Systems, methods, and / or computer readable media for adaptive spatial compounding in ultrasound

Abstract

An ultrasound imaging system (302) includes a transducer array configured to transmit a plurality of ultrasound signals, each at a different sonication angle, receive an echo signal for each ultrasound signal of the plurality of ultrasound signals, and transmit the echo signal to the transducer array. And generating an electrical signal indicative of each of the echo signals. A beamformer (329) is configured to beamform the electrical signal and generate a radio frequency (RF) signal for each of the different acoustic wave action angles. An envelope detector (604) is configured to detect an envelope of each of the RF signals for each of the different acoustic wave action angles. A compressor (606) is configured to compress each of the envelopes for each of the different acoustic wave action angles to produce a data frame. A recombiner (326) is configured to adaptively recombine the data frames for each of the different acoustic action angles to generate a composite image. A display (330) is configured to display the composite image.

Description

Technical Field

[0001] The following generally relates to ultrasound and finds particular application to adaptive spatial compounding in ultrasound imaging, including methods for enhancing needle visualization, improving image contrast, reducing speckle, maintaining sharp and uninterrupted vessel walls and organ boundaries, and / or reducing noise without activating a needle enhancement mode and / or loss of image quality. Background Art

[0002] Ultrasound imaging provides real-time images with information about the interior of an object or subject, such as tissue, an organ, or the like. In one example, an excitation pulse is provided to a transducer array. At least a subset of the elements in the array receive the pulse and convert the electrical pulse into a pressure wave / ultrasound signal. The pressure wave is emitted by the transducer array during a transmit operation, propagates through the medium, and interacts with the medium. Such interaction results in, among other things, an echo, which is a reflection toward the transducer.

[0003] During receive operations, the element receives echoes and converts the reflections into analog signals. For each receive operation, the analog signal is amplified, converted to digital, and beamformed to produce a scanline of radio frequency (RF) data. Using delay-and-sum beamforming, the digital signals are delayed, weighted, and then summed to produce a scanline. The scanline is further processed (e.g., bandpass filtering, envelope detection, logarithmic compression, etc.), scan converted, and displayed as a frame / 2D image (e.g., B-mode image).

[0004] Echoes include specular reflections from smooth surfaces of specular reflectors such as blood vessel walls, muscles, instruments (such as biopsy needles), scattering from diffuse reflectors such as non-smooth surfaces and structures smaller than the wavelength of the emitted waves, and speckle, which is signal-dependent noise due to constructive and destructive interference of echoes from scatterers, which appears as a grainy pattern in B-mode images, thereby reducing contrast and image quality. Methods for reducing or removing speckle include spatial compounding followed by filtering using filters configured for speckle reduction.

[0005] Spatial compounding is the process of registering and combining multiple images of the same structure acquired at different times and at different insonation / incident angles to form a single composite frame. The frames can be acquired by maintaining the transducer in a fixed position and transmitting pressure waves at different angles via electronic beam steering and / or electronically controlled mechanical manipulation of the transducer element array located within the scan head. To form a single composite image, the images acquired at different steering angles are aligned and combined. Alignment can be achieved based on the transducer array geometry, steering angle, sampling frequency, and / or the speed of sound in the insonated tissue.

[0006] Typically, this approach calculates and utilizes the average pixel value across multiple images at different insonation angles for each pixel in the composite image, which can smooth the image and, therefore, reduce speckle. Unfortunately, images acquired using controlled pressure waves tend to have higher levels of clutter, for example due to grating lobes formed by echoes from angles different from the pressure wave direction. This reduces contrast in controlled scans and can obscure the view of blood vessels, cysts, etc., and disrupt the continuous contours of the vessel wall. Additionally, specular reflectors, such as needles, can reflect pressure waves away from the transducer array, which can result in reduced needle visibility.

[0007] The example is described in Figure 1 and Figure 2 In. Figure 1 and Figure 2 In each acquisition, the same position is acquired but at a different insonation angle. Figure 1 In FIG. 1 , the first image 1021 acquired at the first angle includes the blood vessel 104 blurred by the grating lobe (shown via lighter grey shading), and the i-th image 102 acquired at the i-th angle i The nth image 102 includes a blood vessel 104 (shown via a darker grey shading) that is not blurred by the steering angle, and is acquired at the nth angle. n Included is a blood vessel 104 (shown via lighter grey shading) that is blurred by the grating lobe.Thus, the average pixel value calculated and utilized for the composite image will include blur and may significantly blur the blood vessel and disrupt the continuous outline of the vessel wall.

[0008] exist Figure 2 In FIG. 1 , a first pressure wave 2021 emitted at a first angle reflects from the instrument 204 in a direction 206 different from the direction of the emitted pressure wave, resulting in a weaker signal from the instrument (shown via lighter grey shading). The i-th pressure wave 202 emitted at the i-th angle reflects from the instrument 204 in a direction 206 different from the direction of the emitted pressure wave, resulting in a weaker signal from the instrument (shown via lighter grey shading). i is reflected from the instrument 204 in a further direction 208 that is different from the direction of the transmitted pressure wave, resulting in a weaker signal from the instrument (shown via medium shades of grey), and the nth pressure wave 202 transmitted at the nth angle n Reflected from the instrument 204 in a direction 210 that is closer to the direction of the emitted pressure wave relative to the first angle and the i-th angle, thereby providing a stronger signal from the instrument (shown via a darker gray shading). Therefore, the average pixel value calculated and utilized for the composite image will include a weaker signal, which can reduce the visibility of the needle.

[0009] In view of at least the foregoing, there exists an unresolved need for improved spatial compounding methods in ultrasound imaging. Summary of the Invention

[0010] Aspects of this application address the above-mentioned matters and other matters. This Summary introduces the concepts described in more detail in the Detailed Description. It should not be used to identify the essential features of the claimed subject matter, nor should it be used to limit the scope of the claimed subject matter.

[0011] In one aspect, an ultrasound imaging system includes a transducer array configured to transmit a plurality of ultrasound signals, each of the plurality of ultrasound signals being at a different insonation angle. The transducer array is further configured to receive an echo signal for each of the plurality of ultrasound signals. The transducer array is further configured to generate an electrical signal indicative of each of the echo signals. The ultrasound imaging system also includes a beamformer configured to beamform the electrical signal and generate a radio frequency (RF) signal for each of the different insonation angles. The ultrasound imaging system also includes an envelope detector configured to detect an envelope of each of the RF signals for each of the different insonation angles. The ultrasound imaging system also includes a compressor configured to compress each of the envelopes for each of the different insonation angles to generate a data frame. The ultrasound imaging system further includes a compounder configured to adaptively compound the data frames for each of the different insonation angles to generate a compound image. The ultrasound imaging system further includes a display configured to display the compound image.

[0012] In another aspect, a computer-implemented method includes transmitting a plurality of ultrasound signals, each of the plurality of ultrasound signals being at a different insonation angle. The computer-implemented method also includes receiving an echo signal for each of the plurality of ultrasound signals. The computer-implemented method also includes generating an electrical signal indicative of each of the echo signals. The computer-implemented method also includes beamforming the electrical signal to generate a radio frequency (RF) signal for each of the different insonation angles. The computer-implemented method also includes detecting an envelope of each of the RF signals for each of the different insonation angles. The computer-implemented method also includes compressing each of the envelopes for each of the different insonation angles to generate a data frame. The computer-implemented method also includes adaptively compounding the data frames for each of the different insonation angles to generate a composite image.

[0013] In another aspect, a computer-readable medium is encoded with computer-executable instructions that, when executed by a processor, cause the processor to transmit a plurality of ultrasound signals, each of the plurality of ultrasound signals being at a different insonation angle. The instructions further cause the processor to receive an echo signal for each of the plurality of ultrasound signals. The instructions further cause the processor to generate an electrical signal indicative of each of the echo signals. The instructions further cause the processor to beamform the electrical signal and generate a radio frequency (RF) signal for each of the different insonation angles. The instructions further cause the processor to detect an envelope of each of the RF signals for each of the different insonation angles. The instructions further cause the processor to compress each of the envelopes for each of the different insonation angles to generate a data frame. The instructions further cause the processor to adaptively composite the data frames for each of the different insonation angles to generate a composite image.

[0014] Those skilled in the art will appreciate further aspects of the present application upon reading and understanding the accompanying description. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] The present application is illustrated by way of examples and not limitation in the figures of the accompanying drawings, in which like references indicate similar elements.

[0016] Figure 1 A prior art example is shown where composite images of the same structure acquired at different insonation angles can introduce blurring and apparent blurring of blood vessels and disrupt the continuous contours of the vessel wall.

[0017] Figure 2 A prior art example is shown where a composite image of the same structure but at different insonation angles can reduce the visibility of a specular reflector such as a needle.

[0018] Figure 3 Schematically illustrated are non-limiting examples of an imaging system configured for ultrasound imaging including operating modes and timing diagrams according to an aspect of embodiments herein.

[0019] Figure 4 Schematically illustrates a non-limiting example of transmitting operations at different insonation angles to the same structure to acquire data for compounding according to an aspect of the embodiments herein.

[0020] Figure 5 Schematically shows an embodiment of the present invention according to one aspect of the Figure 3 An example of the front-end and beamforming components of an imaging system.

[0021] Figure 6 Schematically shows an embodiment of the present invention according to one aspect of the Figure 3 An example of a scan line processor for an imaging system.

[0022] Figure 7 Schematically illustrating an aspect of the embodiment of the present invention Figure 3 An example of an analyzer for an imaging system comprising a signal-to-noise ratio estimator and a speckle analyzer and / or an image analyzer.

[0023] Figure 8 Schematically shows an embodiment of the present invention according to one aspect of the Figure 3 An example of a multiplexer for an imaging system that uses the output of an analyzer to composite an image.

[0024] Figure 9 An example of a linear function for determining the probability of whether a sample across different insonation angles represents an anechoic cyst, a vascular lumen, or noise is shown in accordance with an aspect of embodiments herein.

[0025] Figure 10 An example of a nonlinear function for determining the probability of whether a sample across different insonation angles represents an anechoic cyst, a vascular lumen, or noise is shown in accordance with an aspect of embodiments herein.

[0026] Figure 11 An example of an analyzer according to an aspect of embodiments herein is schematically shown, the analyzer comprising a signal-to-noise ratio estimator and a speckle analyzer.

[0027] Figure 12 An example of a transfer function for determining the probability that a sample across different insonation angles represents speckle is shown in accordance with an aspect of embodiments herein.

[0028] Figure 13 An example of a compositer configured to composite an image based on a probability that a sample corresponds to a group (vessel, cyst, and noise) and a probability that the sample corresponds to speckle is shown according to an aspect of embodiments herein.

[0029] Figure 14 An example of an analyzer according to an aspect of the embodiments herein is schematically shown, the analyzer comprising a signal-to-noise ratio estimator and an image analyzer.

[0030] Figure 15 An example of an image analyzer that determines a probability that a pixel corresponds to a reflective surface, a probability that a pixel corresponds to noise, and a probability that a pixel corresponds to a group is schematically shown according to an aspect of embodiments herein.

[0031] Figure 16 An example of a compounder configured to compound images based on a probability that a sample corresponds to a group and a probability that a sample corresponds to speckle is shown according to an aspect of embodiments herein.

[0032] Figure 17 A non-limiting example of a flow chart of a computer-implemented method of compounding images based at least on speckle statistics according to embodiments herein is shown.

[0033] Figure 18 Another non-limiting example of a flow chart of a computer-implemented method based at least on image characteristics according to embodiments herein is shown. DETAILED DESCRIPTION

[0034] Ultrasound imaging provides real-time images with information about the interior of an object or subject, such as tissue, an organ, etc. However, ultrasound images are susceptible to speckle, which is signal-dependent noise caused by constructive and destructive interference of echoes from scatterers, which appears as a grainy pattern in the image, which can reduce contrast and / or image quality. Methods for mitigating speckle, such as spatial compounding, which calculates for each pixel in a composite image and utilizes the average pixel value across multiple images, have resulted in composite images with higher levels of clutter, which can reduce image contrast, obscure the view of scanned structures, such as blood vessels, cysts, etc., and disrupt the continuous outline of structures, such as blood vessels, and / or reduce needle visibility.

[0035] Described herein is an adaptive spatial compounding method that mitigates one or more of the aforementioned disadvantages and / or other disadvantages of other spatial compounding methods. As described in more detail below, in one example, the adaptive spatial compounding method determines the pixel position of each pixel in a composite image based on a signal-to-noise ratio (SNR) determined from an envelope signal and speckle analysis of the envelope signal or image analysis of frame data. In one example, the adaptive spatial compounding method described herein can enhance needle visualization, improve image contrast, reduce speckle, maintain sharp and uninterrupted vessel walls and organ boundaries, and / or reduce noise without activating a needle enhancement mode and / or sacrificing image quality.

[0036] Figure 3A non-limiting example of an ultrasound system 302 is schematically shown. The ultrasound system 302 includes a probe 304 and a console 306. In the illustrated embodiment, the probe 304 and the console 306 are coupled to each other via a communication channel 308, which may include wired (e.g., complementary interfaces and cables therebetween) and / or wireless technology (e.g., Wi-Fi, etc.). In another example, the probe 304 and the console 306 are integrated into the same housing, such as part of a handheld ultrasound system.

[0037] The probe 304 includes a transducer array 310. The transducer array 310 includes one or more transducer elements 312. Examples of suitable arrays include 64, 128, 192, 256, and / or other arrays, including larger and smaller arrays, one-dimensional (1D) arrays or two-dimensional (2D) arrays, etc. The transducer array 310 can be linear, curved, and / or otherwise shaped, fully filled, sparse, and / or combinations thereof, etc. The one or more transducer elements 312 are configured to convert an excitation electrical signal into an ultrasonic pressure field and convert a reflected ultrasonic pressure field into an electrical signal.

[0038] As a non-limiting example, one or more transducer elements 312 can be selectively excited via an excitation electrical (pulse) signal, which causes at least a subset of the transducer elements 312 to emit an ultrasonic pressure field into an inspection or scanning field of view. The ultrasonic pressure field may include a focused ultrasonic beam, a defocused (spherical) wave, and / or other ultrasonic signals. The one or more transducer elements 312 receive echo signals and generate analog electrical signals indicative thereof. The echo signals are generated in response to the emitted ultrasonic pressure field interacting with a structure, such as tissue and / or blood cells flowing in a portion of a blood vessel.

[0039] The console 306 includes a transmit circuit 314 configured to generate an excitation electrical signal provided to the transducer array 310 for transmitting an ultrasonic pressure field. In one example, this includes generating delays for individual elements 312 in the transducer array 310, e.g., for transmit focusing, beam steering, etc. For ultrasound scanning including spatial compounding, the transmit circuit 314 generates the excitation electrical signal at each of different insonation angles.

[0040] Temporary turn Figure 4 , schematically shows an example of a transmit operation for spatial compounding. In this example, the transducer array 310 transmits at L+1 different angles, where L is a positive integer greater than 1, and the angles include angles θ0, ..., θ i , ... and angle θ LFor the transmit interval 402, the transducer array 310 transmits the beam 404 at an angle θ0 relative to the transducing surface 406 of the transducer array 310. For the transmit interval 408, the transducer array 310 transmits the beam 404 at an angle θ0 relative to the transducing surface 406 of the transducer array 310. i Transmit beam 410. For a transmit interval 412, the transducer array 310 transmits the beam at an angle θ relative to the transducing surface 406 of the transducer array 310. L Transmit beam 414.

[0041] although Figure 4 Transmit operations for three (3) different angles are shown, but it should be understood that more or fewer transmit operations are contemplated herein. For example, in another example, the transmit circuit 314 transmits for five different angles, etc. This pattern is repeated M times, where M is an integer greater than or equal to 1. Examples of suitable angles θ include ninety (90) degrees, one hundred (100) degrees, eighty (80) degrees, etc. relative to the transmit surface. For this case, in one example, θ0 is equal to the minimum angle, θ i = 0, and the angle θ L Equal to the minimum angle. This angle can be specified in positive and negative degrees, such as positive ten (+10) and negative ten (-10), relative to normal from the emitting surface.

[0042] return Figure 3 The console 306 also includes a receiving circuit 316 configured to receive analog electrical signals. For ultrasound scanning including spatial compounding, the receiving circuit 316 receives analog electrical signals corresponding to echoes received at each of the insonation angles from the element 312. In one example, the receiving circuit 316 is further configured to pre-process the analog electrical signals, for example, to amplify, digitize, focus, and / or otherwise process the analog electrical signals.

[0043] refer to Figure 4 , each of the transmit intervals 402, 408, and 412 will have a corresponding receive operation. For example, for the transmit interval 402 at angle θ0, there will be a receive operation for angle θ0 to receive the echo. i At the launch interval 408, there will be a i The receiving operation is to receive the echo. L At the launch interval 412, there will be a L For each additional transmit interval at another angle, there will be an additional receive operation for that angle to receive the echo.

[0044] Return to Figure 3The console 306 also includes a switch 318 configured to switch between the transmit circuit 314 and the receive circuit 316, for example, by electrically connecting the transmit circuit 314 to the transducer array 310 for transmit operation and electrically connecting the receive circuit 316 to the transducer array 310 for receive operation. In an alternative example, separate switches are employed for each of the transmit circuit 314 and the receive circuit 316.

[0045] Return to Figure 3 The console 306 also includes a switch 318 configured to switch between the transmit circuit 314 and the receive circuit 316, for example, by electrically connecting the transmit circuit 314 to the transducer array 310 for transmit operation and electrically connecting the receive circuit 316 to the transducer array 310 for receive operation. In an alternative example, separate switches are employed for each of the transmit circuit 314 and the receive circuit 316.

[0046] The console 306 also includes a beamformer 320. For receive operations, the beamformer 320 is configured to beamform the signals from the receive circuit 316, for example, via delay summation (e.g., a matched filter beamformer, etc.) and / or perform other beamforming, and to construct a scan plane of scan lines of echoed radio frequency (RF) data for each receive operation.

[0047] Temporary turn Figure 5 , schematically showing the receiving circuit 316 and the beamformer 320 with the acoustic focus 502, the echo 504 and the element 312 ( Figure 3 ) connection.

[0048] In this example, the receiving circuit 316 includes an analog front end (AFE) 506 and an analog-to-digital converter (ADC) 508. The AFE 506 includes a set of amplifiers 510 and a corresponding set of ADCs 512 for each set of channels 514. Each of the amplifiers 510 amplifies the corresponding analog electrical signal from a microvolt level to the voltage range of the ADC 512. In one example, the gain of the channel 308 is set to amplify weak signals as much as possible without causing signal clipping. Each of the ADCs 512 digitizes the amplified electrical signal.

[0049] The beamformer 320 includes delay circuits 516, gain circuits 518, and an adder / summer ("SUM") 520. The delay circuits 516 include a delay circuit 522 for each of the channels 514, and the gain circuits 518 include a gain circuit 524 for each of the channels 514. Each of the delay circuits 522 applies a corresponding delay, and each of the gain circuits 524 applies a corresponding gain / weight. The adder 520 adds / sums the delayed and weighted signals. In one example, a matched filter that matches the desired receive echo pulse shape (bandwidth) operates on the delayed and weighted signals. The output of the beamformer 320 includes an RF signal for a corresponding insonation angle θ, where θ is θ0, ..., θ i , ..., or θ L .

[0050] return Figure 3 The console 306 also includes a scanline processor 322 that is configured to perform other processing on the data, such as filtering (e.g., via a finite impulse response (FIR) filter, an infinite impulse response (IIR) filter, etc.), time gain compensation (TGC), I / Q demodulation, envelope detection, logarithmic compression, noise suppression, and / or other processing, and output a data frame. Figure 6 An example of the scan line processor 322 is schematically shown.

[0051] exist Figure 6 In the example scanline processor 322, an in-phase and quadrature (I / Q) demodulator ("I / Q") 602, an envelope detector ("ENV") 604, and a dynamic range compressor ("DRC") 606 are included. The scanline processor 322 receives as input the RF signal generated by the beamformer 320 for each acoustic wave action angle in parallel and / or serially. The I / Q demodulator 602 is configured to downmix the RF signal and may also apply low-pass filtering and / or decimation. This may include applying a Hilbert transform, a combination of a complex demodulation bandpass filter and optional decimation, and / or other processing. The I / Q demodulator 602 outputs an I / Q signal. In another example, the I / Q demodulator 602 is omitted.

[0052] The envelope detector 604 receives the I / Q signal from the I / Q demodulator 602 (or the RF signal from the beamformer 320 if the I / Q demodulator 602 is omitted) as input and detects, extracts, and outputs the envelope (i.e., amplitude) of the I / Q signal (or RF signal). In one embodiment, this is achieved using a Hilbert transform and / or other methods. The dynamic range compressor 606 compresses the extracted envelope using logarithmic dynamic range compression and / or other methods to reduce the dynamic range, for example, to a predetermined display accuracy, and outputs a scan line. The scan line processor 322 outputs the processed scan line as a frame / image (e.g., a B-mode image).

[0053] return Figure 3 The console 306 also includes an analyzer 324. In the example shown, the analyzer 324 receives data as input from the scanline processor 322. As described in more detail below, the analyzer 324 is configured to analyze the data from the scanline processor 322 by performing at least noise analysis and signal or image analysis, wherein the noise analysis includes determining the SNR, the signal analysis includes determining the speckle statistics, and the image analysis includes determining image characteristics.

[0054] The console 306 also includes a compounder 326 configured to spatially compound the data frames (e.g., via a weighted sum) based on the output from the analyzer 324. As described in more detail below, based on a set of rules or algorithms, the compounder 326 determines a pixel value for each pixel of the compounded image based on the output of the analyzer 324. In other words, the compounder 326 employs an adaptive spatial compounding algorithm for each pixel in the compounded image. As briefly described herein, the output from the analyzer 324 facilitates mitigating one or more disadvantages of existing spatial compounding methods and / or other disadvantages of other spatial compounding methods.

[0055] For example, the methods described herein can mitigate increased clutter and decreased contrast in controlled scans during speckle removal, as well as mitigate blurring of views of vessels, cysts, etc., mitigate disruption of continuous contours of vessel walls, and / or mitigate reduction in specular visibility, relative to configurations that omit the methods described herein. Thus, the methods described herein can reduce speckle and enhance needle visualization, improve image contrast, maintain sharp and uninterrupted vessel wall and organ boundaries, and / or reduce noise without needle enhancement modes and / or loss of image quality.

[0056] The console 306 also includes a scan converter 328 and a display 330. The scan converter 328 is configured to scan-convert the composite image into the coordinate system of the display 330. The scan converter 328 can be configured to use analog and / or digital scan conversion technology. The scan-converted data can be displayed on the display 330 and / or other display monitors.

[0057] The console 306 also includes a user interface (UI) 332. The user interface 332 includes one or more input devices such as buttons, knobs, sliders, a touch screen, a mouse, a keyboard, and / or other input devices, and / or one or more output devices such as visual, audible, and / or other indicators. The UI 332 allows a user to control the operation of the system 302. For example, in one example, the UI 332 receives input indicating the use of an imaging protocol using adaptive spatial compounding as described herein.

[0058] The console 306 also includes a controller 334. The controller 334 includes a processor, such as a microprocessor (μP), a central processing unit (CPU), a graphics processing unit (GPU), etc., and a memory storing the adaptive spatial compounding algorithm described herein. The controller 334 is configured to control one or more of the transmit circuitry 314, the receive circuitry 316, the switch 318, the beamformer 320, the scan line processor 322, the analyzer 324, the compounder 326, the scan converter 328, the display 330, and / or the user interface 332. One or more of these components of the console 306 can be implemented in software and / or hardware.

[0059] Figure 7 An example of an analyzer 324 is schematically shown. The analyzer 324 includes a signal-to-noise ratio (SNR) estimator 702, and at least one of a speckle analyzer 704 and an image analyzer 706. In the case where the analyzer 324 includes both the speckle analyzer 704 and the image analyzer 706, one or both of the speckle analyzer 704 and the image analyzer 706 may be employed. Figure 11 As described in more detail, in another example, the image analyzer 706 is omitted, and the analyzer 324 includes the SNR estimator 702 and the speckle analyzer 704. Figure 14 Described in more detail, in another example, the speckle analyzer 704 is omitted, and the analyzer 324 includes the SNR estimator 702 and the image analyzer 704 .

[0060] The analyzer 324 receives a noise reference level. In the illustrated embodiment, the noise estimator 708 estimates the noise reference level. In one example, the noise estimator 708 estimates the noise reference level based on the noise at different sound wave action angles θ0, ..., θ without first transmitting a pressure wave.i , ..., and θ L The noise reference level is estimated by using data acquired at one or more insonation angles (e.g., θ=0) in the image. Thus, the acquired data will not include any echo signals generated in response to the transmitted pressure waves. In other words, the acquired data will only include noise.

[0061] In one example, the noise estimator 708 estimates the noise reference level based on envelope data before dynamic range compression. In another example, the noise estimator 708 estimates the noise reference level based on a system model. An example of a model is described in U.S. patent application Ser. No. 17 / 304,864 (US 2021 / 0321990 A1), filed June 28, 2012, to Pedersen, entitled “MODEL-BASED CONTROL OF A DYNAMIC RANGEOF AN ULTRASOUND IMAGE,” which is incorporated herein by reference in its entirety.

[0062] The scan line processor 322 receives and processes the scan line from the beamformer 320 ( Figure 3 ) for different sound wave action angles θ0, ..., θ i , ..., and θ L RF data, and output for different sound wave action angles θ0, ..., θ i , ..., and θ L The analyzer 324 receives data from the scan line processor 322. As described in more detail below, in one example, the scan line processor 322 provides the analyzer 324 with the data for different insonation angles θ0, ..., θ i , ..., and θ L In another example, the scan line processor 322 provides the analyzer 324 with envelope data for different insonation angles θ0, ..., θ i , ..., and θ L The envelope data and the different sound wave action angles θ0, ..., θ i , ..., and θ L Frame data.

[0063] The SNR estimator 702 processes the data from the scanline processor 322 and the noise reference level to estimate the SNR. In one example, the SNR estimator 702 estimates the SNR of each sample at position [m, n] based on Equation 1:

[0064] Equation 1 :

[0065]

[0066] where env0 is the envelope data, where θ = 0, noise is the noise reference level, and and The SNR estimator 702 then determines and outputs the probability based on a predetermined set of rules or algorithms. Using a piecewise linear transfer function, for example, Figure 9 As shown in , the SNR estimator 702 estimates the probability based on the rule, as shown in Equation 2:

[0067] Equation 2 :

[0068]

[0069] exist Figure 9 In the example, the first axis 902 represents the SNR in decibels (dB), and the second axis 904 represents the SNR with 0≤P D The probability P of the value range ≤1 D Function 906 represents a piecewise linear transfer function. The function includes a lower SNR cutoff point (SNR L )908, where for SNR≤SNR L , P D becomes 1.0. The function 906 also includes a higher SNR cutoff point (SNR H )910, where for SNR≥SNR H , P D becomes 0.0. The function 906 also includes a region 912 where P D Decrease linearly from 1.0 to 0.0, that is, 0≤P D ≤1.

[0070] In another example, the SNR estimator 702 uses a nonlinear transfer function. Examples of suitable nonlinear transfer functions are given in Figure 10 As shown in Figure 10 In the example, the first axis 1002 represents the SNR in decibels (dB), and the second axis 1004 represents the SNR with 0≤P D The probability P of the value range ≤1 D Function 1006 represents a piecewise linear transfer function. Function 1006 includes a lower SNR cutoff point 1008 representing the SNR, where P D becomes 1.0; indicating a higher SNR cutoff point of 1010, where P D becomes 0.0; and region 1012, where P D Decrease linearly from 1.0 to 0.0, that is, 0≤PD ≤1.

[0071] Figure 9 Function 906 in φ includes abrupt cutoff points 908 and 910 and a linear transition 912 between the cutoff points 908 and 910 . Figure 10 The function 1006 in includes smoother or less abrupt cutoff points 1008 and 1010 and a nonlinear transition 1012 between the cutoff points 1008 and 1010. In general, dark areas in an image can be anechoic cysts, vascular lumens, or noise, with a probability P D It is estimated whether a pixel in the dark region is a member of a class consisting of: anechoic cyst, vessel lumen, or noise, and for all members of the class the minimum value is used for the composite image.

[0072] As described in more detail below, the speckle analyzer 704 processes the data from the scanline processor 322 and determines and outputs the probability that the sample at position [m, n] represents speckle. As described in more detail below, the image analyzer 706 processes the data from the scanline processor 322 and determines and outputs the probability that the sample at position [m, n] represents a reflective surface (e.g., a vessel wall, a septum, a needle, etc.), and the probability that the sample at position [m, n] represents a reflective surface (e.g., a vessel or an anechoic cyst, etc.). Based on a predetermined set of rules, the compositer 326 generates a composite image for different insonation angles θ0, ..., θ θ based on the output of the analyzer 324. i , ..., and θ L Composite the frame data.

[0073] Figure 8 An example of a composite device 326 connected to the analyzer 324 is shown schematically. The composite device receives data from the scan line processor 322 for different insonation angles θ0, ..., θ i , ..., and θ L The composite unit 326 is configured to analyze different acoustic wave action angles θ0, ..., θ based on the probability. i , ..., and θ L In this example, the compositer 326 includes a plurality of operators, including a minimum operator (“MIN”) 802 , an average operator (“MEAN”) 804 , a maximum operator (“MAX”) 806 , and a summation operator (“SUM”) 808 .

[0074] The MIN operator 802 is configured to calculate the sound wave action angles θ0, ..., θ i , ..., and θ LThe minimum pixel value of each pixel of the frame data is identified. The MEAN operator 804 is configured to generate the minimum pixel value for different sound wave action angles θ0, ..., θ i , ... and θ L The average pixel value of each pixel of the frame data is calculated. The MAX operator 806 is configured to calculate the average pixel value of each pixel of the frame data for different sound wave action angles θ0, ..., θ i , ..., and θ L The maximum pixel value for each pixel of the frame data is identified. The SUM operator 808 is configured to sum the outputs of the operators 802, 804, and 806 based on the probabilities from the analyzer 324 to produce a composite image.

[0075] Figure 11 An example of an analyzer 324 configured with an SNR estimator 702 and a speckle analyzer 704 is schematically shown. Figure 7 As described, the SNR estimator 702 determines the probability based on the data from the scan line processor 322 and the noise reference level determined by the noise estimator 708. The SNR estimator 702 provides the probability to the composite 326. Figure 7 Describing further, the speckle analyzer 704 determines possible samples representing speckle based on the envelope data.

[0076] In this example, the speckle analyzer 704 is configured to determine speckle statistics based on Equation 3:

[0077] Equation 3 :

[0078]

[0079] Wherein, μ is the mean value of the envelope data, and σ is the corresponding standard deviation. In one example, the mean value of the sample [m, n] is calculated based on Equation 4:

[0080] Equation 4 :

[0081]

[0082] Among them, env represents envelope data and In one example, the standard deviation value of the sample [m, n] is calculated based on Equation 5:

[0083] Equation 5 :

[0084]

[0085] In one example, a ratio μ / σ = 1.91 represents "perfect" / "fully formed" speckle, eg, for scans of the abdomen, liver, brain, etc.

[0086] Combine Figure 12 A non-limiting example algorithm for determining the probability that a sample represents speckle based on envelope data is described. The first axis 1202 represents And the second axis 1204 represents the value with 0≤P S The probability P of the value range ≤1 s The transfer function 1206 corresponds to: P s [m,n]=f s (v[m,n],ν m ,w,w1,p w ), where w is the width of the function, and 0 <p w <1 is the level of the function at w. In this example, the function includes a generally flat top 1208 having a width w1. The variable v m represents the center of the transfer function 1206.

[0087] Using the transfer function 1206, the speckle analyzer 704 determines the probability (P s ), for example, as shown in Equation 6:

[0088] Equation 6 :

[0089]

[0090] Among them, σ w According to the width w, w1 and level p w to calculate, as shown in Equation 7:

[0091] Equation 7

[0092]

[0093] When w1 == w, the transfer function 1206 is a rectangular window, and when w1 = 0, the transfer function 1206 is a Gaussian window. Other functions such as a Tukey window are also contemplated herein. The speckle analyzer 704 provides the probability (P s ).

[0094] Back to Figure 11 The composite device 326 receives the signals for different acoustic wave action angles θ0, ..., θ from the scan line processor 322. i , ..., and θ L frame data and receives the probability P from the analyzer 324 s and P s . Figure 13 A compositer 326 is schematically shown, which is configured to be based on the probability P s and P s For different sound wave action angles θ0, ..., θ i , ..., and θ L In the illustrated example, operator 808 uses a weighted sum. An example of a suitable weighted sum is shown in Equation 8:

[0095] Equation 8 :

[0096] C=P D min+(1-P D )·(P S ·mean+(1-P S )·max), where 0≤P D ≤1, and 0≤P s ≤ 1. The compositer 326 outputs a composite image C.

[0097] Figure 14 An example of an analyzer 324 is schematically shown, which is configured with an SNR estimator 702 and an image analyzer 706. Figure 7 As described, the SNR estimator 702 determines the probabilities based on the data from the scanline processor 322 and the noise reference level determined by the noise estimator 708. The SNR estimator 702 provides the probabilities to the composite 326. Figure 7 Described further, the image analyzer 706 determines a probability that the sample represents speckle based on the envelope data.

[0098] Figure 15 An example of an image analyzer 706 is schematically illustrated. The image analyzer 706 includes an image enhancer 1502, an interface detector 1504, a maximum operator ("MAX") 1506, an average operator 1508, and a blood vessel detector 1510. The image analyzer 706 receives images for different insonation angles θ0, ..., θ from the scan line processor 322. i , ..., and θ L The image intensifier 1502 processes the frame data of the image for different sound wave action angles θ0, ..., θ i , ..., and θ L The frame data is processed to form continuous regions with similar characteristics. In one embodiment, the processing includes at least unsharp masking and region opening.

[0099] The average operator 1508 is used for different sound wave action angles θ0, ..., θ i , ..., and θ LThe average pixel value of the pixels in the output image of the image intensifier 1502 is calculated. The blood vessel detector 1510 receives the average pixel value of the pixels in the output image of the image intensifier 1502 ... average pixel value of the pixels in the output image of the image intensifier 1502 is calculated. i , ..., and θ L The generated mean image is used as input. In one example, the vessel detector 1510 includes a U-Net-based vessel detector that is trained on application settings such as carotid artery detection, aorta detection, and hepatic vein detection, and uses the trained vessel detector to identify any vessels in the mean image. The vessel detector 1510 outputs a probability P of whether a pixel represents a member of a group consisting of a vessel or an anechoic cyst. v .

[0100] The interface detector 1504 receives signals from the image intensifier 1502 for different acoustic wave action angles θ0, ..., θ i , ..., and θ L The enhanced frame data is used as input. The interface detector 1504 processes the enhanced frame data and detects interfaces. Examples of suitable interface and vessel-like structure detection methods include Frangi filters. For example, the threshold in adaptive thresholding is estimated based on a histogram. The interface detector 1504 outputs the detected sound wave action angles θ0, ..., θ i , ..., and θ L The maximum operator 1506 determines the maximum angles across different acoustic wave action angles θ0, ..., θ i , ..., and θ L The interface detector 1504 outputs the probability P of a reflective surface such as a blood vessel wall, a septum, a needle, etc. r Typically, needles and specular reflectors have strong echoes only in images directed toward the interface.

[0101] exist Figure 14 In the embodiment, the composite device 326 receives the signals for different acoustic wave action angles θ0, ..., θ from the scan line processor 322. i , ..., and θ L frame data and receives the probability P from the analyzer 324 R 、P V and P S . Figure 16 A compositer 326 is schematically shown, which is configured to be based on the probability P R 、P V and P S To deal with different sound wave action angles θ0, ..., θ i , ..., and θ LIn the example shown, the summation operator 808 uses a weighted sum. An example of a suitable weighted sum is shown in Equation 9:

[0102] Equation 9 :

[0103] C=P R max+(1–P R )·(P D min+(1–P D )·mean),

[0104] Where 0≤P D ≤1, 0≤P R ≤1, 0≤P V ≤1, 0≤P N ≤1, and P D =P V ·P N The composite image C is output by the compositer 326 .

[0105] Figure 17 A non-limiting example of a flow chart illustrating a computer-implemented method based at least on speckle statistics is shown. It should be understood that the order of the actions in the method is not limiting. Therefore, other orders are contemplated herein. Furthermore, one or more actions may be omitted, and / or one or more additional actions may be included.

[0106] At 1702, ultrasonic pressure waves are transmitted for a set of insonation angles for use in a composite imaging process, as described herein and / or otherwise. At 1704, echoes generated in response to the interaction of the ultrasonic pressure waves with a structure are received, as described herein and / or otherwise. Typically, a first ultrasonic pressure wave is transmitted at a first insonation angle, a received echo generated in response to the first ultrasonic pressure wave is received, and the process is repeated for each of the set insonation angles, and then repeated multiple times to perform a scan.

[0107] At 1706, the echoes received for each receive operation are converted to analog signals, as described herein and / or otherwise. In one example, for each receive operation, the analog signals are pre-processed, e.g., amplified, and converted to digital signals. At 1708, the pre-processed signals are beamformed to generate scanlines of RF data. Using delay-and-sum beamforming, the digital signals are delayed, weighted, and then summed to generate scanlines of RF data.

[0108] At 1710, the scanlines of RF data are I / Q demodulated, as described herein and / or otherwise. For example, in one embodiment, the scanlines of RF data are demodulated / downmixed to produce I / Q signals. Optionally, the demodulated data is also low-pass filtered and / or decimated. This may include employing a Hilbert transform, a combination of a complex demodulation bandpass filter and optional decimation, and / or other processing.

[0109] At 1712, the envelope / amplitude of the I / Q signal is extracted, as described herein and / or otherwise. For example, in one embodiment, the envelope is extracted using a Hilbert transform and / or other methods. At 1714, the envelope is logarithmically compressed, as described herein and / or otherwise. For example, in one embodiment, the extracted envelope undergoes logarithmic dynamic range compression, e.g., to reduce the dynamic range to a predetermined display accuracy. The resulting signal for each of the different insonation angles is a B-mode image.

[0110] At 1716, the envelope / amplitude of the I / Q signal is analyzed, as described herein and / or otherwise. In this example, the analysis includes speckle and SNR estimation. In one embodiment, speckle is estimated based on the envelope / amplitude of the I / Q signal. For example, in one embodiment, speckle is determined from data acquired at different insonation angles. To this end, the mean and standard deviation of the samples are measured at different insonation angles. The ratio of the mean to the standard deviation is then calculated. A transfer function is then used to determine the probability that the sample corresponds to speckle.

[0111] In one example, the SNR is estimated based on a reference noise level and the envelope / amplitude of the I / Q signals. For example, in one example, the reference noise level is determined from data acquired at one or more different insonation angles without first transmitting a pressure wave, as such data would not include any echo signals and would only include noise. In another example, the reference noise level is determined based on a system model. A linear, nonlinear, and / or other function is employed to determine, based on the SNR, the probability that the sample corresponds to a member of the group consisting of an anechoic cyst, a vascular lumen, and noise.

[0112] At 1718, the B-mode images for the different insonation angles are composited based on the probability of whether the sample corresponds to speckle and the probability of whether the sample corresponds to a group, as described herein and / or otherwise. To this end, a minimum, average, and maximum value are determined for each sample, and a final value for each sample is calculated based on a weighted combination of the probability and the minimum, average, and maximum values. At 1720, the composite image is displayed.

[0113] Figure 18A non-limiting example of a flowchart of a computer-implemented method based at least on image characteristics is shown. It should be understood that the order of the actions in the method is not limiting. Therefore, other orders are contemplated herein. Furthermore, one or more actions may be omitted, and / or one or more additional actions may be included.

[0114] At 1802, ultrasonic pressure waves are transmitted for a set of insonation angles for use in a composite imaging process, as described herein and / or otherwise. At 1804, echoes generated in response to the interaction of the ultrasonic pressure waves with a structure are received, as described herein and / or otherwise. Typically, a first ultrasonic pressure wave is transmitted at a first insonation angle, a received echo generated in response to the first ultrasonic pressure wave is received, and the process is repeated for each of the set insonation angles, and then repeated multiple times to perform a scan.

[0115] At 1806, the echoes received for each receive operation are converted to analog signals, as described herein and / or otherwise. In one example, for each receive operation, the analog signals are pre-processed, e.g., amplified, and converted to digital signals. At 1808, the pre-processed signals are beamformed to generate scanlines of RF data. Using delay-and-sum beamforming, the digital signals are delayed, weighted, and then summed to generate scanlines of RF data.

[0116] At 1810, the scanlines of RF data are I / Q demodulated, as described herein and / or otherwise. For example, in one example, the scanlines of RF data are demodulated / downmixed to produce I / Q signals. Optionally, the demodulated data is also low-pass filtered and / or decimated. This may include employing a Hilbert transform, a combination of a complex demodulation bandpass filter and optional decimation, and / or other processing.

[0117] At 1812, the envelope / amplitude of the I / Q signal is extracted, as described herein and / or otherwise. For example, in one embodiment, the envelope is extracted using a Hilbert transform and / or other methods. At 1814, the envelope is logarithmically compressed, as described herein and / or otherwise. For example, in one embodiment, the extracted envelope undergoes logarithmic dynamic range compression, e.g., to reduce the dynamic range to a predetermined display accuracy. The resulting signal for each of the different insonation angles is a B-mode image.

[0118] At 1816, the envelope / amplitude of the I / Q signals and the B-mode image are analyzed as described herein and / or otherwise. In one example, an SNR is estimated based on a reference noise level and the envelope / amplitude of the I / Q signals. For example, in one example, the reference noise level is determined from data acquired at one or more different insonation angles without first transmitting a pressure wave, as such data would not include any echo signals and would only include noise. In another example, the reference noise level is determined based on a system model. A linear, nonlinear, and / or other function is employed to determine, based on the SNR, a probability that the sample corresponds to a member of the group consisting of an anechoic cyst, a vascular lumen, and noise.

[0119] In this example, the analysis includes image analysis and SNR estimation. For image analysis, frame data for different insonation angles is processed to enhance the image to form continuous regions with similar characteristics. In one embodiment, the processing includes at least unsharp masking and region unwrapping. A mean image of the enhanced images for the different insonation angles is generated. The mean image is then processed to determine the probability that a pixel represents membership in a group consisting of a vessel and an anechoic cyst. Additionally, each of the enhanced images is processed to detect an interface, and then the maximum value of the pixel value across the different insonation angles is determined. Typically, needles and specular reflectors have strong echoes only in images facing the interface.

[0120] At 1818, the B-mode images for the different insonation angles are composited based on the probability that the sample corresponds to a reflective surface, the probability that the sample corresponds to noise, and the probability that the sample corresponds to a vessel or an anechoic cyst, as described herein and / or otherwise. To this end, a minimum, average, and maximum value are determined for each pixel, and a final value for each pixel is calculated based on a weighted combination of the probability and the minimum, average, and maximum values. At 1820, the composited image is displayed.

[0121] The above method can be implemented by computer-readable instructions encoded or embedded on a computer-readable storage medium, which, when executed by a computer processor, causes the processor to perform the described actions or functions. Additionally or alternatively, at least one of the computer-readable instructions is executed by a signal, carrier wave, or other transient medium that is not a computer-readable storage medium.

[0122] As used herein, elements or steps listed in the singular and beginning with the word "one" or "a kind of" should be understood as not excluding a plurality of said elements or steps, unless such exclusion is explicitly stated. In addition, reference to "one embodiment" of the present invention is not intended to be interpreted as excluding the existence of additional embodiments that also include cited features. In addition, unless explicitly stated otherwise, "comprising", "including" or "having" an element or multiple elements with a specific attribute can include such additional elements that do not have this attribute. The terms "comprising" and "in..." are used as the concise language equivalents of the corresponding terms "comprising" and "wherein". In addition, the terms "first", "second" and "third" etc. are only used as marks, and are not intended to impose numerical requirements or specific position order on their objects.

[0123] Various embodiments and / or components (e.g., modules or components and controllers therein) may also be implemented as part of one or more computers or processors. A computer or processor may include a computing device, an input device, a display unit, and an interface, such as for accessing the Internet. A computer or processor may include a microprocessor. The microprocessor may be connected to a communication bus. A computer or processor may also include a memory. The memory may include a random access memory (RAM) and a read-only memory (ROM). A computer or processor may further include a storage device, which may be a hard drive or a removable storage drive, such as a floppy disk drive, an optical disk drive, etc. A storage device may also be other similar devices for loading a computer program or other instructions into a computer or processor.

[0124] As used herein, the term "computer" or "module" may include any processor-based or microprocessor-based system, including systems using microcontrollers, reduced instruction set computers (RISC), application specific integrated circuits (ASICs), logic circuits, and any other circuits or processors capable of performing the functions described herein. The above examples are merely exemplary and are therefore not intended to limit the definition and / or meaning of the term "computer" in any way. A computer or processor executes an instruction set stored in one or more storage elements to process input data. Storage elements may also store data or other information as desired or needed. Storage elements may be in the form of an information source or physical memory element within a processing machine.

[0125] The instruction set may include various commands that instruct a computer or processor to perform specific operations (such as the methods and processes of various embodiments of the present invention) as a processing machine. The instruction set may be in the form of a software program. The software may be in various forms, such as system software or application software. In addition, the software may be in the form of a collection of separate programs or modules, a program module within a larger program, or a portion of a program module. The software may also include modular programming in the form of object-oriented programming. The processing of input data by the processing machine may be in response to operator commands, or in response to the results of previous processing, or in response to a request made by another processing machine.

[0126] As used herein, the terms "software" and "firmware" are interchangeable and include any computer program stored in memory for execution by a computer, including RAM memory, ROM memory, EPROM memory, EEPROM memory, and non-volatile RAM (NVRAM) memory. The above memory types are exemplary only and, therefore, do not limit the types of memory that can be used to store computer programs.

[0127] It should be understood that the above description is intended to be illustrative and not restrictive. For example, the above embodiments (and / or aspects thereof) can be used in combination with each other. In addition, without departing from the scope of the present invention, many modifications can be made to adapt specific situations or materials to the teachings of the various embodiments of the present invention. Although the size and type of the material described herein are intended to limit the parameters of the various embodiments of the present invention, these embodiments are by no means restrictive but exemplary embodiments. After reviewing the above description, many other embodiments will be apparent to those skilled in the art.

[0128] This written description uses examples to disclose various embodiments of the invention, including the best mode, and also to enable those skilled in the art to practice the various embodiments of the invention, including making and using any devices or systems and performing any included methods. The patentable scope of the various embodiments of the invention is defined by the claims and may include other examples that occur to those skilled in the art. Such other examples are intended to be within the scope of the claims if they have structural elements that do not differ from the literal language of the claims, or if the examples include equivalent structural elements with insignificant differences from the literal language of the claims.

[0129] The embodiments of the present disclosure shown in the drawings and described above are exemplary embodiments only and are not intended to limit the scope of the appended claims, including any equivalents included within the scope of the claims. Various modifications are possible and will be apparent to those skilled in the art. It is intended that any combination of non-mutually exclusive features described herein be within the scope of the present disclosure. That is, features of the embodiments may be combined with any appropriate aspect described above, and optional features of any one aspect may be combined with any other appropriate aspect. Similarly, features listed in a dependent claim may be combined with non-mutually exclusive features of other dependent claims, particularly where the dependent claims are dependent on the same independent claim. In some jurisdictions that require single claim dependencies, it may be practice to use such dependencies, but this should not be taken to mean that the features in the dependent claims are mutually exclusive.

Claims

1. An ultrasound imaging system (302), comprising: A transducer array, the transducer array being configured to: Transmitting a plurality of ultrasonic signals, each of the plurality of ultrasonic signals being at a different sonic action angle; receiving an echo signal for each of the plurality of ultrasound signals; and generating an electrical signal indicative of each of the echo signals; a beamformer (329) configured to beamform the electrical signal and generate a radio frequency (RF) signal for each of the different insonation angles; an envelope detector (604) configured to detect an envelope of each of the RF signals for each of the different insonation angles; a compressor (606) configured to compress each of the envelopes for each of the different insonation angles to produce a data frame; a compounder (326) configured to adaptively compound the data frames for each of the different insonation angles to generate a compound image; and A display (330) is configured to display the composite image.

2. The ultrasound imaging system according to claim 1, further comprising: A signal-to-noise ratio (SNR) estimator (702) is configured to estimate the SNR based on the envelope and a reference noise level, and to determine a first probability that a sample of the envelope corresponds to one of noise, an anechoic cyst, and a blood vessel.

3. The ultrasound imaging system according to claim 2, further comprising: A speckle analyzer (704) is configured to analyze the envelope for speckle statistics, wherein the compounder is configured to adaptively compound frames based on the speckle statistics and the first probability. 4 . The ultrasound imaging system of claim 3 , wherein the compounder is configured to calculate a weighted sum of the frames based on the speckle statistics and the first probability to generate the compound image. 5 . The ultrasound imaging system of claim 4 , wherein the speckle statistics include a probability of whether a sample corresponds to speckle.

6. The ultrasound imaging system of claim 5 , wherein the compositer is further configured to identify a minimum pixel value in the frame, calculate an average pixel value of the frame, and identify a maximum pixel value in the frame, and calculate the weighted sum of the frame based on the minimum pixel value, the average pixel value, the maximum pixel value, the speckle probability, and the first probability to generate the composite image.

7. The ultrasound imaging system according to claim 2, further comprising: An image analyzer (706) is configured to analyze the frames for image characteristics, wherein the image compositer is configured to adaptively composite the frames based on the image characteristics and the first probability. 8 . The ultrasound imaging system of claim 7 , wherein the image compositer is configured to calculate a weighted sum of the frames based on the image characteristics and the first probability to generate the composite image.

9. The ultrasound imaging system of claim 8, wherein the image characteristics include an image probability of whether a pixel corresponds to one of a reflective surface, a blood vessel, and an anechoic cyst.

10. The ultrasound imaging system of claim 9, wherein the compositer is configured to identify a minimum pixel value in the frame, calculate an average pixel value of the frame, and identify a maximum pixel value in the frame, and calculate a weighted sum of the frame based on the minimum pixel value, the average pixel value, the maximum pixel value, the image probability, and the first probability to generate the composite image.

11. A computer-implemented method, comprising: Transmitting a plurality of ultrasonic signals, each of the plurality of ultrasonic signals being at a different sonic action angle; receiving an echo signal for each of the plurality of ultrasound signals; generating an electrical signal indicative of each of the echo signals; beamforming the electrical signal to generate a radio frequency (RF) signal for each of the different insonation angles; detecting an envelope of each of the RF signals for each of the different insonation angles; compressing each of the envelopes for each of the different insonation angles to generate a data frame; as well as The data frames are adaptively compounded for each of the different insonation angles to generate a compound image.

12. The computer-implemented method of claim 11 , further comprising: determining a first probability based on the estimated SNR, and The frames are adaptively compounded based on the first probability.

13. The computer-implemented method of claim 12, further comprising: determining speckle statistics based on the envelope; as well as The frames are adaptively compounded based on the speckle statistics and the first probability.

14. The computer-implemented method of claim 12, further comprising: determining image characteristics based on the frame; as well as The frames are adaptively compounded based on the image characteristics and the first probability.

15. A computer-readable medium encoded with computer-executable instructions that, when executed by a processor, cause the processor to: Transmitting a plurality of ultrasonic signals, each of the plurality of ultrasonic signals being at a different sonic action angle; receiving an echo signal for each of the plurality of ultrasound signals; generating an electrical signal indicative of each of the echo signals; beamforming the electrical signal and generating a radio frequency (RF) signal for each of the different insonation angles; detecting an envelope of each of the RF signals for each of the different insonation angles; compressing each of the envelopes for each of the different insonation angles to produce a data frame; and The data frames are adaptively compounded for each of the different insonation angles to generate a compound image.

Images