Estimation of liver disease activity using ultrasonic medical imaging

Abstract

Estimation of liver disease activity using ultrasound medical imaging. An ultrasound-based estimation (40) of disease activity is provided, such as an estimation of the NAS of NAFLD or other activity indices of liver disease. Ultrasound measurements (30, 32) are made of acoustic scattering and shear wave propagation parameters, such as measuring the acoustic backscatter coefficient, shear wave speed, and shear wave damping ratio. A score of disease activity is determined (40) from these scattering and shear wave propagation parameters. In scoring the activity of a disease such as NAFLD, a doctor can get the assistance of relatively inexpensive and rapid ultrasound. Ultrasound imaging (42) is more readily available and less expensive than MRI. Ultrasound is non-invasive.

Description

[0001] Related Applications

[0002] This patent document is a partial continuation application of U.S. Patent Application Serial No. 15 / 716,444, filed on September 26, 2017, which claims the benefit of the filing date under 35 U.S.C. § 119(e) of Provisional U.S. Patent Application Serial No. 62 / 482,606, filed on April 6, 2017. The two applications are incorporated herein by reference. Technical Field

[0003] This embodiment relates to ultrasonic imaging. Ultrasound is used to measure disease-related activity in tissues such as the liver. Background Art

[0004] 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 accumulation of liver fat and liver fibrosis. The fat fraction can be measured as an indicator of NAFLD. The fat fraction and / or other tissue properties (e.g., the degree of fibrosis) in the liver or other tissues such as breast tissue provide diagnostically useful information.

[0005] More than 25% of patients with NAFLD develop non-alcoholic steatohepatitis (NASH). NASH can progress to cirrhosis and hepatocellular carcinoma. The NAFLD activity score (NAS) is used to diagnose and monitor changes or levels of NASH. NAS is provided from histological evaluation of liver biopsy and is calculated as the unweighted sum of the observed steatosis, lobular inflammation, and ballooning scores.

[0006] Magnetic resonance imaging (MRI) can measure the proton density fat fraction (PDFF) as a biomarker of liver fat content. MRI can be used to further estimate NAS. However, MRI is not widely available and is expensive. Summary of the Invention

[0007] As an introduction, the preferred embodiments described below include methods, instructions, and systems for ultrasound-based estimation of disease activity, such as NAS or other activity indices for NAFLD. Ultrasound measures acoustic scattering and shear wave propagation parameters, such as measuring the acoustic backscatter coefficient, shear wave speed, and shear wave damping ratio. A score of disease activity is determined from these scattering and shear wave propagation parameters. In scoring the activity of diseases such as NAFLD, physicians can obtain the assistance of relatively inexpensive and rapid ultrasound compared to biopsy- or MRI-based scoring. Ultrasound is non-invasive and is more readily available and less expensive than MRI.

[0008] In a first aspect, a method for non-alcoholic liver disease activity estimation using an ultrasound scanner is provided. A first measurement of scatter in tissue is generated from a scan of a patient by the ultrasound scanner. The first measurement of scatter is the backscatter coefficient. Second and third measurements of shear wave propagation in the tissue are generated from a scan of the patient by the ultrasound scanner. The second measurement is the shear wave speed, and the third measurement is the shear wave damping ratio. A first value of an ultrasound-derived liver disease activity index is estimated from the backscatter coefficient, the shear wave speed, and the shear wave damping ratio. An ultrasound image is output, the ultrasound image including an indication of the first value of the estimated ultrasound-derived liver disease activity index.

[0009] In a second aspect, a system for disease activity estimation is provided. A beamformer is configured to transmit and receive a sequence of pulses within a patient using a transducer. The sequence of pulses is used for a scatter parameter and for first and second shear wave parameters. An image processor is configured to generate a score of an index of disease activity from a combination of the scatter parameter, the first shear wave parameter, and the second shear wave parameter. A display is configured to display the score of the index of disease activity.

[0010] In a third aspect, a method for liver disease activity estimation using an ultrasound system is provided. The ultrasound system determines a plurality of scatter parameters of a patient's liver tissue. The ultrasound system determines a plurality of shear wave parameters of the patient's liver tissue. A fat component is estimated from at least one of the scatter parameters. A level of liver disease activity is estimated from at least one of the fat component and the shear wave parameters. The level of the liver disease is displayed.

[0011] The invention is defined by the following claims, and nothing in this section should be taken as a limitation on those claims. Further aspects and advantages of the invention are discussed below in connection with the preferred embodiments, and may be claimed independently or in combination later. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] The components and the drawings are not necessarily to scale, but rather the emphasis is placed upon illustrating the principles of the invention. In addition, in the drawings, like reference numerals designate corresponding parts throughout the different views.

[0013] Figure 1 is a flow chart of an embodiment of a method for estimating tissue properties using ultrasound;

[0014] Figure 2 is a block diagram of an embodiment of a system for estimating tissue properties using ultrasound;

[0015] Figure 3 is a flow chart of an embodiment of a method for estimating disease activity such as NAS using ultrasound; and

[0016] Figure 4 It is a graph showing the accuracy in predicting NAS from ultrasonic measurements as compared with histological NAS. Detailed Description

[0017] Estimating disease activity is helpful for diagnosis, screening, monitoring, and / or predicting health conditions. For example, estimating NAS or other liver disease activities. Using the score of an ultrasonic estimation index allows for a rapid, inexpensive, and non-invasive estimation of disease activity. Estimating the ultrasound-derived NAFLD activity score avoids biopsy or MRI.

[0018] Disease activity can be estimated from measurements used to estimate tissue properties such as liver fat component and / or the estimation of liver fat component. The measurement and estimation of fat component or other tissue properties are discussed below with reference to Figure 1 These measurements and / or tissue properties are then discussed with reference to Figure 3 for discussion.

[0019] Regarding tissue property estimation, quantitative ultrasound (QUS) is used for screening, diagnosing, monitoring, and / or predicting health conditions. Multiple QUS parameters can be used to measure the complexity of human tissue in order to accurately characterize the tissue. For example, a multi-parameter method is used to estimate the liver fat component, which combines quantitative parameters extracted from the received signals of different wave phenomena such as the scattering and attenuation of longitudinal waves, the propagation and attenuation of shear waves, and / or the propagation and attenuation of on-axis waves from acoustic radiation force impulse (ARFI).

[0020] In one embodiment, tissue properties (e.g., liver fat component) are estimated by transmitting and receiving a sequence of pulses to estimate scattering parameters, and by transmitting and receiving a sequence of pulses to obtain shear wave parameters. The estimation may also include transmitting and receiving a sequence of pulses to estimate parameters based on the axial displacement caused by acoustic radiation force impulse (ARFI). The QUS parameters are estimated and combined to estimate tissue properties. Other information such as non-ultrasonic data (e.g., blood biomarkers) may be included in the estimation of tissue properties.

[0021] Figure 1 A method for tissue property estimation using an ultrasonic scanner or system is shown. The tissue responses to different types of waves or wave phenomena are measured. The combination of these different response measurements is used to estimate tissue properties.

[0022] The method is performed by Figure 2by a system or a different system. A medical diagnostic ultrasound scanner performs measurements by acoustically generating waves and measuring responses. An image processor of the scanner, computer, server, or other device estimates based on the measurements. A display device, network, or memory is used to output the estimated tissue properties.

[0023] Additional, different, or fewer actions may be provided. For example, actions 33 and / or 38 are not provided. As another example, actions 36 and 37 are alternative or may be used together, such as averaging the results from both. In another example, actions for configuring the ultrasound scanner and / or the scan are provided.

[0024] The actions are performed in the described or shown order (e.g., 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.

[0025] In action 30, the ultrasound scanner generates a measurement of the scatter in tissue from a scan of a patient. The measurement of scatter measures the tissue response to the longitudinal waves transmitted from the ultrasound scanner. The scatter or echo of the longitudinal waves impinging on the tissue is measured.

[0026] Any measurement of scatter may be used. Example scatter parameters include speed of sound, sound dispersion, angular scatter coefficient (e.g., backscatter coefficient), frequency-dependent attenuation coefficient, attenuation coefficient slope, spectral slope of the normalized log-spectrum, spectral intercept of the normalized log-spectrum, spectral mid-band of the normalized log-spectrum, effective scatterer diameter, acoustic concentration, scatterer number density, mean scatterer spacing, nonlinear parameter (B / A), and / or ratio of coherent to incoherent scatter.

[0027] More than one measurement may be performed. For example, the ultrasound system determines values of two or more scatter parameters of the patient tissue. In one embodiment, the spectral slope of the logarithm of the acoustic attenuation coefficient, backscatter coefficient, and / or frequency-dependent backscatter coefficient is measured.

[0028] To measure scattering, an ultrasound scanner scans tissue with ultrasound. A sequence of transmit and receive events is performed to acquire signals to estimate quantitative ultrasound scattering parameters. In one embodiment, a one-dimensional, two-dimensional, or three-dimensional region is scanned in a B-mode sequence (e.g., transmit a broadband (e.g., 1-2 cycles) transmit beam and form one or more response receive beams). Any scan format can be used, such as linear, sector, or vector. The transmit and receive operations can be repeated for each scan line. Narrowband pulses (e.g., 3 or more cycles) can be transmitted and received at different center frequencies with or without overlapping spectra. The narrowband transmit pulses can be used in single or multiple transmit and receive events. The transmit pulse and the corresponding receive beam can be formed at different steering angles, such as sampling the same location in the tissue from different directions. Different steering can be performed only for transmission or only for reception. Different transmit beams can have different transmit powers and / or F-numbers. A single transmit or multiple transmits can be focused, unfocused, or use plane waves. Any scan sequence can be used.

[0029] Repetition with or without different transmit and / or receive settings can be used to measure scattering once or to measure scattering differently. In cases where multiple measurements are provided for the same location with the same scattering parameter, the measurements can be averaged or combined. Measurements from different locations can be averaged, such as adjacent locations or locations within a given range. For example, the measurement of scattering is an average of frequency-dependent measurements from multiple transmits to the same location. The change in the power spectrum as a function of depth, angle, and / or frequency can be measured. As another example, estimates of the attenuation coefficient from different transmit and / or receive angles are averaged to reduce variance or to quantify the angle dependence of attenuation.

[0030] In one embodiment, the scan to be measured is adaptive. The transmit and / or receive can be adaptive. For example, the result of one measurement is used to set the amplitude, angle, frequency, and / or F# for subsequent transmissions.

[0031] In one example, the attenuation coefficient is measured. The reference-phantom method is used, but other measurements of the attenuation coefficient can be used. The acoustic energy has an exponential decay as a function of depth. The measurement of the acoustic intensity as a function of depth is performed before or without depth gain correction. To eliminate system effects, the measurement is calibrated based on the measurement of the acoustic intensity as a function of depth in the phantom. By averaging over a one-dimensional, two-dimensional, or three-dimensional region, the measurement can be subject to less noise. The sampling of beamforming or the acoustic intensity can be transformed to the frequency domain, and the calculations are performed in the frequency domain.

[0032] In another example, the backscatter coefficient is measured. The acoustic attenuation is determined. This acoustic attenuation is used to determine a reference calibration. Through calibration for the acoustic attenuation, the scattered energy is provided as the backscatter coefficient. This calculation can be performed in the frequency domain, providing a measurement as a function of frequency.

[0033] The spectral slope of the logarithm of the frequency-dependent backscatter is measured from the backscatter coefficient. The logarithm of the backscatter coefficient is determined as a function of frequency. A line fit (e.g., least squares) is applied to the logarithm of the backscatter as a function of frequency to determine the spectral slope.

[0034] In operation 32, the ultrasound scanner generates a measurement of shear wave propagation in tissue based on a scan of the patient. For shear wave imaging, an acoustic radiation force impulse (ARFI or pushing pulse) is transmitted into the tissue. This impulse causes displacement of the tissue at a location, resulting in the generation of a shear wave. The shear wave typically travels transversely to the transmission beam of the pushing pulse. By tracking tissue displacement at one or more laterally spaced locations, the shear wave passing through those locations can be detected. The time for the shear wave to travel from an origin to a later position and the distance between the positions provide the shear wave velocity.

[0035] Any shear wave parameter 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, angle- and / or frequency-dependent shear wave attenuation, 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.

[0036] The acoustic absorption coefficient is from the absorption of the acoustic pulse, not from the absorption of the shear wave. The acoustic absorption is determined as F ∝ αI / c, where F is the radiation force, I is the intensity of the ARFI pushing pulse, c is the acoustic sound speed, and α is the acoustic absorption coefficient.

[0037] To measure shear waves, a push pulse or ARFI is transmitted to a focused location in tissue. A reference scan of the tissue position at rest is performed either before the push pulse or after the tissue returns to the resting state. Over time, the displacement or change in position of the tissue at one or more locations spaced from the focused location is measured. The tracking scan is performed repeatedly. Using other measurements of correlation or similarity, the axial, 2D, or 3D shift of the tissue from the reference time compared to the current tracking time is determined. The time of the maximum displacement indicates the time of the shear wave. Other timings can be used, such as the start or end of the displacement. The time the shear wave reaches the tracking location and the distance from the tracking location to the focused location of the push pulse provide the shear wave speed. Other methods can be used, such as solving for the shear wave speed at multiple locations by determining the displacement distribution (profile) of the displacement (as a function of time) at different tracking locations or solving based on the displacement as a function of position.

[0038] The measurement of shear wave parameters can be a function of frequency and / or angle. The measurement is repeated by transmitting the push pulse in the beam at different angles and / or at different frequencies. The spatio-temporal displacement distribution is used to determine the measurement in the time domain or the frequency domain. The results from different angles can be used to determine the angle-dependent measurements.

[0039] The shear wave parameters can be measured at different locations. The measurement can be based on the tissue displacement for one or a single push pulse. The measurement can alternatively be based on the tissue displacement for multiple push pulses. The measurement is repeated for different regions using different push pulses.

[0040] To measure shear wave parameters, both the push pulse and the tracking transmission occur. The displacement is measured by receiving the acoustic response to the tracking transmission and not the push pulse transmission. The same scan used to measure the scattering parameters can be used to measure the shear wave parameters. For example, the reference scan used for tracking before transmitting the push pulse is used to measure scattering. In other embodiments, the scan for the shear wave parameters uses different transmissions and / or receptions compared to the scattering parameters. The scan for the measurement is divided into separate sequences of transmission and reception events for different measurements.

[0041] Compared to the tracking pulse, the push pulse has a relatively long duration, such as tens, hundreds, or thousands of cycles of the push pulse and one to three cycles of the tracking transmission. Different focused locations, frequencies, angles, powers, and / or F-numbers can all be used for the push pulse in the case of providing repetition.

[0042] The same measurements can be repeated for the same location and / or different locations. Different frequencies, F-numbers, angles, powers, focus positions, and / or other differences can be used for any repetition. The resulting measurements can be used together to determine another measurement, or can be combined, such as averaged, to reduce noise.

[0043] The ultrasound scanner can adapt the scan to shear wave parameter measurements. For example, for the estimation of the attenuation coefficient of the shear wave, the push pulse is adapted. For later transmission, the center frequency, duration, F-number, or other characteristics of the push pulse can be changed. The focus can be made tighter or weaker. The displacement that generates the shear wave can be made larger or smaller. As another example, for the estimation of the absorption coefficient using an ARFI push pulse, another push pulse is transmitted with a tighter focus or longer duration. This change can improve the signal-to-noise ratio (SNR) and / or reduce the variability in the measurement.

[0044] The adaptation is based on any information. For example, the displacement distribution is compared with a reference or calibration distribution. As another example, the amount of displacement of the maximum, average, or median displacement is determined. This information can indicate the need for a stronger or higher-intensity push pulse, or can indicate the need for a less-intense push pulse, taking into account a shorter cooling time.

[0045] In operation 33, the ultrasound scanner generates an ARFI measurement of the axial displacement of the tissue. The ARFI transmission causes the tissue to displace along the axis of the transmission beam or scan line. Instead of tracking the shear wave, the tissue displacement along the axis caused by the ARFI or in response to the longitudinal wave generated by the ARFI is tracked over time.

[0046] Any ARFI measurement can be used. For example, the attenuation of the longitudinal wave of the ARFI pulse can be estimated based on the displacement tracked at a position spaced from the focus of the ARFI. The measurement can be at the focus or at other positions along the axial scan line.

[0047] For the measurement, the ARFI is transmitted along the scan line. A tracking scan is performed after the ARFI transmission. Acoustic echoes from the tracking transmission along the scan line are received. The received data is correlated with a reference before or after the displacement caused by the ARFI. The amount of displacement as a function of time, position, transmission angle, and / or transmission frequency is determined. The maximum displacement, the displacement as a function of depth, and / or the amount of displacement as a function of time are used to calculate the ARFI measurement.

[0048] The same measurements can be performed at other times and / or positions. The results from the repetitions can be used to derive another measurement or can be averaged.

[0049] The transmission can be adapted, for example, to the F-number, frequency, duration, power, and / or angle. The adaptation can be in response to any measurement, such as the magnitude of the maximum displacement.

[0050] Other measurements can be used. Measure the response of the tissue to different types of waves and / or scans. Use one or more measurements of the same type. For a given measurement, perform a single instance, an average, or a distribution (e.g., standard deviation over time, duration, frequency, angle, and / or interval). Any number of the same or different types of measurements can be performed.

[0051] In operation 34, an ultrasound scanner or other image processor estimates the tissue properties of the patient's tissue based on different measurements. Use measurements from two or more different wave phenomena. The values of two or more measurements are used to estimate the tissue properties. For example, both the measurement of scattering and the measurement of shear wave propagation are used to estimate the tissue properties. In another example, the measurement of axial displacement (e.g., ARFI measurement) is used together with the measurement of acoustic scattering and / or the measurement of shear wave propagation.

[0052] Other information can be used to estimate the tissue properties. For example, use the patient's clinical information. The clinical information can be medical history, age, body mass index, gender, fasting or not, blood pressure, diabetes or not, and / or blood biomarker measurements. Example blood biomarkers include alanine aminotransferase (ALT) level, aspartate aminotransferase (AST) level, and / or alkaline phosphatase (ALP) level. Any information about the patient can be included.

[0053] Any tissue property can be estimated. For example, estimate the fat component of the tissue. The fat component of the liver, breast, or other tissues is diagnostically useful. The fat component in the patient's liver helps diagnose NAFLD. Other diagnostically useful tissue properties include inflammation, density, fibrosis, and / or nephron characteristics (count and / or diameter). The tissue property is binary, such as present or absent, or is an estimate 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 from the same or different measurements.

[0054] Operations 36 and 37 represent two different embodiments for making the estimate in operation 34. The different embodiments are alternatives. Other embodiments can be used. Two or more embodiments can be used, such as determining the value of the tissue property in two ways and then averaging the results or selecting the most likely accurate result.

[0055] Estimate the value of tissue properties. In an embodiment of action 36, a machine learning classifier estimates tissue properties. The machine-trained classifier provides a non-linear model. Any machine learning and resulting machine learning classifier can be used. For example, support vector machines, probabilistic boosting trees, Bayesian networks, neural networks, or other machine learning can be used.

[0056] Machine learning learns from training data. The training data includes various examples, such as dozens, hundreds, or thousands of samples, as well as ground truth. The examples include input data to be used, such as values of scattering and shear wave propagation parameters. The ground truth is the value of the tissue property for each example. In one embodiment, machine learning learns to classify fat components based on scattering and shear wave propagation parameters. Magnetic resonance (MR) scans that provide proton density fat fraction (PDFF) are used to provide the ground truth for the fat component. MR-PDFF provides the percentage of fat at a certain location or region. The percentage of fat is used as the ground truth, such that machine learning learns to classify the percentage of fat from the input values of ultrasound parameters. Other sources of ground truth can be used for a given tissue property, the given tissue property such as from biopsy, modeling, or other measurements.

[0057] In one embodiment, machine learning trains a neural network. The neural network includes one or more convolutional layers that learn filter kernels to distinguish between values of tissue properties. Machine training learns what weighted combination of input values (e.g., convolution using the learned kernels) indicates the output. The resulting machine learning classifier uses the input values to extract discriminative information and then classifies the tissue property based on the extracted information.

[0058] Training provides one or more matrices. The one or more matrices relate the input information to the output classes. Hierarchical training and resulting classifiers can be used. Different classifiers can be used for different tissue properties. Multiple classifiers can be used for the same tissue property, and the results are averaged or combined.

[0059] In an embodiment of action 37, instead of or in addition to the machine learning model, a linear model is used. A predefined or programmed function relates the input values to 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 MR-PDFF values.

[0060] Any linear function can be used. For example, the value of the tissue property is estimated from one or more scattering parameters and one or more shear wave propagation parameters. Any combination of addition, subtraction, multiplication, or division can be used.

[0061] In one embodiment, 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, ultrasonic-derived fat fraction (UDFF) estimation includes two functions expressed as a weighted combination:

[0062] UDFF = aP1 + bP2 + cP3 + … d for P i：min <P i <P i：max

[0063] UDFF = αP1 + βP2 + cγ + … δ for P k:min <P k <P k:max

[0064] where d and δ are constants, a, b, c, α, and β are weights, and P is a measurement of the parameter. One parameter P i,k 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 the functions, weights, and / or constants may be used. Different selection criteria may be used. The selection parameter may be of one type and the weighting parameters for each function may be of another type. Alternatively, different types (e.g., scatter and shear wave propagation) are included as weighting parameters regardless of one or more types of the parameters used for selection.

[0065] In one example, AC is the acoustic attenuation coefficient (e.g., a scatter parameter), BSC is the backscatter coefficient (e.g., a scatter parameter), and SS is the spectral slope of the logarithm of the frequency-dependent backscatter coefficient (e.g., also a scatter parameter). SWS is the shear wave velocity (e.g., a shear wave propagation parameter). Two functions based on scatter parameters are used, where the function for a given estimation is selected based on the shear wave propagation parameter, expressed as follows:

[0066] UDFF = 55AC + 114BSC - 42 for

[0067] UDFF = -3.8SS + 425BSC - 9.4 for SWS > 1.3 m / s

[0068] The weights and constants are based on minimizing the difference from the fat fraction provided by MR-PDFF. Expert-selected or other weights and / or constants may be used.

[0069] In operation 38, the ultrasound scanner or display device displays the estimated tissue parameter. For example, an image of the fat component is generated. A value representing the estimated fat component is displayed on the screen. Alternatively or additionally, a graph (e.g., a curve or icon) representing the estimated fat component is displayed. A reference to a scale or other reference may be displayed. In other embodiments, the fat component as a function of position is displayed in a one-dimensional, two-dimensional, or three-dimensional representation by color, brightness, hue, luminance, or other modulation of the display value. The tissue property may be linearly or non-linearly mapped to the pixel color.

[0070] The tissue property may be indicated alone or together with other information. For example, shear wave imaging is performed. Shear wave rate, modulus, or other information determined based on the tissue response to the shear wave is displayed. Any shear wave imaging may be used. The displayed image represents the shear wave information of the region of interest or the entire imaging region. For example, in the case where shear rate values are determined for all grid points in the region of interest or the field of view, the pixels of the display represent the shear wave rate for that region. The display grid may be different from the scan grid and / or the grid for which displacements are calculated.

[0071] The shear wave information is used for color overlay or other modulation of the display value. Color, brightness, luminance, hue, or other display properties are modulated as a function of the shear wave characteristic, such as the shear wave rate. The image represents a two-dimensional or three-dimensional region of position. The shear data is in a display format or may be scanned into a display format. The shear data is color or grayscale data, but may be data before being mapped with a grayscale or color scale. The information may be linearly or non-linearly mapped to the display value.

[0072] The image may include other data. For example, the shear wave information is displayed over or together with the B-mode information. B-mode or other data representing tissue, fluid, or contrast agent in the same region may be included, such as displaying B-mode data for any position with a shear wave rate below a threshold or with poor quality. The other data helps the user determine the location of the shear information. In other embodiments, the shear wave characteristic is displayed as an image without other data. In other embodiments, B-mode or other image information is provided without shear wave information.

[0073] Additional estimated values of the tissue property are displayed substantially simultaneously with the shear wave, B-mode, color or flow mode, M-mode, contrast agent mode, and / or other imaging. The visual perception of the view is substantially taken into account. Sequentially displaying two images at a sufficient frequency may allow the viewer to perceive that the images are displayed simultaneously. Component measures for estimating the tissue property may also be displayed in a table, for example.

[0074] Any format for substantially simultaneous display can be used. In one example, the shear wave or anatomical image is a two-dimensional image. The value of the tissue property is text, a graph, a two-dimensional image, or other indicators of an estimated value. The cursor or other position selection can be positioned relative to the shear or anatomical image. The cursor indicates the selection of a position. For example, the user selects pixels associated with the internal region of a lesion, cyst, inclusion, or other structure. Then the tissue property at the selected position is displayed as a value, a pointer along a scale, or other indication. In another example, the tissue property is indicated in a region of interest (a sub-part of the field of view) or over the entire field of view.

[0075] In another embodiment, the shear wave or B-mode and fat fraction images are displayed substantially simultaneously. For example, a dual-screen display is used. The shear wave image (e.g., shear wave rate) and / or B-mode image are displayed in one region of the screen. The fat fraction as a function of position is displayed in another region of the screen. The user can view different images on the screen for diagnosis. Additional information or indication of the tissue property helps the user diagnose the region.

[0076] In one embodiment, the tissue estimate is provided as a real-time digital or quantitative image. Since tissue parameters can be estimated quickly, the values of the tissue parameters are estimated and output within 1 - 3 seconds of the start of the scan. The tissue property can be estimated at different times, such as before, during, and / or after treatment. Estimates from different times are used to monitor the progression of the disease and / or the response to treatment. For example, the percentage change in the value of the tissue property over time is calculated and output.

[0077] The tissue property and / or the measurement used to derive the tissue property can be used in the estimation of disease activity. Figure 3 is a flowchart of an embodiment of a method for estimating disease activity using an ultrasound system. For example, the method is for ultrasound-derived non-alcoholic liver disease activity estimation. The ultrasound scanner measures scattering and / or shear wave propagation in the patient's tissue to directly or indirectly estimate disease activity.

[0078] For example, quantitative ultrasound is used to predict the non-alcoholic fatty liver disease activity score (NAS). The NAS is predicted based on ultrasound estimates of tissue mechanical and acoustic properties. The model predicts the NAS based on tissue mechanical and acoustic properties estimated using medical ultrasound. In one embodiment, the ultrasound system non-invasively obtains an ultrasound-derived NAFLD activity (UDNA) index as a predictor of NAS. The ultrasound system is configured to perform a pulse sequence for generating measurements of scattering and shear wave propagation. A model using at least three properties of the liver, including the acoustic backscatter coefficient, shear wave rate, and shear wave damping ratio, is used to determine the UDNA.

[0079] Histological NAS is the sum of steatosis, lobular inflammation, and ballooning histological scores, but requires a biopsy. In one embodiment, the proposed ultrasound-derived model pairs appropriate mechanical and acoustic properties with NAS features. Based on backscatter, attenuation, and / or sound speed, the ultrasound-derived fat component is used as a measure of steatosis grade. Shear wave damping ratio is used as a measure of inflammation, and shear wave speed is used as a measure of ballooning. Other ultrasound measurements may be used.

[0080] Figure 3 The method is implemented by Figure 2 a system or a different system. A medical diagnostic ultrasound scanner performs the measurements by acoustically generating waves and measuring the responses. An image processor of the scanner, computer, server, or other device estimates based on the measurements. A display device, network, or memory is used to output the estimated disease activity score.

[0081] Additional, different, or fewer actions may be provided. For example, including action 33 from Figure 1 such as using ARFI measurements in the estimation of the fat component or other tissue properties. As another example, not including action 34 such as in the case of estimating the disease activity index score from the measurements without separately estimating tissue properties (e.g., the fat component). In yet another example, not providing action 38. In another example, providing actions for configuring the ultrasound scanner and / or the scan.

[0082] The actions are performed in the described or shown order (e.g., from top to bottom or numerically), but may be performed in other orders. For example, actions 30 and 32 are performed simultaneously, such as using the same transmit and receive pulses, or in any order.

[0083] In action 30, the ultrasound scanner generates one or more measurements of the scatter in the tissue based on an ultrasound scan of the patient. Any acoustic scatter parameter may be used, such as a measurement of the acoustic interaction with the liver tissue. For example, the ultrasound scanner or system measures the backscatter coefficient, frequency-dependent backscatter coefficient, attenuation, sound speed, and / or any other scatter measurement discussed above for Figure 1 The measurements may be frequency-dependent, such as averaged from multiple transmissions. Adaptive scanning may be used.

[0084] The measurements of scatter may be used to estimate the fat component, such as using acoustic backscatter (e.g., frequency-dependent acoustic backscatter) and acoustic attenuation. The measurements of scatter are alternatively or additionally used in the estimation of disease activity, such as using acoustic backscatter or frequency-dependent acoustic backscatter.

[0085] In operation 32, an ultrasound scanner generates one or more measurements of shear wave propagation in tissue based on an ultrasound scan of a patient. Any shear wave propagation parameter may be used. For example, shear wave speed and shear wave damping ratio are used. Any of the shear wave propagation measurements discussed above for Figure 1 may be used. The measurements are for ARFI-induced shear waves in tissue of interest of the patient, such as liver tissue. Adaptive scanning may be used.

[0086] As discussed above for Figure 1 , the ultrasound scans for measuring scatter and for measuring shear wave propagation use the same or different transmit and receive events. For example, separate transmit and receive are used for measuring scatter compared to generating shear waves and measuring the tissue response to the shear waves.

[0087] In one embodiment, a shear wave damping ratio is generated. An ultrasound scan is performed to measure the tissue response to the shear wave in order to determine shear wave viscosity as a complex number, such as the ratio of storage modulus to loss modulus. This complex number represents using the real part of the viscosity as the storage modulus and using the imaginary part of the viscosity as the loss modulus.

[0088] In one method, spatio-temporal displacement measurements are acquired during shear wave propagation. These measurements are Fourier transformed into the frequency domain, such as using a fast Fourier transform, and are used to determine a complex wave number. The logarithm of the spectrum of the displacement as a function of time may be determined for each of the individual locations subjected to the shear wave or other wave. Solving using the logarithm as a function of position provides a complex wavenumber. Various viscoelastic parameters, such as loss modulus and storage modulus, are determined from the complex wave number. In one embodiment, the measurements for determining complex wave number, viscosity, or other damping ratio measurements disclosed in U.S. Published Patent Application No. 2016 / 0302769 are used.

[0089] As another method, shear wave attenuation and shear wave dispersion are measured. Dispersion is the change in shear wave speed or rate as a function of frequency. Shear wave attenuation may also be measured as a function of frequency. For a given frequency or for a combination (e.g., average) from multiple frequencies, a phasor is generated based on the attenuation and dispersion values. The phasor is converted into a complex number, according to which the real part and the imaginary part are used as the damping ratio. Other measurements of the damping ratio from the tissue response to the shear wave may be used.

[0090] An image processor estimates the value of an ultrasound-derived liver disease activity index based on the backscatter coefficient, shear wave speed, and shear wave damping ratio. Other measurements may be used. Non-ultrasound information, such as information from a patient's medical record, may additionally be used.

[0091] Estimate disease activity directly or indirectly from measurements. For direct measurement, the measurement is input into a model that outputs an estimate of the value of disease activity in action 40. Estimate the score directly from the measurement. For indirect measurement, one or more types of measurements are used to determine another value or estimate (e.g., fat component) in action 34, and then this estimate is used alone, together with other types of estimates, together with other measurements, or together with other types of estimates and other measurements to estimate disease activity in action 40.

[0092] In one embodiment, in action 34, the backscatter coefficient, acoustic attenuation, and / or shear wave velocity (e.g., using the backscatter coefficient (acoustic scattering) and acoustic attenuation without shear wave velocity) are used to estimate the fat component. One or more scattering and / or one or more shear wave propagation parameters are used to estimate the fat component. Any of the embodiments discussed above for Figure 1 estimating the fat component can be used. For example, the acoustic attenuation coefficient (e.g., scattering parameter), backscatter coefficient (e.g., scattering parameter), and spectral slope of the logarithm of the frequency-dependent backscatter coefficient (e.g., also a scattering parameter) are used to estimate the fat component. The shear wave velocity (e.g., shear wave propagation parameter) can be used, such as to select a function for estimating the fat component based on the scattering parameters.

[0093] In action 40, the image processor estimates the level of liver disease activity (e.g., NAS or UDNA). For indirect estimation, the level of disease activity is estimated from at least one of the fat component and shear wave parameters. For example, three inputs are used in the estimation of disease activity. Ultrasonic mechanical and acoustic properties replace histological NAS features. The ultrasound-derived fat component, such as based on backscatter, attenuation, and / or sound velocity, is used as a measurement of the steatosis level. The shear wave damping ratio is used as a measurement of inflammation, and the shear wave velocity is used as a measurement of ballooning. The value of liver disease activity for an index to assist a physician is estimated directly or indirectly from various measurements and / or estimates such as the fat component, damping ratio, and shear wave velocity.

[0094] Other measurements and / or estimates can be used as alternatives. Multiple estimates and / or measurements can be appropriately used in place on one histological NAS variable. Activity can be estimated from estimates and / or measurements different from the histological variable, providing different methods for determining disease activity.

[0095] The image processor uses a model to estimate a score or value of a liver disease activity index. The score or value is a part of the index, such as over a range of integers. Any range can be used, such as three levels (e.g., steatohepatitis, cirrhosis, or hepatocellular carcinoma), two levels (e.g., steatosis or fibrosis), or four or more levels. The scoring can relate to a specific stage of the disease or can indicate the level of the disease without referring to a specific stage (e.g., 0 - 7 provides 8 levels with different amounts of activity for each level). The estimated value or score is a stage and / or numerical representation.

[0096] The model can be a function, such as using estimates and / or measurements in variables. In other embodiments, the model is a machine learning classifier. Machine learning, such as using a fully connected neural network, a convolutional neural network, or a support vector machine, trains the model to classify - output a value of disease activity, given the input estimates and / or measurements. In another embodiment, a logistic regression model is used. Logistic regression is utilized to estimate the value of disease activity from measurements and / or estimates. For example, logistic regression with fat fraction, shear wave velocity, and damping ratio is used to estimate disease activity. As another example, disease activity is estimated as a logistic regression of backscatter coefficient, shear wave velocity, and shear wave damping ratio.

[0097] The image processor can use other input information to estimate disease activity. For example, measurements of tissue response to longitudinal waves from ARFI can be used. As another example, clinical information of the patient can be used.

[0098] In action 42, the image processor generates an estimate of disease activity and a display (e.g., a display screen) shows the estimate of disease activity. The level of liver disease activity (e.g., UDNA) is shown to the user to assist in disease diagnosis or monitoring. Since ultrasound is used, assistance is provided without the time and expense of invasive biopsy and / or MRI.

[0099] The level (e.g., value) of the UNDA index or other scale of disease activity is output as alphanumeric text, output as a chart, or output as color coding or markings in an image representing the patient's tissue. For example, an ultrasound image of the liver tissue is shown. The image includes an indication of the value of the estimated ultrasound - derived liver disease activity index. The fat fraction, other tissue property estimates and / or measurements can also be output. Instead of or in addition to the fat fraction, any of the outputs discussed above for action 38 can be used with disease activity.

[0100] Figure 4A graph showing the comparison of predicted NAS with histological NAS is presented. The predicted NAS is an estimate of UDNA based on logistic regression from fat fraction, shear wave velocity, and shear wave damping ratio based on shear wave propagation. The index uses eight levels for scoring (0 - 7). Based on 82 patients, the root mean square error between the predicted NAS and histological NAS is 1.14. The predicted NAS is well correlated with histological NAS, especially for moderate scores of 2 - 5. Good diagnostic performance is provided.

[0101] Figure 2 An embodiment of system 10 for estimating tissue properties and / or disease activity based on measurements in response to different types of waves is shown. System 10 implements Figure 1 the method of, Figure 3 the method of, or other methods. 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.

[0102] System 10 is a medical diagnostic ultrasound imaging system. In alternative embodiments, system 10 is a personal computer, a workstation, a PACS station, or other arrangements at the same location or distributed over a network for real - time or post - acquisition imaging.

[0103] The transmit and receive beamformers 12, 16 form beamformers for transmitting and receiving using transducer 14. A sequence of transmit pulses is transmitted and a response is received based on the operation or configuration of the beamformers. The beamformers scan to measure scatter, shear wave, and / or ARFI parameters. The beamformers 12, 16 are configured to transmit and receive a sequence of pulses in a patient using transducer 14. The sequence of pulses is for one or more scatter parameters and for one or more shear wave parameters.

[0104] The transmit beamformer 12 is an ultrasound transmitter, a memory, a pulser, an analog circuit, a digital circuit, or a combination thereof. The transmit beamformer 12 is operable to generate waveforms with different or relative amplitudes, delays, and / or phasing for multiple channels. When transmitting acoustic waves from transducer 14 in response to the generated electrical waveforms, one or more beams are formed. A sequence of transmit beams is generated to scan a two - dimensional or three - dimensional region. Sector, Linear or other scan formats. 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 shear imaging, a sequence of scans along one or more identical lines is used. In Doppler imaging, the sequence can include multiple beams along the same scan line before scanning adjacent scan lines. For shear imaging, scan or frame interleaving (i.e., scanning the entire region before scanning again) can be used. Line or group of lines interleaving can be used. In an alternative embodiment, the transmit beamformer 12 generates plane waves or diverging waves for faster scanning.

[0105] The same transmit beamformer 12 generates a pulse excitation or an electrical waveform for generating acoustic energy to cause displacement. An electrical waveform for acoustic radiation force impulse is generated. In an alternative embodiment, a different transmit beamformer is provided for generating the pulse excitation. The transmit beamformer 12 causes the transducer 14 to generate a push pulse or an acoustic radiation force impulse.

[0106] The transducer 14 is an array that generates acoustic energy from an electrical waveform. For an array, relative delays focus the acoustic energy. A given transmit event corresponds to the transmission of acoustic energy by different elements at substantially the same time at a given delay. The transmit event can provide a pulse of ultrasonic energy for displacing tissue. The pulse can be a pulse excitation, a tracking pulse, a B-mode pulse, or a pulse for other measurements. The pulse excitation includes a waveform having many cycles (e.g., 500 cycles), but which occurs over a relatively short time to cause tissue displacement over a longer time. The tracking pulse can be a B-mode transmission such as using 1 - 5 cycles. The tracking pulse is used to scan a region of a patient.

[0107] The transducer 14 is a 1, 1.25, 1.5, 1.75, or 2 - dimensional array of piezoelectric or capacitive membrane elements. The transducer 14 includes multiple elements for transducing between acoustic energy and electrical energy. The received signal is generated in response to ultrasonic energy (echoes) impinging on the elements of the transducer 14. The elements are connected to the channels of the transmit and receive beamformers 12, 16. Alternatively, a single element with mechanical focusing is used.

[0108] The receive beamformer 16 includes a plurality of channels having amplifiers, delays, and / or phase rotators, and one or more summers. Each channel is connected to one or more transducer elements. The receive beamformer 16 is configured by hardware or software to apply relative delays, phases, and / or apodization in response to each imaging or tracking transmission to form one or more receive beams. For echoes from a pulsed excitation for tissue displacement, reception operations may not occur. The receive beamformer 16 uses the received signals to output data representing spatial positions. Relative delays and / or phasing and summing of signals from different elements provide beamforming. In an alternative embodiment, the receive beamformer 16 is a processor for generating samples using Fourier or other transforms.

[0109] The receive beamformer 16 may include filters, such as filters for isolating information at second harmonic or other frequency bands relative to the transmission frequency band. Such information may be more likely to include desired tissue, contrast agent, and / or flow information. In another embodiment, the receive beamformer 16 includes a memory or buffer, as well as filters or adders. Two or more receive beams are combined to isolate information at a desired frequency band, such as the second harmonic, cubic fundamental, or another frequency band.

[0110] In coordination with the transmit beamformer 12, the receive beamformer 16 generates data representing a region. To track shear waves or axial longitudinal waves, data representing the region at different times is generated. After an acoustic pulse excitation, the receive beamformer 16 generates beams representing positions along one or more lines at different times. By scanning an area of interest with ultrasound, data (e.g., beamformed samples) is generated. By repeating the scan, ultrasound data representing the region at different times after the pulsed excitation is acquired.

[0111] The receive beamformer 16 outputs beam sum data representing spatial positions. Data for a single position, positions along a line, positions of a region, or positions of a volume may be output. Dynamic focusing may be provided. The data can be used for different purposes. For example, for B-mode or tissue data, different parts of the scan are performed instead of displacement. Alternatively, B-mode data is also used to determine displacement. As another example, data for different types of measurements is acquired with a series of shared scans, and B-mode or Doppler scans are performed separately or using some of the same data.

[0112] The image processor 18 is a B-mode detector, Doppler detector, pulsed wave Doppler detector, correlation processor, Fourier transform processor, application specific integrated circuit, general purpose processor, control processor, image processor, field programmable gate array, digital signal processor, analog circuit, digital circuit, a combination thereof, or other currently known or later developed device for detecting and processing information from beamformed ultrasound samples for display. 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, general purpose processor, digital signal processor, application specific integrated circuit, field programmable gate array, network, server, group of processors, data path, a combination thereof, or other currently known or later developed device for calculating values of different types of parameters based on beamformed and / or detected ultrasound data and / or for making estimates based on values from different types of measurements. For example, the separate image processor is configured by hardware, firmware, and / or software to perform Figure 1 the actions 34-38 shown in Figure 3 and / or the actions 34-42 shown in

[0113] The image processor 18 is configured to estimate values of tissue properties and / or disease activity based on combinations of different types of parameters. For example, measured scatter parameters and one, two, or more measured shear wave parameters are used. Different types of parameters are measured based on transmit and receive sequences and calculations therefrom. One or more measured values of each of at least two types (e.g., scatter, shear wave propagation, or axial ARFI) of parameters are determined for fat fraction estimation. One or more scatter and one or more (e.g., two) shear wave propagation parameter values are determined for liver disease activity estimation.

[0114] In one embodiment, 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 classifier. Input values of measurements with or without other information are used by a learning matrix to output a value of the tissue property. In other embodiments, the image processor 18 uses a weighted combination of values of parameters. For example, two or more functions are provided. Using the value of one or more parameters (e.g., shear wave velocity), one of the functions is selected. The selected function uses the values of the same and / or different parameters to determine the value of the tissue property. A linear or non-linear mapping correlates the values of one or more parameters with the value of the tissue property. For example, two or more scatter parameters are used to determine the value of the tissue property using a shear wave propagation selection function.

[0115] In another embodiment, the image processor 18 is configured to generate a score of the disease activity as a fraction from a combination of one or more scattering parameters and one or more (e.g., two) shear wave parameters. For example, the image processor 18 estimates the fat component of the patient's liver from one or more scattering parameters. The image processor 18 generates a score as a fraction of ultrasound-derived non-alcoholic liver disease activity from the fat component and two or more shear wave parameters. The score is generated using a machine learning classifier or a logistic regression model. For example, the logistic regression model correlates the scattering (e.g., acoustic backscatter coefficient) and two or more shear wave parameters (e.g., shear wave speed and shear wave damping ratio) with the level of disease activity.

[0116] The processor 18 is configured to generate one or more images. For example, generate shear wave speed, B-mode, contrast agent, M-mode, flow or color mode, ARFI, and / or another type of image. The shear wave speed, flow, or ARFI image can be presented alone or as an overlay or region of interest within the B-mode image. The shear wave speed, flow, or ARFI data modulates the color at the location within the region of interest. In the case where the shear wave speed, flow, or ARFI data is below a threshold, the B-mode information can be displayed without modulation by the shear wave speed.

[0117] Other information is included in the image or is displayed sequentially or substantially simultaneously. For example, the tissue property estimation image and / or the disease activity level is displayed simultaneously with other images. One or more values of the tissue property and / or disease activity map can display information. In the case where the tissue property and / or disease activity is measured at different locations, the values of the tissue property and / or disease activity can be generated as a color overlay in the region of interest in the B-mode image. The shear wave speed, tissue property, and / or disease activity data can be combined as a single overlay on one B-mode image. Alternatively, one or more values of the tissue property and / or disease activity are displayed as text or (one or more) numerical values adjacent to or overlaid on the B-mode or shear wave imaging image. The image processor 18 can be configured to generate other displays. For example, the shear wave speed image is displayed next to a graph, text, or graphical indicator of the tissue property and / or disease activity, such as the fat component and / or the degree of fibrosis, and the disease activity such as an index value indicating the UDNA level. The tissue property information and / or disease activity is presented for one or more locations of the region of interest without being presented in a separate two-dimensional or three-dimensional representation, such as when the user selects a location and the ultrasound scanner then presents the tissue property and / or disease activity for that location.

[0118] The image processor 18 operates in accordance with instructions stored in the memory 22 or another memory for making estimates based on measurements of tissue responses to different types of waves (e.g., scattering from transmitted ultrasound, axial tissue displacement, and / or shear waves caused by tissue displacement). The 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 drive, or other computer-readable storage medium. The computer-readable storage medium includes various types of volatile and non-volatile storage media. The functions, acts, or tasks shown in the figures or described herein are executed in response to one or more instruction sets stored in or on the computer-readable storage medium. The functions, acts, or tasks are independent of the particular type of instruction set, storage medium, processor, or processing strategy and may be executed by software, hardware, integrated circuits, firmware, microcode, and the like operating alone or in combination. Also, the processing strategy may include multiprocessing, multitasking, parallel processing, and the like. In one embodiment, the 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 over a telephone line. In other embodiments, the instructions are stored within a given computer, CPU, GPU, or system.

[0119] The display 20 is a device for displaying one-dimensional or two-dimensional images or three-dimensional representations such as a CRT, LCD, projector, plasma, or other display. The two-dimensional image represents the spatial distribution in a region. The three-dimensional representation is rendered from data representing the spatial distribution in a volume. The display 20 is configured by the image processor 18 or other device by inputting a signal to be displayed as an image. The display 20 displays an image representing the tissue properties and / or disease activity (e.g., averaged from estimates of tissue properties including adjacent locations) at a single location in the region of interest, or the entire image. For example, the display 20 displays the value of the fat component and / or the score of the disease activity index. The display of tissue properties and / or disease activity based on different types of waves provides a more accurate level of tissue property or disease information for diagnosis.

[0120] Although the present invention has been described above by reference to various embodiments, it should be understood that many changes and modifications can be made without departing from the scope of the present invention. Accordingly, it is intended that the foregoing detailed description be considered illustrative rather than restrictive, and it should be understood that it is the following claims, including all equivalents, that are intended to define the spirit and scope of the present invention.

Claims

1. A computer-readable storage medium having instructions for implementing a method for non-alcoholic liver disease activity estimation using an ultrasound scanner, the method comprising: generating (30) a first measurement of scatter in tissue from a scan of a patient by the ultrasound scanner, the first measurement of scatter including a backscatter coefficient; generating (32) second and third measurements of shear wave propagation in the tissue from the scan of the patient by the ultrasound scanner, the second measurement including a shear wave velocity, and the third measurement including a shear wave damping ratio; estimating (40) a first value of an ultrasound-derived liver disease activity index from the backscatter coefficient, the shear wave velocity, and the shear wave damping ratio; and outputting (42) an ultrasound image including an indication of the first value of the estimated ultrasound-derived liver disease activity index; wherein estimating (40) the first value includes estimating (34) a fat component of the patient's liver based on acoustic attenuation, the backscatter coefficient, and the shear wave velocity, and estimating (40) the first value of the ultrasound-derived liver disease activity index based on the fat component, the damping ratio, and the shear wave velocity.

2. The computer-readable storage medium according to claim 1, wherein, Generating (30, 32) the first measurement, the second measurement, and the third measurement from the scan includes separate transmit and receive events for (1) the first measurement of scatter and (2) the second and third measurements of shear wave propagation.

3. The computer-readable storage medium according to claim 1, wherein, Generating (30) the first measurement of scatter includes generating (30) a frequency-dependent backscatter coefficient as the backscatter coefficient.

4. The computer-readable storage medium according to claim 1, wherein, Generating (32) the third measurement includes generating (32) the shear wave damping ratio as a ratio of the real and imaginary parts of a complex number from a Fourier transform of a spatio-temporal displacement caused by the shear wave propagation.

5. The computer-readable storage medium according to claim 1, wherein, Generating (32) the third measurement includes generating (32) the shear wave damping ratio based on shear wave attenuation and shear wave dispersion.

6. The computer-readable storage medium according to claim 1, wherein, Estimating (40) the first value includes estimating (40) using a logistic regression of the fat component, the shear wave velocity, and the damping ratio.

7. The computer-readable storage medium according to claim 1, wherein, Estimating (40) includes estimating (40) using a machine learning classifier.

8. The computer-readable storage medium according to claim 1, wherein, Estimating (40) includes estimating (40) using a logistic regression model.

9. The computer-readable storage medium according to claim 8, wherein, Estimating (40) using the logistic regression model includes estimating (40) as a logistic regression of the backscatter coefficient, the shear wave velocity, and the shear wave damping ratio.

10. The computer-readable storage medium according to claim 1, wherein, Generating (30) the measurement of scatter includes generating (30) the measurement of scatter as a frequency-dependent measurement averaged from multiple transmissions.

11. The computer-readable storage medium according to claim 1, wherein, Generating (30, 32) the first, second, and third measurements includes adaptive scanning.

12. The computer-readable storage medium according to claim 1, wherein, Estimating (40) includes estimating (40) as a function of clinical information for the patient.

13. A system for disease activity estimation, the system comprising: a transducer (14); A beamformer (12, 16) configured to transmit and receive a sequence of pulses in a patient using the transducer (14), the sequence of pulses being for scattering parameters and for a first shear wave parameter and a second shear wave parameter; An image processor (18) configured to generate a score for an index of the disease activity based on a combination of the scattering parameter, the first shear wave parameter, and the second shear wave parameter; And A display (20) configured to display the score of the index of the disease activity; Wherein the image processor (18) is configured to estimate a fat component of the patient's liver based on the scattering parameter, and the image processor (18) is configured to generate the score as an ultrasound-derived non-alcoholic liver disease activity based on the fat component, the first shear wave parameter, and the second shear wave parameter.

14. The system according to claim 13, wherein, The image processor (18) is configured to generate the score using a machine learning classifier.

15. The system according to claim 13, wherein The image processor (18) is configured to generate the score using a logistic regression model of the scattering parameter, the first shear wave parameter, and the second shear wave parameter.

16. The system according to claim 13, wherein, The scattering parameter includes an acoustic backscatter coefficient, the first shear wave parameter includes a shear wave speed, and the second shear wave parameter includes a shear wave damping ratio.

17. A computer-readable storage medium having instructions for implementing a method for estimating liver disease activity using an ultrasound system, the method comprising: Determining (30) a plurality of scattering parameters of a patient's liver tissue by the ultrasound system; Determining (32) a plurality of shear wave parameters of the patient's liver tissue by the ultrasound system; Estimating (34) a fat component based on at least one of the scattering parameters; Estimating (40) a level of the liver disease activity based on at least one of the fat component and the shear wave parameters; And Displaying (42) the level of the liver disease activity; Wherein estimating (34) the fat component includes estimating (34) based on acoustic scattering and acoustic attenuation as the scattering parameter, and wherein estimating (40) the level of the liver disease activity includes estimating (40) based on the fat component and based on a shear wave speed and a shear wave damping ratio as the shear wave parameters.

Images