includes applying a corresponding time delay to each channel signal based on a first sound velocity to form delayed channel data for each channel. The first sound velocity may be a first sound velocity in a set of possible sound velocities. This set of possible sound velocities may be predetermined or set by the user. In the example, the set of possible sound velocities may include three, four, five, six or more sound velocities within a reasonable range of sound velocities based on the imaging task (e.g., imaging human anatomy may result in a range of 1400 m / s to 1600 m / s). Applying the time delay may include modifying the channel data based on assumed characteristics of the imaging medium through which the ultrasound waves propagate. As previously explained, the channel signal propagates from a desired source point to the transducer element. With a constant sound velocity in the medium, the arrival time at the transducer element may be a function of the distance from the source point to the transducer element and the sound velocity of the medium through which the ultrasound waves travel, for example, distance(source, transducer_element) / medium_sound_speed. When the speed of sound in the medium is not constant, the arrival time at the transducer element can be the integral of the incremental propagation time along an assumed propagation path, where the incremental propagation time is the incremental distance along the path divided by the speed of sound at each incremental distance. Therefore, beamforming is typically performed using a single assumed speed of sound in the medium to eliminate the assumed time difference of arrival at the transducer elements, and then the time-delayed signals are summed to form an image (as explained below). If the assumed beamforming speed of sound differs from the true speed of sound in the medium, beamforming will be suboptimal and will result in suboptimal image resolution and contrast. Therefore, as explained herein, beamforming can be performed on the same channel signals with different speeds of sound based on a beamforming quality metric to identify the target or optimal speed of sound for image formation.

[0050] At 706, method 700 includes calculating a beamforming quality metric from delayed channel data. The beamforming quality metric may be based on a coherence factor representing the level of similarity between delayed channel signals, which is related to image resolution and contrast, but does not require image formation. See below for further details. Figure 8 Further details regarding the calculation of the beamforming quality metric are provided. In some embodiments of method 700, at 707, method 700 includes calculating the beamforming quality metric on all channel data acquired at 702 and delayed at 704. The calculation on all channel data provides a single beamforming quality metric associated with all acquired data to form an ultrasound image. In some embodiments of method 700, at 708, method 700 includes calculating the beamforming quality metric on a subset of the channel data, such that the beamforming quality metric is calculated for each of multiple scan regions, as... Figure 5Multiple curves are shown in Figure 500. Regions can be formed by subdividing the entire area imaged by the ultrasound imaging system into a default region pattern, or the operator can select a region pattern. The calculation of a subset of channel data results in the calculation of a beamforming quality metric for each region of the ultrasound scan.

[0051] At 709, method 700 includes repeating 702-706 for each different sound speed in the possible set of sound speeds. In this way, beamforming quality metric is calculated for each sound speed in the possible set of sound speeds (whether for all channel data or for each subset of channel data).

[0052] At 710, method 700 includes calculating and fitting each beamforming quality metric as a function of the corresponding beamforming sound velocity. This function can be represented as a graph (such as...). Figure 3 The curve 302 shows the beamforming sound velocity plotted on the x-axis and the beamforming quality metric plotted on the y-axis. Each beamforming quality metric generated by the sound velocity can be plotted as a data point having a first coordinate represented by the beamforming sound velocity and a second coordinate represented by the beamforming quality metric. In some embodiments of method 700, at 711, method 700 includes fitting all beamforming quality metrics calculated for all channel data. In this example, the ultrasound scan may include a region that would be the entire anatomical region operated by the ultrasound scan. All beamforming quality metric data generated due to different sound velocities may be applicable to the entire anatomical region operated by the ultrasound scan, resulting in all data points being fitted as a function of the beamforming sound velocity. In some embodiments of method 700, at 712, method 700 includes fitting beamforming quality metrics for each subset of channel data. In this example, the ultrasound scan may include multiple regions, and the beamforming quality metric may be fitted as a function of the beamforming sound velocity in each region of the ultrasound scan based on the channel data for each region.

[0053] At 713, method 700 includes calculating the target sound velocity from the fitting of beamforming quality metric data. In one example, the curve fitted to the data may be a low-order polynomial, such as a fourth-order polynomial. For each curve associated with the entire scan area or with a corresponding subset of the entire scan area, the polynomial fit may be represented as the best-fit polynomial curve plotted on the data points on the curve. The target sound velocity may be identified as the sound velocity where the polynomial curve fitting of the beamforming quality metric obtains its maximum value. An uncertainty range may also be calculated where the beamforming quality metric of the beamforming sound velocity range is less than the maximum beamforming quality metric in the polynomial fit but within its tolerance value, where in one example the tolerance value is based on the standard deviation of the polynomial fit of the calculated beamforming quality metric data. The uncertainty range may represent a range of values ​​representing the confidence that generating an ultrasound image with the optimal beamforming quality metric can be achieved based on the sound velocity within the range of use. When the channel data is divided into multiple regions, the target beamforming sound velocity and uncertainty range may be identified for each region.

[0054] At 714, method 700 includes generating one or more ultrasound images using a time delay based on a target sound velocity (or a target sound velocity when identified for each region). In one example, an ultrasound image may be generated using a receive beamforming time delay or a transmit beamforming time delay, or both a transmit beamforming time delay and a receive beamforming time delay calculated using the target sound velocity. In another example, an ultrasound image may be generated using a time delay calculated using the sound velocity of the data point closest to the target sound velocity. To generate an ultrasound image, a time delay is applied to the raw (e.g., undelayed) receive channel data based on the selected sound velocity, the time-delayed receive channel signals are summed to form a beam and signal, the logarithm of the absolute values ​​of the beam and signal is determined, a scan conversion to square pixels is performed, and the pixels are scaled to 8-bit grayscale values ​​to form an image. Method 700 then returns.

[0055] While this document describes method 700 as being applied to a single raw set of ultrasonic receive channel signals (generated from a single transmit set), in some examples, new sets of ultrasonic receive channel signals can be obtained for each different sound velocity. When only a single raw set of ultrasonic receive channel signals is acquired, a transmit time can be applied for transmit beamforming, which can be the nominal / assumed sound velocity as described above. When multiple sets of ultrasonic receive channel signals are acquired, a single transmit time delay can be applied for transmit beamforming for each acquired set, or a different transmit time delay (e.g., a matched receive time delay) can be applied for each set.

[0056] Figure 8A flowchart illustrating an exemplary method 800 for calculating a beamforming quality metric is shown. Method 800 may be executed according to instructions stored in a non-transitory memory (such as memory 120) of a computing device and by a processor (such as processor 116) of the computing device. In some examples, method 800 may be executed as part of method 700, for example, to calculate a beamforming quality metric to determine a target velocity of sound for receive beamforming, transmit beamforming, or both transmit and receive beamforming.

[0057] At 802, method 800 includes acquiring channel signals for a range set and a lateral direction set (where the lateral directions are each transverse to the corresponding range direction). This can be used... Figure 2 The receiving channel 234 is used to acquire channel signals. These channel signals are defined for each element, range, and lateral direction, enabling s i (r,θ), where i is the channel index, r is the range index, and θ is the lateral direction index.

[0058] At 804, method 800 includes applying a receive beamforming time delay to the acquired channel signal. The receive beamforming time delay can be applied to the acquired channel signal based on a selected speed of sound, as described above regarding... Figure 7 As described.

[0059] At 806, method 800 includes calculating a coherence factor from the time-delayed channel signal. The coherence factor is a value defined in each range and lateral direction, i.e., C(r,θ). The coherence factor can (at least in one example) be defined as the ratio of the sum of the channel signals. The coherence factor can be calculated according to the following equation.

[0060]

[0061] The coherence factor is the ratio of the following two quantities: the sum of the time delays of the channel signals and the magnitude of the coherence factor. Figure 2 The sum of the coherence and absolute value of A; and the sum of the amplitudes of the channel signal with time delay (the coherence and absolute value of A); Figure 2 The incoherence of B). Beamforming quality metrics can be normalized values ​​within the range of 0 to 1, where 1 indicates a high confidence level in the magnitude of the beam and the scattering from the expected focusing direction, and values ​​close to 0 may indicate the presence of spurious scattering and / or off-axis scattering in the received channel data. Figure 2 The ratio is calculated in the detection processor 252. However, other mechanisms for calculating the coherence factor can be used, such as the ratio of the squared amplitudes of the channel signals, the similarity of the phases of complex channel signals, or the similarity of the symbols of real channel signals.

[0062] At 808, method 800 includes calculating the truncated mean of the coherence factors over the range and lateral direction sets. Calculating the truncated mean may include excluding the minimum value in the dataset before calculating the mean of the remaining values. Calculating the truncated mean may include calculating the cumulative distribution of the coherence factor values ​​over the range and lateral direction sets; deriving the value C from the cumulative distribution. min This makes the coherence factor value fraction p less than C. min ; and calculate those greater than C min The mean of the coherence factor values ​​is used to produce a truncated mean of the coherence factor. This truncated mean is a single value defined for the range and lateral direction sets. In one example, p may be 0.75, but other values ​​are possible without departing from the scope of this disclosure. However, in other examples, C... min This can be a predetermined value determined empirically rather than based on a cumulative distribution. In a further example, the mean of all values ​​can be determined. The coherence factor is a measure of the similarity of channel signals with time delay. Channel signals have high similarity when they consist primarily of echoes from the focus, and the highest similarity occurs when the signal has been delayed with the correct receive beamforming time delay. However, channel signals have low similarity when they consist primarily of echoes from far from the focus, and the lowest similarity occurs when the signal has been correctly delayed. Therefore, as the beamforming velocity changes from suboptimal to optimal, the coherence factor will increase in some ranges and directions, and decrease in others. When averaging samples with low and high coherence factors, the coherence factor is typically largest when the beamforming time delay with the optimal beamforming velocity is applied. By excluding samples with low coherence, the sensitivity of average coherence to changes in the beamforming velocity increases.

[0063] At 810, method 800 includes calculating the filtered derivative magnitude of the coherence factor in the direction transverse to the extent direction. For planar scanning using a one-dimensional probe, there is one direction in the scan plane transverse to the extent direction, namely the azimuth direction. For volumetric scanning using a two-dimensional matrix probe, there are two directions in the volumetric scan transverse to the extent direction, namely the azimuth direction and the elevation direction. Compared to the extent direction, the beamforming point spread function may deteriorate faster in the transverse direction with arrival time error because the expected reduction in response in the transverse direction depends on signal cancellation. The derivative of coherence in the transverse direction is a measure of the width of the received beamforming response profile, i.e., the sharpness of the image reflecting transverse edge-like features. For two-dimensional ultrasound scanning formats, only the azimuth transverse dimension exists in the scan data, so only the derivative in the azimuth direction can be calculated. For volumetric ultrasound scanning formats, both the azimuth and elevation dimensions exist in the scan data, so derivatives in both dimensions can be calculated. However, derivatives have a high-frequency emphasis characteristic, so low-pass filter derivatives are used to weight features in data associated with beamforming performance and mitigate noise amplification. The magnitude of the filter derivative can be calculated by computed a low-pass filter with a characteristic length L, where L approximates the average correlation length of the coherence factors in the lateral direction. In one example, the low-pass filter could be a Gaussian filter with a full width at half maximum (FWHM) of L. The low-pass filter is convolved with a discrete derivative filter [1,-1] to produce a filter derivative filter. The filter derivative filter is applied to the coherence factors in one or more lateral directions within the range set, and the absolute value of the filter derivative filter output is computed to produce the magnitude of the filter derivative for the coherence factors. This magnitude of the filter derivative is a value defined for each range and lateral direction.

[0064] At 812, method 800 includes calculating the truncated mean of the magnitudes of the filtered derivatives of the coherence factors. The truncated mean of the magnitudes of the filtered derivatives of the coherence factors is calculated by the following steps: calculating the cumulative distribution of the magnitudes of the filtered derivatives of the coherence factors over the range and lateral direction sets; deriving the value D. min This makes the fraction q of the filter derivative magnitude less than D. min (In one example, q = 0.75); and calculate greater than D min The mean of those filtered derivative amplitudes is used to produce a truncated mean of the filtered derivative amplitudes. Restricting the mean to those samples with the largest derivative amplitudes more heavily weights the data values ​​corresponding to the edges in the tissue, which increases the sensitivity of the average filtered derivative amplitude to beamforming sound velocity variations. The truncated mean is a single value defined for the range and lateral direction sets. However, in other examples, D... min This can be a predetermined value determined based on experience rather than on a cumulative distribution. In a further example, the mean of all values ​​can be determined.

[0065] At 814, method 800 includes calculating a beamforming quality metric based on the truncated mean of the coherence factor and the truncated mean of the magnitude of the filtered derivative of the coherence factor. The beamforming quality metric can be calculated by multiplying the truncated mean of the coherence factor by the truncated mean of the magnitude of the filtered derivative of the coherence factor. The beamforming quality metric is a single value defined for the range and lateral direction sets. Method 800 then returns.

[0066] Therefore, methods 700 and 800 can be executed to determine the target velocity of sound for receiving beamforming, transmitting beamforming, or both transmitting and receiving beamforming, for image generation. The same processing steps can be applied to the amplitude of the beam and data, or the logarithmically scaled amplitude of the beam and data, instead of to the coherence factor. However, in practice, it has generally been shown that using the coherence factor produces a more noticeable change in beamforming quality metrics and is more reliably correlated with image quality because the coherence factor is a normalized quantity, limited to the range [0,1], unlike the amplitude of the beam and data, or the logarithmically scaled amplitude.

[0067] To select the target beamforming sound velocity, a set of beamforming sound velocities c within the possible range can be used. i The beamforming quality metric is calculated. As explained above, undelayed channel signals can be acquired and stored, and the corresponding beamforming quality metric can be calculated using each beamforming sound velocity. Alternatively, when scanning substantially the same tissue, different channel signals acquired with different beamforming sound velocity time delays can be used to calculate the beamforming quality metric.

[0068] Then, in the least-squares sense, the low-order polynomial is fitted to the calculated beamforming quality metric set Q. i In one example, a fourth-order polynomial is fitted. The transformation x is used. i =(2c i -c max -c min ) / (c max -c min Numerically, we aim to normalize this set of sound speed values ​​to a range of -1 to 1, where c i c is a sound speed value in the set. max Let c be the loud speed value in the set, and c min Let f(x) be the minimum sound speed value in the set. Then, least squares fitting yields the quartic polynomial f(x) = ax. 4 +bx 3 +cx 2 The coefficients a, b, c, d, and e of +d x+e are used to minimize the difference between the fit and the quality metric, ∑|f i -Q i |2 .

[0069] The maximum value of the fitted polynomial can be obtained through analysis or numerical calculation. The value x at which the polynomial reaches its maximum value. max Corresponding to the optimal speed of sound, c opt =(c max +c min ) / 2+x max (c max -c min ) / 2.

[0070] Typically, polynomial fitting will not match the set of sound speeds c. i The quality metric Q in i The mean square deviation of the fit is σ = [∑|f i -Q i | 2 ] 1 / 2 This can be used to estimate a reasonable range of values ​​for the optimal speed of sound. The lower bound of the possible optimal normalized speed of sound can be chosen to be less than x. max The value of x is 0, such that f(x) max f(x0) = σ. Similarly, the upper limit of the possible optimal normalized speed of sound can be chosen to be greater than x. max The value of x1 makes f(x) max )-f(x1)=σ. These aspects define the range of normalized sound speeds that the fit deviates from its maximum value by σ (a measure of typical deviation of the fit).

[0071] The advantage of using a computed beamforming quality metric to automatically determine the optimal beamforming velocity parameters for ultrasound image generation is that it can improve ultrasound image quality without operator intervention. Ultrasound system manufacturers have found that most operators rarely use features requiring manual intervention, such as turning knobs or pressing buttons, even when such features produce better image quality. Another advantage is that adaptive beamforming algorithms can use the computed beamforming quality metric to select the time delay for a given scan area, minimizing image artifacts that typically result from below-optimal time delay estimates. The beamforming quality metric can also be combined with other image enhancement algorithms, such as adaptive beamforming, to quantify the degree of image improvement. For example, if the image quality metric in a tissue region indicates that the adaptive beamforming algorithm has unintentionally degraded image quality, the algorithm can be suppressed for that tissue region. Furthermore, the beamforming quality metric can be used to automatically select imaging parameters, such as the number of transmit focus areas, the number of transmit directions, the transmit f-number, and transmit and receive apodization functions, as input to a cost function to optimally balance image quality with other parameters of interest, such as frame rate and field of view. Beamforming quality metrics can also be used to automatically select fixed parameters (such as filter length or threshold) for designing algorithms to improve image quality, thus avoiding time-consuming testing and experimentation to select these parameters.

[0072] This disclosure also provides support for a method comprising: calculating a corresponding beamforming quality metric for each of a plurality of beamforming sound velocities, each beamforming quality metric being calculated based on an ultrasound receiving channel signal delayed based on the corresponding beamforming sound velocity; identifying a target beamforming sound velocity based on each beamforming quality metric; and generating an ultrasound image using the target beamforming sound velocities. In a first example of the method, the method further comprises: acquiring the ultrasound receiving channel signal and storing it in a memory; and time-delaying the ultrasound receiving channel signal multiple times, each time using a different corresponding beamforming sound velocity. In a second example of the method, optionally including the first example, each corresponding different beamforming sound velocity is selected from a set of possible beamforming sound velocities, such that a beamforming quality metric is calculated for each beamforming sound velocities in the set of possible beamforming sound velocities. In a third example of the method, optionally including one or both of the first and second examples, generating the ultrasound image using the target beamforming sound velocities includes time-delaying the ultrasound receiving channel signal based on the target beamforming sound velocities. In a fourth example of the method, which optionally includes one, two, or each of the first to third examples, generating the ultrasound image using the target beamforming sound velocity includes time-delaying the ultrasound signal output by each ultrasound transducer of the ultrasound probe based on the target beamforming sound velocity. In a fifth example of the method, which optionally includes one, two, or each of the first to fourth examples, identifying the target beamforming sound velocity based on each beamforming quality metric includes fitting a corresponding beamforming quality metric of each corresponding beamforming sound velocity to a function of the beamforming sound velocity, and identifying the target beamforming sound velocity from the fitted function. In a sixth example of the method, which optionally includes one, two, or each of the first to fifth examples, identifying the target beamforming sound velocity from the fitted function includes identifying the target beamforming sound velocity as the beamforming sound velocity that produces the beamforming quality metric closest to the maximum beamforming quality metric of the fitted function. In a seventh example of the method, which optionally includes one or both or each of the first to sixth examples, identifying the target beamforming sound velocity from the fitted function includes identifying the target beamforming sound velocity as the beamforming sound velocity that produces the maximum beamforming quality metric of the fitted function.In an eighth example of the method, optionally including one, two, or each of the first to seventh examples, calculating the corresponding beamforming quality metric includes: calculating a first beamforming quality metric for the ultrasonic receiving channel signal with a time delay based on the first beamforming sound velocity; calculating a first coherence factor from the ultrasonic receiving channel signal for each range and lateral direction; calculating a first truncated mean from each first coherence factor for each range and lateral direction; calculating the filtered derivative amplitude of each first coherence factor for each lateral direction; calculating a second truncated mean from each filtered derivative amplitude for each range and lateral direction; and calculating the first beamforming quality metric by multiplying the first truncated mean by the second truncated mean. In a ninth example of the method, optionally including one, two, or each of the first to eighth examples, the ultrasonic receiving channel signal is received via a plurality of channels, each channel coupled to a corresponding ultrasonic transducer of an ultrasonic probe, and wherein each first coherence factor is calculated as the ratio of the absolute value of the summed output of each channel to the sum of the absolute values ​​of the outputs of each channel.

[0073] This disclosure also provides support for a system comprising: a plurality of ultrasonic transducers configured to transmit and receive ultrasonic signals; a memory storing instructions; and a processor configured to execute the instructions to: acquire a set of ultrasonic receive channel signals via the plurality of ultrasonic transducers; calculate a corresponding beamforming quality metric for each time-delayed set of ultrasonic receive channel signals in a plurality of time-delayed sets of ultrasonic receive channel signals, wherein each time-delayed set of ultrasonic receive channel signals is time-delayed from the set of ultrasonic receive channel signals based on a different beamforming velocity; identify a target beamforming velocity based on each beamforming quality metric; and generate an ultrasonic image using the target beamforming velocity. In a first example of the system, the set of ultrasonic receive channel signals is received via a set of receive channels, each receive channel being coupled to an ultrasonic transducer in the plurality of ultrasonic transducers, and wherein each beamforming quality metric is calculated from a coherence factor, which is a measure of the similarity between channel signals in each time-delayed set of ultrasonic receive channel signals. In a second example of a system optionally including the first example, a first coherence factor is calculated for each range and lateral direction from a set of ultrasonic received channel signals with a first time delay, time-delayed by the first beamforming sound velocity; a first truncated mean is calculated from each first coherence factor in each range and lateral direction; a filtered derivative magnitude of each first coherence factor is calculated in each lateral direction; a second truncated mean is calculated from each filtered derivative magnitude in each range and lateral direction; and a first beamforming quality metric is calculated by multiplying the first truncated mean by the second truncated mean. In a third example of a system optionally including one or both of the first and second examples, identifying the target beamforming sound velocity based on the beamforming quality metric includes fitting a corresponding beamforming quality metric for each corresponding beamforming sound velocity to a function of the beamforming sound velocity, and identifying the target beamforming sound velocity from the fitted function. In a fourth example of a system that optionally includes one or both or each of the first and third examples, the processor is configured to execute the instructions to calculate each beamforming quality metric before generating the ultrasound image, and wherein generating the ultrasound image using the target beamforming sound velocity includes applying the target beamforming sound velocity to calculate the transmit beamforming time delay, the receive beamforming time delay, or both the transmit beamforming time delay and the receive beamforming time delay.

[0074] This disclosure also provides support for a method comprising: generating an ultrasound image from a plurality of received channel signals, including, for each of a plurality of regions of the ultrasound image, time-delaying a set of the plurality of received channel signals corresponding to that region based on a corresponding beamforming speed, wherein each beamforming speed is independently selected for each region based on a set of beamforming quality metrics calculated for each region. In a first example of the method, the plurality of regions of the ultrasound image includes a first region generated from a first set of the plurality of received channel signals and a second region generated from a second set of the plurality of received channel signals, wherein the first set of the plurality of received channel signals is time-delayed based on a first beamforming speed and the second set of the plurality of received channel signals is time-delayed based on a second beamforming speed different from the first beamforming speed. In a second example of the method, optionally including the first example, the method further includes: selecting the first beamforming sound velocity by calculating a first set of beamforming quality metrics for the first region, the first set of beamforming quality metrics being calculated by: time-delaying the first set of a plurality of received channel signals multiple times, each time based on a different beamforming sound velocity selected from the set of beamforming sound velocities, and calculating the beamforming quality metric each time. In a third example of the method, optionally including one or both of the first and second examples, each calculation of the beamforming quality metric includes calculating the coherence ratio for each range and lateral direction of the first set of a plurality of received channel signals, and determining an average coherence ratio, each coherence ratio including the ratio of the sum of the absolute values ​​of the time-delayed channel signals to the sum of the absolute values ​​of the time-delayed channel signals. In a fourth example of the method that optionally includes one, two, or each of the first to third examples, selecting the first beamforming sound velocity includes fitting the first set of beamforming quality metrics as a function applied to calculate the corresponding sound velocity for each beamforming quality metric, and selecting the first beamforming sound velocity from the fit.

[0075] When describing elements of various embodiments of this disclosure, the terms “an,” “a,” and “the” are intended to refer to one or more of these elements. The terms “first,” “second,” etc., do not indicate any order, quantity, or importance, but are used to distinguish one element from another. The terms “comprising,” “including,” and “having” are intended to be inclusive and mean that additional elements may exist in addition to the listed elements. As used herein, the terms “connected to,” “linked to,” etc., indicate that an object (e.g., a material, element, structure, component, etc.) may be connected to or linked to another object, regardless of whether the object is directly connected to or linked to the other object, or whether one or more intervening objects exist between the object and the other. Furthermore, it should be understood that references to “an embodiment” or “an embodiment” of this disclosure are not intended to be construed as excluding the existence of additional embodiments that also include the referenced features.

[0076] In addition to any modifications previously indicated, those skilled in the art can devise many other variations and alternative arrangements without departing from the spirit and scope of this description, and the appended claims are intended to cover such modifications and arrangements. Therefore, although the information has been described above in a specific and detailed manner in conjunction with what is currently considered to be the most practical and preferred aspects, it will be apparent to those skilled in the art that many modifications can be made without departing from the principles and concepts set forth herein, including but not limited to changes in form, function, mode of operation, and use. Likewise, as used herein, in all respects, examples and embodiments are intended to be illustrative only and should not be construed as restrictive in any way.

Claims

1. A method for generating ultrasound images, comprising: For each of the multiple beamforming sound velocities, a corresponding beamforming quality metric is calculated, and each beamforming quality metric is calculated based on the ultrasonic receiving channel signal with time delay based on the corresponding beamforming sound velocity. Identify the target beamforming sound velocity based on each beamforming quality metric; and The target beam is used to generate the sound velocity and produce an ultrasonic image. The calculation of each beamforming quality metric includes the corresponding beamforming quality metric calculated for the ultrasonic received channel signal based on a time delay of the sound velocity for each beamforming: The coherence factor for each range and lateral direction is calculated from the ultrasonic received channel signal; Calculate the first truncated mean in each range and lateral direction for each coherence factor; Calculate the magnitude of the filter derivative for each coherence factor in each horizontal direction; Calculate the second truncated mean for each range and lateral direction from each filter derivative magnitude; as well as The corresponding beamforming quality metric is calculated by multiplying the first truncated mean by the second truncated mean.

2. The method according to claim 1 further includes acquiring the ultrasonic receiving channel signal and storing it in a memory, and performing time delay on the ultrasonic receiving channel signal multiple times, each time using a different corresponding beam to form the speed of sound.

3. The method of claim 2, wherein each corresponding distinct beamforming sound velocity is selected from a set of possible beamforming sound velocities, such that a beamforming quality metric is calculated for each beamforming sound velocity in the set of possible beamforming sound velocities.

4. The method of claim 2, wherein generating the ultrasound image using the target beamforming speed of sound includes time-delaying the ultrasound receiving channel signal based on the target beamforming speed of sound.

5. The method of claim 2, wherein generating the ultrasound image using the target beamforming velocity comprises time-delaying the ultrasound signal output by each ultrasound transducer of the ultrasound probe based on the target beamforming velocity.

6. The method of claim 1, wherein identifying the target beamforming sound velocity based on each beamforming quality metric comprises fitting a corresponding beamforming quality metric of each corresponding beamforming sound velocity to a function of the beamforming sound velocity, and identifying the target beamforming sound velocity from the fitted function.

7. The method of claim 6, wherein identifying the target beamforming sound velocity from the fitted function comprises identifying the target beamforming sound velocity as the beamforming sound velocity that produces the beamforming quality metric closest to the maximum beamforming quality metric of the fitted function.

8. The method of claim 6, wherein identifying the target beamforming velocity from the fitted function comprises identifying the target beamforming velocity as the beamforming velocity that produces the maximum beamforming quality metric of the fitted function.

9. The method of claim 1, wherein the ultrasonic receiving channel signal is received through a plurality of channels, each channel being coupled to a corresponding ultrasonic transducer of the ultrasonic probe, and wherein each first coherence factor is calculated as the ratio of the absolute value of the summation output of each channel to the sum of the absolute values ​​of the outputs of each channel.

10. A system for generating ultrasound images, comprising: Multiple ultrasonic transducers, the multiple ultrasonic transducers being configured to transmit and receive ultrasonic signals; The memory stores instructions; and Processor, the processor being configured to execute the instructions to: The ultrasonic receiving channel signal set is acquired via the plurality of ultrasonic transducers; For each time-delayed set of ultrasonic received channel signals in a set of multiple time-delayed ultrasonic received channel signals, a corresponding beamforming quality metric is calculated, wherein each time-delayed set of ultrasonic received channel signals is time-delayed from the set of ultrasonic received channel signals based on a different beamforming sound velocity. The coherence factor is calculated for each range and lateral direction from the set of ultrasonic received channel signals that are time-delayed by the sound speed of each beamforming. Calculate the first truncated mean from each coherence factor in each range and lateral direction; Calculate the magnitude of the filter derivative for each coherence factor in each horizontal direction; Calculate the second truncated mean for each range and lateral direction from each filter derivative magnitude; as well as Each beamforming quality metric is calculated by multiplying the first truncated mean by the second truncated mean; Identify the target beamforming sound velocity based on each beamforming quality metric; as well as The target beam is used to generate the sound velocity and produce an ultrasonic image.

11. The system of claim 10, wherein the set of ultrasonic receiving channel signals is received via a set of receiving channels, each receiving channel being coupled to an ultrasonic transducer among the plurality of ultrasonic transducers, and wherein each beamforming quality metric is calculated from a coherence factor, the coherence factor being a measure of the similarity between channel signals in the set of ultrasonic receiving channel signals at each time delay.

12. The system of claim 10, wherein identifying the target beamforming sound velocity based on each beamforming quality metric comprises fitting a corresponding beamforming quality metric of each corresponding beamforming sound velocity to a function of the beamforming sound velocity, and identifying the target beamforming sound velocity from the fitted function.

13. The system of claim 10, wherein the processor is configured to execute the instructions to calculate each beamforming quality metric prior to generating the ultrasound image, and wherein generating the ultrasound image using the target beamforming sound velocity includes applying the target beamforming sound velocity to calculate a transmit beamforming time delay, a receive beamforming time delay, or both the transmit beamforming time delay and the receive beamforming time delay.

14. A method for generating ultrasound images, comprising: Generating an ultrasound image from multiple received channel signals includes, for each of multiple regions of the ultrasound image, time-delaying a set of the multiple received channel signals corresponding to that region based on a corresponding beamforming speed, wherein each beamforming speed is selected independently for each region based on a set of beamforming quality metrics calculated for each region. The calculation of the beamforming quality metric set includes: The coherence factor is calculated for each range and lateral direction from the set of signals from the plurality of received channels that are time-delayed by the sound speed of each beamforming. Calculate the first truncated mean in each range and lateral direction for each coherence factor; Calculate the magnitude of the filter derivative for each coherence factor in each horizontal direction; Calculate the second truncated mean for each range and lateral direction from each filter derivative magnitude; and The beamforming quality metric is calculated by multiplying the first truncated mean by the second truncated mean.

15. The method of claim 14, wherein the plurality of regions of the ultrasound image comprises a first region generated from a first set of the plurality of received channel signals and a second region generated from a second set of the plurality of received channel signals, wherein the first set of the plurality of received channel signals is time-delayed based on a first beamforming speed and the second set of the plurality of received channel signals is time-delayed based on a second beamforming speed different from the first beamforming speed.

16. The method of claim 15, further comprising selecting the first beamforming sound velocity by calculating a first beamforming quality metric set for the first region, the first beamforming quality metric set being calculated by the following steps: The first set of the plurality of received channel signals is time-delayed multiple times, each time based on a different beamforming sound velocity selected from the set of beamforming sound velocities, and each time a beamforming quality metric is calculated.

17. The method of claim 16, wherein each calculation of the beamforming quality metric comprises calculating the coherence ratio for each range and lateral direction of the first set of the plurality of received channel signals, and determining an average coherence ratio, each coherence ratio comprising the ratio of the sum of the absolute values ​​of the time-delayed channel signals to the sum of the absolute values ​​of the time-delayed channel signals.

18. The method of claim 16, wherein selecting the first beamforming sound velocity comprises fitting the first set of beamforming quality metrics as a function applied to calculate the corresponding sound velocity for each beamforming quality metric, and selecting the first beamforming sound velocity from the fitting.

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