Estimation of fat content from tissue nonlinear response using medical ultrasound imaging.

Abstract

To estimate tissue characteristics (e.g., fat content). [Solution] Ultrasound is used to measure the nonlinear response of tissue (e.g., liver tissue). Fat content is estimated from the measured nonlinear response. The estimated fat content becomes more accurate by estimation from the measured nonlinear response of the tissue. By combining it with other ultrasound-based measurements such as scattering, attenuation, and / or sound velocity, the ultrasound-based estimation of fat content can become even more accurate. Other tissue properties can be estimated from the nonlinear response of the tissue alone or in combination with other measurements.

Description

Technical Field

[0001] The embodiments disclosed herein relate to ultrasonic imaging (ultrasonic imaging). Tissue characteristics such as liver fat fraction are measured using ultrasound.

Background Art

[0002] Non-alcoholic fatty liver disease (NAFLD) is the most common liver disease in adults and children in the United States. NAFLD is characterized by excessive liver fat accumulation and liver fibrosis. The fat fraction can be measured as an indicator of NAFLD. The fat fraction in the liver or other tissues (e.g., breast tissue), and / or other tissue characteristics (e.g., degree of fibrosis) provide diagnostically useful information.

[0003] Magnetic resonance imaging (MRI) accurately measures the proton density fat fraction (PDFF) as a biomarker of liver fat content. However, MRI is not widely available and is expensive. Ultrasonic imaging is more readily available and less expensive. Ultrasonic-based techniques for quantifying liver fat can advance clinical care. In one approach, ultrasonic-based shear wave imaging is used to estimate the fat fraction. This approach may not adequately address tissue complexity. In another approach, ultrasonic-based scattering and / or shear waves are used to estimate the fat fraction. For example, the attenuation coefficient and the backscattering coefficient are measured ultrasonically and used to estimate the fat fraction (ultrasound-derived fat fraction). Although accurate, the accuracy of fat fraction estimation using ultrasound can be improved.

Summary of the Invention

[0004] As a preface, the preferred embodiments described below include methods, commands, and systems for ultrasound-based estimation of tissue properties (e.g., fat content). Ultrasound is used to measure the nonlinear response of a tissue (e.g., liver tissue). The fat content is estimated from the measured nonlinear response. The estimated fat content may be significantly more accurate as a result of estimation based on the measured tissue nonlinear response. Ultrasound-based estimation of fat content may become even more accurate by combining it with other ultrasound-based measurements, such as scattering, attenuation, and / or sound velocity. Other tissue properties may be estimated from the tissue's nonlinear response alone or in combination with other measurements.

[0005] In a first embodiment, a method for estimating fat percentage using an ultrasound scanner is provided. One or more measurements of scattering and / or shear wave propagation in the tissue are generated from a patient scan with an ultrasound scanner. Measurements of the tissue's nonlinear response are generated from a patient scan with an ultrasound scanner. A processor estimates the fat percentage of the patient's tissue from (1) one or more measurements of scattering and / or shear wave propagation and (2) the measurements of the tissue's nonlinear response. An ultrasound image containing an index of the estimated fat percentage is displayed.

[0006] In a second embodiment, a system for estimating fat percentage is provided. A beamformer is configured to transmit pulses at different outputs to a patient, and a transducer receives ultrasound data in response to these pulses. An image processor is configured to determine the tissue nonlinear response from the ultrasound data and to estimate the fat percentage from the tissue nonlinear response. A display is configured to display the fat percentage value.

[0007] In a third embodiment, a method for estimating tissue properties using an ultrasonic system is provided. The ultrasonic system determines several scattering parameters of the tissue, several shear wave parameters of the tissue, and the nonlinear response of the tissue. The tissue properties are estimated from these scattering parameters, shear wave parameters, and nonlinear response. The tissue properties are then displayed.

[0008] The exemplary embodiments listed below outline other features or aspects. One or more aspects described above or in the exemplary embodiments may be used alone or in combination with other exemplary embodiments, features, or aspects. One aspect or feature of a method, system, or computer-readable medium may be used in other methods, systems, or computer-readable mediums. These, along with other aspects, features, and advantages, will become apparent from the detailed description of the preferred embodiments below, which should be read in conjunction with the drawings. The present invention is defined by the claims, and nothing in this section should be construed as a limitation to the claims. Further aspects and advantages of the present invention are discussed below in connection with the preferred embodiments and may be claimed later, independently or in combination. [Brief explanation of the drawing]

[0009] The components and figures are not necessarily to scale; instead, the emphasis is on illustrating the principle of the invention. Furthermore, in the figures, the same reference numerals indicate corresponding parts throughout the figures. [Figure 1] A flowchart illustrating one embodiment of a method for estimating tissue characteristics using ultrasound. [Figure 2] A block diagram of one embodiment of a system for estimating tissue characteristics using ultrasound. [Figure 3] A flowchart illustrating one embodiment of a method for estimating tissue properties (e.g., fat content) from ultrasound-based measurement of tissue nonlinear response. [Modes for carrying out the invention]

[0010] [Detailed description of drawings and currently preferred embodiments]

[0011] Quantitative ultrasound (QUS) is used for screening, diagnosis, monitoring, and / or predicting health status. The complexity of human tissue can be measured using multiple QUS parameters to accurately characterize the tissue. For example, liver fat percentage is estimated using a multiparametric approach that combines quantitative parameters extracted from received signals of various wave phenomena, such as longitudinal wave scattering and attenuation, shear wave propagation and attenuation, and / or on-axis wave propagation and attenuation from acoustic radiant force impulses (ARFI).

[0012] In one embodiment, tissue properties (e.g., liver fat percentage) are estimated by transmitting and receiving sequences of pulses to estimate scattering parameters, and by transmitting and receiving sequences of pulses to obtain shear wave parameters. The estimation may also include transmitting and receiving sequences of pulses to estimate parameters from axial displacements caused by acoustic radiant force impulses (ARFI). QUS parameters are estimated and combined to estimate tissue properties. Other information can also be included in the estimation of tissue properties, such as non-ultrasonic data (e.g., blood biomarkers).

[0013] In alternative or additional approaches, tissue nonlinearity is measured. For example, a nonlinearity coefficient is estimated from the backscatter signals of multiple ultrasonic transmissions at different power levels. Tissue nonlinearity is used alone or in combination with other parameters (e.g., scattering, attenuation, and / or sound velocity) to estimate tissue properties (e.g., fat content). By integrating tissue nonlinearity with other parameters in a model to estimate ultrasonic-derived fat content or tissue properties, the estimates are more accurate than those that do not use tissue nonlinearity.

[0014] Figures 1 and 2 illustrate the estimation of tissue properties from ultrasound measurements that do not include tissue nonlinearity, as described below. Figure 3 then illustrates the estimation of tissue properties from ultrasound measurements that include tissue nonlinearity.

[0015] Figure 1 illustrates a method for estimating tissue properties using an ultrasonic scanner or system. Tissue responses to different types of waves or wave phenomena are measured. A combination of these different response measurements is used to estimate tissue properties.

[0016] This method is performed by the system shown in Figure 2 or another system. A medical diagnostic ultrasound scanner performs the measurement by acoustically generating waves and measuring the response. An image processor in the scanner, computer, server, or other device makes estimations from the measurements. A display device, network, or memory is used to output the estimated tissue characteristics.

[0017] Additional or different processes may be provided, or there may be fewer processes. For example, processes 33 and / or 38 may not be provided. In another example, processes 36 and 37 may be mutually exclusive or may be used together, for example, by averaging the results from both. In yet another example, processes for configuring an ultrasound scanner and / or scanning are provided.

[0018] Each process is performed in the order described or illustrated (e.g., from top to bottom or in numerical order), but may be performed in other orders. For example, processes 30, 32, and 33 may be performed simultaneously, for example, using the same transmit and receive pulses, or in any order.

[0019] In process 30, the ultrasound scanner generates scattering measurements in the tissue from the patient's scan. The scattering measurements measure the tissue response to longitudinal waves transmitted from the ultrasound scanner. The scattering or echo of longitudinal waves incident on the tissue is measured.

[0020] Any scattering measurement may be used. Examples of scattering parameters include sound velocity, sound velocity dispersion, angular scattering coefficient (e.g., backscattering coefficient), frequency-dependent decay coefficient, decay coefficient slope, spectral slope of the normalized logarithmic spectrum, spectral intercept of the normalized logarithmic spectrum, spectral midband of the normalized logarithmic spectrum, effective scattering diameter, acoustic density, scattering number density, mean scattering spacing, nonlinearity parameter (B / A), and / or the ratio of coherent to incoherent scattering.

[0021] More than one measurement may be performed. For example, an ultrasound system may determine the values ​​of two or more scattering parameters in patient tissue. In one embodiment, the spectral slope of the logarithm of the acoustic attenuation coefficient, backscattering coefficient, and / or frequency-dependent backscattering coefficient is measured.

[0022] To measure scattering, an ultrasonic scanner scans the tissue with ultrasound. A sequence of transmit and receive events is performed to obtain a signal for estimating quantitative ultrasonic scattering parameters. In one embodiment, a one-dimensional, two-dimensional, or three-dimensional region is scanned by a B-mode sequence (e.g., transmitting a broadband (e.g., 1-2 cycles) transmit beam to form one or more reacted receive beams). Any scan format, such as linear, sector, or vector, may be used. The transmit and receive process may be repeated for each scan line. Narrowband pulses (e.g., 3 or more cycles) may be transmitted and received at separate center frequencies with or without overlapping spectra. Narrowband transmit pulses may be used in one or more transmit and receive events. Transmit pulses and corresponding receive beams can be formed at different steering angles, for example, to sample the same location in the tissue from different directions. Different steering angles may be performed for transmit only or for receive only. Each transmit beam may have a different transmit power and / or F number. Single or multiple transmissions may be focused or unfocused, or plane waves may be used. Any scan sequence may be used.

[0023] Repetition with or without different transmission and / or reception settings can be used to measure scattering once or to measure scattering separately. If multiple measurements of the same scattering parameter are provided for the same location, the measurements can be averaged or combined. Measurements from different locations, such as adjacent locations or locations within a predetermined range, can be averaged. For example, the measured value of scattering is a frequency-dependent measurement averaged from multiple transmissions to the same location. Changes in the power spectrum according to depth, angle, and / or frequency can be measured. As another example, estimated values of attenuation coefficients from different transmission and / or reception angles are averaged to reduce variance or used to quantify the angle dependence of attenuation.

[0024] In one embodiment, the scan for measurement is adaptive. Transmission and / or reception can be adaptive. For example, the result of one measurement is used to set the amplitude, angle, frequency, and / or F# for subsequent transmissions.

[0025] In one example, the attenuation coefficient is measured. The reference phantom method is used, but other measurements of the attenuation coefficient can also be used. The acoustic energy has exponential attenuation according to depth. Measurements of acoustic intensity according to depth are performed before depth gain correction or without depth gain correction. To remove the influence of the system, the measurements are calibrated based on the measurements of acoustic intensity according to depth in the phantom. The measurements can be made less noisy by averaging through a one-dimensional, two-dimensional, or three-dimensional region. The beam-formed sample or acoustic intensity can be converted to the frequency domain, and the calculations can be performed in the frequency domain.

[0026] In another example, the backscatter coefficient is measured. Acoustic attenuation is determined. This acoustic attenuation is used to determine the reference calibration. By calibrating for acoustic attenuation, the scattered energy is provided as the backscatter coefficient. The calculations can be performed in the frequency domain, providing measurements according to frequency.

[0027] The logarithmic spectral slope of frequency-dependent backscattering is measured from the backscattering coefficient. The logarithm of the backscattering coefficient is determined according to the frequency. A line (e.g., least squares) is fitted to the logarithm of backscattering according to the frequency, and the spectral slope is determined.

[0028] In process 32, the ultrasound scanner generates measurements of shear wave propagation in the tissue from a patient scan. For shear wave imaging, an acoustic radiant force impulse (ARFI or pushing pulse) is transmitted to the tissue. This impulse causes tissue displacement at a certain location, resulting in the generation of shear waves. Shear waves generally propagate laterally relative to the transmitting beam of the pushing pulse. By tracking tissue displacement at one or more laterally spaced locations, shear waves passing through those locations can be detected. The time it takes for the shear wave to travel from its origin to a later location and the distance between those locations provide the shear wave velocity.

[0029] Arbitrary shear wave parameters can be determined. For example, the shear wave velocity or speed in the tissue is measured. Other shear wave parameters include angle and / or frequency-dependent shear wave velocity, dispersion, angle and frequency-dependent shear wave attenuation, viscosity, angle and / or frequency-dependent storage modulus, angle and / or frequency-dependent loss modulus, viscosity, and / or angle and / or frequency-dependent acoustic absorption coefficient.

[0030] The acoustic absorption coefficient is derived from the absorption of acoustic pulses, not from the absorption of shear waves. Acoustic absorption is, The image was identified as JPEG2026065593000002.jpg1956, where F is the radiant force, I is the intensity of the ARFI push pulse, c is the acoustic speed of sound, and α is the acoustic absorption coefficient.

[0031] To measure shear waves, a pushing pulse or ARFI is transmitted to a focal point within the tissue. A reference scan of the stationary tissue position is performed before the pushing pulse or after the tissue returns to a stationary state. Changes or displacements of the tissue position at one or more locations spaced apart from the focal point are measured over time. Tracking scans are performed iteratively. Using correlation or other similarity measurements, the axial, 2D, or 3D shift of the tissue from the reference point is determined in comparison to the current tracking point. The point of maximum displacement indicates the point of the shear wave. Other timings, such as the start or end of displacement, may be used. The time it takes for the shear wave to reach the tracking point, and the distance from the tracking point to the focal point of the pushing pulse, provide the shear wave velocity. Other approaches may be used, such as solving the shear wave velocity at multiple locations by determining the shift of the displacement profile (displacement over time) for different tracking points, or solving it from the displacement over location.

[0032] Shear wave parameters can be measured depending on frequency and / or angle. Measurements are repeated by transmitting pushing pulses with beams from different angles and / or at different frequencies. Spatiotemporal displacement profiles are used to determine the measurements in the time domain or frequency domain. Results from different angles may be used to determine angle-dependent measurements.

[0033] Shear wave parameters can be measured at different locations. Measurements may be based on tissue displacement for a pushing pulse or a single pushing pulse. Alternatively, measurements may be based on tissue displacement for multiple pushing pulses. Measurements are repeated for different regions using different pushing pulses.

[0034] To measure shear wave parameters, both pushing pulses and tracking transmissions are performed. Displacement is measured by receiving the acoustic response to tracking transmissions rather than pushing pulse transmissions. The same scan used to measure scattering parameters may be used to measure shear wave parameters. For example, a reference scan used for tracking, before transmitting pushing pulses, may be used to measure scattering. In other embodiments, scans for shear wave parameters use different transmissions and / or receptions than those for scattering parameters. Scans for measurement are divided into separate sequences of transmission and reception events for different measurements.

[0035] Pushing pulses have a relatively longer duration compared to tracking pulses; for example, tens, hundreds, or thousands of cycles for pushing pulses, compared to 1-3 cycles for tracking transmissions. Different focal positions, frequencies, angles, outputs, and / or F-numbers may be used for pushing pulses when repetitions are provided.

[0036] The same measurement may be repeated for the same and / or different locations. For any repetition, different frequencies, F-numbers, angles, power outputs, focal positions, and / or other differences may be used. The obtained measurements may be used together to determine other measurements, or combined, for example, by averaging to reduce noise.

[0037] Ultrasonic scanners can adapt scans for shear wave parameter measurements. For example, push pulses can be adapted to estimate the attenuation coefficient of a shear wave. The center frequency, duration, f-number, or other characteristics of the push pulse are modified for subsequent transmission. The focus is made tighter or weaker. The displacement for generating the shear wave is made larger or smaller. As another example, another push pulse is transmitted with tighter focus or a longer duration for estimating the absorption coefficient using an ARFI push pulse. This modification can improve the signal-to-noise ratio (SNR) and / or reduce variability in the measurement.

[0038] Such adaptations are based on arbitrary information. For example, a displacement profile is compared to a reference or calibration profile. Another example is the determination of maximum, mean, or median displacements. The information may indicate the need for stronger or higher intensity pushing pulses, or lower intensity pushing pulses, which can shorten the cool-down time.

[0039] In process 33, the ultrasound scanner generates ARFI measurements of the axial displacement of the tissue. ARFI transmission causes the tissue to displace along the axis of the transmitted beam or the scan line. Rather than tracking shear waves, the axial tissue displacement caused by ARFI, or the axial tissue displacement in response to the longitudinal waves generated by ARFI, is tracked over time.

[0040] Any ARFI measurement may be used. For example, the longitudinal wave attenuation of an ARFI pulse can be estimated from the displacement tracked at a position spaced away from the ARFI focal point. The measurement may be at the focal point or at other positions along the axial scanline.

[0041] To measure, an ARFI is transmitted along the scanline. A tracking scan is performed after the ARFI is transmitted. Acoustic echoes from the tracking transmission along the scanline are received. The received data is correlated with reference information before or after the displacement caused by the ARFI. The amount of displacement is determined based on time, position, transmission angle, and / or transmission frequency. The maximum displacement, displacement based on depth, and / or displacement based on time are used to calculate the ARFI measurement.

[0042] The same measurement may be performed at other times and / or locations. The results from the repetitions may be used to derive yet another measurement or averaged.

[0043] The transmission can be adapted by adjusting the F-number, frequency, duration, output, and / or angle. This adaptation may be in response to any measured value, such as the magnitude of the maximum displacement.

[0044] Other measurements may be used. The tissue's response to different types of waves and / or scans is measured. One or more measurements of the same type are used. For a given measurement, a single instance, mean, or distribution (e.g., standard deviation over time, duration, frequency, angle, and / or space) is performed. The same or different types of measurements may be performed any number of times.

[0045] In process 34, an ultrasound scanner or other image processor estimates the tissue properties of the patient's tissue from each measurement. Measurements from two or more different wave phenomena are used. The values ​​of two or more measurements are used to estimate the tissue properties. For example, both scattering measurements and shear wave propagation measurements are used to estimate the tissue properties. In another example, on-axial displacement measurements (e.g., ARFI measurements) are used together with acoustic scattering measurements and / or shear wave propagation measurements.

[0046] Other information may be used to estimate tissue characteristics. For example, clinical information about the patient may be used. Clinical information may include medical history, age, body mass index, sex, fasting status, blood pressure, diabetes status, and / or blood biomarker measurements. Examples of blood biomarkers include alanine aminotransferase (ALT) levels, aspartate aminotransferase (AST) levels, and / or alkaline phosphatase (ALP) levels. Any other information about the patient may be included.

[0047] Any tissue property can be estimated. For example, tissue fat percentage can be estimated. Fat percentages of liver, breast, or other tissues are diagnostically useful. Fat percentage in a patient's liver aids in the diagnosis of NAFLD. Other diagnostically useful tissue properties include inflammation, density, fibrosis, and / or nephron properties (number and / or diameter). Tissue properties are either binary, such as present or absent, or estimates along a scale (i.e., level or magnitude of the tissue property). In one embodiment, only one tissue property is estimated. In other embodiments, two or more different tissue properties are estimated from the same or different measurements.

[0048] Processes 36 and 37 represent two different embodiments of the estimation in process 34. These different embodiments are alternatives. Other embodiments may also be used. Two or more embodiments may be used, for example, determining the values ​​of the tissue characteristics in two ways and then averaging the results or selecting the result that is most likely to be accurate.

[0049] The values ​​of the organizational characteristics are estimated. In the embodiment of process 36, a machine learning-trained classifier estimates the organizational characteristics. The machine learning-trained classifier provides a nonlinear model. Any machine learning method and the resulting machine learning-trained classifier can be used. For example, support vector machines, stochastic boosting trees, Bayesian networks, neural networks, or other machine learning methods can be used.

[0050] Machine learning learns from training data. The training data includes various examples (e.g., tens, hundreds, or thousands of samples) and ground truth. The examples include input data used, such as values ​​for scattering and shear wave propagation parameters. Ground truth is the tissue characteristic value for each example. In one embodiment, machine learning is for learning to classify fat percentages based on scattering and shear wave propagation parameters. Ground truth for fat percentage is provided by a magnetic resonance (MR) scan that provides proton density fat percentage (PDFF). MR-PDFF provides the fat percentage for a location or region. The fat percentage is used as ground truth, thereby allowing machine learning to learn to classify fat percentages from input values ​​for ultrasound parameters. Other sources of ground truth, such as those from biopsies, modeling, or other measurements, may be used for given tissue characteristics.

[0051] In some embodiments, machine learning trains a neural network. The neural network includes one or more convolutional layers that learn filter kernels to distinguish between values ​​of an organizational characteristic. Machine training learns which weighted combinations of input values ​​(e.g., convolutions using the trained kernels) produce an output. The resulting trained classifier extracts discriminative information using the input values ​​and then classifies the organizational characteristics based on the extracted information.

[0052] Training involves providing one or more matrices that associate input information with output classes. Hierarchical training and the resulting classifiers can be used. Different classifiers can be used for different organizational characteristics. Multiple classifiers can be used for the same organizational characteristic, and the results can be averaged or combined.

[0053] In the embodiment of process 37, a linear model is used instead of or in addition to a machine-trained model. A pre-configured or programmed function associates input values ​​with output values. The function and / or weights used in the function may be determined experimentally. For example, the weights are obtained by least-squares minimization using MR-PDFF values.

[0054] Any linear function may be used. For example, the values ​​of tissue properties are estimated from one or more scattering parameters and one or more shear wave propagation parameters. Any combination of addition, subtraction, multiplication, or division may be used.

[0055] In some embodiments, two or more functions (e.g., weighted combinations of measurements) are provided. One of the functions is selected based on the value of one of the parameters. For example, ultrasound-derived fat percentage (UDFF) estimation involves two functions, expressed as a weighted combination: In the formula JPEG2026065593000003.jpg21163, d and δ are constants, a, b, c, α, β, and γ are weights, and P is a measured value of a parameter. One parameter P i,k However, this is used to determine which function to select. Possible functions include two or three other parameters and weights. Additional, different, or fewer numbers of functions, parameters in functions, weights, and / or constants may be used. Different selection criteria may be used. The selection parameter may be of one type, and the weighted parameters of each function may be of another type. Alternatively, different types (e.g., scattering and shear wave propagation) may be included as weighted parameters, regardless of one or more types of parameters used for selection.

[0056] In one example, AC is the acoustic attenuation coefficient (e.g., the scattering parameter), BSC is the backscattering coefficient (e.g., the scattering parameter), and SS is the spectral slope of the logarithm of the frequency-dependent backscattering coefficient (e.g., also the scattering parameter). SWS is the shear wave velocity (e.g., the shear wave propagation parameter). Two functions based on the scattering parameter are used, in this case a function for a given estimation is selected based on the shear wave propagation parameter and is expressed as follows: JPEG2026065593000004.jpg27147

[0057] The weights and constants are based on minimizing the difference from the fat content percentage provided by MR-PDFF. Expertly selected or other weights and / or constants may be used.

[0058] In another embodiment, for example, the following single function may be used: In the formula JPEG2026065593000005.jpg2197, a, b, and c are weights, and P is a measured value of a parameter such as the backscattering coefficient.

[0059] In process 38, the ultrasound scanner or display device displays the estimated tissue parameters. For example, an image of the fat percentage is generated. A value representing the estimated fat percentage is displayed on the screen. Alternatively or additionally, a graphic (e.g., a curve or icon) representing the estimated fat percentage is displayed. A scale or reference to other criteria may be displayed. In other embodiments, the location-dependent fat percentage is displayed by color, brightness, hue, luminance, or other modulation of the displayed value in a one-dimensional, two-dimensional, or three-dimensional representation. Tissue characteristics may be mapped linearly or non-linearly to pixel color.

[0060] Tissue characteristics are presented alone or in conjunction with other information. For example, shear wave imaging is performed. Shear wave velocity, modulus of elasticity, or other information determined from the tissue's response to shear waves is displayed. Any shear imaging may be used. The displayed image represents shear wave information for the region of interest or the entire imaging area. For example, if shear velocity values ​​are determined for all grid points in the region of interest or field of view, the pixels in the display represent the shear wave velocity for that region. The display grid may differ from the scanning grid and / or the grid on which displacements are calculated.

[0061] Shear wave information is used for color overlays or other modulation of display values. Color, brightness, luminance, hue, or other display characteristics are modulated according to shear wave characteristics such as shear wave velocity. The image represents a two-dimensional or three-dimensional region of location. The shear data is in display format or can be scan-converted to display format. The shear data is color or grayscale data, but may be data before mapping to grayscale or color scales. The information can be mapped linearly or non-linearly to display values.

[0062] The image may contain other data. For example, shear wave information may be displayed overlaid on or together with the B-mode information. It may also contain B-mode or other data representing tissue, fluid, or contrast agent in the same region, for example, displaying B-mode data for any location with a shear wave velocity below a threshold or for a location of low quality. The other data may assist the user in determining the location of the shear information. In other embodiments, shear wave characteristics are displayed as an image without other data. In yet another embodiment, B-mode or other image information is provided without shear wave information.

[0063] Additional estimates of tissue properties are displayed substantially simultaneously with shear wave, B-mode, color or flow mode, M-mode, contrast-enhanced mode, and / or other imaging. Substantially, the visual perception of the field of view is taken into consideration. By displaying two images sequentially at a sufficient frequency, it may be possible for the viewer to perceive the images as being displayed simultaneously. Measurements of the components used to estimate tissue properties may also be displayed, for example, as a table.

[0064] Any format can be used for virtually simultaneous display. In one example, the shear wave image or anatomical image is a two-dimensional image. Tissue property values ​​are text, graphs, two-dimensional images, or other indicators of estimated values. A cursor or other location selection may be positioned relative to the shear wave image or anatomical image. The cursor points to a location selection. For example, the user selects a pixel associated with an internal region of a lesion, cyst, inclusion body, or other structure. The tissue property for the selected location is then displayed as a value, a pointer along a scale, or other indicator. In another example, tissue property is shown within a region of interest (a subregion of the field of view) or across the entire field of view.

[0065] In another embodiment, a shear wave image or B-mode image and a fat percentage image are displayed substantially simultaneously. For example, a dual-screen display is used. The shear wave image (e.g., shear wave velocity) and / or B-mode image are displayed in one area of ​​the screen. The location-dependent fat percentage is displayed in another area of ​​the screen. The user can view the different images on the screen for diagnostic purposes. Additional information or indicators of tissue characteristics may be provided to help the user diagnose the area.

[0066] In one embodiment, tissue estimation is provided as real-time numerical or quantitative images. Since tissue parameters can be estimated rapidly, the values ​​of tissue parameters are estimated and output within 1 to 3 seconds from the start of the scan. Tissue characteristics can be estimated at different points in time, such as before, during, and / or after treatment. Estimates from different points in time are used to monitor disease progression and / or response to treatment. For example, the percentage change in tissue characteristic values ​​over time is calculated and output.

[0067] Figure 2 shows one embodiment of a system 10 for estimating tissue characteristics from measurements responding to different types of waves. System 10 performs the method shown in Figure 1 or other methods. System 10 includes a Transmit Beamformer 12, a Transducer (XDCR) 14, a Receive Beamformer 16, an Image Processor 18, a Display 20, and Memory 22. Additional or different components may be provided, or there may be fewer components. For example, a user input device may be provided for user-to-system interaction.

[0068] System 10 is a medical diagnostic ultrasound imaging system. In an alternative embodiment, System 10 is a personal computer, a workstation, a PACS station, or other configuration located in the same location or distributed over a network for real-time or post-acquisition imaging.

[0069] Transmit and receive beamformers 12,16 form beamformers used for transmission and reception using transducer 14. A sequence of pulses is transmitted and a response is received based on the operation or configuration of the beamformer. The beamformer scans to measure scattering, shear wave, and / or ARFI parameters.

[0070] The transmitting beamformer 12 is an ultrasonic transmitter, memory, pulser, analog circuitry, digital circuitry, or a combination thereof. The transmitting beamformer 12 is capable of generating waveforms for multiple channels having different or relative amplitudes, delays, and / or phases. When acoustic waves are transmitted from the transducer 14 in response to the generated electrical waveforms, one or more beams are formed. A sequence of transmitting beams is generated to scan a two-dimensional or three-dimensional region. Sector, Vector®, linear, or other scan formats may be used. The same region may be scanned multiple times using different scan line angles, F numbers, and / or waveform center frequencies. For flow or Doppler imaging and shear imaging, a sequence of scans along the same one or more lines is used. In Doppler imaging, the sequence may include multiple beams along the same scan line before scanning adjacent scan lines. In shear imaging, scan or frame interleaving may be used (i.e., scanning the entire region before scanning again). Line or group of line interleaving may be used. In an alternative embodiment, the transmitting beamformer 12 generates a plane wave or a divergent wave for faster scanning.

[0071] The same transmitting beamformer 12 generates an impulse excitation or electrical waveform to produce acoustic energy to cause displacement. An electrical waveform for an acoustic radiance impulse is generated. In an alternative embodiment, another transmitting beamformer is provided to generate an impulse excitation. The transmitting beamformer 12 causes the transducer 14 to generate a pushing pulse or an acoustic radiance pulse.

[0072] The transducer 14 is an array for generating acoustic energy from an electrical waveform. In the case of an array, relative delay focuses the acoustic energy. A given transmission event corresponds to the transmission of acoustic energy by different elements at substantially the same time, given a delay. A transmission event may provide a pulse of ultrasonic energy to displace tissue. The pulse may be an impulse excitation, a tracking pulse, a B-mode pulse, or a pulse for other measurements. An impulse excitation involves a waveform with many cycles (e.g., 500 cycles), but the waveform occurs for a relatively short time and causes tissue displacement over a longer period of time. A tracking pulse may be a B-mode transmission using, for example, 1 to 5 cycles. A tracking pulse is used to scan a patient area.

[0073] The transducer 14 is a 1-, 1.25-, 1.5-, 1.75-, or 2-dimensional array of piezoelectric or capacitive film elements. The transducer 14 includes multiple elements for converting between acoustic and electrical energy. A received signal is generated in response to ultrasonic energy (echo) incident on the elements of the transducer 14. The elements are connected to the channels of the transmit and receive beamformers 12 and 16. Alternatively, a single element with a mechanical focus is used.

[0074] The receiving beamformer 16 includes multiple channels, each having an amplifier, delay, and / or phase rotater, and one or more adders. Each channel is connected to one or more transducer elements. The receiving beamformer 16 is configured by hardware or software and applies relative delay, phase, and / or apodization to form one or more received beams in response to each imaging or tracking transmission. Receiving operation may not occur for echoes from impulse excitations used to displace tissue. The receiving beamformer 16 outputs data representing spatial position using the received signals. Relative delay and / or phasing (fading) and addition of signals from different elements provide beamforming. In an alternative embodiment, the receiving beamformer 16 is a processor for generating samples using Fourier or other transforms.

[0075] The receiving beamformer 16 may include filters, for example, filters for separating information in the second harmonic or other frequency bands relative to the transmission frequency band. Such information is more likely to include planned tissue, contrast agent, and / or flow information. In another embodiment, the receiving beamformer 16 includes memory or buffers and filters or adders. Two or more receiving beams are coupled to separate information in a desired frequency band, such as the second harmonic, third fundamental, or other bands.

[0076] In coordination with the transmitting beamformer 12, the receiving beamformer 16 generates data representing a region. Data representing the region at different time points is generated to track shear waves or axial longitudinal waves. After acoustic impulse excitation, the receiving beamformer 16 generates beams representing positions along one or more lines at different time points. Data (e.g., beamformed samples) is generated by scanning the region of interest with ultrasound. By repeating the scan, ultrasonic data representing the region at different time points after impulse excitation is acquired.

[0077] The receiving beamformer 16 outputs beam summation data representing spatial position. Data is output for a single position, a position along a line, a position relative to a region, or a position relative to a volume. Dynamic focusing may be provided. The data may be for different purposes. For example, different parts of a scan may be performed for B-mode or tissue data, different from those for displacement. Alternatively, B-mode data may also be used to determine displacement. As another example, data for different types of measurements may be acquired by a series of common scans, and then B-mode or Doppler scans may be performed separately or using parts of the same data.

[0078] The image processor 18 is a B-mode detector, a Doppler detector, a pulsed-wave Doppler detector, a correlation processor, a Fourier transform processor, an application-specific integrated circuit, a general-purpose processor, a control processor, an image processor, a field-programmable gate array, a digital signal processor, an analog circuit, a digital circuit, a combination thereof, or any other currently known or future-developed device for detecting and processing information for display from a beamformed ultrasonic sample. In one embodiment, the image processor 18 includes one or more detectors and a separate image processor. The separate image processor is a control processor, a general-purpose processor, a digital signal processor, an application-specific integrated circuit, a field-programmable gate array, a network, a server, a group of processors, a data path, a combination thereof, or any other currently known or future-developed device for calculating values ​​of different types of parameters from beamformed and / or detected ultrasonic data and / or for estimating tissue characteristics from values ​​from different types of measurements. For example, the separate image processor consists of hardware, firmware, and / or software that perform any combination of one or more processes shown in Figure 1.

[0079] The image processor 18 is configured to estimate tissue characteristics from combinations of each type of parameter. For example, measured scattering parameters and measured shear wave parameters are used. Different types of parameters are measured based on transmission and reception sequences, as well as calculations from the results. At least two types of parameters (e.g., scattering, shear wave propagation, or axial ARFI) are determined from one or more measurements of each type.

[0080] The image processor 18 estimates tissue properties based on different types of parameters or measurements of tissue responses to different types of wavefronts. The estimation applies a machine learning-trained classifier. The input values ​​of the measurements, with or without other information, are used by the trained matrix to output values ​​of the tissue properties. In other embodiments, the image processor 18 uses weighted combinations of parameter values. For example, two or more functions are provided. One of the functions is selected using the values ​​of one or more parameters (e.g., shear wave velocity). The selected function determines the values ​​of the tissue properties using the same and / or different parameter values. Linear or nonlinear mapping associates the values ​​of one or more parameters with the values ​​of the tissue properties. For example, two or more scattering parameters are used to determine the values ​​of the tissue properties using the function selected by shear wave propagation.

[0081] The processor 18 is configured to generate one or more images. For example, shear wave velocity, B-mode, contrast agent, M-mode, flow or color mode, ARFI, and / or other types of images are generated. The shear wave velocity, flow, or ARFI images may be presented alone, or as an overlay or region of interest within a B-mode image. The shear wave velocity, flow, or ARFI data modulates the color of the location in the region of interest. At locations where the shear wave velocity, flow, or ARFI data is below a threshold, the B-mode information may be displayed without modulation by the shear wave velocity.

[0082] Other information may be included in the image or displayed sequentially or substantially simultaneously. For example, a tissue property estimation image may be displayed simultaneously with other images. One or more values ​​from a tissue property map may be mapped to display information. If tissue properties are measured at different locations, the tissue property values ​​may be generated as a color overlay within the region of interest in the B-mode image. Shear wave velocity and tissue property data may be combined as a single overlay in a single B-mode image. Alternatively, tissue property values ​​may be displayed as text or numbers adjacent to or overlaid on the B-mode or shear wave imaging image. The image processor 18 may be configured to generate other displays. For example, a shear wave velocity image may be displayed next to a graph, text, or graphical indicator of tissue properties such as fat percentage and / or degree of fibrosis. Tissue property information may be presented for one or more locations in the region of interest without being presented as a separate two-dimensional or three-dimensional representation, such as when the user selects a location and the ultrasound scanner then presents the tissue properties for that location.

[0083] The image processor 18 operates according to instructions stored in memory 22 or another memory for estimating tissue characteristics from measurements of tissue responses to different types of waves (e.g., scattering from transmitted ultrasound, on-axial tissue displacement, and / or shear waves resulting from tissue displacement). Memory 22 is a non-temporary computer-readable storage medium. Instructions for performing the processes, methods, and / or techniques discussed herein are provided to computer-readable storage media or memory, such as caches, buffers, RAM, removable media, hard drives, or other computer-readable storage media. Computer-readable storage media include various volatile and non-volatile storage media. The functions, processes, or tasks illustrated or described herein are performed in response to one or more sets of instructions stored in computer-readable storage media. The functions, processes, or tasks are independent of any particular type of instruction set, storage medium, processor, or processing strategy and may be performed by software, hardware, integrated circuits, firmware, microcode, etc., operating alone or in combination. Similarly, processing strategies may include multiprocessing, multitasking, parallel processing, etc. In one embodiment, instructions are stored on a removable media device to be read by a local or remote system. In another embodiment, instructions are stored in a remote location that can be transmitted over a computer network or telephone line. In yet another embodiment, instructions are stored in a given computer, CPU, GPU, or system.

[0084] The display 20 is a device such as a CRT, LCD, projector, plasma, or other display for displaying one-dimensional or two-dimensional images or three-dimensional representations. Two-dimensional images represent spatial distributions in a region. Three-dimensional representations are rendered from data representing spatial distributions in a volume. The display 20 is configured by an image processor 18 or other device, based on the input of signals to be displayed as images. The display 20 displays an image representing tissue properties for a single location (e.g., averaged from tissue property estimations including adjacent locations), within a region of interest, or across the entire image. For example, the display 20 displays a value for fat percentage. Displaying tissue properties based on different types of waves provides more accurate tissue property information for diagnosis.

[0085] Figure 3 illustrates how tissue properties are estimated using an ultrasound scanner or system. The nonlinear response of the tissue is measured by ultrasound. The nonlinearity of the tissue provides insights into its properties. When fat accumulates in the liver, its nonlinearity increases, leading to a greater conversion of ultrasound energy from the fundamental spectral region to the harmonics. As a result, the backscatter signals in the fundamental region decrease, while those in the harmonic region increase. The nonlinearity of the tissue response is used alone or in combination with other measurements and information to estimate fat content and / or other tissue properties.

[0086] The descriptions of the measurements and / or different measurements described for Figures 1 and 2 are not repeated in the description of Figure 3 below. The description of tissue property estimation is also not repeated below. In Figure 3, the measurement of the tissue's nonlinear response is performed in process 31, and regardless of the model used, the estimation incorporates the tissue's nonlinear response or data derived therefrom as input for estimating tissue property. In the following example, fat content is used as a tissue property, but other tissue properties may also be estimated, either alternatively or additionally.

[0087] The method in Figure 3 is performed by the system in Figure 2 or a different system. A medical diagnostic ultrasound scanner performs the measurement by acoustically generating waves and measuring the response. An image processor in the scanner, computer, server, or other device makes estimations from the measurements. A display device, network, or memory is used to output the estimated tissue characteristics.

[0088] Additional or different processes may be provided, or there may be fewer processes. For example, processes 30, 32, 33, and / or 38 may not be provided. As another example, processes 36 and 37 may be interchangeable or may be used together, for example, by averaging the results from both. As yet another example, processes for configuring and / or scanning with an ultrasonic scanner are provided.

[0089] The processes are performed in the order described or shown (e.g., from top to bottom or in numerical order), but they may be performed in other orders. For example, processes 30, 31, 32, and 33 may be performed simultaneously, for example, using the same transmit and receive pulses, or in any order.

[0090] In processes 30, 32, and 33, the ultrasound scanner generates one or more measurements of scattering, shear wave propagation, and / or axial displacement in the tissue from the ultrasound scanner scan of the patient. For example, the attenuation coefficient, scattering coefficient, and / or shear wave velocity or sound velocity are measured. The values ​​may be determined for one or more scattering parameters of the tissue. The values ​​may be determined for one or more shear wave parameters of the tissue. The values ​​may be determined for one or more axial displacement parameters.

[0091] In Figure 1, any of the above-mentioned measured values ​​and corresponding parameter values ​​may be generated. Only some of the measured values ​​and corresponding parameter values ​​may be generated, or none of these measured values ​​may be generated at all.

[0092] In process 31, the ultrasound scanner generates one or more measurements of the tissue's nonlinear response. The ultrasound scanner scans the patient's tissue, for example, liver tissue. The ultrasound scanner determines the tissue nonlinearity from this scan.

[0093] Tissue nonlinearity responses are determined from echoes generated in response to ultrasonic transmissions of different power levels. By transmitting at different power levels for a given location, line, region, and / or volume, the tissue response for that location, line, region, and / or volume can be measured.

[0094] Any number of transmissions and corresponding different output levels may be used. For example, 3, 5, 10, 20, or more output levels may be used. The range of output levels is set by the ultrasonic scanner. For example, one transmission may be made at the maximum output allowed by safety, hardware, and / or other limits. Another transmission may be made at a lower output. Different outputs may be sequentially stepped, for example, 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of the maximum output. Different step widths and / or variations may be used for the step widths throughout the set of output levels.

[0095] Backscatter from transmissions at different power levels is received by an ultrasonic scanner. Echoes are received from the tissue. These echoes have different amplitudes based on various factors, including the tissue response to the power level in the transmission. After beamforming and before detection, the received signal is analyzed to estimate the tissue nonlinear response. The processor analyzes the backscattered signals from transmissions at changed power levels to estimate the tissue nonlinear response.

[0096] Variations in the response to transmissions at different power levels, depending on the transmitted power, indicate structural nonlinearity. Any measurement of the variation in the response depending on the transmitted power can be used.

[0097] In one approach, the processor determines the nonlinear coefficients of the response depending on the transmission power. The nonlinearity of the structural response is characterized by the nonlinear coefficients. Any currently known or future-developed nonlinear coefficient determination may be used. For example, the B / A of the Taylor series expansion for nonlinear acoustics is determined. Variations in the received signal level for fundamental (transmission frequency) and / or harmonic information are analyzed. Finite-amplitude insertion substitution (FAIS) may be used, taking into account the effects of both sample attenuation and transducer diffraction on the measurement. An improved thermodynamic method (ITO) based on the measurement of phase shift in sound waves due to changes in ambient pressure may be used. Any of the measurements described in U.S. Patent No. 4,664,123 may be used.

[0098] In an alternative approach, the values ​​of the nonlinear coefficients are not calculated. Instead, a dataset or one or more curves are used that fit the received amplitude to the transmit power at one or more frequencies (e.g., the fundamental and second harmonics). This dataset or one or more fitted curves characterize the tissue nonlinear response. The dataset (e.g., a table of received amplitudes to transmit power) or the curves fitted to this data are input into a model for estimating fat content.

[0099] Characterizations are directed towards one or more locations. Characterizations for different locations may be maintained separately for estimation at each location. Alternatively, low-pass filtering or other combinations are used to determine values ​​from information at different locations. Characterizations or resulting estimates are averaged or combined. Alternatively, different characterizations are used as different inputs for estimation.

[0100] The nonlinear response of tissue varies with frequency. The nonlinearity occurs inversely between the fundamental (transmission) frequency and the harmonic frequencies (e.g., the second harmonic). The tissue nonlinear response can be characterized individually for two or more frequency bands. These characterizations and / or estimates derived from them can be combined. Alternatively, different characterizations can be used as different inputs for estimation.

[0101] In process 34, the processor estimates the fat content and / or other tissue characteristics of the patient's tissue. The estimation uses the machine learning model or classifier estimation in process 36 and / or the linear model estimation in process 37.

[0102] The input to a linear or machine-learned model for estimation includes one or more values ​​that characterize the tissue's nonlinear response. For example, values ​​of nonlinear coefficients are input. As another example, a dataset or fitted curve is input to an ultrasound-derived fat percentage or tissue property model. The tissue's nonlinear response can be used to select a function for estimating fat percentage and / or as a variable in the estimation.

[0103] Other inputs may also be used. For example, one or more measurements of scattering, longitudinal wave response, and / or shear wave propagation, and one or more measurements of tissue nonlinear response may be input. In another example, non-ultrasonic data (e.g., clinical data) may also be input. In yet another example, the attenuation coefficient, backscattering coefficient, and / or sound velocity may be input along with the measurements of tissue nonlinear response.

[0104] In response to the input to the model, the processor estimates the fat content and / or other tissue properties. The values ​​for the fat content and / or other tissue properties are output in response to the input. The processor uses the model to determine the fat content or other tissue properties. When one or more measurements of tissue nonlinearity are combined with one or more other parameters such as the damping coefficient, backscattering coefficient, and sound velocity, this information can improve the accuracy of the fat content estimate.

[0105] In process 38, estimated tissue characteristics (e.g., fat percentage values) are displayed. For example, an ultrasound image of the patient (e.g., a B-mode image) is displayed. Annotations are placed on or next to the image. These annotations indicate estimated tissue characteristics for the selected location or region of interest. Any of the displays described above are available with respect to the explanation of Figure 1.

[0106] Referring to Figure 2, the transmitting beamformer 12 is configured to scan to measure the tissue nonlinear response. By using the transmit and receive signals for other measurements and / or using the transmit and receive signals solely for measuring the tissue nonlinear response, the transmitting beamformer 12 and the receiving beamformer 16 provide ultrasonic data in response to the transmit pulse at different outputs.

[0107] The image processor 18 is configured to determine the nonlinear response of tissue from ultrasound data. The image processor 18 characterizes the tissue nonlinear response by calculating nonlinear coefficients, collecting a dataset of ultrasound data, or fitting a curve.

[0108] The image processor 18 is configured to estimate fat content and / or other tissue properties from the tissue nonlinear response. In response to the input of a measured characterization of the tissue nonlinear response, a model run by the image processor 18 outputs tissue properties. The image processor 18 may also use other measured values ​​such as scattering parameters, shear wave parameters, and the tissue nonlinear response to estimate tissue properties (e.g., fat content).

[0109] Display 20 displays tissue characteristic values. For example, the estimated fat percentage for the patient is displayed. The fat percentage or other tissue characteristic value may be more accurate in the estimation by using the tissue nonlinear response measured by ultrasound.

[0110] The following is a list of non-limiting exemplary embodiments disclosed herein. Exemplary embodiments relating to one set or type (e.g., a method or a system) may also be provided in exemplary embodiments of other sets or types, and may be combined with exemplary embodiments of other sets or types.

[0111] Exemplary Embodiment 1: A method for estimating fat content using an ultrasound scanner, To generate one or more measurements of scattering and / or shear wave propagation in tissue from a patient scan using the aforementioned ultrasound scanner, To generate measured values ​​of the tissue nonlinear response from the patient's scan using the aforementioned ultrasound scanner, (1) Estimate the fat content of the patient's tissue from one or more measured values ​​of scattering and / or shear wave propagation and (2) measured values ​​of the tissue nonlinear response. A method comprising outputting an ultrasound image containing an estimated index of the fat content.

[0112] Exemplary Embodiment 2: The method of Exemplary Embodiment 1, A method for generating each of the measurements from the scan, comprising separate transmit and receive events for one or more measurements of the scattering and / or shear wave propagation and measurements of the structural nonlinear response.

[0113] Exemplary Embodiment 3: A method according to exemplary embodiment 1 or 2, A method for generating one or more measurements of the scattering and / or shear wave propagation, comprising generating measurements of frequency-dependent acoustic attenuation coefficients, frequency-dependent backscattering coefficients, sound velocity, or a combination thereof.

[0114] Exemplary Embodiment 4: A method of any of the exemplary embodiments 1 to 3, A method for generating one or more measurements of the scattering and / or shear wave propagation, comprising generating a shear wave velocity.

[0115] Exemplary Embodiment 5: A method of any of the exemplary embodiments 1 to 4, A method for generating the measured values ​​of the tissue nonlinear response, comprising transmitting ultrasound at different power levels and characterizing the variation in the response to the transmission corresponding to the different power levels.

[0116] Exemplary Embodiment 6: The method of exemplary embodiment 5, The characterization is a method that includes determining the nonlinear coefficients of the response corresponding to the different outputs.

[0117] Exemplary Embodiment 7: The method of exemplary embodiment 5, The characterization method comprises generating curves or datasets of the response corresponding to the different outputs.

[0118] Exemplary Embodiment 8: The method of exemplary embodiment 5, The characterization method comprises characterizing the response as an echo at the fundamental frequency and / or harmonic frequency of the transmitted ultrasound.

[0119] Exemplary Embodiment 9: The method of exemplary embodiment 5, A method for transmitting ultrasound at different power levels, comprising transmitting the ultrasound at at least five different power levels.

[0120] Exemplary Embodiment 10: A method according to any of the exemplary embodiments 1 to 9, The estimation described above is a method that includes estimating with a machine learning-trained classifier.

[0121] Exemplary Embodiment 11: A method according to any of the exemplary embodiments 1 to 9, The estimation described above is a method that includes estimation using a linear model.

[0122] Exemplary Embodiment 12: A method of any of the exemplary embodiments 1 to 11, The estimation method includes estimating when the measured values ​​of the tissue nonlinear response include nonlinear coefficients input to the fat content model.

[0123] Exemplary Embodiment 13: A method in any of the exemplary embodiments 1 to 12, The estimation method includes estimating when the measured values ​​of the tissue nonlinear response include a curve input to a fat content model.

[0124] Exemplary Embodiment 14: A method of any of the exemplary embodiments 1 to 13, The method further includes generating measurements of the axial displacement of the said tissue, The estimation method includes estimating according to the measured values ​​of the axial displacement.

[0125] Exemplary Embodiment 15: A system for estimating fat content, Transducer and, A beamformer configured to transmit pulses to a patient at different outputs and receive ultrasound data in response to the pulses with the transducer, An image processor configured to determine tissue nonlinearity response from the ultrasound data and to estimate fat content from the tissue nonlinearity response, A system including a display configured to display the value of the fat content.

[0126] Exemplary Embodiment 16: The system of exemplary embodiment 15, The system comprises an image processor configured to estimate the fat content using a machine learning classifier.

[0127] Exemplary Embodiment 17: A system of exemplary embodiment 15 or 16, The system is configured such that the image processor estimates the fat content from the tissue nonlinear response and scattering parameters and / or shear wave parameters.

[0128] Exemplary Embodiment 18: The system of exemplary embodiment 17, The system is configured such that the image processor determines the tissue nonlinear response as a nonlinear coefficient.

[0129] Exemplary Embodiment 19: A method for estimating tissue properties using an ultrasound system, The ultrasonic system determines multiple scattering parameters of the tissue. The ultrasonic system determines multiple shear wave parameters of the tissue. The ultrasonic system determines the nonlinear response of the tissue, Estimating tissue characteristics from the scattering parameters, the shear wave parameters, and the nonlinear response, A method including displaying the aforementioned organizational characteristics.

[0130] Exemplary Embodiment 20: The method of exemplary embodiment 19, A method for determining the nonlinear response, including determining it from acoustic echoes responding to different transmission powers.

[0131] While the present invention has been described above with reference to various embodiments, it should be understood that many changes and modifications are possible without departing from the scope of the invention. That is, the above detailed description is intended to be illustrative rather than restrictive, and the claims, including all equivalents, are intended to define the spirit and scope of the invention.

Claims

1. A method for estimating fat content using an ultrasound scanner, To generate one or more measurements of scattering and / or shear wave propagation in tissue from a patient scan using the aforementioned ultrasound scanner, To generate measured values ​​of the tissue nonlinear response from the patient's scan using the aforementioned ultrasound scanner, The processor estimates the fat content of the patient's tissue from (1) one or more measurements of the scattering and / or shear wave propagation and (2) measurements of the tissue nonlinear response. A method comprising outputting an ultrasound image containing an estimated index of the fat content.

2. The method according to claim 1, wherein generating each of the measurements from the scan includes separate transmit and receive events for one or more measurements of the scattering and / or shear wave propagation and measurements of the structural nonlinear response.

3. The method according to claim 1, wherein generating one or more measurements of scattering and / or shear wave propagation includes generating a measurement of a frequency-dependent acoustic attenuation coefficient, a frequency-dependent backscattering coefficient, a sound velocity, or a combination thereof.

4. The method according to claim 1, wherein generating one or more measurements of the scattering and / or shear wave propagation includes generating a shear wave velocity.

5. The method according to claim 1, wherein generating the measured values ​​of the tissue nonlinear response includes transmitting ultrasound at different powers and characterizing the variation in the response to the transmission corresponding to the different powers.

6. The method according to claim 5, wherein the characterization includes determining the nonlinear coefficients of the response corresponding to the different outputs.

7. The method according to claim 5, wherein the characterization includes generating a curve or dataset of the response corresponding to the different outputs.

8. The method according to claim 5, wherein transmitting the ultrasound at different power levels includes transmitting the ultrasound at at least five different power levels.

9. The method according to claim 1, wherein generating a measurement of the tissue nonlinear response comprises characterizing the response as echoes at the fundamental and / or harmonic frequencies of the transmitted ultrasound.

10. The method according to claim 1, wherein the estimation is performed using a machine learning-trained classifier.

11. The method according to claim 1, wherein the estimation includes estimating using a linear model.

12. The method according to claim 1, wherein the estimation includes estimating when the measured value of the tissue nonlinear response includes a nonlinear coefficient input to the fat content model.

13. The method according to claim 1, wherein the estimation includes estimating when the measured values ​​of the tissue nonlinear response include a curve provided to a fat content model.

14. The method further includes generating measurements of the axial displacement of the said tissue, The method according to claim 1, wherein the estimation is performed according to the measured value of the axial displacement.

15. A system for estimating fat content, Transducer and, A beamformer configured to transmit pulses to a patient at different outputs and receive ultrasound data in response to the pulses with the transducer, An image processor configured to determine tissue nonlinearity response from the ultrasound data and to estimate fat content from the tissue nonlinearity response, A system including a display configured to display the value of the fat content.

16. The system according to claim 15, wherein the image processor is configured to estimate the fat content using a machine learning classifier.

17. The system according to claim 15, wherein the image processor is configured to estimate the fat content from the tissue nonlinear response and scattering parameters and / or shear wave parameters.

18. The system according to claim 17, wherein the image processor is configured to determine the tissue nonlinear response as a nonlinear coefficient.

19. A method for estimating tissue properties using an ultrasound system, The ultrasonic system determines multiple scattering parameters of the tissue. The ultrasonic system determines multiple shear wave parameters of the tissue. The ultrasonic system determines the nonlinear response of the tissue, Estimating tissue characteristics from the scattering parameters, the shear wave parameters, and the nonlinear response, A method including displaying the aforementioned organizational characteristics.

20. The method according to claim 19, wherein determining the nonlinear response includes determining it from acoustic echoes responding to different transmission powers.

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