Fat fraction estimation from tissue non-linear response using ultrasound medical imaging

Abstract

And performing fat fraction estimation according to the tissue nonlinear response by utilizing ultrasonic medical imaging. For a tissue property (e.g., fat fraction) estimate, ultrasound is used to measure a non-linear response of a tissue (e.g., liver tissue). A fat fraction is estimated from the measured non-linear response. The estimated fat fraction may be more accurate due to estimation from the measured tissue non-linear response. By combining with other ultrasound-based measurements, such as scattering, attenuation, and / or sound velocity, ultrasound-based fat fraction estimates may even be more accurate. Other tissue properties may be estimated separately from the tissue nonlinear response or in combination with other measurements.

Description

Technical Field

[0001] This embodiment relates to ultrasound imaging. Ultrasound is used to measure tissue properties, such as the fat fraction in the liver. Background Technology

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

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

[0004] By way of introduction, the preferred embodiments described below include methods, instructions, and systems for estimating tissue properties (e.g., fat fraction) using ultrasound. Ultrasound is used to measure the nonlinear response of a tissue (e.g., liver tissue). The fat fraction is estimated based on the measured nonlinear response. Because the estimation is based on the measured tissue nonlinear response, the estimated fat fraction can be more accurate. By combining it with other ultrasound-based measurements (such as scattering, attenuation, and / or sound velocity), ultrasound-based fat fraction estimation can be even more accurate. Other tissue properties can be estimated individually or in combination with other measurements based on the tissue nonlinear response.

[0005] In a first aspect, a method for estimating fat fraction using an ultrasound scanner is provided. One or more measures of scattered and / or shear wave propagation in the tissue are generated by the ultrasound scanner based on a patient's scan. A measure of the tissue's nonlinear response is generated by the ultrasound scanner based on the patient's scan. A processor estimates the fat fraction of the patient's tissue based on (1) one or more measures of scattered and / or shear wave propagation and (2) a measure of the tissue's nonlinear response. An ultrasound image—including an indication of the estimated fat fraction—is displayed.

[0006] In a second aspect, a system for estimating fat fraction is provided. A beamformer is configured to emit pulses at different power levels within a patient's body, and a transducer receives ultrasound data in response to the pulses. An image processor is configured to determine a tissue nonlinear response based on the ultrasound data, and is configured to estimate the fat fraction based on the tissue nonlinear response. A display is configured to show the value of the fat fraction.

[0007] In a third aspect, a method for estimating tissue properties using an ultrasound system is provided. The ultrasound system determines multiple scattering parameters, multiple shear wave parameters, and the nonlinear response of the tissue. Tissue properties are estimated based on the scattering parameters, shear wave parameters, and nonlinear response. The tissue properties are then displayed.

[0008] The illustrative embodiments listed below summarize other features or aspects. Any one or more of the aspects described above or in the illustrative embodiments may be used alone or in combination with other illustrative embodiments, features, or aspects. Any aspect or feature of one of the methods, systems, or computer-readable media may be used in other aspects or features of the methods, systems, or computer-readable media. These and other aspects, features, and advantages will become apparent from the following detailed description of preferred embodiments, which should be read in conjunction with the accompanying drawings. The invention is defined by the following claims, and nothing in this section should be considered as limiting those claims. Further aspects and advantages of the invention are discussed below in conjunction with preferred embodiments and may be claimed thereafter, independently or in combination. Attached Figure Description

[0009] The components and figures are not necessarily to scale; instead, the emphasis is on illustrating the principles of the invention. Furthermore, similar reference numerals are used throughout the figures to designate corresponding parts in different views.

[0010] Figure 1 This is a flowchart of an embodiment of a method for estimating tissue properties using ultrasound; Figure 2 This is a block diagram of one embodiment of a system for estimating tissue properties using ultrasound; and Figure 3 This is a flowchart of an embodiment of a method for estimating tissue properties (e.g., fat fraction) based on ultrasound-based measurements of tissue nonlinear response. Detailed Implementation

[0011] Quantitative ultrasound (QUS) is used for screening, diagnosis, monitoring, and / or prediction of health conditions. Multiple QUS parameters can be used to measure the complexity of human tissues for accurate characterization. For example, a multi-parameter approach can be used to estimate liver fat fraction, combining quantitative parameters extracted from received signals from different 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 pulses (ARFI).

[0012] In one embodiment, tissue properties (e.g., liver fat fraction) are estimated by transmitting and receiving pulse sequences to estimate scattering parameters and by transmitting and receiving pulse sequences to obtain shear wave parameters. The estimation may also include transmitting and receiving pulse sequences to estimate parameters based on axial displacement induced by acoustic radiation force pulses (ARFI). QUS parameters are estimated and combined to estimate tissue properties. Other information may be included in the estimation of tissue properties, such as non-ultrasound data (e.g., blood biomarkers).

[0013] In alternative or additional methods, tissue nonlinearity is measured. For example, the nonlinearity coefficient is estimated based on the backscattered signals from multiple ultrasound emissions at different power levels. Tissue nonlinearity, alone or in combination with other parameters (e.g., scattering, attenuation, and / or sound velocity), is used to estimate tissue properties (e.g., fat fraction). By integrating tissue nonlinearity with other parameters in the model to estimate ultrasound-derived fat fraction or tissue properties, the estimated values ​​can be more accurate than estimates that do not utilize tissue nonlinearity.

[0014] The following describes the estimation of tissue properties based on ultrasound measurements (excluding tissue nonlinearity). Figure 1 and Figure 2 Then, the tissue properties are estimated based on ultrasound measurements using tissue nonlinearity to describe... Figure 3 .

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

[0016] This method is by Figure 2 This is implemented using a system or different systems. Medical diagnostic ultrasound scanners perform measurements by generating acoustic waves and measuring the response. An image processor in the scanner, computer, server, or other device makes estimates based on these measurements. A display device, network, or memory is used to output the estimated tissue properties.

[0017] Additional, different, or fewer actions may be provided. For example, actions 33 and / or 38 may not be provided. As another example, actions 36 and 37 are alternatives, or may be used together, such as averaging results from both. In yet another example, actions for configuring the ultrasound scanner and / or scanning are provided.

[0018] The actions are performed in the order described or shown (e.g., from top to bottom or numerically), but may also be performed in other orders. For example, actions 30, 32, and 33 may be performed simultaneously, such as using the same transmit and receive pulses, or in any order.

[0019] In action 30, the ultrasound scanner generates a measure of scattering in the tissue based on a scan of the patient. This scattering measure measures the tissue's response to longitudinal waves emitted from the ultrasound scanner. It measures the scattering or echo of longitudinal waves impacting the tissue.

[0020] Any scattering metric can be used. Example scattering parameters include sound velocity, sound dispersion, angular scattering coefficient (e.g., backscattering coefficient), frequency-dependent attenuation coefficient, attenuation coefficient slope, spectral slope of the normalized logarithmic spectrum, spectral intercept of the normalized logarithmic spectrum, spectral band of the normalized logarithmic spectrum, effective scatterer diameter, acoustic concentration, scatterer number density, average scatterer spacing, nonlinear parameter (B / A), and / or the ratio of coherent to incoherent scattering.

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

[0022] To measure scattering, an ultrasound scanner uses ultrasound to scan tissue. Sequences of emission and reception events are performed to acquire signals to estimate quantitative ultrasound scattering parameters. In one embodiment, a one-dimensional, two-dimensional, or three-dimensional region is scanned by a mode B sequence (e.g., emitting a broadband (e.g., 1-2 cycles) transmit beam and forming one or more response receive beams). Any scan format can be used, such as linear, fan-shaped, or vector. The emission and reception operations can be repeated for each scan line. Narrowband pulses (e.g., 3 or more cycles) can be emitted and received at different center frequencies with or without overlapping spectra. Narrowband transmit pulses can be used in single or multiple emission and reception events. The transmit pulses and corresponding receive beams can be formed at different steer angles, such as sampling the same location of the tissue from different directions. Different steer angles can be performed only for emission or only for reception. Different transmit beams can have different transmit powers and / or F-numbers. Single or multiple emissions can be focused, unfocused, or use plane waves. Any scan sequence can be used.

[0023] Repeats, with or without different transmit and / or receive settings, can be used to measure single scattering or to measure scattering differently. In the case of multiple measures providing the same scattering parameters for the same location, these measures can be averaged or combined. Measures from different locations (such as neighboring locations or locations within a given range) can be averaged. For example, a scattering measure is a frequency-dependent measure averaged from multiple transmits to the same location. Changes in the power spectrum as a function of depth, angle, and / or frequency can be measured. As another example, estimates of attenuation coefficients from different transmit and / or receive angles are averaged to reduce variance or used to quantify the angle dependence of attenuation.

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

[0025] In one example, the attenuation coefficient is measured. A reference phantom method is used, but other measures of the attenuation coefficient can also be used. Acoustic energy attenuates exponentially as a function of depth. A measure of acoustic intensity as a function of depth is performed before or without depth gain correction. To remove system effects, the measurement is calibrated based on the measure of acoustic intensity as a function of depth within the phantom. By averaging over one-dimensional, two-dimensional, or three-dimensional regions, the measurement can withstand less noise. Beamforming samples or acoustic intensities can be converted to the frequency domain, and calculations performed in the frequency domain.

[0026] In another example, the backscattering coefficient is measured. Acoustic attenuation is determined. This acoustic attenuation is used to determine a reference calibration. By calibrating against the acoustic attenuation, the scattered energy is provided as the backscattering coefficient. This calculation can be performed in the frequency domain, thus providing a metric as a function of frequency.

[0027] The spectral slope is determined by measuring the frequency-dependent logarithm of the backscattering coefficients. The logarithm of the backscattering coefficients is used as a function of frequency. A line is fitted (e.g., least squares) to the logarithm of backscattering as a function of frequency to determine the spectral slope.

[0028] In action 32, the ultrasound scanner generates a measure of shear wave propagation in the tissue based on the patient's scan. For shear wave imaging, an acoustic radiation force pulse (ARFI or push pulse) is emitted into the tissue. The pulse causes tissue displacement at the location, resulting in the generation of a shear wave. The shear wave generally travels transversely to the emitted beam of the push pulse. By tracking tissue displacement at one or more transversely spaced locations, the shear wave passing through those locations can be detected. The time it takes for the shear wave to travel from the origin to a later location and the distance between those locations provide the shear wave velocity.

[0029] Any shear wave parameter can be determined. For example, shear wave velocity or rate in tissue can be 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, and viscosity and / or angle- and / or frequency-dependent acoustic absorption coefficient.

[0030] The acoustic absorption coefficient originates from the absorption of the acoustic pulse, not from the absorption of the shear wave. Acoustic absorption is determined to be... Where F is the radiative force, I is the intensity of the ARFI driving pulse, c is the acoustic velocity, and It is the acoustic absorption coefficient.

[0031] To measure shear waves, a push pulse or ARFI is emitted to the focal location in the tissue. A reference scan of the quiescent tissue is performed before the push pulse or after the tissue returns to a quiescent state. Over time, changes in the tissue's location or displacement are measured at one or more locations spaced apart from the focal location. Tracking scans are repeated. Using correlation or other similarity measures, the axial, 2D, or 3D displacement from the reference time is determined compared to the current tracking time. The time of maximum displacement indicates the timing of the shear wave. Other timing parameters, such as the start or end of the displacement, can be used. The time it takes for the shear wave to reach the tracking location and the distance from the tracking location to the focal location of the push pulse provide the shear wave rate. Other methods can be used, such as solving for the shear wave rate at multiple locations by determining the displacement profile (displacement as a function of time) at different tracking locations, or based on the displacement as a function of location.

[0032] Shear wave parameters can be measured as a function of frequency and / or angle. Measurements are repeated by transmitting push pulses from the beam at different angles and / or frequencies. The metric is determined using spatiotemporal displacement profiles in the time or frequency domain. Results from different angles can be used to determine angle-dependent metrics.

[0033] Shear wave parameters can be measured at different locations. The measurements can be based on tissue displacement in response to one or a single push pulse. Alternatively, the measurements can be based on tissue displacement in response to multiple push pulses. The measurements are repeated for different zones using different push pulses.

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

[0035] Compared to tracking pulses, push pulses have a relatively long duration, such as tens, hundreds, or thousands of cycles, while tracking pulses have one to three cycles. When repetition is provided, different focal positions, frequencies, angles, power, and / or F-numbers can be used for push pulses.

[0036] The same measurement can be repeated at the same and / or different locations. Different frequencies, F-numbers, angles, power, focal positions, and / or other variations can be used for any repetition. The resulting measurements can be used together to determine another measurement, or they can be combined, such as by averaging, to reduce noise.

[0037] Ultrasonic scanners can be adapted for shear wave parameter measurements. For example, for estimating the shear wave attenuation coefficient, a push pulse adaptation is used. The center frequency, duration, f-number, or other characteristics of the push pulse are altered for subsequent transmissions. The focus is tighter or weaker. The displacement used to create the shear wave is larger or smaller. As another example, for estimating the absorption coefficient using an ARFI push pulse, another push pulse is transmitted with a tighter focus or a longer duration. These alterations can improve the signal-to-noise ratio (SNR) and / or reduce variability in the measurement.

[0038] The adaptation is based on any information. For example, the displacement profile is compared to a reference or calibration profile. As another example, the amount of displacement for the maximum, average, or intermediate displacement is determined. This information may indicate the need for a stronger or higher intensity push pulse, or it may indicate the need for a weaker push pulse, thus allowing for a shorter cooling time.

[0039] In action 33, the ultrasound scanner generates an ARFI metric of the axial displacement of the tissue. ARFI emission causes tissue displacement along the axis or scan line of the emitted beam. Instead of tracking shear waves, it tracks the axial tissue displacement caused by or in response to longitudinal waves generated by ARFI over time.

[0040] Any ARFI metric can be used. For example, the attenuation of the longitudinal wave of an ARFI pulse can be estimated based on the displacement tracked at a location spaced apart from the ARFI focal point. This metric can be at the focal point or at other locations along the axial scan line.

[0041] For measurement, ARFI is emitted along the scan line. A tracking scan is performed after the ARFI emission. Acoustic echoes from the tracking emission along the scan line are received. The received data are correlated with a reference before or after the displacement induced by the ARFI. The displacement amount as a function of time, position, emission angle, and / or emission frequency is determined. The maximum displacement amount, displacement as a function of depth, and / or displacement as a function of time are used to calculate the ARFI metric.

[0042] The same measurement can be performed at other times and / or locations. Results from repetitions can be used to derive another metric or can be averaged.

[0043] The transmission can be adapted, such as to the F-number, frequency, duration, power, and / or angle. The adaptation can be responsive to any metric, such as the magnitude of the maximum displacement.

[0044] Other measures can be used. Measure the tissue response to different types of waves and / or scans. Use one or more measures of the same type. For a given measure, perform a single instance, average, or distribution (e.g., standard deviation over time, duration, frequency, angle, and / or space). Any number of the same or different types of measures can be performed.

[0045] In action 34, an ultrasound scanner or other image processor estimates the tissue properties of the patient's tissue based on different metrics. Metrics from two or more different wave phenomena are used. The values ​​of two or more metrics are used to estimate tissue properties. For example, both a scattering metric and a shear wave propagation metric are used to estimate tissue properties. In another example, a metric of on-axis displacement (e.g., an ARFI metric) is used in conjunction with a scattering metric and / or a shear wave propagation metric.

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

[0047] Any tissue property can be estimated. For example, the fat fraction of a tissue can be estimated. The fat fraction in the liver, breast, or other tissues is diagnostically useful. The fat fraction in a patient's liver aids in the diagnosis of NAFLD. Other diagnostically useful tissue properties include inflammation, density, fibrosis, and / or nephron characteristics (count and / or diameter). Tissue properties are binary, such as present or absent, or estimates along a scale (i.e., the 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 based on the same or different measures.

[0048] Actions 36 and 37 represent two different embodiments for making an estimate in action 34. These different embodiments are alternatives. Other embodiments may be used. These two or more embodiments may be used, such as determining the value of the tissue property in two ways and then averaging the results or selecting the result most likely to be accurate.

[0049] Estimate the values ​​of the tissue properties. In the embodiment of action 36, a machine learning classifier estimates the tissue properties. The machine-trained classifier provides a non-linear model. Any machine learning method and the resulting machine learning classifier can be used. For example, support vector machines, probability 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, such as tens, hundreds, or thousands of samples, and a ground truth. The examples include input data to be used, such as values ​​for scattering and shear wave propagation parameters. The ground truth is the value of the tissue property for each example. In one embodiment, the machine learning learns to classify fat fractions based on scattering and shear wave propagation parameters. The ground truth for fat fractions is provided using magnetic resonance (MR) scans that provide proton density fat fraction (PDFF). MR-PDFF provides the percentage of fat at a location or region. The fat percentage is used as the ground truth, allowing the machine learning to classify the fat percentage based on the input values ​​of the ultrasound parameters. For a given tissue property, other ground truth sources can be used, such as those from biopsy, modeling, or other measurements.

[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 values ​​of tissue properties. The machine learns what weighted combination of input values ​​(e.g., convolutions using the learned kernels) indicates the output. The resulting machine learning classifier uses the input values ​​to extract discriminative information and then classifies tissue properties based on the extracted information.

[0052] The training provides one or more matrices. These matrices correlate the input information with the output class. Hierarchical training and the resulting classifiers can be used. Different classifiers can be used for different organizational properties. Multiple classifiers can be used for the same organizational property, and the results can be averaged or combined.

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

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

[0055] In some embodiments, two or more functions (e.g., a weighted combination of metrics) are provided. One function is selected based on the value of one of the parameters. For example, ultrasound-derived fat fraction (UDFF) estimation includes two functions, represented as a weighted combination: ,for ,for Where d and They are constants, a, b, c, , and These are the weights, and P is the measure of the parameter. A parameter... Used to determine which function to select. Possible functions include two or three additional parameters and weights. Additional, different, or fewer functions, parameters within functions, weights, and / or constants can be used. Different selection criteria can be used. The selection parameters can be of one type, and the weighting parameters for each function can be of another type. Alternatively, different types (e.g., scattering and shear wave propagation) are included as weighting parameters, regardless of the type of one or more parameters used for selection.

[0056] In one example, AC is the acoustic attenuation coefficient (e.g., a scattering parameter), BSC is the backscattering coefficient (e.g., a scattering parameter), and SS is the spectral slope of the logarithm of the frequency-dependent backscattering coefficient (e.g., also a scattering parameter). SWS is the shear wave velocity (e.g., a shear wave propagation parameter). Two functions based on the scattering parameters are used, where the function chosen for a given estimate is based on the shear wave propagation parameter, as expressed as: ,for ,for The weights and constants are based on minimizing the difference between the fat fraction and the result provided by MR-PDFF. Expert-selected or other weights and / or constants may be used.

[0057] In other embodiments, a single function is used, such as: Where a, b, and c are weights, and P is a measure of a parameter such as the backscattering coefficient.

[0058] In action 38, an ultrasound scanner or display device displays the estimated tissue parameters. For example, an image of the fat fraction is generated. A value representing the estimated fat fraction is displayed on a screen. Alternatively or additionally, a graph (e.g., a curve or icon) representing the estimated fat fraction is displayed. References to scales or other references may be displayed. In other embodiments, the fat fraction as a function of position is displayed in one-dimensional, two-dimensional, or three-dimensional representation using color, brightness, hue, luminance, or other modulation of the display value. Tissue properties may be mapped linearly or non-linearly to pixel colors.

[0059] Tissue properties are indicated individually or together with other information. For example, shear wave imaging is performed. Shear wave rate, modulus, or other information determined based on the tissue's response to shear waves is displayed. Any shear imaging method can be used. The displayed image represents shear wave information for the region of interest or the entire imaging area. For example, in the case where shear rate values ​​are determined for all grid points in the region of interest or field of view, the pixels on the display represent the shear wave rate of that area. The displayed grid may differ from the scan grid and / or the grid for which displacement is calculated.

[0060] Shear wave information is used for color overlay or other modulation of display values. Color, brightness, luminance, hue, or other display characteristics are modulated as a function of shear wave characteristics, such as shear wave rate. The image represents a two-dimensional or three-dimensional location area. The shear data is in a display format, or can be scanned and converted to a display format. The shear data is color or grayscale data, but can also be data before mapping using a grayscale or color scale. Information can be mapped to display values ​​linearly or non-linearly.

[0061] The image may include other data. For example, shear wave information may be displayed on or together with B-mode information. B-mode or other data representing tissue, fluid, or contrast agent in the same region may be included, such as B-mode data showing any location with a shear wave rate below a threshold or with poor quality. Other data assists the user in determining the location of 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.

[0062] Additional estimates of tissue properties are typically displayed simultaneously with shear wave, B-mode, color or flow mode, M-mode, contrast agent mode, and / or other imaging. Visual perception of the view is largely taken into account. Displaying two images sequentially at a sufficient frequency allows the observer to perceive that the images are being displayed simultaneously. Component measures used to estimate tissue properties can also be displayed, such as in tables.

[0063] Any format that can be displayed substantially simultaneously can be used. In one example, the shear wave or anatomical image is a two-dimensional image. The values ​​of the tissue properties are text, charts, two-dimensional images, or other indicators of estimated values. A cursor or other location can be positioned relative to the shear wave or anatomical image. The cursor indicates the location selection. For example, the user selects pixels associated with an internal region of a lesion, cyst, inclusion, or other structure. The tissue properties at the selected location are then displayed as values, pointers along the scale, or other indicators. In another example, the tissue properties are indicated in a region of interest (a sub-section of the field of view) or across the entire field of view.

[0064] In another embodiment, shear wave or B-mode and fat fraction images are displayed substantially simultaneously. For example, a dual-screen display is used. A shear wave image (e.g., shear wave rate) and / or a B-mode image are displayed in one area of ​​the screen. The fat fraction, as a function of location, is displayed in another area of ​​the screen. The user can view different images on the screen for diagnostic purposes. Additional information or indications regarding tissue properties aid the user in diagnosing the areas.

[0065] In one embodiment, tissue estimates are provided as real-time digital or quantitative images. Because tissue parameters can be estimated rapidly, their values ​​are estimated and output within 1-3 seconds of the scan's commencement. Tissue properties can be estimated at different times, such as before, during, and / or after treatment. Estimates from different times are used to monitor disease progression and / or response to therapy. For example, the percentage change in tissue property values ​​over time is calculated and output.

[0066] Figure 2An embodiment of a system 10 for estimating tissue properties based on metrics responding to different types of waves is shown. System 10 is implemented... Figure 1 The system 10 includes a transmit beamformer 12, a transducer 14, a receive beamformer 16, an image processor 18, a display 20, and a memory 22. Additional, different, or fewer components may be provided. For example, user input is provided for user interaction with the system.

[0067] System 10 is a medical diagnostic ultrasound imaging system. In alternative embodiments, system 10 is a personal computer, workstation, PACS station, or other arrangement located in the same location or distributed over a network for real-time or post-acquisition imaging.

[0068] Transmitting and receiving beamformers 12 and 16 form a beamformer for transmitting and receiving using transducer 14. Based on the operation or configuration of the beamformer, a sequence of pulses is transmitted and a response is received. The beamformer is scanned for measuring scattered, sheared wave, and / or ARFI parameters.

[0069] Transmit beamformer 12 is an ultrasonic transmitter, memory, pulse generator, analog circuitry, digital circuitry, or a combination thereof. Transmit beamformer 12 is operable to generate waveforms for multiple channels with different or relative amplitudes, delays, and / or phases. One or more beams are formed when acoustic waves are emitted from transducer 14 in response to the generated electrical waveforms. A sequence of transmit beams is generated to scan a two-dimensional or three-dimensional region. Sector, vector®, linear, or other scan formats can be used. The same region can be scanned multiple times using different scan line angles, F-numbers, and / or waveform center frequencies. For flow or Doppler imaging and for shearing imaging, a scan sequence 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. For shearing imaging, scan or frame interleaving (i.e., scanning the entire region before rescanning) can be used. Line or group-of-lines interleaving can be used. In alternative embodiments, transmit beamformer 12 generates plane waves or divergent waves for more rapid scanning.

[0070] The same transmit beamformer 12 generates a pulsed excitation or electrical waveform to generate acoustic energy to induce displacement. This generates an electrical waveform that produces an acoustic radiating force pulse. In an alternative embodiment, a different transmit beamformer is provided to generate the pulsed excitation. The transmit beamformer 12 causes the transducer 14 to generate a driving pulse or an acoustic radiating force pulse.

[0071] Transducer 14 is an array used to generate acoustic energy based on an electrical waveform. For the array, the acoustic energy is focused with a relative delay. A given emission event corresponds to different elements emitting acoustic energy at substantially the same time, given a given delay. The emission event can provide an ultrasound energy pulse for displacing tissue. The pulse can be a pulse excitation, a tracking pulse, a B-mode pulse, or a pulse for other measurements. A pulse excitation includes a waveform with many cycles (e.g., 500 cycles), but it occurs in a relatively short time, thus causing tissue displacement over a longer period. A tracking pulse can be a B-mode emission, such as using 1-5 cycles. The tracking pulse is used to scan areas of the patient.

[0072] Transducer 14 is a 1-dimensional, 1.25-dimensional, 1.5-dimensional, 1.75-dimensional, or 2-dimensional array of piezoelectric or capacitive film elements. Transducer 14 includes multiple elements for converting acoustic energy and electrical energy. The received signal is generated in response to ultrasonic energy (echoes) impacting the elements of transducer 14. The elements are connected to the channels of transmitting and receiving beamformers 12, 16. Alternatively, a single element with a mechanical focus is used.

[0073] The receiver beamformer 16 includes multiple channels having amplifiers, delayers, and / or phase rotators, as well as one or more adders. Each channel is connected to one or more transducer elements. The receiver beamformer 16 is configured, either in hardware or software, to apply relative delays, phases, and / or apodization to form one or more receiver beams in response to each imaging or tracking emission. No receiving operation may occur for echoes from pulsed excitations used to displace tissue. The receiver beamformer 16 outputs data representing spatial location using the received signals. The relative delays and / or phase summation of signals from different elements provide beamforming. In an alternative embodiment, the receiver beamformer 16 is a processor for generating samples using Fourier or other transforms.

[0074] The receiving beamformer 16 may include filters, such as filters for isolating second harmonics or information at other frequency bands relative to the transmitting frequency band. Such information may more likely include desired tissue, contrast agent, and / or flow information. In another embodiment, the receiving beamformer 16 includes a memory or buffer and filters or adders. Two or more receiving beams are combined to isolate information at desired frequency bands, such as second harmonics, cubic fundamentals, or another band.

[0075] In cooperation with the transmitting beamformer 12, the receiving beamformer 16 generates data representing the region. To track shear waves or axial longitudinal waves, data representing the region at different times is generated. After acoustic pulse excitation, the receiving beamformer 16 generates a beam representing the position along one or more lines at different times. Data (e.g., samples of beamforming) is generated by ultrasonically scanning the region of interest. By repeated scanning, ultrasonic data representing the region at different times after pulse excitation is acquired.

[0076] The beamformer 16 receives beamsum data representing spatial location from its output. Data on individual locations, along lines, areas, or volumes are output. Dynamic focusing can be provided. The data can be used for different purposes. For example, a scan performed for B-mode or organization data differs from a scan performed for displacement. Alternatively, B-mode data can also be used to determine displacement. As another example, a series of shared scans can be used to acquire data for different types of measurements, and B-mode or Doppler scans can be performed separately or using some of the same data.

[0077] Image processor 18 is a device for detecting and processing information for display from beamforming ultrasound samples, including a mode-B detector, a Doppler detector, a pulse-wave Doppler detector, a correlation processor, a Fourier transform processor, an application-specific integrated circuit (ASIC), a general-purpose processor, a control processor, an image processor, a field-programmable gate array (FPGA), a digital signal processor (DSP), analog circuitry, digital circuitry, combinations thereof, or other devices now known or hereafter developed. In one embodiment, 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 DSP, an ASIC, a DSP, a network, a server, a processor group, a data path, combinations thereof, or other devices now known or hereafter developed for calculating different types of parameter values ​​and / or estimating tissue properties based on values ​​from different types of metrics, from beamforming and / or detected ultrasound data. For example, the separate image processor is configured by hardware, firmware, and / or software to perform... Figure 1 Any combination of one or more of the actions shown.

[0078] Image processor 18 is configured to estimate values ​​of tissue properties based on combinations of different types of parameters. For example, measured scattering parameters and measured shear wave parameters are used. Different types of parameters are measured based on the transmission and reception sequences and calculations based on the results. Values ​​of one or more measures of each of at least two of the types (e.g., scattering, shear wave propagation, or axial ARFI) are determined.

[0079] Image processor 18 estimates tissue properties based on different types of parameters or measures of tissue response to different types of wavefronts. The estimation applies a machine learning classifier. The learned matrix outputs values ​​of the tissue properties using input values ​​with or without other information, based on the measures. In other embodiments, image processor 18 uses a weighted combination 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 uses the values ​​of the same and / or different parameters to determine the values ​​of the tissue properties. Linear or nonlinear mappings correlate the values ​​of one or more parameters with the values ​​of the tissue properties. For example, a shear wave propagation selection function is used to determine the values ​​of the tissue properties using two or more scattering parameters.

[0080] Processor 18 is configured to generate one or more images. For example, it may generate shear wave rate, B-mode, contrast agent, M-mode, flow or color mode, ARFI, and / or another type of image. Shear wave rate, flow, or ARFI images may be presented individually or as overlays or regions of interest within a B-mode image. The shear wave rate, flow, or ARFI data modulates the color at locations within the region of interest. If the shear wave rate, flow, or ARFI data is below a threshold, B-mode information may be displayed without modulation by the shear wave rate.

[0081] Other information is included in the image or displayed sequentially or substantially simultaneously. For example, an image estimating tissue properties is displayed simultaneously with another image. Values ​​of one or more tissue properties are mapped to the displayed information. In cases where tissue properties are measured at different locations, the values ​​of the tissue properties can be generated as a color overlay in the region of interest (ROI) of a B-mode image. Shear wave velocity and tissue property data can be combined into a single overlay on a B-mode image. Alternatively, the values ​​of the tissue properties are displayed as text or numerical values ​​adjacent to or overlaid on the B-mode or shear wave imaging image. Image processor 18 can be configured to generate additional displays. For example, a shear wave velocity image is displayed alongside charts, text, or graphical indicators of the tissue properties, such as fat fraction and / or degree of fibrosis. Tissue property information is presented for one or more locations within the ROI, rather than in a separate two-dimensional or three-dimensional representation, such as where the user selects a location and then the ultrasound scanner presents the tissue properties at that location.

[0082] Image processor 18 operates according to instructions stored in memory 22 or another memory to estimate tissue properties based on measures of tissue response to different types of waves, such as scattering from emitted ultrasound, on-axis tissue displacement, and / or shear waves caused by tissue displacement. Memory 22 is a non-transitory computer-readable storage medium. Instructions for implementing the processes, methods, and / or techniques discussed herein are provided on a computer-readable storage medium or memory, such as a cache, buffer, RAM, removable media, hard disk drive, or other computer-readable storage medium. Computer-readable storage media include various types of volatile and non-volatile storage media. Functions, actions, or tasks illustrated in the figures or described herein are performed in response to one or more sets of instructions stored in or on a computer-readable storage medium. The functions, actions, or tasks are independent of a particular type of instruction set, storage medium, processor, or processing strategy and can be operated individually or in combination by software, hardware, integrated circuits, firmware, microcode, and the like. Similarly, processing strategies can include multiprocessing, multitasking, parallel processing, and the like. In one embodiment, instructions are stored on a removable media device for reading by a local or remote system. In other embodiments, the instructions are stored at a remote location for transmission over a computer network or telephone line. In yet another embodiment, the instructions are stored within a given computer, CPU, GPU, or system.

[0083] Display 20 is a device such as a CRT, LCD, projector, plasma display screen, or other display used to display one-dimensional or two-dimensional images or three-dimensional representations. A two-dimensional image represents the spatial distribution within a region. A three-dimensional representation is rendered based on data representing the spatial distribution within a volume. Display 20 is configured by image processor 18 or other devices by inputting signals to be displayed as images. Display 20 displays an image representing the tissue properties of a single location within the region of interest (e.g., averaged from tissue property estimates including neighboring locations), or an entire image. For example, display 20 displays the value of a fat fraction. Displaying tissue properties based on different types of waves provides more accurate tissue property information for diagnosis.

[0084] Figure 3 A method for estimating tissue properties using an ultrasound scanner or system is illustrated. The tissue nonlinear response is measured using ultrasound. Tissue nonlinearity provides insight into tissue properties. As fat accumulates in the liver, its nonlinearity increases, resulting in a greater conversion of ultrasound energy from the fundamental frequency region to the harmonics. As a result, the backscattered signal in the fundamental region weakens, while those in the harmonic region are enhanced. The nonlinearity of the tissue response, alone or in combination with other measurements or information, is used to estimate fat fraction and / or another tissue property.

[0085] Below Figure 3 The description will no longer repeat the target Figure 1 and Figure 2 The description of the measurements and / or the acquisition of different measurements is given. The description of the estimation of tissue properties will not be repeated below. For Figure 3 In action 31, one or more measurements of the tissue nonlinear response are performed, and regardless of the model used, the estimates are incorporated into the tissue nonlinear response or data derived therefrom as input for estimating tissue properties. In the example below, fat fraction is used as a tissue property, but other tissue properties may be estimated alternatively or additionally.

[0086] Figure 3 The method is by Figure 2 This is implemented using a system or different systems. Medical diagnostic ultrasound scanners perform measurements by generating acoustic waves and measuring the response. An image processor in the scanner, computer, server, or other device makes estimates based on these measurements. A display device, network, or memory is used to output the estimated tissue properties.

[0087] Additional, different, or fewer actions may be provided. For example, actions 30, 32, 33, and / or 38 may not be provided. As another example, actions 36 and 37 are alternatives, or may be used together, such as averaging results from both. In yet another example, actions for configuring the ultrasound scanner and / or scanning are provided.

[0088] The actions are performed in the order described or shown (e.g., from top to bottom or numerically), but may also be performed in other orders. For example, actions 30, 31, 32, and 33 may be performed simultaneously, such as using the same transmit and receive pulses, or in any order.

[0089] In actions 30, 32, and 33, the ultrasound scanner generates one or more measures of scattering, shear wave propagation, and / or axial displacement in the tissue based on scans of the patient performed by the ultrasound scanner. For example, attenuation coefficient, scattering coefficient, and / or shear wave or sound velocity are measured. Values ​​can be determined for one or more scattering parameters of the tissue. Values ​​can be determined for one or more shear wave parameters of the tissue. Values ​​can be determined for one or more axial displacement parameters.

[0090] The above can be generated Figure 1 The measurement and value of any of the corresponding parameters discussed in the text. Measurements and values ​​of some or none of the corresponding parameters may be generated.

[0091] In action 31, the ultrasound scanner generates one or more measures of the tissue's nonlinear response. The ultrasound scanner scans the patient's tissues, such as liver tissue. The ultrasound scanner determines the tissue nonlinearity based on the scan.

[0092] The nonlinear response of a tissue is determined by the echoes generated in response to ultrasound emission at different power levels. The tissue response at a location, line, region, and / or volume can be measured by emitting at different power levels into the location, line, region, and / or volume.

[0093] Any number of emitters and corresponding power levels can be used. For example, three, five, ten, twenty, or more power levels can be used. The range of power levels is set by the ultrasound scanner. For example, one emitter may be at the maximum power allowed by safety, hardware, and / or other limitations. Other emitters may be at lower power levels. The different power levels are linearly stepped, such as 10%, 20%, 30%, 40%, 50%, 60%, 70%, 80%, 90%, and 100% of the maximum power. Variations in step size and / or step size can be used across the power level set.

[0094] Backscattered signals emitted from varying power levels are received by an ultrasound scanner. Echoes from the tissue are also received. These echoes have different amplitudes based on various factors, including the tissue's response to the emitted power. The received signals are analyzed after beamforming and before detection to estimate the tissue's nonlinear response. The processor analyzes the backscattered signals emitted at varying power levels to estimate the tissue's nonlinear response.

[0095] The variation in the response to emission at different power levels as a function of the emitted power indicates organizational nonlinearity. Any measure of the variation in the response as a function of the emitted power can be used.

[0096] In one approach, the processor determines nonlinear coefficients of the response as a function of different transmit powers. The nonlinearity of the response is characterized by these nonlinear coefficients. Any nonlinear coefficient now known or developed later can be used for determination. For example, the B / A ratio of the Taylor series expansion of nonlinear acoustics has been discovered. Variations in the received signal level of the fundamental (transmit frequency) and / or harmonic information are analyzed. The Finite Amplitude Interpolation Substitution (FAIS) method can be used, which takes into account the effects of both sound attenuation of the sample and transducer diffraction on the measurement. An Improved Thermodynamic Method (ITO) based on the measurement of phase shift in the sound wave due to changes in ambient pressure can be used. Any of the measurements described in U.S. Patent No. 4,664,123 can be used.

[0097] In another approach, the values ​​of the nonlinear coefficients are not calculated. Instead, a dataset or curve(s) fitted to the received amplitude is used as a function of the transmitted power at one or more frequencies (e.g., fundamental and second harmonic). This dataset or curve(s) characterizes the organization's nonlinear response. The dataset (e.g., a table of received amplitudes as a function of transmitted power) or the curve fitted to it is input into a model used to estimate the fat fraction.

[0098] The representation is specific to one or more locations. Representations for different locations can be maintained separately for estimation at those locations. Alternatively, low-pass filtering or other combinations are used to determine values ​​based on information at different locations. The representations or the resulting estimates are averaged or combined. Alternatively, different representations are used as different inputs for estimation.

[0099] The organization's nonlinear response varies as a function of frequency. For the fundamental (emission) frequency and harmonic frequencies (e.g., the second harmonic), the nonlinearity occurs in reverse. The organization's nonlinear response can be characterized separately for two or more frequency bands. These characterizations and / or the estimates derived from them can be combined. Alternatively, different characterizations can be used as different inputs for estimation.

[0100] In action 34, the processor estimates the fat fraction and / or other tissue properties of the patient's tissue. This estimation uses a machine learning model or classifier from action 36 and / or a linear model from action 37.

[0101] The input to the linear model or machine learning model used for estimation includes one or more values ​​characterizing the tissue's nonlinear response. For example, the values ​​of the nonlinear coefficients are input. As another example, a dataset or fitted curve is input to an ultrasound-derived fat fraction or tissue property model. The tissue nonlinear response can be used to select a function for estimating the fat fraction and / or used as a variable in the estimation.

[0102] Other inputs may be used. For example, one or more measures of scattering, longitudinal wave response, and / or shear wave propagation, as well as one or more measures of tissue nonlinear response, may be entered. In another example, non-ultrasound data (e.g., clinical data) may also be entered. In yet another example, attenuation coefficient, backscattering coefficient, and / or sound velocity may be entered along with measurements of tissue nonlinear response.

[0103] In response to input to the model, the processor estimates the fat fraction and / or another tissue property. In response to said input, it outputs values ​​for the fat fraction and / or another tissue property. The processor uses the model to determine the fat fraction or another tissue property. This information can enhance the accuracy of the fat fraction estimation when combined with one or more measurements of tissue nonlinearity (such as attenuation coefficient, backscattering coefficient, and sound velocity).

[0104] In action 38, the estimated tissue properties (e.g., a value for fat fraction) are displayed. For example, an ultrasound image of the patient (e.g., a B-mode image) is displayed. Annotations are provided on or near the image. The annotations indicate the estimated tissue properties at selected locations or regions of interest. This can be used for the purposes described above. Figure 1 The description refers to any of the displays.

[0105] refer to Figure 2 The transmit beamformer 12 is configured to scan for measuring tissue nonlinear response. The transmit and received signals are used for other measurements and / or used solely for measuring tissue nonlinear response. The transmit beamformer 12 and receive beamformer 16 provide ultrasound data in response to transmit pulses at different power levels.

[0106] Image processor 18 is configured to determine tissue nonlinear response based on ultrasound data. Image processor 18 characterizes tissue nonlinear response by calculating nonlinear coefficients, collecting a dataset of ultrasound data, or fitting a curve.

[0107] Image processor 18 is configured to estimate fat fraction and / or another tissue property based on tissue nonlinear response. The model implemented by image processor 18 outputs the tissue property in response to input of the measured characteristics of the tissue nonlinear response. Image processor 18 can also use different measurements, such as scattering parameters, shear wave parameters, and tissue nonlinear response, to estimate tissue properties (e.g., fat fraction).

[0108] Display 20 shows values ​​for tissue properties. For example, the patient's estimated fat fraction is displayed. The fat fraction or other tissue property values ​​can be more accurate due to the use of tissue nonlinear responses, such as those measured by ultrasound, in the estimation.

[0109] The following is a list of non-limiting illustrative embodiments disclosed herein. Illustrative embodiments of a set or type (e.g., method or system) may be provided or in combination with illustrative embodiments of other sets or types.

[0110] Illustrative Example 1. A method for estimating fat fraction using an ultrasound scanner, the method comprising: generating one or more measures of scattered and / or shear wave propagation in tissues from a scan of a patient by an ultrasound scanner; generating a measure of tissue nonlinear response of tissues from a scan of a patient by an ultrasound scanner; estimating the fat fraction of the patient's tissues according to: (1) the one or more measures of scattered and / or shear wave propagation and (2) a measure of tissue nonlinear response; and outputting an ultrasound image including an indication of the estimated fat fraction.

[0111] Illustrative Example 2. According to the method of Illustrative Example 1, wherein the scan generates separate transmit and receive events, including one or more metrics for scattering and / or shear wave propagation and a metric for the organization's nonlinear response.

[0112] Illustrative Example 3. The method according to any one of Illustrative Examples 1-2, wherein the one or more measures of generating scattering and / or shear wave propagation include a measure of generating frequency-dependent acoustic attenuation coefficient, frequency-dependent backscattering coefficient, sound speed, or a combination thereof.

[0113] Illustrative Example 4. The method according to any one of Illustrative Examples 1-3, wherein the one or more measures of generating scattering and / or shear wave propagation include generating shear wave velocity.

[0114] Illustrative Example 5. The method according to any one of Illustrative Examples 1-4, wherein generating a measure of the tissue nonlinear response includes emitting ultrasound at different powers and characterizing the variation in the response to the emission as a function of the different powers.

[0115] Illustrative Example 6. The method according to Illustrative Example 5, wherein characterization includes determining nonlinear coefficients of the response as a function of different powers.

[0116] Illustrative Example 7. The method according to Illustrative Example 5, wherein characterization includes generating curves or datasets of responses as functions of different powers.

[0117] Illustrative Example 8. According to the method of Illustrative Example 5, the characterization includes characterizing the response as an echo in the fundamental and / or harmonic frequencies of the emitted ultrasound.

[0118] Illustrative Example 9. The method according to Illustrative Example 5, wherein emitting ultrasound at different power levels includes emitting ultrasound at at least five different power levels.

[0119] Illustrative Example 10. The method according to any one of Illustrative Examples 1-9, wherein the estimation includes estimation using a classifier derived from machine learning.

[0120] Illustrative Example 11. The method according to any one of Illustrative Examples 1-9, wherein the estimation includes estimation using a linear model.

[0121] Illustrative Example 12. The method according to any one of Illustrative Examples 1-11, wherein the estimation includes estimating the nonlinear response of the tissue, wherein the measure of the nonlinear response includes the nonlinear coefficients input to the fat fraction model.

[0122] Illustrative Example 13. The method according to any one of Illustrative Examples 1-12, wherein the estimation includes estimating the measure of the tissue's nonlinear response, which includes a curve input to the fat fraction model.

[0123] Illustrative Example 14. The method according to any one of Illustrative Examples 1-13 further includes generating a measure of the axial displacement of the tissue, and wherein the estimation includes estimation as a function of the measure of the axial displacement.

[0124] Illustrative Example 15. A system for estimating fat fraction, the system comprising: a transducer; a beamformer configured to emit pulses at different power levels within a patient and to receive ultrasound data in response to the pulses using the transducer; an image processor configured to determine a tissue nonlinear response based on the ultrasound data and to estimate the fat fraction based on the tissue nonlinear response; and a display configured to display a value of the fat fraction.

[0125] Illustrative Example 16. The system according to Illustrative Example 15, wherein the image processor is configured to estimate fat fraction using a machine learning classifier.

[0126] Illustrative Example 17. The system according to any one of Illustrative Examples 15-16, wherein the image processor is configured to estimate fat fraction based on tissue nonlinear response and scattering parameters and / or shear wave parameters.

[0127] Illustrative Example 18. The system according to Illustrative Example 17, wherein the image processor is configured to determine the nonlinear response of the tissue as nonlinear coefficients.

[0128] Illustrative Example 19. A method for estimating tissue properties using an ultrasound system, the method comprising: determining multiple scattering parameters of the tissue by the ultrasound system; determining multiple shear wave parameters of the tissue by the ultrasound system; determining the nonlinear response of the tissue by the ultrasound system; estimating tissue properties based on the scattering parameters, shear wave parameters, and nonlinear response; and displaying the tissue properties.

[0129] Illustrative Example 20. According to the method of Illustrative Example 19, determining the nonlinear response includes determining it based on the acoustic echoes in response to different transmission powers.

[0130] While the invention has been described above with reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the invention. Therefore, it is intended that the foregoing detailed description be regarded as illustrative rather than restrictive, and it should be understood that the following claims, including all equivalents, are intended to define the spirit and scope of the invention.

Claims

1. A method for estimating fat fraction using an ultrasound scanner, the method comprising: One or more measures of the propagation of scattered and / or shear waves in the tissue are generated by an ultrasound scanner scan of the patient; Based on a measure of the tissue's nonlinear response generated by an ultrasound scanner scanning a patient; The processor estimates the fat fraction of the patient's tissue based on: (1) one or more measures of scattering and / or shear wave propagation and (2) a measure of the tissue's nonlinear response; as well as The output includes ultrasound images indicating the estimated fat fraction.

2. The method of claim 1, wherein the measurement generated according to the scan includes separate transmission and reception events for one or more measures of scattering and / or shear wave propagation and a measure of the organization's nonlinear response.

3. The method of claim 1, wherein the one or more measures of generating scattering and / or shear wave propagation include measures of generating frequency-dependent acoustic attenuation coefficient, frequency-dependent backscattering coefficient, sound speed, or combinations thereof.

4. The method of claim 1, wherein the one or more measures of generating scattering and / or shear wave propagation include generating shear wave velocity.

5. The method of claim 1, wherein generating a measure of the tissue's nonlinear response comprises emitting ultrasound at different powers and characterizing the variation in the response to the emission as a function of the different powers.

6. The method of claim 5, wherein characterization includes determining nonlinear coefficients of the response as a function of different powers.

7. The method of claim 5, wherein characterization includes generating curves or datasets of responses as a function of different powers.

8. The method of claim 5, wherein emitting ultrasound at different power levels comprises emitting ultrasound at at least five different power levels.

9. The method of claim 1, wherein the metric for generating a tissue nonlinear response comprises characterizing the response as an echo in the fundamental and / or harmonic frequencies of the emitted ultrasound.

10. The method of claim 1, wherein the estimation includes estimating using a machine learning classifier.

11. The method of claim 1, wherein the estimation comprises using a linear model.

12. The method of claim 1, wherein the estimation includes estimating the nonlinear response of the tissue, wherein the measure of the nonlinear response includes the nonlinear coefficients input to the fat fraction model.

13. The method of claim 1, wherein the estimation includes estimating the tissue's nonlinear response in the presence of a curve provided to a fat fraction model.

14. The method of claim 1, further comprising generating a measure of the axial displacement of the tissue, wherein the estimation comprises estimating as a function of the measure of the axial displacement.

15. A system for estimating body fat percentage, the system comprising: Transducer; A beamformer is configured to emit pulses at different power levels within the patient's body and to receive ultrasound data in response to the pulses using a transducer. An image processor is configured to determine tissue nonlinear response based on ultrasound data and to estimate fat fraction based on tissue nonlinear response. as well as The display is configured to show the value of the fat fraction.

16. The system of claim 15, wherein the image processor is configured to use a machine learning classifier to estimate the fat score.

17. The system of claim 15, wherein the image processor is configured to estimate fat fraction based on tissue nonlinear response and scattering parameters and / or shear wave parameters.

18. The system of claim 17, wherein the image processor is configured to determine the nonlinear response of the tissue as nonlinear coefficients.

19. A method for estimating tissue properties using an ultrasound system, the method comprising: Multiple scattering parameters of the tissue are determined by the ultrasound system; Multiple shear wave parameters of the tissue are determined by the ultrasound system; The nonlinear response of tissues is determined by an ultrasound system; Tissue properties are estimated based on scattering parameters, shear wave parameters, and nonlinear response; as well as This indicates the nature of the organization.

20. The method of claim 19, wherein determining the nonlinear response comprises determining it based on acoustic echoes in response to different transmit powers.

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