Patent Description:
Ultrasound imaging systems are widely used for medical imaging. A conventional medical ultrasound system may include an ultrasound transducer probe coupled to a processing system and one or more display devices. The ultrasound transducer probe may include an array of acoustic elements that transmit acoustic waves into an object (e.g., a patient's body) and record acoustic waves reflected from the object. The transmission of the acoustic waves and/or the reception of reflected acoustic waves or echo responses can be performed by the same set of ultrasound transducer elements or different sets of acoustic elements. The processing system reconstructs or creates an image of the object from the echo responses received by the acoustic elements. For conventional ultrasound imaging, the processing system may perform beamforming by delaying and summing the received echo response signals to achieve receive focusing along imaging depths. The processing system may reconstruct the image from the beamformed signals by applying signal processing and/or image processing techniques.

There are often tradeoffs between resolution, contrast, penetration depth, signal-to-noise ratio (SNR), and/or acquisition speed or frame rate in conventional ultrasound imaging. For example, image quality or resolution in conventional ultrasound imaging is limited by diffraction. One approach to reducing the effect of diffraction is to employ a transducer with a larger aperture size. In another example, an ultrasound imaging system may utilize unfocused ultrasound beams or diverging waves to illuminate a larger portion of a region of interest (ROI) with a single transmission in order to reduce image acquisition time. However, images obtained from a limited number of diverging waves can have a lower image quality than images obtained from focused imaging. Thus, the ultrasound image quality in a conventional ultrasound imaging system can be limited by the capability (e.g., the transducer aperture size) of the system and/or the acquisition process.

<NPL> describes the use of deep neural networks for suppressing off-axis scattering in ultrasound channel data.

<NPL> also describes an approach for suppressing off-axis scattering in ultrasound images.

While existing ultrasound imaging has proved useful for clinical guidance and diagnosis, there remains a need for improved systems and techniques for providing high-quality ultrasound images. Embodiments of the present disclosure provide a deep learning framework to map ultrasound echo channel signals to beamformed signals instead of performing conventional delay-and-sum (DAS)-based beamforming. For example, an imaging probe including a transducer array may be used for ultrasound imaging. The transducer array may include an array of acoustic elements that emit ultrasound pulses into an object (e.g., a patient's anatomy) and receive ultrasound channel signals corresponding to ultrasonic waves reflected from the object. A predictive network (e.g., a convolutional neural network (CNN)) can be trained to map the per-channel ultrasound echo channel signals to beamformed signals on a pixel-by-pixel basis. In an example, the per-channel ultrasound echo channel signals are time-aligned and normalized prior to applying the predictive network. Thus, the predictive network is trained to learn beamforming instead of amplitude mapping and/or time-delay mapping. For example, a transducer array of a certain aperture size and/or an acquisition with a certain number of transmit firings can provide a certain image quality using DAS-based beamforming. In an embodiment, the predictive network can be trained to provide beamformed signals with a higher image quality or resolution than the actual transducer aperture size in use can provide. In an embodiment, the predictive network is trained to provide beamformed signals with a higher image quality or resolution than the actual number of transmit firings used in an acquisition can provide. The predictive network can be trained using a combination of simulation data, data acquired from phantoms in experimental test setups, and/or data acquired from patients in clinical settings. The disclosed embodiments are suitable for use in two-dimensional (2D) imaging, three-dimensional (3D) volumetric imaging, focused imaging, and/or unfocused imaging.

In one embodiment, an ultrasound imaging system according to claim <NUM> is provided.

In some embodiments, wherein the processor circuit is further configured to apply time delays to the normalized ultrasound channel data based on an imaging depth. In some embodiments, wherein the ultrasound channel data includes a plurality of samples for a plurality of channels, wherein the beamformed data includes a plurality of output values, wherein the processor circuit is further configured to select a subset of the plurality of samples based on an imaging depth, wherein the processor circuit normalizing the ultrasound channel data includes scaling a first signal level of a first sample of the subset of the plurality of samples based on second signal levels of the subset of the plurality of samples to produce a subset of the normalized ultrasound channel data, and wherein the processor circuit generating the beamformed data includes applying the predictive network to the subset of the normalized ultrasound channel data to produce a first output value of the plurality of output values in the beamformed data. In some embodiments, wherein the first sample and the first output value correspond to a same pixel location in the image. In some embodiments, wherein the processor circuit normalizing the ultrasound channel data includes scaling the first signal level of the first sample based on a root-mean-square (RMS) value of the subset of the plurality of samples. In some embodiments, wherein the array of acoustic elements includes a first aperture size, and wherein the beamformed data is associated with a second aperture size larger than the first aperture size. In some embodiments, wherein the predictive network is trained by providing test ultrasound channel data generated based on the first aperture size and first target beamformed data generated based on the second aperture size; and training the predictive network to produce the first target beamformed data from the test ultrasound channel data. In some embodiments, wherein the predictive network is trained by providing second target beamformed data generated based on the first aperture size; and training the predictive network to produce the second target beamformed data from the test ultrasound channel data before training the predictive network to produce the first target beamformed data. In some embodiments, wherein the ultrasound channel data is generated from a first quantity of ultrasound transmit trigger events, and wherein the beamformed data is associated with a second quantity of ultrasound transmit trigger events greater than the first quantity of ultrasound transmit trigger events. In some embodiments, wherein the predictive network is trained by providing test ultrasound channel data generated based on the first quantity of ultrasound transmit trigger events and first target beamformed data generated based on the second quantity of ultrasound transmit trigger events; and training the predictive network to produce the first target beamformed data from the test ultrasound channel data. In some embodiments, wherein the predictive network is trained by providing second target beamformed data generated based on the first quantity of ultrasound transmit trigger events; and training the predictive network to produce the second target beamformed data from the test ultrasound channel data before training the predictive network to produce the first target beamformed data. In some embodiments, wherein the ultrasound channel data is associated with a first signal-to-noise (SNR), and wherein the beamformed data is associated with a second SNR greater than the first SNR. In some embodiments, wherein the array of acoustic elements includes a one-dimensional array of acoustic elements. In some embodiments, wherein the array of acoustic elements includes a two-dimensional array of acoustic elements.

In one embodiment, a method of ultrasound imaging according to claim <NUM> is provided.

In some embodiments, the method further comprises applying time delays to the normalized ultrasound channel data based on an imaging depth. In some embodiments, wherein the ultrasound channel data includes a plurality of samples for a plurality of channels, wherein the beamformed data includes a plurality of output values, wherein the method includes selecting a subset of the plurality of samples based on an imaging depth, wherein the normalizing the ultrasound channel data includes scaling a first signal level of a first sample of the subset of the plurality of samples based on second signal levels of the subset of the plurality of samples to produce the normalized ultrasound channel data, the first sample corresponding to a pixel location in the image, and generating the beamformed data by applying the predictive network to the subset of the normalized ultrasound channel data to produce a first output value of the plurality of output values in the beamformed data, the first output value corresponding to the pixel location. In some embodiments, wherein the array of acoustic elements includes a first aperture size, and wherein the beamformed data is associated with a second aperture size larger than the first aperture size. In some embodiments, wherein the ultrasound channel data is generated from a first quantity of ultrasound transmit trigger events, and wherein the beamformed data is associated with a second quantity of ultrasound transmit trigger events greater than the first quantity of ultrasound transmit trigger events. In some embodiments, wherein the ultrasound channel data is associated with a first signal-to-noise (SNR), and wherein the beamformed data is associated with a second SNR greater than the first SNR.

In one embodiment, a non-transitory computer-readable medium storing computer readable instructions according to claim <NUM> is provided.

It is fully contemplated that the features, components, and/or steps described with respect to one embodiment may be combined with the features, components, and/or steps described with respect to other embodiments of the present disclosure.

<FIG> is a schematic diagram of an ultrasound imaging system <NUM>, according to aspects of the present disclosure. The system <NUM> is used for scanning an area or volume of a patient's body. The system <NUM> includes an ultrasound imaging probe <NUM> in communication with a host <NUM> over a communication interface or link <NUM>. The probe <NUM> includes a transducer <NUM>, an analog front end (AFE) <NUM>, a beamformer <NUM>, a processor circuit <NUM>, and a communication interface <NUM>. The host <NUM> includes a display <NUM>, a processor circuit <NUM>, a communication interface <NUM>, and a memory <NUM>.

The probe <NUM> may be in any suitable form for imaging various body parts of a patient while positioned inside or outside of the patient's body. In an embodiment, the probe <NUM> is an external ultrasound imaging device including a housing configured for handheld operation by a user. The transducer <NUM> can be configured to obtain ultrasound data while the user grasps the housing of the probe <NUM> such that the transducer <NUM> is positioned adjacent to and/or in contact with a patient's skin. The probe <NUM> is configured to obtain ultrasound data of anatomy within the patient's body while the probe <NUM> is positioned outside of the patient's body. In some other embodiments, the probe <NUM> may be in the form of a catheter, an intravascular ultrasound (IVUS) catheter, an intracardiac echocardiography (ICE) catheter, a transesophageal echocardiography (TEE) probe, a transthoracic echocardiography (TTE) probe, an endo-cavity probe, a handheld ultrasound scanner, or a patch-based ultrasound device.

The transducer <NUM> emits ultrasound signals towards an anatomical object <NUM> and receives echo signals reflected from the object <NUM> back to the transducer <NUM>. The object <NUM> may include any anatomy (e.g., lung, blood vessel, tissues, heart, kidney, and/or liver) of a patient that is suitable for ultrasound imaging examination. The ultrasound transducer <NUM> can include any suitable number of acoustic elements, including one or more acoustic elements and/or plurality of acoustic elements. In some instances, the transducer <NUM> includes a single acoustic element. In some instances, the transducer <NUM> may include an array of acoustic elements with any number of acoustic elements in any suitable configuration. For example, the transducer <NUM> can include between <NUM> acoustic element and <NUM> acoustic elements, including values such as <NUM> acoustic elements, <NUM> acoustic elements, <NUM> acoustic elements, <NUM> acoustic elements, <NUM> acoustic elements, <NUM> acoustic elements, <NUM> acoustic elements, and/or other values both larger and smaller. In some instances, the transducer <NUM> may include an array of acoustic elements with any number of acoustic elements in any suitable configuration, such as a linear array, a planar array, a curved array, a curvilinear array, a circumferential array, an annular array, a phased array, a matrix array, a one-dimensional (1D) array, a <NUM>. x dimensional array (e.g., a <NUM>. 5D array), or a two-dimensional (2D) array. The array of acoustic elements (e.g., one or more rows, one or more columns, and/or one or more orientations) that can be uniformly or independently controlled and activated. The transducer <NUM> can be configured to obtain 1D, 2D, and/or three-dimensional (3D) images of patient anatomy. The acoustic elements may also be referred to as transducer elements or imaging elements. In some embodiments, the transducer <NUM> may include a piezoelectric micromachined ultrasound transducer (PMUT), capacitive micromachined ultrasonic transducer (CMUT), single crystal, lead zirconate titanate (PZT), PZT composite, other suitable transducer types, and/or combinations thereof.

The AFE <NUM> is coupled to the transducer <NUM>. The AFE <NUM> may include components that control the transmissions of ultrasound waves at the transducer <NUM> and/or the receptions of echo responses at the transducer <NUM>. For example, in a transmit path, the AFE <NUM> may include a digital-to-analog converter (DAC), filters, gain controls, and/or a high-voltage (HV) transmitter that drives or triggers ultrasound pulse emissions at the acoustic elements or transducer elements of the transducer <NUM>. In a receive path, the AFE <NUM> may include gain controls, filters, amplifiers, and analog-to-digital converts (ADCs) that receive echo responses from the transducer elements of the transducer <NUM>. The AFE <NUM> may further include a plurality of transmit/receive (T/R) switches that control the switching between transmit and receive at the transducer elements and prevent the high-voltage pulses from damaging the transducer elements for the transducer <NUM>.

In an embodiment, the transducer <NUM> includes M plurality of transducer elements (e.g., acoustic elements <NUM> of <FIG>). In some embodiments, M can be about <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, or greater than <NUM>. In the receive path, each transducer element can convert ultrasound energy received from a reflected ultrasound pulse to an electrical signal, forming a single receive channel. In other words, the transducer <NUM> can generate M analog ultrasound echo channel signals <NUM>. The AFE <NUM> can be coupled to the transducer <NUM> via M signal lines. The ADCs (e.g., ADCs <NUM> of <FIG>) in the AFE <NUM> can produce M digital ultrasound echo channel signals <NUM>, each corresponding to an analog ultrasound echo channel signal <NUM> received at one of the transducer element in the transducer <NUM>. The digital ultrasound echo channel signals <NUM> can also be referred to as ultrasound echo data streams or ultrasound echo channel data.

The beamformer <NUM> is coupled to the AFE <NUM>. The beamformer <NUM> may include delay elements and summing elements configured to control transmit and/or receive beamforming at the transducer <NUM>. The beamformer <NUM> may apply appropriate time-delays to at least a subset of the digital ultrasound echo channel signals <NUM> and combine the time-delayed digital ultrasound echo channel signals to form a beamformed signal <NUM> (e.g., a focused beam). For example, the beamformer <NUM> may produce L plurality of beamformed signals <NUM>, where L is a positive integer smaller than M. In some embodiments, the beamformer <NUM> may include multiple stages of beamforming. For example, the beamformer <NUM> may perform partial beamforming to combine a subset of the digital ultrasound echo channel signals <NUM> to form partially beamformed signals and subsequently beamform the partial beamformed signals to produce fully beamformed signals. While the beamformer <NUM> is described in the context of digital beamforming, in some embodiments, the AFE <NUM> can include electronics and/or dedicated hardware for analog partial beamforming.

The processor circuit <NUM> is coupled to the beamformer <NUM>. The processor circuit <NUM> may include a central processing unit (CPU), a digital signal processor (DSP), an application specific integrated circuit (ASIC), a controller, a field programmable gate array (FPGA) device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. The processor circuit <NUM> may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration. The processor circuit <NUM> is configured to process the beamformed signals <NUM>. For example, the processor circuit <NUM> may perform a series of coherent and/or incoherent signal processing, such as compounding, envelope detection, logarithmic compression, and/or non-linear image filtering, to the beamformed signals <NUM> to produce image signals <NUM>.

The communication interface <NUM> is coupled to the processor circuit <NUM>. The communication interface <NUM> may include one or more transmitters, one or more receivers, one or more transceivers, and/or circuitry for transmitting and/or receiving communication signals. The communication interface <NUM> can include hardware components and/or software components implementing a particular communication protocol suitable for transporting signals over the communication link <NUM> to the host <NUM>. The communication interface <NUM> can be referred to as a communication device or a communication interface module.

The communication link <NUM> may be any suitable communication link. For example, the communication link <NUM> may be a wired link, such as a universal serial bus (USB) link or an Ethernet link. Alternatively, the communication link <NUM> nay be a wireless link, such as an ultra-wideband (UWB) link, an Institute of Electrical and Electronics Engineers (IEEE) <NUM> WiFi link, or a Bluetooth link.

At the host <NUM>, the communication interface <NUM> may receive the image signals <NUM>, transducer element signals (e.g., the analog ultrasound echo channel signals <NUM>), or partially beamformed signals. The communication interface <NUM> may be substantially similar to the communication interface <NUM>. The host <NUM> may be any suitable computing and display device, such as a workstation, a personal computer (PC), a laptop, a tablet, or a mobile phone.

The processor circuit <NUM> is coupled to the communication interface <NUM>. The processor circuit <NUM> may be implemented as a combination of software components and hardware components. The processor circuit <NUM> may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a controller, a FPGA device, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. The processor circuit <NUM> may also be implemented as a combination of computing devices, e.g., a combination of a DSP and a microprocessor, a plurality of microprocessors, one or more microprocessors in conjunction with a DSP core, or any other such configuration.

The processor circuit <NUM> can be configured to generate or reconstruct images <NUM> of the object <NUM> from the image signals <NUM> received from the probe <NUM>, beamform images <NUM> from transducer signals (e.g., the analog ultrasound echo channel signals <NUM>), or partially beamformed signals <NUM>. The processor circuit <NUM> can further apply image processing techniques to the image signals <NUM>. In some embodiments, the processor circuit <NUM> can perform scan conversions to form 2D or 2D volume images from the image signals <NUM>. In some embodiments, the processor circuit <NUM> can perform real-time processing on the image signals <NUM> to provide a streaming video of ultrasound images <NUM> of the object <NUM>. The images <NUM> can include morphological information, functional information, and/or quantitative measurement of the object <NUM> depending on the acquisition modalities used at the probe <NUM>. The morphological information may include anatomical structural information (e.g., B-mode information) of the object <NUM>. Examples of functional information may include tissue strain, elasticity, Doppler flow, tissue Doppler flow, and/or blood flow information associated with the object <NUM>. Examples of quantitative measurements may include a blood flow velocity, blood flow volume, lumen diameter, lumen area, stenosis length, plaque burden, and/or tissue elasticity. In some embodiments, the processor circuit <NUM> can perform image analysis on the image signals <NUM> to determine clinical conditions associated with the object <NUM>.

The display <NUM> is coupled to the processor circuit <NUM>. The display <NUM> may be a monitor or any suitable display. The display <NUM> is configured to display ultrasound images, image videos, and/or information associated with the object <NUM> under examination.

While the system <NUM> is illustrated with beamforming and signal processing functions performed by the beamformer <NUM> and the processor circuit <NUM>, respectively, at the probe <NUM>, in some embodiments, at least some of the beamforming and/or signal processing functions may be performed at the host <NUM>. In other words, the probe <NUM> may transfer digital ultrasound echo channel signals <NUM> or beamformed signals <NUM> to the host <NUM> for processing. In some other embodiments, the probe <NUM> may transfer the analog ultrasound echo channel signals <NUM>, for example, with some gain controls, filtering, and/or partial analog beamforming to the host <NUM> for processing. In such embodiments, the host <NUM> may further include ADCs and a beamformer. In addition, the communication interface <NUM> at the probe <NUM> may be an industry standard physical connector and/or a proprietary physical connector and the communication link <NUM> may include any industry standard cables, coaxial cables, and/or proprietary cables. In general, the system <NUM> may represent any types of ultrasound imaging system, where ultrasound imaging functionalities may be partitioned in any suitable manner across a probe (e.g., including a transducer <NUM>), a host, and/or any intermediate processing subsystem between the probe and the host.

According to embodiments of the present disclosure, the system <NUM> uses a predictive model (e.g., a deep learning model) for beamforming instead of the delay-and-sum (DAS)-based beamformer <NUM> described above. The system <NUM> can be used in various stages of ultrasound imaging. In an embodiment, the system <NUM> may be used for collecting ultrasound images to form a training dataset <NUM> for training a machine learning network <NUM> for ultrasound beamforming. For example, the host <NUM> may include a memory <NUM>, which may be any suitable storage device, such as a cache memory (e.g., a cache memory of the processor circuit <NUM>), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, solid state drives, other forms of volatile and non-volatile memory, or a combination of different types of memory. The memory <NUM> can be configured to store the training image dataset <NUM> and the machine learning network <NUM>. For example, the training image dataset <NUM> can store the digital ultrasound echo channel signals <NUM> in association with beamformed signals generated using the system <NUM> or simulated beamformed signals. In an embodiment, the system <NUM> may utilize the trained machine learning network <NUM> for beamforming instead of the DAS beamformer <NUM> in a clinical setting (e.g., during an ultrasound examination). Mechanisms for training a deep learning model for ultrasound beamforming and applying the trained deep learning model for ultrasound beamforming are described in greater detail herein.

<FIG> is a schematic diagram illustrating an ultrasound imaging system <NUM> implementing DAS-based beamforming, according to embodiments of the present disclosure. The system <NUM> corresponds to a portion of the system <NUM> and provides a more detailed view of components along the receive signal path of the system <NUM> (e.g., within the probe <NUM> and/or the host <NUM>). As shown in <FIG>, the transducer <NUM> includes a plurality of acoustic elements <NUM>. Each acoustic element <NUM> forms a receive channel, where an analog ultrasound echo channel signal <NUM> may be received when the acoustic element <NUM> is activated for receiving after a transmit trigger. For example, the transducer <NUM> may include M quantity of acoustic elements <NUM>. Thus, the receive channels can be referred to as Channel(<NUM>) to Channel(M). In an embodiment, the AFE <NUM> may include a plurality of ADCs <NUM>. Each ADC <NUM> may be coupled to an acoustic element <NUM>. While not shown, the AFE <NUM> may additionally include other components, such as filters and amplifiers, coupled to each acoustic element <NUM>. Each ADC <NUM> may sample a corresponding analog ultrasound echo channel signal <NUM> to form a digital ultrasound echo channel signal <NUM>. Each digital ultrasound echo channel signal <NUM> includes a series of samples along an imaging depth of field. In some other embodiments, the AFE <NUM> may include a less number of ADCs <NUM> than the number of receive channels. In such embodiments, each ADC <NUM> may be coupled to a subset of the receive channels and configured to sample analog ultrasound echo channel signals <NUM> from the subset of receive channels, for example, in a multiplexing manner.

The beamformer <NUM> is coupled to the ADCs <NUM>. The beamformer <NUM> includes a plurality of delay elements <NUM> coupled to a summing element <NUM>. Each delay element <NUM> is configured to apply a time-delay to a corresponding digital ultrasound echo channel signal <NUM> to produce a delayed ultrasound echo channel signal <NUM>. The delay elements <NUM> may be dynamically configured to apply appropriate time-delays to the digital ultrasound echo channel signal <NUM>. For example, one or more of the acoustic elements <NUM> may be triggered to transmit ultrasonic energy into an anatomy (e.g., the anatomy object <NUM>) and a group of acoustic elements <NUM> may be activated to receive ultrasound echoes reflected from the anatomy due to the ultrasound signal transmission. Due to the different propagation paths, receive echoes may arrive at the acoustic elements <NUM> at different times. Thus, the delay elements <NUM> delays the ultrasound echo channel signal <NUM> such that the ultrasound echo channel signal <NUM> are aligned in time. The summing element <NUM> is configured to combine the delayed ultrasound echo channel signals <NUM> to produce beamformed data <NUM>. The beamformed data <NUM> corresponds to the beamformed signals <NUM>.

In general, the goal of beamforming is to reverse the acoustic wave propagation effect so that ultrasound or acoustic energy can be focused at various locations along a main axis of the ultrasound echo signal path. For example, the delay elements <NUM> can be dynamically configured to provide receive focusing at each echo location along the main axis of the ultrasound echo signal path. In other words, the delay elements <NUM> can be configured with different delays to provide focusing at different echo locations.

The beamformed data <NUM> can be further processed by the processor circuit <NUM> and/or the processor circuit <NUM>, for example, including frequency compounding, envelope detection, logarithmic compression, and/or non-linear image filtering as described above with respect to <FIG>, to produce an image <NUM>.

Some performance measures, such as image quality or resolution and/or data acquisition rate or frame rates, may be important for ultrasound imaging. For example, the image quality, resolution, or contrast may impact a clinician's ability to differentiate anatomical details within an acquired ultrasound image. The data acquisition rate or frame rates may impact the amount of time required for acquiring an ultrasound image or video, and thus the real-time imaging capability and ultrasound examination time.

Ultrasound imaging quality or resolution can be limited by diffraction, which is determined by the aperture size of a transducer. In other words, the imaging quality or resolution of the systems <NUM> and/or <NUM> can be limited by the aperture size <NUM> (see <FIG>) of the transducer <NUM> in use for an examination. The aperture size <NUM> refers to the physical size or dimensions of the transducer <NUM>. The aperture size <NUM> may correspond to the number of acoustic elements <NUM> in the transducer <NUM>. One approach to improving image quality or image resolution is to employ a transducer with a larger aperture size. In general, image resolution varies proportionally with transducer's aperture size. For example, a transducer having about <NUM> acoustic elements <NUM> can provide about twice the imaging resolution compared to a transducer having about <NUM> acoustic elements <NUM>.

Data acquisition rates can be of a concern for 3D imaging or volumetric imaging, where a large amount of imaging data is acquired in order to produce a 3D image. Conventional ultrasound imaging acquisition schemes utilize focused transmit beams (shown in <FIG>). A focused transmit beam can illuminate a limited region. Thus, multiple transmit beams are typically used to sweep through or illuminate an entire region of interest. As such, the use of focused transmit beams can pose a time limit for real-time volumetric imaging and/or applications where a high frame rate is important, for example, in cardiac imaging.

<FIG> is a schematic diagram illustrating an ultrasonic wave transmission scheme <NUM> for ultrasound imaging, according to aspects of the present disclosure. The scheme <NUM> can be employed by the systems <NUM> and/or <NUM>. The scheme <NUM> configures the transducer <NUM> to emit a focused ultrasound beam <NUM> for ultrasound imaging. As shown, a group of acoustic elements <NUM> is activated to emit the focused ultrasound beam <NUM>. The focused ultrasound beam <NUM> has an hour-glass shape with a focus <NUM> at an imaging depth of <NUM>. As can be observed, multiple focused ultrasound beams <NUM> are required in order to sweep through a region of interest (ROI) <NUM>, and thus may take a certain amount of time.

To improve the frame rates or reduce the image acquisition time, a faster imaging method may use unfocused ultrasound beams (shown in <FIG>). An unfocused beam can illuminate a larger portion of the ROI <NUM>, and thus may reduce the number of transmissions required to illuminate or sweep through the entire ROI <NUM>.

<FIG> is a schematic diagram illustrating an ultrasonic wave transmission scheme <NUM> for ultrasound imaging, according to aspects of the present disclosure. The scheme <NUM> can be employed by the systems <NUM> and/or <NUM>. The scheme <NUM> configures the transducer <NUM> to emit an unfocused ultrasound beam <NUM> for ultrasound imaging. As shown, a group of acoustic elements <NUM> is activated to produce the unfocused ultrasound beam <NUM>. The unfocused ultrasound beam <NUM> includes plane waves or diverging waves, where the focus <NUM> is located behind the transducer <NUM>. The unfocused ultrasound beam <NUM> can illuminate a large portion of the ROI <NUM> than the focused ultrasound beam <NUM>, and thus a less number of transmissions is required to sweep the entire ROI <NUM> when using the unfocused ultrasound beam <NUM> compared to using the focused ultrasound beam <NUM>.

While the diverging waves can illuminate a larger portion of the ROI <NUM>, the image quality may degrade due to the lack of transmission focusing. One approach to compensating the image quality loss due to unfocused imaging is to repeat the transmission or increase the number of diverging wave transmissions and coherently compounding received beams from the multiple transmissions. Thus, there is a trade-off between frame rates or acquisition time and image quality.

The use of unfocused ultrasound beams <NUM> may have additional impacts with 3D imaging. 3D imaging uses a 2D transducer array, which may include a large number of acoustic elements (e.g., the acoustic elements <NUM>), for example, in the order of thousands of acoustic elements. However, ultrasound imaging systems may typically have a limited number of system channels or receive channels (e.g., about <NUM>) for transporting received ultrasound echoes received from the transducer to a processor circuit (e.g., the processor circuit <NUM> and/or the host <NUM>). One approach to overcoming the limited number of system channels is to use micro-beamformers, where partial beamforming is performed prior to sending the received ultrasound echoes signals to the system channels. While micro-beamformers may provide a good receive focusing performance with the use of focused transmit beams (e.g., the beam <NUM>), the receive focusing performance may be sub-optimal when the transmit beam is steered away from the main axis of the transmit beam (e.g., the unfocused beam <NUM>). Further, in some instances, a micro-beamformed array may result in an under-sampled array, where the inter-element spacing (e.g., the spacing between the acoustic elements <NUM>) may exceed the grating lobe limit of λ/<NUM>, where λ represents the wavelength of the transmit beam. As a result, grating lobes may appear in the reconstructed images. The grating lobes may not overlap with focused transmit beams, and thus may not be an issue when focused transmit beams are used. However, grating lobes can create artefacts with wider insonifications (e.g., when unfocused beams <NUM> are used).

Accordingly, the present disclosure provides techniques to overcome the image quality and data acquisition rate issues described above. The present disclosure uses deep learning techniques for beamforming instead of the conventional DAS-based beamforming. In one embodiment, a deep learning network is trained to map per-channel ultrasound echo data (e.g., the ultrasound echo channel signals <NUM>) generated from a certain aperture size to beamformed data with a higher resolution than the aperture size can provide. In other words, the deep learning-based beamformed data includes a resolution corresponding to images generated from a larger transducer aperture size (e.g., about twice the aperture size of the transducer used for collecting the per-channel ultrasound echo data). In one embodiment, a deep learning network is trained to map per-channel ultrasound echo data generated from unfocused transmit beams (e.g., the unfocused ultrasound beam <NUM>) with a certain number of transmit triggering events to beamformed data with a higher image quality (e.g., a higher SNR, better contrast, and/or better contrast to noise) than number of transmit triggering events can provide. In other words, the deep learning-based beamformed data includes an image quality corresponding to images generated from a greater number of transmit triggering events. Accordingly, the present disclosure can improve image quality and/or reduce data acquisition time.

<FIG> is a schematic diagram of an ultrasound imaging system <NUM> implementing deep learning-based beamforming, according to embodiments of the present disclosure. The system <NUM> is substantially similar to the system <NUM>, but utilizes a deep learning-based beamformer <NUM> instead of the DAS-based beamformer <NUM> for beamforming. The system <NUM> includes a signal conditioning component <NUM> and the deep learning-based beamformer <NUM>. The signal conditioning component <NUM> and the deep learning-based beamformer <NUM> can be implemented by a combination of hardware and software. The deep learning-based beamformer <NUM> includes a time-alignment component <NUM>, a normalization component <NUM>, a deep learning network <NUM>, and a de-normalization component <NUM>.

Similar to the systems <NUM> and <NUM>, the system <NUM> may include a transducer array (e.g., the transducer <NUM>). The transducer array may include M number of acoustic elements (e.g., the acoustic elements <NUM>) that can be configured to transmit ultrasound energy into an anatomy (e.g., the anatomical object <NUM>) and receive ultrasound echoes reflected from the anatomy back to the transducer array. The ultrasound echoes may be received in the form of M number of channels, each carrying an ultrasound echo channel signal <NUM> (e.g., the digital ultrasound echo channel signals <NUM>). The ultrasound echo channel signals <NUM> may be raw radio frequency (RF) channel signals. The ultrasound echo channel signals <NUM> may be referred to as per-channel ultrasound RF echo data.

The signal conditioning component <NUM> can include one or more filters configured to receive the ultrasound echo channel signals <NUM> and condition the received ultrasound echo channel signals <NUM> prior to beamforming. In an example, the signal conditioning component <NUM> may apply a bandpass filter to the ultrasound echo channel signals <NUM> to remove electronic noise. The bandpass filter may span all quadrature band pass filters (QBPs) that are used by the system <NUM> for subsequent frequency compounding during image reconstruction. As an example, the transducer array may generate ultrasound beams at a center frequency of about <NUM> and the ultrasound echo channel signals <NUM> are sampled at about <NUM> (e.g., by ADCs such as the <NUM>). The ultrasound echo channel signals <NUM> can be decimated at about <NUM> to reduce subsequent computational speed requirements. Thus, the bandpass filter may be centered at about <NUM> and may have a bandwidth between about <NUM> and about <NUM>. Typically, decimation can be performed after time-alignment since there are a greater number of samples available to make a more accurate estimation of delayed samples.

The time-alignment component <NUM> is coupled to the signal conditioning component <NUM>. The time-alignment component <NUM> is configured to time-align the conditioned ultrasound echo channel signals <NUM>. The time-alignment component <NUM> may include delay elements similar to the delay elements <NUM> and perform substantially similar time-delay operations as the delay elements <NUM> described above with respect to <FIG>.

The normalization component <NUM> is coupled to the time-alignment component <NUM>. The normalization component <NUM> is configured to normalize the signal levels of the time-aligned per-channel ultrasound echo signals <NUM> by scaling signal levels or amplitudes of the time-aligned per-channel ultrasound echo signals <NUM> by the local energy of the signals <NUM>. The normalization component <NUM> performs the signal level normalization in subsets of samples from the time-aligned per-channel ultrasound echo signals <NUM>, as described in greater detail herein.

The deep learning network <NUM> is coupled to the normalization component <NUM>. The deep learning network <NUM> maps the normalized, time-aligned per-channel ultrasound echo signals <NUM> to normalized beamformed data <NUM>. In an example, the deep learning network <NUM> can be a CNN network. Configurations or architectures of the deep learning network <NUM> and/or training of the deep learning network <NUM> are described in greater detail herein.

The applying of the deep learning network <NUM> to the normalized, time-aligned per-channel ultrasound echo channel signals <NUM> can reduce the complexity of the deep learning network <NUM> and improve the beamforming or beam-summing prediction performance of the deep learning network. For example, the performing of the time-alignment or time-delaying prior to the deep learning network <NUM> can allow the deep learning network <NUM> to be trained to learn beamforming without having to learn the time-alignment. The time-alignment or time-delay operations have relatively low computational complexity, and thus can be performed outside of the deep learning network <NUM> without a high computational cost. The normalization prior to the deep learning network <NUM> can avoid having samples with large amplitudes or signal levels dominate samples with lower amplitudes or signal levels. Thus, the deep learning network <NUM> can be trained to learn the summing operations in beamforming, and not amplitude mapping. As such, the normalization can prevent numerical imbalance in the loss function of the deep learning network <NUM>. The loss function is a measure of how well the deep learning network <NUM> performs and is used as an error measure during training as described in greater detail herein.

The denormalization component <NUM> is coupled to the deep learning network <NUM>. The denormalization component <NUM> is configured to de-normalize the beamformed data <NUM> based on the normalization performed at the normalization component <NUM>. In other words, the de-normalization component <NUM> reverses the operations of the normalization component <NUM> as described in greater detail herein. The de-normalization component <NUM> produces de-normalized beamformed data <NUM>. The beamformed data <NUM> can be further processed by the processor circuit <NUM> and/or the processor circuit <NUM>, for exampling, including frequency compounding, envelope detection, logarithmic compression, and/or non-linear image filtering as described above with respect to <FIG>, to produce an image.

According to embodiments of the disclosure, the deep learning network <NUM> is trained such that the beamformed data <NUM> has a higher image quality or resolution than the DAS-based beamformed data <NUM>. As such, images generated from the beamformed data <NUM> can have a higher image quality or resolution than images generated from the DAS-based beamformed data <NUM>.

<FIG> is a schematic diagram illustrating a normalization scheme <NUM> for deep learning-based beamforming, according to aspects of the present disclosure. The scheme <NUM> is implemented by the normalization component <NUM> of <FIG>. The scheme <NUM> applies normalization to the M channels of time-aligned ultrasound echo channel signals <NUM>. Each ultrasound echo channel signal <NUM> in a receive channel includes a plurality of time samples along an imaging depth (e.g., in a y-dimension). The time samples are shown as symbols "X" in <FIG>.

The scheme <NUM> partitions the samples in ultrasound echo channel signals <NUM> into multiple subsets <NUM> based on an imaging depth. For simplicity of discussion and illustration, three subsets 610a, 610b, and 610c are shown, each corresponding to an imaging depth range. However, the number of subsets <NUM> may vary depending on the embodiments. In some examples, the imaging depth range for each subset may correspond to about four times the wavelength (e.g., <NUM>×λ) of a corresponding ultrasound transmission beam.

The normalization component <NUM> normalizes each subset <NUM> by scaling the signal levels or amplitudes of the samples in the corresponding subset <NUM> based on the signal energy of the corresponding subset <NUM>. The normalization component <NUM> produces a subset of samples in the normalized ultrasound echo channel signals <NUM> from each subset <NUM>. For example, the subset 610a is normalized to produce a subset 620a of the samples in the normalized ultrasound echo channel signals <NUM>, the subset 610b is normalized to produce a subset 620b of samples in the normalized ultrasound echo channel signals <NUM>, and the subset 610c is normalized to produce a subset 620c of samples in the normalized ultrasound echo channel signals <NUM>. After the normalization, the normalized ultrasound echo channel signals <NUM> may include signal levels between about <NUM> and -<NUM>.

The deep learning network <NUM> is applied to the normalized ultrasound echo channel signals <NUM> to produce the beamformed data <NUM>. As an example, the deep learning network <NUM> outputs a beamformed output sample or pixel 632a for the subset 610a, a beamformed output sample or pixel 632b for the subset 610b, and a beamformed output sample or pixel 632c for the subset 610c. The pixel 632a corresponds to a center time sample 612a of the subset 610a. The pixel 632b corresponds to a center time sample 612b of the subset 610b. The pixel 632c corresponds to a center time sample 612c of the subset 610c. In an example, the subset 610a includes about <NUM> samples for each channel along the imaging depths. The sample 612a may correspond to the <NUM>th sample in the Channel(i). The time sample 612a and the beamformed output pixel 632a may correspond to the same pixel location in the final image. Similarly, the time sample 612b and the beamformed output pixel 632b may correspond to the same pixel location in the final image. The time sample 612c and the beamformed output pixel 632c may correspond to the same pixel location in the final image.

In an embodiment, the normalization component <NUM> performs the scaling by dividing the subset of samples by the root-mean-square (RMS) of the signal level of a sample corresponding to a beamform output sample or pixel. For example, the normalization component <NUM> scales the sample 612a by dividing the sample 612a with the RMS of all the samples in subset 610a, scales the sample 612b by dividing the sample 612b with the RMS of all the samples in subset 610b, and scales the sample 612c by dividing the sample 612c with the RMS of all the samples in subset 610c. Accordingly, each sample 612a, 612b, or 612c is sampled with respect to the signal energy in its neighborhood. Thus, the normalized echo channel signals <NUM> may mostly include samples with a signal energy between about <NUM> and about <NUM>.

Referring to <FIG>, for denormalization <NUM>, the factor or RMS value used for the normalization of each subset <NUM> may be stored and the denormalization component <NUM> may apply the same factor or RMS value to each corresponding beamformed pixel value 632a, 632b, and 632c. In other words, the denormalization component <NUM> multiplies the output 632a by the RMS value of signal level of the subset 610a, multiplies the output 632b by the RMS value of signal level of the subset 610b, and multiplies the output 632c by the RMS value of signal level of the subset 610c.

While the subsets <NUM> are illustrated as non-overlapping in <FIG>, the scheme <NUM> can be applied to overlapping samples in a sliding window manner along the imaging depths. As an example, the subset 610a may include K rows (e.g., row <NUM> to row K) of samples along the imaging depths. A second subset <NUM> may be formed by including samples from row <NUM> to row K+<NUM> along the imaging depths. A third subset <NUM> may be formed by including samples from row <NUM> to row K+<NUM> along the imaging depths and so forth. For each subset <NUM>, a normalization value (e.g., RMS) is calculated from all the samples in the corresponding subset and the sample (e.g., the sample 612a) located in the center of the subset is divided by the normalization value. The denormalization may be performed using similar sliding window mechanisms. Thus, after applying the sliding windows to the normalization and the denormalization, all samples for the final beamformed data <NUM> are calculated.

In an embodiment, the deep learning network <NUM> is trained to map per-channel ultrasound echo data acquired from a transducer of a certain aperture size (e.g., the aperture size <NUM>) or including a certain number of acoustic elements (e.g., the acoustic elements <NUM>) to beamformed data corresponding to beamformed data obtained from a larger transducer aperture size (e.g., about double) or a greater number of acoustic elements. In other words, the beamformed data <NUM> predicted by the deep learning network <NUM> has a higher image quality (e.g., higher resolution and/or reduced clutters or artefacts) than what the transducer in use can provide.

While the scheme <NUM> is described in the context of a 2D dataset including a number of channels along the x-axis and imaging depths along the y-axis, similar mechanisms can be applied to a 3D dataset including a number of transmit triggers or firing along the z-axis, for example, when the deep learning network <NUM> is trained to map per-channel ultrasound echo data acquired from a certain number of transmit triggers to beamformed data corresponding to a greater number of transmit triggers. For example, the 3D dataset is partitioned into 3D data subsets based on imaging depths, the normalization component <NUM> may scale a center sample in each 3D data subset by dividing the centered sample with the RMS of all samples in the corresponding 3D subset, and the deep learning network <NUM> maps each 3D data subset to a beamformed output sample or pixel.

It should be noted that in some other embodiments, the normalization can be performed by scaling the entire set of ultrasound echo channel data (e.g., the ultrasound echo channel signals <NUM>) based on a signal energy of the set of ultrasound echo channel data instead of applying the normalization per subset based on an imaging depth as in the scheme <NUM>.

<FIG> is a schematic diagram illustrating a configuration <NUM> of the deep learning network <NUM>, according to aspects of the present disclosure. The deep learning network <NUM> may include one or more CNNs <NUM>. The CNN <NUM> may operate on per-channel ultrasound channel data <NUM>. The CNN <NUM> maps the per-channel ultrasound channel data <NUM> to beamformed data <NUM>. In an example, the ultrasound channel data <NUM> may correspond to the normalized, time-aligned ultrasound echo channel signals <NUM> and the beamformed data <NUM> may correspond to the beamformed data <NUM> in the system <NUM>. The CNN <NUM> provides per-channel pixel-based mapping of 2D data and/or 3D data to beamformed data.

The CNN <NUM> includes a set of N convolutional layers <NUM> followed by a set of K fully connected layers <NUM>, where N and K may be any positive integers. The convolutional layers <NUM> are shown as <NUM>(<NUM>) to <NUM>(N). The fully connected layers <NUM> are shown as <NUM>(<NUM>) to <NUM>(K). In an example, the convolutional layers <NUM>(<NUM>) to <NUM>(N) and the fully connected layers <NUM><NUM>(<NUM>) to <NUM>(K-<NUM>) may utilize a rectified non-linear (ReLU) activation function. The last output layer <NUM>(K) may utilize a linear activation function. Each convolutional layer <NUM> may include a set of filters <NUM> configured to extract features from the ultrasound channel data <NUM>. The values N and K and the sizes of the filters <NUM> in each convolutional layer <NUM> may vary depending on the embodiments. It should be noted that the CNN <NUM> does not include pooling layers that are commonly used to reduce the size of the convolutional layers. The exclusion of pooling layers allows all convolutions to contribute to the output of the CNN <NUM>. Alternatively the CNN may include convolutional layers <NUM> only, or fully connected layers <NUM> only.

In an example, the ultrasound channel data <NUM> may include a 2D dataset spanning an x-dimension corresponding to receive channels (e.g., Channel(<NUM>) to Channel (M) of <FIG> and <FIG>) and a y-dimension corresponding to imaging depths. The CNN <NUM> may include about five convolutional layers <NUM> (e.g., N = <NUM>) and about two fully connected layers <NUM> (e.g., K = <NUM>). The convolution layers <NUM> may include 2D convolutional kernels (e.g., the filters <NUM>) spanning in the x and y dimensions. The 2D convolutional kernel size may vary depending on the embodiments. In some examples, the same 2D convolutional kernel size is used for all convolutional layers <NUM>. In some examples, different 2D convolutional kernel sizes may be used for the convolutional layers <NUM>. In some examples, the 2D convolutional kernel size may be dependent on the ultrasound transmission configuration used for collecting the ultrasound channel data <NUM>. The first convolutional layer <NUM>(<NUM>) layer may include about sixty-four filters <NUM> or 2D convolutional kernels, the second convolutional layer <NUM>(<NUM>) layer may include about thirty-two filters <NUM>, the third convolutional layer <NUM>(<NUM>) layer may include about sixteen filters <NUM>, the fourth convolutional layer <NUM>(<NUM>) layer may include about eight filters <NUM>, and the fifth convolutional layer <NUM>(<NUM>) layer may include about four filters <NUM>. The first fully connected layer <NUM>(<NUM>) may have a size of about <NUM> and the last fully connected layer <NUM>(<NUM>) may have a size of about <NUM>. The output at the last fully connected layer <NUM>(<NUM>) corresponds to a single beamformed output sample or pixel (e.g., the beamformed output 632a, 632b, or 632c).

In another example, the ultrasound channel data <NUM> may include a 3D dataset spanning an x-dimension corresponding to receive channels (e.g., Channel(<NUM>) to Channel (M) of <FIG> and <FIG>), a y-dimension corresponding to imaging depths, and a z-dimension corresponding to transmit triggers or transmit events. The CNN <NUM> may include about six convolutional layers <NUM> (e.g., N = <NUM>) and about four fully connected layers <NUM> (e.g., K = <NUM>). The convolution layers <NUM> may include 3D convolutional kernels spanning in the x, y, and z dimensions. The 3D convolutional kernel size may vary depending on the embodiments. In some examples, the same 3D convolutional kernel size is used for all convolutional layers <NUM>. In some examples, different 3D convolutional kernel size may be used for the convolutional layers <NUM>. In some examples, the 3D convolutional kernel size may be dependent on the ultrasound transmission configuration used for collecting the ultrasound channel data <NUM>. The first convolutional layer <NUM>(<NUM>) layer may include about sixty-four filters <NUM> or 3D convolutional kernels, the second convolutional layer <NUM>(<NUM>) layer may include about thirty-two filters <NUM>, the third convolutional layer <NUM>(<NUM>) layer may include about sixteen filters <NUM>, the fourth convolutional layer <NUM>(<NUM>) layer may include about eight filters <NUM>, the fifth convolutional layer <NUM>(<NUM>) layer may include about four filters <NUM>, and the sixth convolutional layer <NUM>(<NUM>) layer may include about two filters <NUM>. The first fully connected layer <NUM>(<NUM>) may have a size of about <NUM>, the second fully connected layer <NUM>(<NUM>) may have a size of about <NUM>, the third fully connected layer <NUM>(<NUM>) may have a size of about <NUM>, and the last fully connected layer <NUM>(<NUM>) may have a size of about <NUM>. The output at the last fully connected layer <NUM>(<NUM>) corresponds to a single beamformed output sample or pixel (e.g., the beamformed output 632a, 632b, or 632c).

In some examples, the CNN <NUM> may include a flattening layer at the output of the last convolutional layer <NUM>(N) to convert the convolutional part of the CNN <NUM> into a 1D feature vector for the subsequent fully connected layers <NUM>. In some examples, the convolutional layers <NUM> can include zero padding such that the input and output size of the convolution or filter <NUM> are the same.

In some examples, the CNN <NUM> can include an additional layer before the first convolutional layer <NUM>(<NUM>) for normalization (e.g., including similar normalization operations as the normalization component <NUM>) and an additional layer after the last fully connected layer <NUM>(K) for denormalization (e.g., including similar denormalization operations as the denormalization component <NUM>). Thus, the CNN <NUM> can be applied without explicitly normalizing the time-align per-channel ultrasound echo signals (e.g., the signals <NUM>) and without explicitly de-normalizing the output of the CNN <NUM>. In some examples, the CNN <NUM> can be trained to perform beamforming including the pre-normalization layer and the post-denormalization layer for a particular ultrasound center frequency since the partitioning of ultrasound echo samples in the normalization can be dependent on the ultrasound center frequency.

<FIG> is a schematic diagram illustrating a deep learning network training scheme <NUM>, according to aspects of the present disclosure. The scheme <NUM> can be implemented by a computer system such as the host <NUM>. The scheme <NUM> can be employed to train the deep learning network <NUM> for ultrasound beamforming. The scheme <NUM> trains the deep learning network <NUM> to predict or imitate beamformed data obtained from a transducer with a larger aperture size than a transducer in use.

The scheme <NUM> trains the deep learning network <NUM> in two stages <NUM> and <NUM>. In the first stage <NUM>, the scheme <NUM> trains the deep learning network <NUM> using an input-output pair, where the input includes ultrasound channel data <NUM> and the output includes target beamformed data <NUM>. The ultrasound channel data <NUM> may be normalized, time-aligned ultrasound echo channel signals similar to the normalized, time-aligned ultrasound echo channel signals <NUM>. The ultrasound channel data <NUM> may be acquired from a transducer array (e.g., the transducer <NUM>) including an aperture size M (e.g., the aperture size <NUM>) or M number of acoustic elements (e.g., the acoustic elements <NUM>). The ultrasound channel data <NUM> may correspond to ultrasound echo responses received from a certain subject (e.g., the object <NUM>). The ultrasound channel data <NUM> can be a 2D dataset with an x-dimension corresponding to receive channels and a y-dimension corresponding to imaging depths. The target data <NUM> may correspond to beamformed data generated from the ultrasound channel data <NUM> using a DAS-based beamformer (e.g., the beamformer <NUM>). The target data <NUM> is also normalized so that the training does not have to learn amplitude mapping. During training, the deep learning network <NUM> can be applied to the ultrasound channel data <NUM> using forward propagation to produce an output <NUM> (e.g., the beamformed data <NUM>). The coefficients of the filters <NUM> in the convolutional layers <NUM> and the weightings in the fully connected layers <NUM> can be adjusted using backward propagation to minimize the error between the predicted or mapped output <NUM> and the target output <NUM>. In some embodiments, the error function or the loss function may be a mean-square-error (MSE) function or any other suitable error measure function. In other words, the scheme <NUM> trains the deep learning network <NUM> to approximate the beamforming provided by the beamformer <NUM>. The training or the adjusting of the coefficients for the filters <NUM> may be repeated for multiple input-output pairs. The first stage <NUM> functions as an initialization of filter coefficients and/or weights in the deep learning network <NUM>.

In the subsequent stage <NUM>, the scheme <NUM> uses the filter coefficients and/or weights obtained for the deep learning network <NUM> from the first stage <NUM> as a start and continues with the training. The scheme <NUM> trains the deep learning network <NUM> using an input-output pair, where the input includes ultrasound channel data <NUM> and the output includes target beamformed data <NUM>. The target data <NUM> may correspond to beamformed data of the same subject generated from a transducer with a larger aperture size than the aperture size M, for example, an aperture size of k×M or k×M number of acoustic elements, where k is greater than <NUM>. Similarly, the target data <NUM> is normalized data. In an example, the target data <NUM> may be generated for an aperture size including about <NUM> acoustic elements (e.g., the acoustic elements <NUM>) and the target data <NUM> may be generated for an aperture size including about <NUM> acoustic elements (e.g., based on the Tukey-apodization). Similar to the first stage <NUM>, the deep learning network <NUM> is trained by applying the ultrasound channel data <NUM> using forward propagation to produce an output <NUM> (e.g., the beamformed data <NUM>). The coefficients of the filters <NUM> in the convolutional layers <NUM> and the weightings in the fully connected layers <NUM> can be adjusted using backward propagation to minimize the error between the output <NUM> and the target output <NUM>. The training or the adjusting of the coefficients for the filters <NUM> may be repeated for multiple input-output pairs. While the scheme <NUM> utilizes two stages of training, in some embodiments, the scheme <NUM> may perform the second stage <NUM> of training without performing the first stage <NUM> of the training.

As can be observed, the scheme <NUM> trains the deep learning network <NUM> to map per-channel ultrasound echo signals to beamformed data corresponding to a larger transducer aperture size than the aperture size of the transducer used for collecting the ultrasound echo channel signals. Accordingly, the deep learning network <NUM> can provide a higher image quality (e.g., improved resolution and/or enhanced contrast) in the final reconstructed images than a conventional DAS-based beamformer (e.g., the beamformed <NUM>).

<FIG> illustrates pre-scan converted ultrasound images generated from DAS-based beamforming and deep learning-based beamforming, according to aspects of the present disclosure. The ultrasound images <NUM> and <NUM> are generated from the same set of per-channel ultrasound echo signals (e.g., the digital ultrasound channel echo signals <NUM> and <NUM> and the ultrasound channel data <NUM> and <NUM>) acquired from an in-vivo scan of a patient's heart in the apical four chamber view. The ultrasound image <NUM> is generated using a conventional DAS-based beamformer (e.g., the beamformer <NUM>) to beamform the acquired per-channel ultrasound echo signals, whereas the ultrasound image <NUM> is generated by applying a deep learning network (e.g., the deep learning network <NUM> trained using the scheme <NUM>) to map the per-channel ultrasound echo signals to beamformed data (e.g., the beamformed data <NUM> and <NUM>). As can be observed, the ultrasound image <NUM> provides an improved contrast and resolution without a significant loss of cardiac structures (endocardium) compared to the ultrasound image <NUM>. Accordingly, deep learning-based beamforming can provide a higher image quality or resolution than conventional DAS beamforming.

<FIG> is a schematic diagram illustrating a deep learning network training scheme <NUM>, according to aspects of the present disclosure. The scheme <NUM> can be implemented by a computer system such as the host <NUM>. The scheme <NUM> can be employed to train the deep learning network <NUM> or the CNN <NUM> for ultrasound beamforming. The scheme <NUM> is substantially similar to the scheme <NUM>. However, the scheme <NUM> uses different types of input and/or target data. The scheme <NUM> trains the deep learning network <NUM> to predict or imitate beamformed data obtained from a greater number of transmit firings or events than the actual number of transmit firings used in an acquisition.

In the first stage <NUM>, the scheme <NUM> trains the deep learning network <NUM> using an input-output pair, where the input includes ultrasound channel data <NUM> and the output includes target beamformed data <NUM>. The ultrasound channel data <NUM> may be normalized, time-aligned ultrasound echo channel signals similar to the normalized, time-aligned ultrasound echo channel signals <NUM>. The ultrasound channel data <NUM> may be acquired from T number of transmit events. For example, the transmission of an ultrasound beam is repeated T times and T sets of per-channel ultrasound echo signals are received. The ultrasound channel data <NUM> may correspond to ultrasound echo responses received from a certain subject (e.g., the object <NUM>).

In some examples, the ultrasound beams are focused beams (e.g., the focused ultrasound transmission beams <NUM>). In some other examples, the ultrasound beams are unfocused beams or diverging beams (e.g., the unfocused ultrasound transmission beams <NUM>).

In some examples, the ultrasound channel data <NUM> can be a 3D dataset with an x-dimension corresponding to receive channels, a y-dimension corresponding to imaging depths, and a z-dimension corresponding to transmit events.

The target data <NUM> may correspond to beamformed data generated from the ultrasound channel data <NUM> using a DAS-based beamformer (e.g., the beamformer <NUM>). The target data <NUM> is also normalized so that the training does not have to learn amplitude mapping. During training, the deep learning network <NUM> can be applied to the ultrasound channel data <NUM> using forward propagation to produce an output <NUM> (e.g., the beamformed data <NUM>). The coefficients of the filters <NUM> in the convolutional layers <NUM> and the weightings in the fully connected layers <NUM> can be adjusted using backward propagation to minimize the error between the output <NUM> and the target output <NUM>. In some embodiments, the error function may be a MSE function or any other suitable error measure function. In other words, the scheme <NUM> trains the deep learning network <NUM> to approximate the beamforming provided by the beamformer <NUM>. The training or the adjusting of the coefficients for the filters <NUM> may be repeated for multiple input-output pairs. The first stage <NUM> functions as an initialization of filter coefficients and/or weights in the deep learning network <NUM>.

In the subsequent stage <NUM>, the scheme <NUM> uses the filter coefficients and/or weights obtained for the deep learning network <NUM> from the first stage <NUM> as a start and continues with the training. The scheme <NUM> trains the deep learning network <NUM> using an input-output pair, where the input includes ultrasound channel data <NUM> and the output includes target beamformed data <NUM>. The target data <NUM> may correspond to beamformed data of the same subjected generated from ultrasound echo channel signals collected from a greater number of transmit events, for example, m×T number of transmit events or triggers, where m is greater than <NUM>. Similarly, the target data <NUM> is normalized data. In an example, the target data <NUM> may be generated from <NUM> transmit events (e.g., with <NUM> repeated ultrasound transmissions) and the target data <NUM> may be generated from <NUM> transmit events. Similar to the first stage <NUM>, the deep learning network <NUM> is trained by applying the ultrasound channel data <NUM> using forward propagation to produce an output <NUM> (e.g., the beamformed data <NUM>). The coefficients of the filters <NUM> in the convolutional layers <NUM> and the weightings in the fully connected layers <NUM> can be adjusted using backward propagation to minimize the error between the output <NUM> and the target output <NUM>. The training or the adjusting of the coefficients for the filters <NUM> may be repeated for multiple input-output pairs. While the scheme <NUM> utilizes two stages of training, in some embodiments, the scheme <NUM> may perform the second stage <NUM> of training without performing the first stage <NUM> of the training.

As can be observed, the scheme <NUM> trains the deep learning network <NUM> to map per-channel ultrasound echo signals to beamformed data corresponding to a greater number of transmit events. Accordingly, the deep learning network <NUM> can provide a higher image quality than a conventional DAS-based beamformer (e.g., the beamformer <NUM>). Further, when using diverging beams for unfocused imaging, the scheme <NUM> can train the deep learning network <NUM> to compensate for artefacts caused by the use of diverging beams and improve the final ultrasound image quality without a significant increase in acquisition time.

<FIG> is a schematic diagram illustrating a deep learning network training scheme <NUM>, according to aspects of the present disclosure. The scheme <NUM> can be implemented by a computer system such as the host <NUM>. The scheme <NUM> can be employed to train the deep learning network <NUM> or the CNN <NUM> for ultrasound beamforming.

The scheme <NUM> trains the deep learning network <NUM> in two stages <NUM> and <NUM>. In the first stage <NUM>, the scheme <NUM> trains the deep learning network <NUM> using an input-output pair, where the input includes ultrasound channel data <NUM> and the output includes target beamformed data <NUM>. The ultrasound channel data <NUM> may be normalized, time-aligned ultrasound echo channel signals similar to the normalized, time-aligned ultrasound echo channel signals <NUM>. The ultrasound channel data <NUM> may be acquired from a patient during a clinical setting or from a phantom in a test setup. The ultrasound channel data <NUM> can be a 2D dataset with an x-dimension corresponding to receive channels and a y-dimension corresponding to imaging depths. The target data <NUM> may correspond to beamformed data of the same subjected generated from the ultrasound channel data <NUM> using a DAS-based beamformer (e.g., the beamformer <NUM>). The target data <NUM> may have a first SNR (e.g., S decibels (dB)). The target data <NUM> is also normalized so that the training does not have to learn amplitude mapping. During training, the deep learning network <NUM> can be applied to the ultrasound channel data <NUM> using forward propagation to produce an output <NUM> (e.g., the beamformed data <NUM>). The coefficients of the filters <NUM> in the convolutional layers <NUM> and the weightings in the fully connected layers <NUM> can be adjusted using backward propagation to minimize the error between the output <NUM> and the target output <NUM>. In some embodiments, the error function may be a MSE function or any other suitable error measure function. In other words, the scheme <NUM> trains the deep learning network <NUM> to approximate the beamforming provided by the beamformer <NUM>. The training or the adjusting of the coefficients for the filters <NUM> may be repeated for multiple input-output pairs. The first stage <NUM> functions as an initialization of filter coefficients and/or weights in the deep learning network <NUM>.

In the subsequent stage <NUM>, the scheme <NUM> uses the filter coefficients and/or weights obtained for the deep learning network <NUM> from the first stage <NUM> as a start and continues with the training. The scheme <NUM> trains the deep learning network <NUM> using an input-output pair, where the input includes ultrasound channel data <NUM> and the output includes target beamformed data <NUM>. The target data <NUM> may correspond to beamformed data of the same subject, but with a second SNR higher than the first SNR (e.g., n×S dB, where n is greater than <NUM>). The higher SNR can be due to the use of more advance signal and/or imaging processing techniques, a larger transducer aperture size, and/or a greater number of transmit firings. Similarly, the target data <NUM> is normalized data. Similar to the first stage <NUM>, the deep learning network <NUM> is trained by applying the ultrasound channel data <NUM> using forward propagation to produce an output <NUM> (e.g., the beamformed data <NUM>). The coefficients of the filters <NUM> in the convolutional layers <NUM> and the weightings in the fully connected layers <NUM> can be adjusted using backward propagation to minimize the error between the output <NUM> and the target output <NUM>. The training or the adjusting of the coefficients for the filters <NUM> may be repeated for multiple input-output pairs. While the scheme <NUM> utilizes two stages of training, in some embodiments, the scheme <NUM> may perform the second stage <NUM> of training without performing the first stage <NUM> of the training.

As can be observed, the scheme <NUM> trains the deep learning network <NUM> to map per-channel ultrasound echo signals to beamformed data corresponding to a higher SNR than beamformed data from a conventional DAS-based beamformer (e.g., the beamformer <NUM>).

<FIG> illustrates ultrasound images generated from DAS-based beamforming and deep learning-based beamforming, according to aspects of the present disclosure. The ultrasound images <NUM>, <NUM>, and <NUM> are acquired from in-vivo scan of a patient's heart. Initially, a first set of per-channel ultrasound echo signals (e.g., the digital ultrasound channel echo signals <NUM> and <NUM> and the ultrasound channel data <NUM> and <NUM>) is collected after <NUM> transmit triggers of unfocused ultrasound beams or diverging beams (e.g., the unfocused ultrasound beam <NUM>). Subsequently, a second set of per-channel ultrasound echo signals is collected after <NUM> transmit triggers of unfocused ultrasound beams or diverging beams. The ultrasound image <NUM> is generated using a DAS-based beamformer (e.g., the beamformer <NUM>) to beamform the second set of per-channel ultrasound echo signals from the <NUM> transmit triggers. The image <NUM> is generated using the DAS beamformer to beamform the first set of per-channel ultrasound echo signals from the <NUM> transmit triggers. The image <NUM> is generated by applying the deep learning network <NUM> to map the first set of per-channel ultrasound echo signals with the <NUM> transmit triggers to beamformed data (e.g., the beamformed data <NUM> and <NUM>) from the <NUM> transmit triggers.

Comparing the image <NUM> and the image <NUM>, the image <NUM> from the <NUM> transmit triggers provides a higher image quality (e.g., better contrast, better contrast-to-noise) than the image <NUM> from the <NUM> transmit triggers as expected. Comparing the image <NUM>, <NUM>, and <NUM>, the deep learning-based beamformed image <NUM> from the <NUM> transmit triggers provides an image quality or resolution comparable to the DAS-based beamformed image <NUM> from the <NUM> transmits triggers. The amount of clutters or artefacts in the image <NUM> generated from the deep learning-based beamforming is significantly less than the image <NUM> generated from the DAS-based beamforming with the same number of transmit triggers. Accordingly, deep learning-based beamforming can provide a higher image quality or resolution than conventional DAS-based beamforming.

In general, the schemes <NUM>, <NUM>, and <NUM> can use any suitable combination of simulation data generated offline, data acquired from a patient in a clinical setting, and data acquired from a phantom in a test setup to train the deep learning network <NUM>. Given target beamformed data with a high SNR, for example, generated from a larger aperture size, an increased number of transmits, and/or coherently compounding echo signals received from the multiple transmits, the schemes <NUM>, <NUM>, and <NUM> can train the deep learning network <NUM> to output beamformed data with a higher SNR. In addition, using actual data acquired from an ultrasound system (e.g., the systems <NUM> and <NUM>) instead of simulation data as input-output data pairs, the deep learning network <NUM> can be trained to suppress clutters from noise sources, such as acoustic noise, thermal noise, electronic noise, aberration, and/or reverberation, that are introduced due to poor acoustic conditions and cannot be addressed along the signal paths of the ultrasound system (e.g., the systems <NUM>, <NUM>, and/or <NUM>).

In some embodiments, the deep learning network <NUM> can be trained to learn mapping of micro-beamformed data instead of per-channel ultrasound echo data to beamformed data. As an example, a system (e.g., the systems <NUM> and <NUM>) may have <NUM> receive channels. The system may include micro-beamformers for micro-beamforming. For example, the system may group four adjacent acoustic elements (e.g., the acoustic elements <NUM>) together and apply a beamformer to the group of acoustic elements to focus and steer delays to corresponding receive channels such that the micro-beamformed points are along the main axis of the transmit beam. Thus, after micro-beamforming, the <NUM> receive channels are reduced to <NUM> channels. The deep learning network <NUM> can be trained and applied to map the <NUM> micro-beamformed channel signals to beamformed data (e.g., the beamformed data <NUM>) using substantially similar mechanisms as described above.

While the error functions or loss functions in the schemes <NUM>, <NUM>, and <NUM> described above are error or cost functions between the ground truth pixel values (e.g., in the target data <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>) and the deep learning network <NUM> predicted pixel values (e.g., in the outputs <NUM>, <NUM>, <NUM>, <NUM>, <NUM>, and <NUM>), the deep learning network <NUM> can be trained to predict other signal values at an earlier stage (e.g., prior to beamforming) in the signal path of the ultrasound systems <NUM> and/or <NUM>.

In an example, the deep learning network <NUM> can be trained to learn mapping transmit compounding from a limited number of transmits to an increased number of transmits. Thus, the loss function for the deep learning network <NUM> may be the difference between the ground truth transmit compounded channel data and network predicted compounded channel data corresponding to a greater number of transmits. For example, the input to the deep learning network <NUM> may be a 3D ultrasound echo channel dataset as described above, where with the x-dimension may correspond to receive channels, the y-dimension may correspond to imaging depths, and the z-dimension correspond to transmit events (e.g., T). The deep learning network <NUM> can be trained to output a compounded echo channel dataset corresponding to m×T transmits, where m greater than <NUM>. Alternatively, the 3D ultrasound echo channel dataset can be converted to a 2D dataset by summing the per-channel ultrasound echo signals from the T transmit events (e.g., collapsed in the transmit or z-dimension) and the deep learning network <NUM> can be trained to provide the same compounded echo channel dataset corresponding to m×T transmits.

In general, the deep learning network <NUM> can output the compounded channel data or beamformed data in any suitable dimension or representations and the loss function can be modified accordingly. In an example of deep-learning based transmit compounding, the deep learning network <NUM> can be trained to provide a 1D compounded channel data collapsed in the transmit or z-dimension and sampled at the depth or y-dimension. In an example of deep-learning based beamforming, the deep learning network <NUM> can be trained to provide a 1D DAS vector collapsed in the channel or x-dimension and sampled in the depth or y-dimension or the scalar value of the corresponding pixel point collapsed in the channel or x-dimension and the transmit or z-dimension and sampled in the depth or y-dimension.

While the input data in the schemes <NUM>, <NUM>, and <NUM> described above is a 3D matrix for each pixel, a 3D matrix of aligned data for each beam may be used as input. The fully convolutional architecture may operate on the larger dataset using substantially similar mechanisms as described above.

While the input data in the schemes <NUM>, <NUM>, and <NUM> described above is per-channel ultrasound echo data, beamformed data can be used as input. For example, the input beamformed data may be produced from a limited number of transmits and may include grating lobe artefacts. The deep learning network <NUM> can be trained to provide beamformed data corresponding to a greater number of transmits and with a higher image quality and resolution.

Generally, aspects of the present disclosure describe using a machine learning network to replace one or more conventional ultrasound image processing steps, such as beamforming, that are required to generate conventional ultrasound images. The machine learning network is applied to the raw channel data obtained by the ultrasound transducer, rather than one or more of the conventional image processing steps being carried out on the raw channel data (e.g., beamforming and/or compounding of multiple transmits). The machine learning network is trained using a plurality of target beamformed data. Application of the machine learning network to the raw channel data results in modified data. A processor generates the ultrasound image using the modified data, which includes a trait of the target images (e.g., anatomical structure, speckle, etc.). While the disclosed embodiments are described in the context of mapping ultrasound echo channel data RF data to beamformed data using deep learning, in some embodiments, similar deep learning techniques can be applied to map ultrasound echo channel data in an intermediate frequency (IF) or baseband (BB) to beamformed data.

<FIG> is a schematic diagram of a processor circuit <NUM>, according to embodiments of the present disclosure. The processor circuit <NUM> may be implemented in the probe <NUM> and/or the host <NUM> of <FIG>. As shown, the processor circuit <NUM> may include a processor <NUM>, a memory <NUM>, and a communication module <NUM>. These elements may be in direct or indirect communication with each other, for example via one or more buses.

The processor <NUM> may include a CPU, a DSP, an ASIC, a controller, an FPGA, another hardware device, a firmware device, or any combination thereof configured to perform the operations described herein. In an example, the processor <NUM> may correspond to the processor circuit <NUM> of <FIG>. In an example, the processor <NUM> may correspond to the processor circuit <NUM> of <FIG>.

The memory <NUM> may include a cache memory (e.g., a cache memory of the processor <NUM>), random access memory (RAM), magnetoresistive RAM (MRAM), read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read only memory (EPROM), electrically erasable programmable read only memory (EEPROM), flash memory, solid state memory device, hard disk drives, other forms of volatile and non-volatile memory, or a combination of different types of memory. In an embodiment, the memory <NUM> includes a non-transitory computer-readable medium. The instructions <NUM> may include instructions that, when executed by the processor <NUM>, cause the processor <NUM> to perform the operations described herein, for example, aspects of <FIG>, <FIG>, and <FIG> and with reference to the host <NUM> and/or the probe <NUM> (<FIG>). Instructions <NUM> may also be referred to as code. The terms "instructions" and "code" should be interpreted broadly to include any type of computer-readable statement(s). For example, the terms "instructions" and "code" may refer to one or more programs, routines, sub-routines, functions, procedures, etc. "Instructions" and "code" may include a single computer-readable statement or many computer-readable statements. In an example, the memory <NUM> may correspond to the memory <NUM> of <FIG>.

The communication module <NUM> can include any electronic circuitry and/or logic circuitry to facilitate direct or indirect communication of data between the processor circuit <NUM>, the probe <NUM>, and/or the display <NUM>. In that regard, the communication module <NUM> can be an input/output (I/O) device. In some instances, the communication module <NUM> facilitates direct or indirect communication between various elements of the processor circuit <NUM> and/or the host <NUM> (<FIG>). In some instances, the communication module <NUM> may correspond to the communication interface <NUM> (<FIG>). In some instances, the communication module <NUM> may correspond to the communication interface <NUM> (<FIG>).

<FIG> is a flow diagram of a deep learning-base ultrasound imaging method <NUM>, according to aspects of the present disclosure. Steps of the method <NUM> can be executed by the system <NUM>, <NUM>, and/or <NUM>, for example, by a processor such as the processor circuits <NUM>, <NUM>, or the processor <NUM>, processor circuit such as the processor circuit <NUM>, and/or other suitable component such as the probe <NUM> and/or the host <NUM>. As illustrated, the method <NUM> includes a number of enumerated steps, but embodiments of the method <NUM> may include additional steps before, after, and in between the enumerated steps.

At step <NUM>, the method <NUM> includes receiving, at a processor circuit in communication with an array of acoustic elements, ultrasound channel data corresponding to ultrasound echoes associated with an anatomy. The processor circuit may be similar to the processor circuits <NUM> and <NUM> and the processor circuit <NUM>. The acoustic elements may be similar to the acoustic elements <NUM>. The ultrasound channel data may be similar to the digital ultrasound echo channel signals <NUM> and <NUM>, the ultrasound channel data <NUM>, <NUM>, <NUM>, and <NUM>.

At step <NUM>, the method <NUM> includes normalizing the ultrasound channel data by applying a first scaling function to the ultrasound channel data based on signal levels of the ultrasound channel data, for example, utilizing the normalization component <NUM> and/or the scheme <NUM>.

At step <NUM>, the method <NUM> includes generating beamformed data by applying a predictive network (e.g., the deep learning network <NUM>) to the normalized ultrasound channel data (e.g., the ultrasound echo channel signals <NUM>).

At step <NUM>, the method <NUM> includes de-normalizing the beamformed data by applying a second scaling function to the beamformed data based on the signal levels of the ultrasound channel data, for example, utilizing the denormalization component <NUM>.

In an example, the first scaling function may include scaling signal levels of the ultrasound channel data by a first factor corresponding a signal energy or an RMS value of the ultrasound channel data. The second scaling function may include scaling signal levels of the beamformed data by an inverse of the first factor (e.g., an inverse of the signal energy or the RMS value).

At step <NUM>, the method <NUM> includes generating an image of the anatomy from the beamformed data.

At step <NUM>, the method <NUM> includes outputting, to a display (e.g., the display <NUM>) in communication with the processor circuit, the image of the anatomy.

In an embodiment, time delays are applied to the normalized ultrasound channel data based on an imaging depth, for example, utilizing the time-alignment component <NUM> to facilitate receive focusing.

In an embodiment, the ultrasound channel data includes a plurality of samples for a plurality of channels (e.g., the receive channels <NUM> to M of <FIG> and <FIG>). The beamformed data includes a plurality of output values (e.g., beamformed output sample or pixels <NUM>). The normalization can include selecting a subset (e.g., the subset 610a, 610b, or 610c) of the plurality of samples based on an imaging depth and scaling a first signal level of a first sample (e.g., the sample 612a, 612b, or 612c) of the subset of the plurality of samples based on second signal levels (e.g., RMS) of the plurality of samples to produce a subset of the normalized ultrasound channel data (e.g., the subset 620a, 620b, or 620c). The first sample corresponds to a pixel location in the image. The generating the beamformed data includes applying the predictive network to the subset of the normalized ultrasound channel data to produce a first output value of the plurality of output values in the beamformed data, where the first output value correspond to the same pixel location in the image as the first sample.

In an embodiment, the array of acoustic elements includes a first aperture size (e.g., the aperture size <NUM>) and the beamformed data is associated with a second aperture size larger than the first aperture size. For example, the prediction network is trained using the scheme <NUM>.

In an embodiment, the ultrasound channel data is generated from a first quantity of ultrasound transmit trigger events and the beamformed data is associated with a second quantity of ultrasound transmit trigger events greater than the first quantity of ultrasound transmit trigger events. For example, the prediction network is trained using the scheme <NUM>.

In an embodiment, the ultrasound channel data is associated with a first SNR and the beamformed data is associated with a second SNR greater than the first SNR. For example, the prediction network is trained using the scheme <NUM>.

Aspects of the present disclosure can provide several benefits. For example, the use of a deep learning network (e.g., the deep learning network <NUM>) for beamforming raw RF channel data (e.g., the ultrasound echo channel signals <NUM> and <NUM>) acquired from a probe (e.g., the probes <NUM>) can provide superior ultrasound image quality (e.g., improved resolution, enhanced contrast, and/or reduced side lobes, clutters and/or artefacts) compared to conventional DAS-based beamformers and/or reduce image acquisition time or improve imaging frame rates. The use of normalized, time-aligned ultrasound echo channel signals as inputs to the deep learning network allows the deep learning network to be trained for beamforming or beam-summing without having to learn amplitude mapping and/or time-delay mapping, and thus reduces the complexity of the network. Further, the use of the deep learning network can provide a computational cost advantage compared to conventional DAS-based beamformer (e.g., the beamformer <NUM>) since operations in the inference stage of the deep learning network are mostly convolutions (e.g., multiply-adds) and matrix multiplications.

Claim 1:
An ultrasound imaging system (<NUM>), comprising:
an array (<NUM>) of acoustic elements configured to transmit ultrasound energy into an anatomy and to receive ultrasound echoes associated with the anatomy; and
a processor circuit (<NUM>) in communication with the array of acoustic elements and configured to:
receive (<NUM>), from the array, ultrasound channel data (<NUM>, <NUM>) corresponding to the received ultrasound echoes;
time-align (<NUM>) the ultrasound channel data;
normalize (<NUM>, <NUM>) the time-aligned ultrasound channel data (<NUM>) by applying a first scaling function to the time-aligned ultrasound channel data based on signal levels of the time-aligned ultrasound channel data;
generate (<NUM>) beamformed data (<NUM>, <NUM>) by applying a predictive network (<NUM>, <NUM>) to the normalized time-aligned ultrasound channel data (<NUM>);
de-normalize (<NUM>, <NUM>) the beamformed data by applying a second scaling function to the beamformed data based on the signal levels of the ultrasound channel data;
generate (<NUM>) an image (<NUM>, <NUM>, <NUM>) of the anatomy from the beamformed data; and
output, to a display in communication with the processor circuit, the image of the anatomy,
characterized in that the predictive network (<NUM>, <NUM>) is configured to perform the summing operations in beamforming the normalized ultrasound channel data (<NUM>).