METHOD AND SYSTEM FOR AUTOMATIC ESTIMATION OF SHEAR MODULUS AND VISCOSITY FROM SHEAR WAVE IMAGING

Abstract

A method and system for the automated, non-invasive estimation of shear modulus and viscosity of biological tissue from shear wave imaging are presented. Shear wave images are acquired to assess the mechanical properties of a patient's organ. Shear wave propagation within the tissue, as seen in the shear wave images, is simulated based on shear modulus and viscosity values ​​for the tissue using a shear wave propagation model. The simulated shear wave propagation is compared to observed shear wave propagation in the tissue shear wave images using a cost function.Patient-specific shear modulus and viscosity values ​​for the tissue are estimated to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation.

Description

I. BACKGROUND OF THE INVENTION The present invention relates to an image-based estimation of tissue mechanical properties, and more particularly to an automatic estimation of tissue shear modulus and viscosity from shear-wave imaging. Shear-wave imaging (SWI) is an ultrasound-based imaging modality that can provide important information about tissue structure and integrity. In SWI, an ultrasound probe is used to generate and track shear waves propagating through biological tissue. A radiation force from an acoustic radiation force impulse (ARFI) generates the shear waves in the tissue. The propagation velocity of the shear waves in the tissue is measured, and a time-domain sequence of images showing the shear wave displacement iscaptured by the ultrasound probe. Qualitative measurements have been derived from SWI, for example, to highlight stiffer or softer tissues in a region of interest. SWI has numerous clinical applications, especially in oncology, where it can be used to differentiate between malignant and benign lesions. However, accurately estimating tissue elasticity and viscosity from SWI remains a significant challenge. While the elastic modulus can be directly related to shear wave velocity in homogeneous isotropic solids, this relationship does not hold in viscous media such as biological tissues, where viscosity also affects shear wave velocity. Therefore, it is important to consider both properties in the estimation process to achieve an accurate assessment of tissue mechanics and tissue structure. 3035531 2 BREFSUMMARY OF THE INVENTION The present invention provides a method and system for estimating patient-specific values ​​for the shear modulus and viscosity of biological tissue from shear wave imaging (SWI). Embodiments of the present invention couple a forward shear wave propagation model in soft media with an optimization algorithm to automatically estimate both the shear modulus and viscosity from shear wave images. The shear wave displacement is calculated from a forward shear wave propagation model, taking into account the shear modulus and viscosity. A cost function, which evaluates the difference between the computer-calculated shear wave propagation and the shear wave propagation observed in the shear wave images, is then minimized, for example, using a zero-intercept optimizer.gradient, to estimate customized values ​​for the shear modulus and viscosity of the tissue. Embodiments of the present invention may use a hierarchical approach, which allows for the estimation of spatial maps of shear modulus and viscosity, which can then be displayed to an end user for diagnostic purposes. A map obtained from an initial shear modulus estimate from shear wave imaging using the homogeneous tissue approximation can also be used as the initialization for the proposed approach, which estimates both the shear modulus and viscosity. In one embodiment of the present invention, shear wave propagation in a patient's tissue is simulated based on shear modulus and viscosity values ​​for the tissue using a shear wave propagation computation model. The propagationThe simulated shear wave propagation is compared to observed shear wave propagation in tissue shear wave images using a cost function. Patient-specific shear modulus and viscosity values ​​for the tissue are estimated to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation. In a first aspect, a method for estimating a patient's tissue shear modulus and viscosity based on tissue shear wave images comprises: simulating shear wave propagation in the tissue based on shear modulus and viscosity values ​​for the tissue using a shear wave propagation computation model; the comparison of simulated shear wave propagation to observed shear wave propagation in wave imagestissue shear using a cost function; and the estimation of patient-specific tissue shear modulus and viscosity values ​​to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation. Depending on the embodiment, the method may include one or more of the following features: - visualization of patient-specific shear modulus and viscosity values ​​estimated by generating spatial maps of shear modulus and viscosity. - simulation of shear wave propagation in the tissue based on tissue shear modulus and viscosity values ​​using a shear wave propagation calculation model includes: calculating a shear displacement of the tissue in a spatial domain corresponding to at least one region of the shear wave images.each of a plurality of time steps based on the shear modulus and viscosity values ​​for the tissue. - the calculation of a shear displacement of the tissue in a spatial domain corresponding to at least one region of the 30 shear wave images at each of a plurality of time steps based on the shear modulus and viscosity values ​​for the tissue includes: the calculation of the shear displacement of the tissue in the spatial domain by resolving p&iz=iV2u+?J8V2u at each time step, where p is the shear modulus, q is the viscosity, p is a density of the tissue, and u is the shear displacement of the tissue. - The simulation of shear wave propagation in tissue 5 based on shear modulus and viscosity values ​​for the tissue using a shear wave propagation calculation model includes: the simulation of shear wave propagation in the tissueusing the shear wave propagation calculation model with predetermined initial shear modulus and viscosity values ​​for the tissue. - the simulation of shear wave propagation in the tissue based on shear modulus and viscosity values ​​for the tissue using a shear wave propagation calculation model includes: the calculation of an initial shear modulus value for the tissue based on a measured shear wave velocity in the shear wave images with an assumption that the tissue is non-viscous; and the simulation of shear wave propagation in the tissue using the shear wave propagation calculation model with the calculated initial shear modulus value and a predetermined initial viscosity value. - the simulation of shear wave propagation in the tissue based on modulus valuesof shear and viscosity for the fabric using a shear wave propagation calculation model includes: the determination of initial boundary conditions for a 25 shear displacement in the fabric based on a number of first frames of the shear wave images; and the simulation of shear wave propagation in the fabric starting from the initial boundary conditions for shear displacement in the fabric based on the shear modulus and viscosity values ​​for the fabric using the 30 shear wave propagation calculation model. - the simulation of shear wave propagation in the fabric based on shear modulus and viscosity values ​​for the fabric in 3035531 5 using a shear wave propagation calculation model includes: the determination of initial boundary conditions for shear wave propagation using aa direct model of an acoustic radiation force impulse (ARFI) used to generate the shear waves in the shear wave images, wherein the direct ARFI model is fitted to current probe parameters of an ultrasonic probe used to acquire the shear wave images; and the simulation of shear wave propagation in the tissue starting from initial boundary conditions based on shear modulus and viscosity values ​​for the tissue using the shear wave propagation calculation model. - the comparison of the simulated shear wave propagation to an observed shear wave propagation in the tissue shear wave images using a cost function includes: the direct comparison of a simulated shear wave displacement to an observed shear wave displacement at a particular time frame for a pluralityof locations in a spatial domain of shear wave images using the cost function. - the direct comparison of a simulated shear wave displacement 20 to an observed shear wave displacement at a particular time frame for a plurality of locations in a spatial domain of shear wave images using the cost function includes: the calculation of a cross-correlation normalized cost function that directly compares the simulated shear wave displacement to the observed shear wave displacement 25 at the particular time frame for the plurality of locations in a spatial domain of shear wave images. - the direct comparison of a simulated shear wave displacement to an observed shear wave displacement at a particular time frame 30 for a plurality of locations in a spatial domain of shear wave images using the functionThe cost function includes: calculating a sum of squared distances that directly compares the simulated shear wave displacement to the observed shear wave displacement at the particular time frame for the plurality of locations in the spatial domain of the shear wave images. The comparison of simulated shear wave propagation to observed shear wave propagation in the tissue shear wave images using a cost function includes: directly comparing a simulated shear wave displacement to an observed shear wave displacement for a particular location in a spatial domain of the shear wave image frame at each of a plurality of time steps using the cost function. The comparison of simulated shear wave propagation to observed shear wave propagation in the shear wave images.tissue shear using a cost function includes: 15 - direct comparison of a simulated shear wave displacement to an observed shear wave displacement for each of a plurality of locations in a spatial domain of the shear wave image frame at each of a plurality of time steps using the cost function. 20 - comparison of simulated shear wave propagation to observed shear wave propagation in tissue shear wave images using a cost function includes: computing a cost function in radio-frequency space that compares a measured radio-frequency shift of the observed shear wave propagation 25 with a calculated radio-frequency shift of the simulated shear wave propagation. - estimation of patient-specific tissue shear modulus and viscosity values ​​to optimize the functionThe cost of comparing simulated shear wave propagation to observed shear wave propagation includes: estimating an initial shear modulus and viscosity value for the tissue in a target region of the shear wave images; simulating shear wave propagation using the shear wave propagation calculation model with current values ​​for the shear modulus and viscosity of the tissue in the target region; generating an error map showing a spatial distribution of error values ​​between the simulated shear wave propagation using current values ​​for the shear modulus and viscosity and the observed spatial propagation; and segmenting the error map to identify tissue subregions within the target region based on the error values ​​between the shear wave propagation.simulated using current values ​​for the shear modulus and viscosity and observed spatial propagation; and estimation of spatially varying shear modulus and viscosity values ​​for the tissue in the target region by calculating separate values ​​for the shear modulus and viscosity for each of the identified tissue subregions to optimize the cost function using multi-criteria optimization. - estimation of patient-specific tissue shear modulus and viscosity values ​​to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation further includes: repeating the steps of shear wave propagation simulation using the shear wave propagation calculation model with current values ​​for the shear modulus and viscosity of the tissue in the regiontarget, error map generation, error map segmentation, and estimation of spatially varying shear modulus and viscosity values ​​for the tissue in the target region until a number of sub-regions in the target region converge. - the repetition of the simulation steps of shear wave propagation in tissue based on shear modulus and viscosity values ​​for the tissue using a shear wave propagation calculation model, comparison of the simulated shear wave propagation to observed shear wave propagation in tissue shear wave images using a cost function, and estimation of patient-specific shear modulus and viscosity values ​​for the tissue to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation,until the simulated shear wave propagation 5 converges to the shear wave propagation observed in the shear wave images. - The estimated patient-specific tissue shear modulus and viscosity values ​​are spatially varying shear modulus and viscosity values, and the visualization 10 of the estimated patient-specific tissue shear modulus and viscosity values ​​by generating spatial shear modulus and viscosity maps includes: generating a color shear modulus map by mapping the estimated spatially varying patient-specific shear modulus values ​​onto a spatial domain of the shear wave images 15 and assigning a color to each pixel in the shear modulus color map based on the estimated patient-specific shear modulus value forthis pixel; and the generation of a viscosity color map by mapping the estimated patient-specific viscosity values ​​to spatial variations on the spatial domain 20 of the shear wave images and assigning a color to each pixel in the viscosity color map based on the estimated patient-specific viscosity value for that pixel. In another aspect, the invention relates to a non-transient, computer-readable medium storing computer program instructions 25 (code) for estimating a patient's shear modulus and tissue viscosity based on tissue shear wave images, the computer program instructions, when executed by a processor, causing the processor to perform operations implementing the steps of the present method. 30 In another aspect, an apparatus for estimating a patient's shear modulus and tissue viscosity based onTissue shear wave imaging includes: 3035531 9 a means for simulating shear wave propagation in the tissue based on shear modulus and viscosity values ​​for the tissue using a shear wave propagation calculation model; a means for comparing the simulated shear wave propagation 5 to observed shear wave propagation in tissue shear wave images using a cost function; and a means for estimating patient-specific shear modulus and viscosity values ​​for the tissue to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation 10. Depending on the embodiment, the apparatus may include one or more of the following features: - a means for visualizing the patient-specific shear modulus and viscosity values ​​estimated ingenerating spatial maps of 15 shear modulus and viscosity. - the means for simulating shear wave propagation in the tissue based on shear modulus and viscosity values ​​for the tissue using a shear wave propagation calculation model includes: a means for calculating a shear displacement of the tissue 20 in a spatial domain corresponding to at least one region of the shear wave images at each of a plurality of time steps based on the shear modulus and viscosity values ​​for the tissue. - the means for comparing the simulated shear wave propagation to an observed shear wave propagation in the shear wave images 25 of the tissue using a cost function includes: a means for directly comparing a simulated shear wave displacement to an observed shear wave displacement at a particular time frame for aplurality of locations in a spatial domain of shear wave images using the cost function. 30 - The means for comparing simulated shear wave propagation to observed shear wave propagation in tissue shear wave images using a cost function includes: a means 3035531 10 for directly comparing a simulated shear wave displacement to an observed shear wave displacement for a particular location in a spatial domain of the shear wave image frame at each of a plurality of time steps using the cost function. 5 - The means for comparing simulated shear wave propagation to observed shear wave propagation in tissue shear wave images using a cost function includes: a means for directly comparing a simulated shear wave displacement to a shear wave displacementobserved shear for each of a plurality of locations in a spatial domain of the shear wave image frame at each of a plurality of time steps using the cost function. - the means for comparing simulated shear wave propagation 15 to observed shear wave propagation in tissue shear wave images using a cost function includes: a means for comparing a measured radiofrequency shift of the observed shear wave propagation with a calculated radiofrequency shift of the simulated shear wave propagation. 20 - the means for estimating patient-specific tissue shear modulus and viscosity values ​​to optimize the cost function by comparing simulated shear wave propagation to observed shear wave propagation includes: a means for estimating an initial shear modulus value and a valueinitial viscosity for 25 tissue in a target region of shear wave images; a means for simulating shear wave propagation using the shear wave propagation calculation model with current values ​​for the shear modulus and viscosity of the tissue in the target region; a means for generating an error map showing a spatial distribution of error values ​​between the simulated shear wave propagation using current values ​​for the shear modulus and viscosity and the observed spatial propagation; a means for segmenting the error map to identify tissue subregions within the target region based on the error values ​​between the simulated shear wave propagation using current values ​​for the shear modulus and viscosity and the observed spatial propagation; and a means for estimating modulus values ​​of 5shear and viscosity with spatial variations for the tissue in the target region by calculating separate values ​​for the shear modulus and viscosity for each of the identified tissue subregions to optimize the cost function. These and other advantages of the invention will become apparent to the person skilled in the art by reference to the detailed description that follows and the accompanying drawings. BRIEF DESCRIPTION OF THE DRAWINGS Figure 1 illustrates a method for non-invasive estimation of the shear modulus and viscosity of a target region of biological tissue according to an embodiment of the present invention; Figure 2 illustrates an application infrastructure for estimating patient-specific shear modulus and viscosity values ​​from shear wave images using a shear wave propagation computation model according to an embodiment of the present invention; Figure 3 illustrates aA method for estimating patient-specific shear modulus and viscosity values ​​from shear wave images using a shear wave propagation model according to an embodiment of the present invention; Figure 4 illustrates examples of observed and simulated shear wave propagation after estimation of shear modulus and viscosity parameters; and Figure 5 is a high-level functional diagram of a computer capable of implementing the present invention. DETAILED DESCRIPTION The present invention relates to an automatic estimation of the shear modulus and viscosity of biological tissue from shear wave imaging (SWI). Embodiments of the present invention are described herein to give a visual understanding of the method for estimating patient-specific values ​​for shear modulus and viscosity.of a tissue region. A digital image is often composed of digital representations of one or more objects (or shapes). The digital representation of an object is often described here in terms of object identification and manipulation. Such manipulations are virtual manipulations performed in the memory or other circuits / hardware of a computer system. Accordingly, it should be understood that embodiments of the present invention can be implemented within a computer system using data stored within the computer system. Embodiments of the present invention use an inverse modeling approach to estimate patient-specific shear modulus and viscosity values ​​from shear wave images using a forward shear wave propagation model. The forward shear wave propagation model is used toGiven a set of shear modulus and viscosity parameters and an initial condition, calculate the shear wave propagation in a multidimensional (2D / 3D) grid lattice whose extent covers the observed domain. The calculated shear wave propagation is then compared with the observed shear wave propagation in shear wave images. If the calculated shear wave propagation does not match the observed shear wave propagation, an optimizer process is used to automatically estimate the optimal shear modulus and viscosity, based on a selected cost function. For example, in one possible implementation, a normalized cross-correlation measure could be used as a cost function to compare the calculated and observed shear wave propagations, but any similarity measure could be used.The optimization process can be performed using a gradient-free optimizer, but the present invention is not limited to this. A hierarchical method 5 can be used to estimate spatially varying values ​​for the shear modulus and viscosity over a target tissue region. A first map of shear modulus estimates can be obtained using the shear wave velocity, which is related to the shear modulus in non-viscous media. This map can be used to initialize the estimation procedure 10 and also to identify regions with varying biomechanical properties. Figure 1 illustrates a method for non-invasive estimation of the shear modulus and viscosity of a target region of biological tissue according to an embodiment of the present invention. In step 102, 15 shear wave images are received. The shear wave images are acquired by imagingShear wave imaging (SWI) uses an ultrasound probe to generate and track shear waves propagating through biological tissue. An acoustic radiation force pulse (ARFI) generates 20 shear waves in the tissue, and a time-dependent sequence of images showing the movement of the shear waves through the tissue over time is captured by the ultrasound probe. The shear wave images can be a time-dependent sequence of two-dimensional (2D) images or a time-dependent sequence of three-dimensional (3D) images. The 25 shear wave images can be received directly from the ultrasound probe, or they can be received by loading previously acquired shear wave images from a computer system's memory or storage, or by receiving previously acquired shear wave images over a data network. The 30 shear wave imagesmay include a target tissue region of a patient. For example, shear wave images may include a tissue region in which a tumor or lesion is present. 3035531 14 In step 104, patient-specific shear modulus and viscosity values ​​for tissue in a target region are estimated based on the shear wave images using a computational model for shear wave propagation. The target tissue region may refer to the entire tissue in the shear wave images or may refer to a particular region of interest within the shear wave images. Figure 2 illustrates an application infrastructure for estimating patient-specific shear modulus and viscosity values ​​from shear wave images using a computational model for shear wave propagation according to one embodiment of the presentinvention. As shown in Figure 2, Model 202 is a direct computational model of shear wave propagation. Given values ​​for the shear modulus and viscosity (p', rin) of the tissue in the target region, and an initial condition, Model 202 generates a simulation of shear wave propagation in the target region. The shear modulus and viscosity estimation application infrastructure is initialized with initial shear modulus and viscosity values ​​of P', rin. A cost function 204 compares the simulation 206 with a ground-reality shear modulus propagation 208 observed in 20 shear wave images, and sends the result of the comparison to an optimizer 210. The optimizer 210 calculates new parameter values ​​for the shear modulus and viscosity to minimize the cost function 204 and sends the new parameter values ​​for the modulus ofshear and viscosity are compared to model 202, which generates another simulation 206 25 using the new parameter values. When, based on the comparison result calculated by the cost function 204, the optimizer 210 determines that optimal values ​​for the shear modulus and viscosity have been found, minimizing the cost function 204, the optimizer 210 outputs the optimal values ​​for the shear modulus and viscosity. Model 202, cost function 204, and optimizer 210 are described in more detail in the description for the process in Figure 3. Figure 3 illustrates a method for estimating patient-specific shear modulus and viscosity values ​​from shear wave images using a shear wave propagation model according to an embodiment of the present invention. Method 5 in Figure 3 can be used to implementStep 104 of Figure 1. In step 302, shear wave propagation is simulated in the target region using a shear wave propagation model. The shear wave propagation model calculates the shear wave displacement for a given shear modulus and viscosity. For this purpose, the following partial differential equation for transverse waves can be used: μparu = μpeu + νnateu, where μpeu is the shear modulus, μeu is the viscosity, ρeu is the density (which can be assumed to be 1070 kg / m³ in biological tissues), and μeu is the tissue shear displacement. The wave propagates in 1-, 2-, or 3-dimensional space depending on Neumann, O-Dirichlet, or absorption boundary conditions defined according to the SWI acquisition parameterization. In one possible implementation, Neumann or absorption boundary conditions 20 can be used by default.In another embodiment, the organ of interest is segmented from a B-mode ultrasound image. The resulting boundaries are used to establish appropriate shear wave boundary conditions (e.g., Neumann), while in SWI areas where no boundary is visible, absorption boundary conditions are used. The B-mode and SW images are aligned by design if the probe does not move between the two acquisitions. This can be achieved completely transparently to the user by the hardware. The computational model can calculate the shear wave displacement on a multidimensional (2D / 3D) grid array whose extent covers the domain of the target tissue region in the shear wave images. As described above, the target tissue region can be the entire shear wave image or a defined region of interest within the shear wave images.In one possible implementation, generic values, such as values ​​from the literature or average values ​​stored in a database of known cases, can be used as initial values ​​for the shear modulus and tissue viscosity. In another possible implementation, the shear modulus can be estimated directly from the shear wave velocity in the shear wave images. In this case, the viscosity can be given an initial value of zero or a generic value, such as a value from the literature or an average value in a database of known cases. Initial boundary conditions are determined for the shear wave propagation calculation. In an advantageous embodiment, the first N frames of the acquired shear wave images are used to establish the initial conditions (e.g., N = 2). In particular,The displacement of the observed shear wave in the first N frames of the shear wave images provides an initial boundary condition, and then the computational model is used for simulated shear wave propagation. This ensures that the observed ground-reality shear wave in the shear wave images and the calculated shear wave begin in exactly the same state, and protects against a time lag between the observed and calculated shear waves. In another possible embodiment, a direct ARFI impulse model is used to determine the initial conditions for shear wave propagation. In this embodiment, the ultrasonic probe parameters are directly used to calculate the force due to the ARFI impulse. In an advantageous implementation, the finite element method (FEM) can be used to calculatethe propagation of shear waves in 1, 2, or 3-dimensional space. An implicit time-step approach can be used for greater stability. However, it should be understood that the present invention is not limited to the FEM method with an implicit time-step approach for computing shear wave propagation, and that other methods, such as explicit or finite-difference FEM methods, can also be used to compute shear wave propagation. Furthermore, the present invention is not limited to the use of the differential equation described above for a transverse wave as the shear wave propagation model, and other shear wave propagation models based on more complex models of viscoelastic materials can also be used. In step 304, the simulated shear wave propagation is compared to the propagationobserved shear wave propagation in shear wave images is estimated using a cost function. The shear modulus and viscosity are estimated by minimizing a cost function that calculates a similarity (or difference) between the calculated (simulated) shear wave propagation and the measured (observed) shear wave propagation in the shear wave images. In a first embodiment, the calculated shear wave propagation is directly compared with the shear wave displacement observed in the shear wave images. According to an advantageous implementation, a normalized cross correlation (NCC) can be used as a cost function that measures a similarity between the calculated and observed shear wave propagations. However, the present invention is not limited to the NCC and any other cost function, such as a high distance sum.squared, can be similarly used to measure the similarity between calculated and observed shear wave propagations. The NCC cost function can be expressed as: (f(x, Y) - (t(x, Y) - 3035531 18 where at is the standard deviation of the measured shear wave propagation t(x,y), of is the standard deviation of the calculated shear wave propagation f(X,y), is the mean of the measured dataset, f is the mean of the simulation dataset, and n is the number of elements compared. In one possible implementation, the NCC is calculated at a user-defined (or automatically selected) time frame over the entire domain of the target region. In this case, n refers to a number of locations (pixels / voxels), denoted as (x,y) in the equation above, against which the simulated shear wave displacement is compared to the observed shear wave displacement at a certain point in time. In anotherIn this implementation, the NCC can be calculated at a user-defined (or automatically selected) spatial position over the entire time domain. In this case, n refers to the number of time steps at which to compare the simulated shear wave displacement 15 to the observed shear wave displacement for a particular spatial location. In another possible implementation, the NCC can be calculated over the entire spacetime domain. In a second embodiment, as an alternative to the cost function directly comparing the simulated and observed shear wave propagations 20, an indirect estimation can be performed by working directly in radio frequency (RF) space. Intuitively, the shear wave displacement is captured by a shift in the radio frequency signal. By tracking this shift over time, the shear wave displacement can be estimated and thendisplayed for 25 the user. In this mode, the shear modulus and viscosity can be estimated by minimizing the differences between the measured RF shift and a calculated RF shift obtained as the difference in the amplitude of the simulated shear displacement at a specific location. One advantage of working directly in RF space is that the cost function 30 is not affected by any post-processing performed on the RF signal during shear wave displacement estimation. Another advantage of working directly in RF space is that the measured shear wave displacement 3035531 19 can be automatically smoothed via the fitted model after shear modulus and viscosity estimation (i.e., once the simulated shear wave propagation converges to the measured shear wave propagation). 5 In step 306, it is determined whether the shear wave propagationThe simulated shear wave propagation has converged to the observed shear wave propagation. For example, a difference value between the simulated and observed shear wave propagations, as calculated by the cost function, can be compared to a predetermined threshold value. The simulated shear wave propagation is determined to have converged to the observed shear wave propagation when the difference value is less than the threshold value. Otherwise, the simulated shear wave propagation is determined to have not converged to the observed shear wave propagation. Furthermore, in one possible implementation, the simulated shear wave propagation can be considered to have converged to the observed shear wave propagation if a predetermined maximum number of iterations is reached. If it is determined that the simulated shear wave propagation has not converged to the wave propagationIf the observed shear wave propagation is determined to converge to the observed shear wave propagation, the process proceeds to step 308. If it is determined that the simulated shear wave propagation has converged to the observed shear wave propagation, the process proceeds to step 310. In step 308, the shear modulus and viscosity parameters of the tissue in the target region are estimated by minimizing the cost function. A gradient-free optimizer can be used to calculate new values ​​for the shear modulus and viscosity that minimize the cost function. In an advantageous embodiment, the shear modulus and viscosity values ​​of the tissue in the target region can be calculated using a hierarchical coarse-to-fine approach to estimate spatially variable parameters for the shear modulus and viscosity. In such a hierarchical approach, in a first step, it is assumed that the tissue in the target region is homogeneous and has a single value.is calculated for 3035531 20 each of the shear modulus and viscosity for the entire target region. Then, the spatial domain of the target region is divided into sub-regions based on the spatial distribution of a simulation error (e.g., sum of high squared distances or measurement of NCC errors between the 5 calculated and measured shear wave propagations at each point in the spatial domain). Shear modulus and viscosity values ​​are estimated separately for each of the sub-regions. Specifically, after the initial estimation of the shear modulus and viscosity with the assumption of homogeneous tissue, another shear wave propagation simulation is run by the computational model using the updated shear modulus and viscosity values ​​and the error (e.g., sum of high squared differences or NCC) between the calculated and observed shear wave propagations.In an advantageous implementation, the same measure used to calculate the cost function (e.g., 15 NCC, squared high difference sum, etc.) can be used to calculate the error, but the present invention is not limited to this, and it is also possible that different measures could be used for cost function evaluation and error calculation at this stage. An error map can be generated by mapping the calculated error value at each point in the spatial domain onto an image of the spatial domain. For example, the error map can be an image in which the intensity or color at each pixel depends on the calculated error at that pixel. The error map is segmented to identify regions of the error map having similar error values. The error map can be segmented using any automated or semi-automated image segmentation technique, such as the creationfrom an intensity threshold, region growth, graphical cross-sections, random Walker segmentation, etc. Once the different regions are identified in the error map, a multi-criteria optimization can be used to estimate different shear modulus and viscosity parameters for each of the identified spatial regions to minimize as much as possible the cost function that compares the calculated shear wave propagation to the observed shear wave propagation. In one possible implementation, this optimization may only be performed for regions with high error values ​​(e.g., above a threshold). The steps of generating an error map, segmenting the error map to identify regions with different error values, and calculating the shear modulus and viscosity parameters for the identified regions can be repeated, and at eachIn each iteration, regions identified in the previous iteration can be divided into smaller regions. These steps can be iterated until the number of distinct regions identified in the spatial domain converges, meaning that the average or maximum global error is less than a certain threshold. Once the spatially variable shear modulus and viscosity parameters for the tissue in the target region are estimated, the process returns to step 302, and steps 302, 304, and 306 are repeated with the updated shear modulus and viscosity parameters. In particular, the computational model is used to simulate shear wave propagation with updated shear modulus and viscosity values ​​(step 302), the simulated shear wave propagation is compared to the shear wave propagation observed in shear wave images using the cost function (step 304), and it isIt is determined whether the simulated shear wave propagation has converged to the observed shear wave propagation (step 306). In step 310, once it is determined that the simulated shear wave propagation has converged to the observed shear wave propagation in the shear images, the process in Figure 3 ends and the final estimated values ​​of the shear modulus and viscosity parameters are returned. Returning to Figure 1, in step 106, the estimated shear modulus and viscosity values ​​for the target region are output. The estimated shear modulus and viscosity values ​​can be output by visualizing them on 1-, 2-, or 3-dimensional maps, which can be displayed on a computer system display device. For example, the 3035531 22 shear modulus valuesIdentified for each location within the spatial domain of the target tissue region, these values ​​can be mapped onto an image of the target region, which is then displayed on the display device. Shear modulus and viscosity values ​​can be visualized using respective color maps, where the color (or intensity) at each pixel or voxel in the spatial domain depends on the calculated shear modulus or viscosity value at that location. In one possible implementation, the error maps generated in step 308 of Figure 3 can be used to generate these color maps, as they are already divided into regions for which common shear modulus and viscosity values ​​have been calculated. The spatial maps of shear modulus and viscosity can then be displayed to a user (e.g., physician, technician, etc.) for diagnostic purposes.For example, in oncology applications, the target region in shear wave images may include a lesion or tumor, and the patient-specific shear modulus and viscosity values ​​estimated for the lesion or tumor may allow the user to diagnose the lesion or tumor as malignant or benign. In a possible application, the shear modulus and viscosity values ​​for a lesion or tumor and / or the surrounding region may be used as features and fed into a machine learning-based classifier, such as a support vector machine (SVM), which can then automatically classify the lesion or tumor. For example, based on the estimated shear modulus and viscosity values, the machine learning-based classifier may classify the lesion or tumor as malignant or benign.lesion / tumor as a particular type of lesion / tumor, or can classify the lesion / tumor to assign a particular grade to the lesion / tumor. It should be understood that the oncology applications described herein are not intended to limit the present invention, and that the method of Figure 1 can also be applied to other applications. In addition to spatial maps of shear modulus and viscosity, simulated shear wave propagation and shear wave images showing observed shear wave propagation can also be displayed, for example on a display device of a computer system, as well as the error maps generated in step 308 of Figure 3. Figure 4 illustrates examples of observed and simulated shear wave propagation. As shown in Figure 4, image 402 shows the measured (observed) shear wave propagation in the shear wave images for theThe entire spatial domain of the shear wave images is represented in Figure 10, and Figure 404 shows the measured (observed) shear wave propagation for a region of interest used for a cost function evaluation. Figure 406 shows the simulated shear wave propagation over the entire spatial domain, and Figure 408 shows the simulated shear wave propagation in the region of interest used for a cost function evaluation. In the example in Figure 4, the first two frames of the shear wave images are used as initial conditions for the shear wave propagation simulation, and only a portion of the space (region of interest) is used to evaluate the cost function to minimize the effects of boundary conditions. The actual field conditions for the fabric properties in the region of interest are a shear modulus of 4 kPa and a viscosity of 0 Pa·s. The results of the estimation inUsing the methods of Figures 1 and 3 for this example, the shear modulus is 4.01 kPa and the viscosity is 4.4 e⁻¹° Pa·s. The total space (Figures 402 and 406) shows a difference for wave propagation, but the method nevertheless manages to estimate values ​​close to the actual field values ​​since the evaluation was performed in a region of interest (Figures 404 and 408) far from the boundary conditions. The methods described above for a non-invasive estimation of patient-specific shear modulus and viscosity values ​​for biological tissue based on shear wave imaging (SWI) can be implemented on a computer using computer processors, memory units, storage devices, computer software, and other well-known components. A high-level functional diagram of such a computer is illustrated in Figure 5.Computer 502 contains a processor 504, which controls all of Computer 502's operation by executing computer program instructions that define such operation. The computer program instructions can be stored in the storage device 512 (for example, a magnetic disk) and loaded into memory 510 when execution of the computer program instructions is desired. Thus, the steps of the processes in Figures 1, 2, and 3 can be defined by the computer program instructions stored in memory 510 and / or storage 512 and controlled by the processor 504 executing the computer program instructions. An ultrasonic acquisition device 520, such as an ultrasonic probe, can be connected to Computer 502 to acquire shear wave images and input the shear wave images into Computer 502. It is possible that the ultrasonic acquisition device520 and computer 502 communicate wirelessly via a network. In one possible embodiment, computer 502 can be located remotely from the ultrasonic acquisition device 520, and the process steps 20 can be performed by computer 502 as part of a server-based or cloud-based service. Computer 502 also includes one or more network interface(s) 506 for communicating with other devices via a network. Computer 502 also includes other input / output devices 508 that allow user interaction with computer 502 (e.g., a display, keyboard, mouse, speakers, buttons, etc.). Such input / output devices 508 can be used in conjunction with a set of computer programs as an annotation tool to annotate volumes received from the ultrasonic acquisition device 520. Those skilled in the art will recognize that the implementationThe work of a real computer may also contain other components, and Figure 5 is a high-level representation of some of the components of such a computer for illustrative purposes. The preceding detailed description should be understood as being in all respects illustrative and by way of example, but not restrictive. It should be understood that the embodiments shown and described herein are only illustrative of the principles of the present invention and that various modifications may be implemented by those skilled in the art without departing from the spirit of the invention. Those skilled in the art may implement various other combinations of features without departing from the spirit of the invention.

Claims

Claims

1. A method for estimating shear modulus and viscosity of tissue of a patient based on shear wave images of the tissue, comprising: simulating shear wave propagation in the tissue based on shear modulus and viscosity values for the tissue using a shear wave propagation computational model; comparing the simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function; and estimating patient-specific shear modulus and viscosity values for the tissue to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation;wherein comparing the simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function comprises: calculating a cost function in radio frequency space that compares a measured radio frequency shift of the observed shear wave propagation with a calculated radio frequency shift of the simulated shear wave propagation.;

2. The method of claim 1, further comprising: visualizing the estimated patient-specific shear modulus and viscosity values by generating spatial shear modulus and viscosity maps.

3. The method of claim 1, wherein simulating shear wave propagation in the tissue based on shear modulus and viscosity values for the tissue using a shear wave propagation computational model comprises: computing a shear displacement of the tissue in a spatial domain corresponding to at least one region of the shear wave images at each of a plurality of time steps based on the shear modulus and viscosity values for the tissue.

4. The method of claim 3, wherein calculating a shear displacement of the tissue in a spatial domain corresponding to at least one region of the shear wave images at each of a plurality of time steps based on the shear modulus and viscosity values for the tissue comprises: calculating the shear displacement of the tissue in the spatial domain by solving for + at each time step, where j u is the shear modulus, T] is the viscosity, q is a density of the tissue, and u is the shear displacement of the tissue.

5. The method of claim 1, wherein simulating shear wave propagation in the tissue based on shear modulus and viscosity values for the tissue using a shear wave propagation computational model comprises: simulating shear wave propagation in the tissue using the shear wave propagation computational model with predetermined initialized shear modulus and viscosity values for the tissue.

6. The method of claim 1, wherein simulating shear wave propagation in the tissue based on shear modulus and viscosity values for the tissue using a shear wave propagation calculation model comprises: calculating an initial shear modulus value for the tissue based on a measured shear wave velocity in the shear wave images with an assumption that the tissue is inviscid; and simulating shear wave propagation in the tissue using the shear wave propagation calculation model with the calculated initial shear modulus value and a predetermined initial viscosity value.

7. The method of claim 1, wherein simulating shear wave propagation in the tissue based on shear modulus and viscosity values for the tissue using a shear wave propagation computational model comprises: determining initial boundary conditions for shear displacement in the tissue based on a number of first frames of the shear wave images; and simulating shear wave propagation in the tissue starting from the initial boundary conditions for shear displacement in the tissue based on shear modulus and viscosity values for the tissue using the shear wave propagation computational model.

8. The method of claim 1, wherein simulating shear wave propagation in the tissue based on shear modulus and viscosity values for the tissue using a shear wave propagation computational model comprises: determining initial boundary conditions for the shear wave propagation using a forward model of an acoustic radiation force pulse (ARFI) used to generate the shear waves in the shear wave images, wherein the forward model of the ARFI is fitted to current probe parameters of an ultrasound probe used to acquire the shear wave images;and simulation of shear wave propagation in the tissue starting from initial boundary conditions based on shear modulus and viscosity values for the tissue using the shear wave propagation computational model.;

9. The method of claim 1, wherein comparing simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function comprises: directly comparing simulated shear wave displacement to observed shear wave displacement at a particular time frame for a plurality of locations in a spatial domain of the shear wave images using the cost function.

10. The method of claim 9, wherein directly comparing a simulated shear wave displacement to an observed shear wave displacement at a particular time frame for a plurality of locations in a spatial domain of the shear wave images using the cost function includes: calculating a cross-correlation normalized cost function that directly compares the simulated shear wave displacement to the observed shear wave displacement at the particular time frame for the plurality of locations in a spatial domain of the shear wave images.

11. The method of claim 9, wherein directly comparing a simulated shear wave displacement to an observed shear wave displacement at a particular time frame for a plurality of locations in a spatial domain of the shear wave images using the cost function comprises: calculating a cost function sum of squared distances that directly compares the simulated shear wave displacement to the observed shear wave displacement at the particular time frame for the plurality of locations in the spatial domain of the shear wave images.

12. The method of claim 1, wherein comparing simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function comprises: directly comparing simulated shear wave displacement to observed shear wave displacement for a particular location in a spatial domain of the shear wave image frame at each of a plurality of time steps using the cost function.

13. The method of claim 1, wherein comparing simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function comprises: directly comparing simulated shear wave displacement to observed shear wave displacement for each of a plurality of locations in a spatial domain of the shear wave image frame at each of a plurality of time steps using the cost function.

14. The method of claim 1, wherein estimating tissue-specific shear modulus and viscosity values

15.

16. to the patient to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation includes: estimating an initial shear modulus value and an initial viscosity value for the tissue in a target region of the shear wave images; simulation of shear wave propagation using the shear wave propagation computational model with current values for the shear modulus and viscosity of the tissue in the target region; generating an error map showing a spatial distribution of error values between simulated shear wave propagation using current values for shear modulus and viscosity and observed spatial propagation; segmenting the error map to identify tissue subregions within the target region based on error values between simulated shear wave propagation using current values for shear modulus and viscosity and observed spatial propagation; and estimating spatially varying shear modulus and viscosity values for the tissue in the target region by calculating separate values for shear modulus and viscosity for each of the identified tissue subregions to optimize the cost function using multi-criteria optimization. The method of claim 14, wherein estimating patient-specific shear modulus and viscosity values for the tissue to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation further comprises: repeating the steps of simulating the shear wave propagation using the shear wave propagation computational model with current values for the shear modulus and viscosity of the tissue in the target region, generating an error map, segmenting the error map, and estimating spatially varying shear modulus and viscosity values for the tissue in the target region until a number of the subregions in the target region converge. The method of claim 1, further comprising:

17.

18. repeating the steps of simulating shear wave propagation in the tissue based on shear modulus and viscosity values for the tissue using a shear wave propagation computational model, comparing the simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function, and estimating patient-specific shear modulus and viscosity values for the tissue to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation, until the simulated shear wave propagation converges to the observed shear wave propagation in the shear wave images. The method of claim 2, wherein the estimated patient-specific shear modulus values and tissue viscosity values are spatially varying shear modulus values and viscosity values, and visualizing the estimated patient-specific shear modulus and tissue viscosity values by generating spatial shear modulus and viscosity maps comprises: generating a shear modulus color map by mapping the spatially varying estimated patient-specific shear modulus values onto a spatial domain of the shear wave images and assigning a color to each pixel in the shear modulus color map based on the estimated patient-specific shear modulus value for that pixel;and generating a viscosity color map by mapping the spatially varying estimated patient-specific viscosity values onto a spatial domain of the shear wave images and assigning a color to each pixel in the viscosity color map based on the estimated patient-specific viscosity value for that pixel.; Apparatus for estimating a patient's shear modulus and tissue viscosity based on tissue shear wave images, comprising: means for simulating shear wave propagation in the tissue based on shear modulus and viscosity values for the tissue using a shear wave propagation computational model; means for comparing the simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function; and means for estimating patient-specific shear modulus and viscosity values for the tissue to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation;wherein the means for comparing the simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function comprises: means for comparing a measured radio frequency shift of the observed shear wave propagation with a calculated radio frequency shift of the simulated shear wave propagation.;

19. The apparatus of claim 18, further comprising: means for visualizing the estimated patient-specific shear modulus and viscosity values by generating spatial shear modulus and viscosity maps.

20. The apparatus of claim 18, wherein the means for simulating shear wave propagation in the tissue based on shear modulus and viscosity values for the tissue using a shear wave propagation computational model comprises: means for computing a shear displacement of the tissue in a spatial domain corresponding to at least one region of the shear wave images at each of a plurality of time steps based on the shear modulus and viscosity values for the tissue.

21. Apparatus according to claim 18, wherein the means for comparing the simulated shear wave propagation to observed shear wave propagation in the images of tissue shear waves using a cost function comprises: means for directly comparing a simulated shear wave displacement to an observed shear wave displacement at a particular time frame for a plurality of locations in a spatial domain of the shear wave images using the cost function.

22. Apparatus according to claim 18, wherein the means for comparing the simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function comprises: means for directly comparing a simulated shear wave displacement to an observed shear wave displacement for a particular location in a spatial domain of the shear wave image frame at each of a plurality of time steps using the cost function.

23. The apparatus of claim 18, wherein the means for comparing the simulated shear wave propagation to observed shear wave propagation in the shear wave images of the tissue using a cost function comprises: means for directly comparing a simulated shear wave displacement to an observed shear wave displacement for each of a plurality of locations in a spatial domain of the shear wave image frame at each of a plurality of time steps using the cost function.

24. The apparatus of claim 18, wherein the means for estimating patient-specific shear modulus and viscosity values for the tissue to optimize the cost function by comparing the simulated shear wave propagation to the observed shear wave propagation comprises: means for estimating an initial shear modulus value and an initial viscosity value for the tissue in a target region of the shear wave images; means for simulating the shear wave propagation using the shear wave propagation computational model

25. with current values for the shear modulus and viscosity of the tissue in the target region; means for generating an error map showing a spatial distribution of error values between the simulated shear wave propagation using the current values for the shear modulus and viscosity and the observed spatial propagation; means for segmenting the error map to identify tissue subregions within the target region based on the error values between the simulated shear wave propagation using the current values for the shear modulus and viscosity and the observed spatial propagation; and a means for estimating spatially varying shear modulus and viscosity values for the tissue in the target region by calculating separate values for the shear modulus and viscosity for each of the identified tissue subregions to optimize the cost function. A non-transitory computer-readable medium storing computer program instructions for, when the computer program instructions are executed by a computer, implementing the method of any one of claims 1 to 17.

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