Viscoelastic parameter inversion method and related devices

Abstract

The application discloses a viscoelasticity parameter inversion method, and belongs to the field of medical image processing and computational imaging. The method obtains the time-space image sequence data of the measured soft tissue after excitation, extracts the shear wave group velocity by using the time-of-flight method, and equivalently takes the shear wave group velocity as the phase velocity anchor point under the zero-frequency limit condition to determine the static shear modulus; the two-dimensional fast Fourier transform is used to extract the middle-high frequency band phase velocity dispersion curve; the rheological model of the measured soft tissue is constrained based on the static shear modulus, and the single-parameter fitting of the viscosity coefficient is combined with the middle-high frequency band phase velocity dispersion curve to obtain the viscoelasticity parameter.

Description

Technical Field

[0001] This invention relates to the field of medical image processing and computational imaging, specifically to a viscoelastic parameter inversion method and related equipment. Background Technology

[0002] Shear wave mechanics imaging can analyze the propagation characteristics of shear waves in the tested soft tissue and invert the viscoelastic parameters of the soft tissue, thus providing a basis for non-invasive detection of tissues such as the liver, breast, and muscle. Existing viscoelastic parameter inversion methods mainly include time-domain and frequency-domain methods. Time-domain methods, such as the time-of-flight method, are relatively simple to calculate and can extract shear wave group velocities, but they often struggle to effectively separate viscoelastic parameters. Frequency-domain methods, such as the two-dimensional fast Fourier transform, can extract phase velocity dispersion curves and combine them with rheological models for parameter inversion, but due to limitations such as the length of the region of interest, low-frequency information is easily missing or contains significant errors. Furthermore, when performing parameter inversion on rheological models based on incomplete mid-to-high frequency phase velocity dispersion curves, problems such as severe parameter coupling, local optima, and insufficient inversion stability easily arise, affecting the accuracy and reliability of the viscoelastic parameter inversion results. Summary of the Invention

[0003] The purpose of this invention is to provide a viscoelastic parameter inversion method and related equipment to overcome the technical problems of insufficient constraints on the rheological model when performing parameter inversion based on the phase velocity dispersion curve in the mid-to-high frequency band due to the lack of low-frequency information in the prior art, which leads to severe parameter coupling, insufficient inversion stability and low accuracy of viscoelastic parameter inversion.

[0004] To achieve the above objectives, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for inverting viscoelastic parameters, comprising: Acquire spatiotemporal image sequence data of the tested soft tissue after stimulation; Based on the spatiotemporal image sequence data, the time-of-flight method is used to extract the shear group velocity, and the shear group velocity is equivalent to the phase velocity anchor point under the zero-frequency limit condition to determine the static shear modulus of the tested soft tissue. Based on the spatiotemporal image sequence data, a two-dimensional fast Fourier transform is used to extract the phase velocity dispersion curve in the mid-to-high frequency band. The rheological model of the tested soft tissue is constrained based on the static shear modulus, and the viscosity coefficient is fitted with a single parameter using the mid-to-high frequency phase velocity dispersion curve to obtain the viscoelastic parameters of the tested soft tissue.

[0005] In one embodiment of the present invention, the step of extracting shear group velocity using the time-of-flight method based on the spatiotemporal image sequence data includes: Determine the arrival time of the shear wave at different spatial locations along the propagation direction; Establish a distance-time correspondence between each spatial location and the corresponding shear wave arrival time, and determine the shear wave group velocity based on the distance-time correspondence.

[0006] In one embodiment of the present invention, determining the arrival time of the shear wave at different spatial locations along the propagation direction includes: The target for tracking is determined to be either a positive wave peak or a negative wave trough based on the initial wave field energy distribution. Under the polarity locking condition of the tracked target, the arrival time of shear waves at different spatial locations of the same wavefront is extracted.

[0007] In one embodiment of the present invention, the extraction of the arrival time of shear waves at different spatial locations on the same wavefront includes: Parabolic interpolation is used to achieve subpixel-level shear wave arrival time extraction.

[0008] In one embodiment of the present invention, the shear wave group velocity is determined by performing robust linear regression on the distance-time correspondence using a random sampling consensus algorithm and removing outliers.

[0009] In one embodiment of the present invention, the step of extracting the mid-to-high frequency phase velocity dispersion curve using a two-dimensional fast Fourier transform based on the spatiotemporal image sequence data includes: A Hanning window is applied to the wavefield time rate of change matrix corresponding to the spatiotemporal image sequence data; A two-dimensional fast Fourier transform is performed on the wavefield time rate of change matrix after processing with the Hanning window to obtain the frequency-wavenumber spectrum. Search for energy ridges in the frequency-wavenumber spectrum and extract the mid-to-high frequency phase velocity dispersion curves based on the energy ridges.

[0010] In one embodiment of the present invention, the rheological model of the tested soft tissue is a Kelvin-Voigt rheological model; the static shear modulus is determined based on the density of the tested soft tissue and the shear group velocity; when performing single-parameter fitting on the viscosity coefficient, the static shear modulus is substituted as a known constant into the Kelvin-Voigt rheological model, and the viscosity coefficient is fitted in conjunction with the mid-to-high frequency phase velocity dispersion curve, so that the inversion of the viscoelastic parameters is transformed from multi-parameter optimization to single-parameter optimization for the viscosity coefficient.

[0011] In a second aspect, the present invention provides a viscoelastic parameter inversion system, comprising: The data preprocessing module is used to reconstruct the acquired wave field signal to obtain the spatiotemporal image sequence data of the tested soft tissue after excitation; The time-domain wave velocity tracking module is used to extract the shear wave group velocity based on the spatiotemporal image sequence data using the time-of-flight method, and to convert the shear wave group velocity into a phase velocity anchor point under the zero-frequency limiting condition in order to determine the static shear modulus of the tested soft tissue. The frequency domain dispersion extraction module is used to extract the mid-to-high frequency phase velocity dispersion curves based on the spatiotemporal image sequence data using a two-dimensional fast Fourier transform. The joint inversion module is used to constrain the rheological model of the tested soft tissue based on the static shear modulus, and to perform single-parameter fitting of the viscosity coefficient by combining the mid-to-high frequency phase velocity dispersion curve to obtain the viscoelastic parameters of the tested soft tissue.

[0012] Thirdly, the present invention provides a computer device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the steps of the viscoelastic parameter inversion method as described above.

[0013] Fourthly, the present invention provides a computer-readable storage medium storing a computer program that, when executed by a processor, implements the steps of the viscoelastic parameter inversion method as described above.

[0014] Compared with the prior art, the present invention has the following beneficial technical effects: Firstly, this invention provides a viscoelastic parameter inversion method. By acquiring spatiotemporal image sequence data of a tested soft tissue after excitation, the time-of-flight method is used to extract shear group velocities, which are then equated to phase velocity anchor points under zero-frequency limiting conditions to determine the static shear modulus of the tested soft tissue. Simultaneously, a two-dimensional fast Fourier transform is used to extract mid-to-high frequency phase velocity dispersion curves, and the rheological model of the tested soft tissue is constrained based on the static shear modulus. The viscosity coefficient is then fitted using a single parameter in conjunction with the mid-to-high frequency phase velocity dispersion curves to obtain the viscoelastic parameters of the tested soft tissue. Therefore, constraint information corresponding to the low-frequency response is introduced into the parameter inversion process, which can compensate for the insufficient constraint on the rheological model when relying solely on mid-to-high frequency phase velocity dispersion curves for parameter inversion, reduce parameter coupling, and improve inversion stability and accuracy of viscoelastic parameter inversion.

[0015] Secondly, this invention provides a viscoelastic parameter inversion system. Through the coordinated operation of a data preprocessing module, a time-domain wave velocity tracking module, a frequency-domain dispersion extraction module, and a joint inversion module, it achieves the processing of spatiotemporal image sequence data of the tested soft tissue after excitation, extraction of shear group velocity, extraction of mid-to-high frequency phase velocity dispersion curves, and inversion of viscoelastic parameters. This system can combine the phase velocity anchor point under the zero-frequency limiting condition with the mid-to-high frequency phase velocity dispersion curves to effectively constrain the rheological model of the tested soft tissue and complete single-parameter fitting of the viscosity coefficient. This improves the problems of severe parameter coupling, insufficient inversion stability, and low accuracy of viscoelastic parameter inversion that are prone to occur in existing systems when low-frequency information is missing.

[0016] Thirdly, the present invention provides a computer device that, through a processor executing a specific computer program, can efficiently implement the steps of the method of the present invention. When performing data processing tasks, the computer device can accurately perform numerical calculations and logical judgments, avoiding errors caused by human factors. At the same time, since the computer program has high stability and reliability, it can ensure the accuracy and consistency of the data processing results.

[0017] Fourthly, the present invention provides a computer-readable storage medium. By programming the steps of the method of the present invention into a computer program and storing it on the computer-readable storage medium, users can easily load these programs onto any compatible computer device and execute them without rewriting or converting the code, which greatly improves the convenience and flexibility of program execution. Attached Figure Description

[0018] Figure 1 This is a flowchart of the viscoelastic parameter inversion method in an embodiment of the present invention.

[0019] Figure 2 This is a schematic diagram of the single-parameter fitting results of the rheological model using the time-of-flight method group velocity as the anchor point in an embodiment of the present invention.

[0020] Figure 3 This is a schematic diagram of the viscoelastic parameter inversion system in an embodiment of the present invention. Detailed Implementation

[0021] Among existing viscoelastic parameter inversion methods, although the time-of-flight method can extract shear wave group velocities, it is difficult to achieve effective separation of viscoelastic parameters based solely on this result. Although the two-dimensional fast Fourier transform can extract phase velocity dispersion curves in the mid-to-high frequency bands and combine them with rheological models for parameter inversion, it is prone to insufficient constraints on the rheological model when low-frequency information is missing. This leads to severe parameter coupling, insufficient inversion stability, and low accuracy of viscoelastic parameter inversion.

[0022] Based on the above background, this invention proposes a viscoelastic parameter inversion method and related equipment. By equating the shear group velocity extracted using the time-of-flight method to the phase velocity anchor point under the zero-frequency limit condition, the static shear modulus of the tested soft tissue is determined. Furthermore, the viscosity coefficient is fitted with a single parameter by combining the mid-to-high frequency phase velocity dispersion curve extracted using two-dimensional fast Fourier transform. This enhances the constraint on the rheological model, reduces the degree of parameter coupling, and improves the stability and accuracy of viscoelastic parameter inversion.

[0023] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] Example 1: In this embodiment, as Figure 1 As shown, a viscoelastic parameter inversion method is provided. This method is used to analyze the mechanical response of tested soft tissues and output viscoelastic parameters reflecting the mechanical properties of the tested soft tissues. This viscoelastic parameter inversion method can be applied to mechanical testing scenarios of tested soft tissues such as the liver, breast, and muscle, and can also be integrated into relevant image processing workflows to support quantitative analysis of the tested soft tissues.

[0025] The viscoelastic parameter inversion method includes the following steps: S101, acquire spatiotemporal image sequence data of the tested soft tissue after stimulation.

[0026] In this step, the acquired object is the spatiotemporal image sequence data of the tested soft tissue under external excitation. This spatiotemporal image sequence data characterizes the dynamic response process of the tested soft tissue in both spatial and temporal dimensions, reflecting the propagation of shear waves within the soft tissue. Acquiring this spatiotemporal image sequence data provides a unified data foundation for subsequent extraction of shear wave group velocities and mid-to-high frequency phase velocity dispersion curves. In other words, the spatiotemporal image sequence data carries both low-frequency response information and mid-to-high frequency response information; therefore, it serves as the fundamental input for viscoelastic parameter inversion.

[0027] S102, based on the spatiotemporal image sequence data, the shear group velocity is extracted using the time-of-flight method, and the shear group velocity is equivalent to the phase velocity anchor point under the zero-frequency limiting condition to determine the static shear modulus of the tested soft tissue.

[0028] In this step, the time-of-flight method is used to process the spatiotemporal image sequence data to obtain the shear wave group velocity. This shear wave group velocity characterizes the velocity properties during the overall propagation of the shear wave. Since the response under the zero-frequency limiting condition can reflect the static mechanical properties of the tested soft tissue, the shear wave group velocity can be equivalently converted to the phase velocity anchor point under the zero-frequency limiting condition, allowing for the further determination of the static shear modulus of the tested soft tissue. Thus, the static shear modulus is no longer merely a parameter to be determined, but becomes known constraint information in the subsequent parameter inversion process. The purpose of this step is to transform the time-domain analysis results into physical quantities that can constrain the rheological model of the tested soft tissue, thereby providing low-frequency constraints for subsequent parameter inversion.

[0029] S103, Based on the spatiotemporal image sequence data, a two-dimensional fast Fourier transform is used to extract the phase velocity dispersion curve in the mid-to-high frequency band.

[0030] In this step, a two-dimensional fast Fourier transform is used to perform frequency domain analysis on the spatiotemporal image sequence data to extract the mid-to-high frequency phase velocity dispersion curve. This mid-to-high frequency phase velocity dispersion curve characterizes the propagation response changes of the tested soft tissue under different frequency conditions, reflecting the dispersion characteristics of the tested soft tissue. Compared with the static shear modulus obtained in S102, the mid-to-high frequency phase velocity dispersion curve describes the dynamic response characteristics of the tested soft tissue in the mid-to-high frequency range. Therefore, the mid-to-high frequency phase velocity dispersion curve and the static shear modulus are correlated in terms of their characterization objects and complement each other in terms of frequency band information, jointly constituting the input basis for subsequent viscoelastic parameter inversion.

[0031] S104. The rheological model of the tested soft tissue is constrained based on the static shear modulus, and the viscosity coefficient is fitted with a single parameter by combining the mid-to-high frequency phase velocity dispersion curve to obtain the viscoelastic parameters of the tested soft tissue.

[0032] In this step, the static shear modulus determined in S102 is introduced as a constraint into the rheological model of the tested soft tissue, and the mid-to-high frequency phase velocity dispersion curve extracted in S103 is used as the fitting basis to perform single-parameter fitting on the viscosity coefficient. Since the static shear modulus has already been determined by the shear group velocity, there is no need to simultaneously optimize the static shear modulus during the parameter inversion process; therefore, the parameter inversion object is focused on the viscosity coefficient. Based on the synergistic effect of the static shear modulus and the mid-to-high frequency phase velocity dispersion curve, the rheological model of the tested soft tissue can utilize both the constraint information under the zero-frequency limit condition and the dispersion information in the mid-to-high frequency range during the fitting process, thereby obtaining the viscoelastic parameters of the tested soft tissue. This approach helps alleviate the problem of insufficient constraint on the rheological model of the tested soft tissue when performing parameter inversion solely based on the mid-to-high frequency phase velocity dispersion curve, and also helps reduce the degree of parameter coupling, improving the stability and accuracy of viscoelastic parameter inversion.

[0033] Therefore, in the viscoelastic parameter inversion method provided in this embodiment, spatiotemporal image sequence data provides a common data source for shear group velocity extraction and mid-to-high frequency phase velocity dispersion curve extraction. The static shear modulus determined after the shear group velocity is equivalent to the phase velocity anchor point under the zero-frequency limit condition provides an effective constraint for the rheological model of the tested soft tissue, while the mid-to-high frequency phase velocity dispersion curve provides a dynamic response basis for single-parameter fitting of the viscosity coefficient. Through the synergistic cooperation between the above steps, the joint utilization of low-frequency constraint information and mid-to-high frequency dispersion information can be achieved in the same technical solution, thereby obtaining the viscoelastic parameters of the tested soft tissue.

[0034] Example 2: The viscoelastic parameter inversion method provided in this embodiment, based on Embodiment 1, further explains the implementation of extracting shear wave group velocity using the time-of-flight method based on the spatiotemporal image sequence data.

[0035] In this embodiment, based on the spatiotemporal image sequence data, the time-of-flight method is used to extract the shear wave group velocity, including: determining the arrival time of the shear wave at different spatial locations along the propagation direction; establishing a distance-time correspondence between each spatial location and the corresponding shear wave arrival time; and determining the shear wave group velocity based on the distance-time correspondence. Thus, the propagation process of shear waves within the tested soft tissue can be converted into a quantitative characterization result associated with propagation distance and propagation time, thereby providing a basis for determining the shear wave group velocity.

[0036] To determine the arrival time of a shear wave at different spatial locations along its propagation direction, in this embodiment, the tracking target is determined to be either a positive peak or a negative trough based on the initial wavefield energy distribution. Under the condition of polarity locking of the tracking target, the arrival times of the shear waves at different spatial locations of the same wavefront are extracted. Specifically, the dominant polarity corresponding to the wavefield energy distribution can be determined by analyzing the front-end wavefield data closest to the acoustic radiation force excitation source, and the tracking target can be locked as either a positive peak or a negative trough accordingly. After the tracking target is locked, the same wavefront is continuously tracked along the shear wave propagation direction, so that the shear wave arrival times extracted at different spatial locations all correspond to the same propagation event. Using the above method, the physical consistency of the shear wave arrival time extraction process can be guaranteed, the identification deviation caused by the mixing of waves of different polarities can be reduced, and the subsequently established distance-time correspondence can more accurately reflect the true propagation law of the shear wave.

[0037] In this embodiment, parabolic interpolation is used to extract the arrival time of shear waves at different spatial locations for the same wavefront, achieving sub-pixel level extraction. Specifically, interpolation relationships can be constructed around the local extrema corresponding to the tracking target to further characterize the changing trends between discrete sampling points, thereby obtaining more refined time positioning results. Due to the inherent limitations in the sampling resolution of spatiotemporal image sequence data, directly determining the arrival time of shear waves based solely on discrete sampling points can easily lead to insufficient positioning accuracy. By employing parabolic interpolation to achieve sub-pixel level extraction of shear wave arrival times, the physical time resolution limitation corresponding to the sampling rate can be overcome, improving the accuracy of shear wave arrival time extraction and thus providing a higher quality data foundation for subsequent determination of shear wave group velocity.

[0038] After obtaining the arrival times of shear waves at different spatial locations, a distance-time correspondence is established between each spatial location and the corresponding shear wave arrival time, and the shear wave group velocity is determined based on this correspondence. In this embodiment, the distance-time correspondence is used to characterize the variation of the propagation delay of the shear wave along the propagation direction at different spatial locations, and the shear wave group velocity is determined by the overall trend of the distance-time correspondence. Since the distance-time correspondence comprehensively utilizes propagation information from multiple spatial locations, it better reflects the overall propagation characteristics of the shear wave in the tested soft tissue compared to judging based solely on a single-point response.

[0039] In this embodiment, to determine the shear wave group velocity based on the distance-time correspondence, a robust linear regression of the distance-time correspondence using a random sampling consensus algorithm is employed to eliminate outliers and determine the shear wave group velocity. Specifically, after establishing the distance-time correspondence, reasonable upper and lower thresholds can be set for the distance-time slope, and multiple random sampling iterations can be performed to identify and eliminate abnormal outliers that severely deviate from the wavefront trajectory. Since the arrival time of shear waves extracted from different spatial locations may be affected by local noise, abnormal responses, or data disturbances in actual processing, directly performing linear fitting on all data points can easily cause the determined shear wave group velocity to deviate from the true propagation trend. After using a robust linear regression of the random sampling consensus algorithm, the tolerance of the distance-time correspondence to abnormal data can be enhanced, making the data points involved in velocity determination more consistent with the overall law of shear wave propagation, thereby improving the stability and reliability of the shear wave group velocity determination results.

[0040] In this embodiment, the technical approach for extracting shear wave group velocity using the time-of-flight method can be understood as follows: Multiple spatial locations along the propagation direction of the shear wave are determined based on spatiotemporal image sequence data; the arrival time of the shear wave at each spatial location is extracted around a unified tracking target; a distance-time correspondence is established using these arrival times; and robust linear regression of this distance-time correspondence is performed using a random sampling consensus algorithm to obtain the shear wave group velocity. Through this technical approach, target polarity locking provides a consistent identification benchmark for shear wave arrival time extraction; parabolic interpolation provides higher time resolution; and the random sampling consensus algorithm provides resistance to outlier interference in determining the shear wave group velocity. These technical features work together to give the process of extracting shear wave group velocity using the time-of-flight method better accuracy and stability.

[0041] Example 3: The viscoelastic parameter inversion method provided in this embodiment, based on Embodiment 1, further explains the implementation method of extracting the mid-to-high frequency phase velocity dispersion curve using two-dimensional fast Fourier transform based on the spatiotemporal image sequence data.

[0042] In this embodiment, based on the spatiotemporal image sequence data, a two-dimensional fast Fourier transform is used to extract the mid-to-high frequency phase velocity dispersion curve. This includes: applying a Hanning window to the wavefield time rate of change matrix corresponding to the spatiotemporal image sequence data; performing a two-dimensional fast Fourier transform on the wavefield time rate of change matrix after the Hanning window processing to obtain a frequency-wavenumber spectrum; searching for energy ridges in the frequency-wavenumber spectrum; and extracting the mid-to-high frequency phase velocity dispersion curve based on the energy ridges. Through this processing path, the spatiotemporal variation information characterizing the shear wave propagation properties in the spatiotemporal image sequence data can be converted into spectral distribution information in both the frequency and wavenumber dimensions, thus providing a foundation for the extraction of the mid-to-high frequency phase velocity dispersion curve.

[0043] In this embodiment, the wavefield time-rate-of-change matrix corresponding to the spatiotemporal image sequence data is used to characterize the response features of the wavefield signal as it changes over time. Since the spatiotemporal image sequence data contains dynamic change information in both spatial and temporal dimensions, converting it into the wavefield time-rate-of-change matrix better highlights the dynamic change features related to the propagation process, allowing subsequent frequency domain analysis to focus more on the shear wave propagation process. Therefore, the wavefield time-rate-of-change matrix serves as a direct input for extracting the mid-to-high frequency phase velocity dispersion curve using a two-dimensional fast Fourier transform.

[0044] In this embodiment, the Hanning window is applied to the wavefield time-rate-of-change matrix to improve the spectral distribution quality during subsequent frequency domain transformation. Since finite-length data is susceptible to boundary truncation effects during frequency domain transformation, directly performing a two-dimensional fast Fourier transform on the wavefield time-rate-of-change matrix may cause energy diffusion in the frequency and wavenumber components within the spectral domain. By applying the Hanning window to the wavefield time-rate-of-change matrix, the changes at boundary positions are smoother, thereby improving the energy concentration in the resulting frequency-wavenumber spectrum and providing a clearer spectral basis for energy ridge search.

[0045] In this embodiment, a two-dimensional fast Fourier transform (FFT) is performed on the wavefield time-rate-of-change matrix processed by the Hanning window. The FFT transforms the matrix from the spatiotemporal domain to the frequency-wavenumber domain to obtain a frequency-wavenumber spectrum. This spectrum characterizes the correspondence between different frequency components and different wavenumber components, and reflects the propagation characteristics of shear waves under different frequency conditions. Since the propagation response in the mid-to-high frequency band can be reflected by the energy distribution in the frequency-wavenumber spectrum, it provides a direct basis for extracting the phase velocity dispersion curve in the mid-to-high frequency band.

[0046] In this embodiment, the energy ridge line is used to characterize the dominant distribution trajectory of energy concentration in the frequency-wavenumber spectrum when searching for energy ridge lines in the frequency-wavenumber spectrum. Since shear waves have a primary propagation mode, the corresponding frequency and wavenumber components often form relatively concentrated energy bands in the frequency-wavenumber spectrum. By searching for energy ridge lines in the frequency-wavenumber spectrum, spectral trajectories related to the dominant propagation mode can be identified, thereby converging complex spectral distribution information into key feature information that is easy to extract and characterize subsequently.

[0047] In this embodiment, the mid-to-high frequency phase velocity dispersion curve is extracted based on the energy ridge line. This curve is used to characterize the phase velocity variation of the tested soft tissue under different frequency conditions. Since the energy ridge line provides the dominant correspondence between frequency and wavenumber, the propagation characteristics within the mid-to-high frequency range can be obtained from the energy ridge line, further forming the mid-to-high frequency phase velocity dispersion curve. This curve reflects the dynamic response characteristics of the tested soft tissue within the mid-to-high frequency range and provides a dispersion information basis for subsequent viscoelastic parameter inversion.

[0048] In this embodiment, the technical approach of using two-dimensional fast Fourier transform (FFT) to extract mid-to-high frequency phase velocity dispersion curves can be understood as follows: A wavefield time-rate-of-change matrix is ​​constructed based on the spatiotemporal image sequence data; the spectral analysis conditions of the wavefield time-rate-of-change matrix are improved through Hanning window processing; a two-dimensional fast Fourier transform is performed on the wavefield time-rate-of-change matrix after Hanning window processing to obtain a frequency-wavenumber spectrum; and energy ridges are searched in the frequency-wavenumber spectrum to extract the mid-to-high frequency phase velocity dispersion curves. Through this technical approach, Hanning window processing provides better data conditions for constructing the frequency-wavenumber spectrum; two-dimensional fast Fourier transform provides a spectral domain expression for the correspondence between frequency components and wavenumber components; and energy ridge searching provides a key positioning basis for extracting mid-to-high frequency phase velocity dispersion curves. The cooperation of these technical features makes the extraction process of mid-to-high frequency phase velocity dispersion curves more stable and usable.

[0049] Example 4: The viscoelastic parameter inversion method provided in this embodiment, based on Embodiment 1, further explains the implementation method of constraining the rheological model of the tested soft tissue based on the static shear modulus and combining it with the single-parameter fitting of the viscosity coefficient using the mid-to-high frequency phase velocity dispersion curve. For example... Figure 2 As shown, in this embodiment, the shear wave group velocity extracted using the time-of-flight method is used as an anchor point to constrain the rheological model of the tested soft tissue, and on this basis, a single-parameter fitting of the viscosity coefficient is completed.

[0050] Figure 2 The diagram illustrates the results of single-parameter fitting of a rheological model using shear wave group velocities extracted via the time-of-flight method as anchor points. Figure 2 In the results shown, the shear group velocity extracted using the time-of-flight method is equivalent to the phase velocity anchor point under the zero-frequency limiting condition, and the static shear modulus is determined accordingly. After substituting the static shear modulus as a known constant into the rheological model, the viscosity coefficient is fitted using a single parameter by combining the phase velocity dispersion curve in the mid-to-high frequency range, thereby obtaining the viscoelastic parameters. Figure 2 This reflects the process of jointly utilizing low-frequency constraint information and mid-to-high-frequency dispersion information.

[0051] In this embodiment, the rheological model of the tested soft tissue is the Kelvin-Voigt rheological model. The Kelvin-Voigt rheological model is used to characterize the viscoelastic response of the tested soft tissue under external excitation. Since the propagation response of the tested soft tissue under different frequency conditions is simultaneously affected by elastic and viscous factors, the Kelvin-Voigt rheological model can provide a physical model basis for the inversion of the viscoelastic parameters of the tested soft tissue, establishing a correspondence between the mid-to-high frequency phase velocity dispersion curve and the mechanical parameters of the tested soft tissue.

[0052] In this embodiment, the phase velocity expression of the Kelvin-Voigt rheological model is:

[0053] in, Represents phase velocity, Indicates shear modulus, Represents angular frequency. Indicates the viscosity coefficient. This represents the density of the tested soft tissue. Using the above phase velocity expression, a correspondence can be established between phase velocity and shear modulus, viscosity coefficient, and the density of the tested soft tissue, thus providing a rheological model basis for parameter fitting based on the mid-to-high frequency phase velocity dispersion curve.

[0054] In this embodiment, the static shear modulus is determined based on the density of the tested soft tissue and the shear group velocity, and satisfies the following equivalent relationship:

[0055] in, The static shear modulus locked by the time-of-flight method through group velocity conversion. The group velocity calculated using the time-of-flight method. The density of the tested soft tissue is given. Therefore, the static shear modulus can be determined based on the density of the tested soft tissue and the shear group velocity. This means that the static shear modulus is no longer a completely unknown parameter in the subsequent fitting process, but rather a known condition constraining the Kelvin-Voigt rheological model. Using this method, physical quantities obtained from time-domain analysis can be further transformed into constraint quantities in the rheological model, thereby enhancing the physical constraint capability in the viscoelastic parameter inversion process.

[0056] When performing single-parameter fitting on the viscosity coefficient, the static shear modulus is substituted as a known constant into the Kelvin-Voigt rheological model, so that the inversion of the viscoelastic parameters is transformed from multi-parameter optimization to single-parameter optimization for the viscosity coefficient. That is, in this embodiment, the static shear modulus is already determined by the density of the tested soft tissue and the shear group velocity. Therefore, during the fitting process, it is not necessary to simultaneously optimize the static shear modulus and the viscosity coefficient; instead, the solution is only performed around the viscosity coefficient. Compared to directly fitting multiple parameters based on the mid-to-high frequency phase velocity dispersion curve, substituting the static shear modulus as a known constant into the Kelvin-Voigt rheological model reduces the number of fitting variables, simplifies the solution process of the objective function, and reduces the adverse effects of coupling between different parameters on the inversion results.

[0057] In this embodiment, the mid-to-high frequency phase velocity dispersion curve provides dynamic response information of the tested soft tissue in the mid-to-high frequency range, while the static shear modulus provides static mechanical constraint information of the tested soft tissue under low-frequency limiting response. After substituting the static shear modulus as a known constant into the Kelvin-Voigt rheological model, the mid-to-high frequency phase velocity dispersion curve and the static shear modulus work synergistically within the Kelvin-Voigt rheological model. The static shear modulus limits the fitting range, and the mid-to-high frequency phase velocity dispersion curve characterizes the dispersion variation. Both work together in the determination of the viscosity coefficient. This approach enables the combined use of low-frequency constraint information and mid-to-high frequency dispersion information in the same parameter inversion process, thus allowing the obtained viscoelastic parameters to better reflect the actual mechanical properties of the tested soft tissue.

[0058] In this embodiment, the inversion of the viscoelastic parameters is transformed from multi-parameter optimization to single-parameter optimization for the viscosity coefficient. This reduces the solution dimensionality and fitting complexity during the viscoelastic parameter inversion process. Since the static shear modulus is already used as a known constant in the constraints of the Kelvin-Voigt rheological model, even with insufficient low-frequency information, the Kelvin-Voigt rheological model can still obtain effective constraints from the low-frequency limiting response. This alleviates the constraint deficiency problem that easily occurs when relying solely on the mid-to-high-frequency phase velocity dispersion curve for parameter inversion. Therefore, it helps reduce the probability of local optima and improves the stability, reliability, and accuracy of the viscoelastic parameter inversion results.

[0059] In this embodiment, the technical path for parameter inversion based on the Kelvin-Voigt rheological model can be understood as follows: The static shear modulus is determined based on the density of the tested soft tissue and the shear group velocity; the static shear modulus is substituted as a known constant into the Kelvin-Voigt rheological model; and the viscosity coefficient is fitted using a single parameter in conjunction with the mid-to-high frequency phase velocity dispersion curve to obtain the viscoelastic parameters of the tested soft tissue. Through this technical path, the density of the tested soft tissue and the shear group velocity provide the basis for determining the static shear modulus; the static shear modulus provides constraints for the Kelvin-Voigt rheological model; and the mid-to-high frequency phase velocity dispersion curve provides a dynamic response basis for solving the viscosity coefficient. The cooperation of these technical features gives the viscoelastic parameter inversion process better physical constraints and higher solution stability.

[0060] Example 5: The viscoelastic parameter inversion method provided in this embodiment, based on embodiments 1 to 4, further illustrates the application process of the viscoelastic parameter inversion method in actual use scenarios.

[0061] In this embodiment, the actual application process of the viscoelastic parameter inversion method is explained using image data acquired by medical ultrasound equipment as an example. The viscoelastic parameter inversion method can be applied to mechanical testing scenarios of tested soft tissues such as the liver, breast, and muscle, as well as to mechanical testing scenarios of biomimetic tissue materials. It can also be integrated into the image processing system of diagnostic equipment to achieve viscoelastic parameter inversion of the tested soft tissues or biomimetic tissue materials.

[0062] In this embodiment, the viscoelastic parameter inversion method can be run in a medical computing device including a processor and memory. After the wavefield signal acquired by the medical ultrasound equipment is transmitted to the medical computing device, the wavefield signal is processed by the medical computing device. Specifically, the acquired wavefield signal can be reconstructed into the spatiotemporal image sequence data. The spatiotemporal image sequence data can correspond to a three-dimensional data matrix containing spatial width, spatial depth, and time dimensions. The mechanical wave energy in human soft tissue propagates and diffuses along the width direction of the three-dimensional data matrix. The spatial sampling resolution and temporal sampling frequency of the three-dimensional data matrix can be set and adjusted according to the hardware parameters of the ultrasound probe and the required detection depth to adapt to the data acquisition needs of different detection objects and under different detection conditions.

[0063] In this embodiment, to improve the quality of the spatiotemporal image sequence data, digital processing operators can be introduced during the reconstruction of the wavefield signal or the processing of the spatiotemporal image sequence data. These digital processing operators may include a bandpass digital filter, a moving average filter, and a two-dimensional Hanning window. The bandpass digital filter can suppress low-frequency baseline drift caused by patient physiological respiration, slow tissue movement, and probe micro-jitter; the moving average filter can improve data smoothness; and the two-dimensional Hanning window can improve the spectral distribution quality during frequency domain analysis. Through the synergistic cooperation of these digital processing operators, the signal-to-noise ratio and resolution of the spatiotemporal image sequence data can be improved, thereby providing a more stable data foundation for subsequently obtaining shear group velocity and mid-to-high frequency phase velocity dispersion curves.

[0064] In a practical clinical testing procedure, an ultrasound probe pre-emits acoustic radiation force pulses onto the soft tissue of a target area in the human body, causing transient shear waves to propagate laterally within the soft tissue. The displacement field changes formed by the specific tissue after forced vibration can be transmitted back to the receiving system via the ultrasound tracking pulse sequence, and further converted into spatiotemporal image sequence data, which is then transmitted to the algorithm processor running in the background. After receiving the spatiotemporal image sequence data, the algorithm processor can execute the processing procedures described in Examples 1 to 4 to obtain the viscoelastic parameters of the tested soft tissue.

[0065] Specifically, the algorithm processor can extract shear group velocities using the time-of-flight method based on the spatiotemporal image sequence data, and equate these velocities to phase velocity anchor points under zero-frequency limiting conditions to determine the static shear modulus of the tested soft tissue. Simultaneously, the algorithm processor can also extract mid-to-high frequency phase velocity dispersion curves using a two-dimensional fast Fourier transform based on the spatiotemporal image sequence data. Furthermore, the algorithm processor constrains the rheological model based on the static shear modulus and performs single-parameter fitting of the viscosity coefficient using the mid-to-high frequency phase velocity dispersion curves to obtain the viscoelastic parameters of the tested soft tissue. The rheological model can be the Kelvin-Voigt rheological model, or it can be replaced and adjusted according to the microstructural characteristics of different organs and tissues in clinical practice. Therefore, the viscoelastic parameter inversion method is not only applicable to different tissue types but also adapts to the mechanical characteristic expression requirements corresponding to different tissue microstructures.

[0066] In this embodiment, if only the time-of-flight method is used, only the average velocity information corresponding to the pulse envelope propagation can be obtained, making it difficult to effectively separate the viscoelastic parameters. If only the two-dimensional fast Fourier transform is used, when the physical window length of the region of interest of the ultrasound probe is limited, low-frequency information is easily missing or contains large errors, which may lead to distortion or underestimation of the low-frequency end of the mid-to-high frequency phase velocity dispersion curve. Furthermore, if a multi-parameter rheological model is directly used to fit the incomplete dispersion information when the low-frequency constraints are insufficient, problems such as ill-posed inversion, local optima, and low clinical repeatability are likely to occur. In contrast, this embodiment, by equating the shear group velocity to the phase velocity anchor point under the zero-frequency limit condition and combining it with the mid-to-high frequency phase velocity dispersion curve for parameter inversion, can jointly utilize low-frequency constraint information and mid-to-high frequency dispersion information in the same technical path, thereby improving the limitations of using time-domain methods or frequency-domain methods alone.

[0067] In this embodiment, the viscoelastic parameter inversion method can be used to distinguish the mechanical properties of human tissues at different pathological stages. For example, for different degrees of fibrosis or fatty liver, the algorithm processor can obtain the viscoelastic parameters of the tested soft tissue to achieve precise separation of elastic and viscous components, thereby providing highly robust mechanical testing results for quantitative assessment of tissue stiffness and early disease diagnosis. Therefore, the viscoelastic parameter inversion method not only completes data processing tasks but also provides quantitative results with practical reference value for clinical diagnosis.

[0068] Therefore, in the practical application scenario provided in this embodiment, the medical ultrasound equipment is responsible for exciting the tested soft tissue and acquiring wavefield signals, while the medical computing equipment is responsible for reconstructing the wavefield signals into spatiotemporal image sequence data and completing data processing. This spatiotemporal image sequence data provides a unified data source for shear group velocity extraction, mid-to-high frequency phase velocity dispersion curve extraction, and viscoelastic parameter inversion. Digital processing operators provide auxiliary support for improving data quality, and the rheological model provides the physical constraint basis for viscoelastic parameter inversion. The cooperation between these processing steps enables the viscoelastic parameter inversion method to operate stably in actual detection processes and obtain highly reliable viscoelastic parameter inversion results.

[0069] Example 6: The viscoelastic parameter inversion system provided in this embodiment, such as Figure 3 As shown, the system includes a data preprocessing module, a time-domain wave velocity tracking module, a frequency-domain dispersion extraction module, and a joint inversion module. These modules work in sequence to process the spatiotemporal image sequence data of the excited soft tissue under test, extract shear group velocities, extract mid-to-high frequency phase velocity dispersion curves, and invert viscoelastic parameters. Through the synergistic effect of these modules, a complete processing flow from data acquisition to parameter output results can be completed within the same system architecture.

[0070] In this embodiment, the data preprocessing module is used to reconstruct the acquired wavefield signal to obtain spatiotemporal image sequence data of the tested soft tissue after excitation. Specifically, the data preprocessing module can reconstruct the wavefield signal acquired by the medical ultrasound equipment into a three-dimensional data matrix containing spatial width, spatial depth, and time dimensions to form the spatiotemporal image sequence data. The spatiotemporal image sequence data is used to characterize the dynamic response process of the tested soft tissue in the spatial and temporal dimensions, and provides a unified data input basis for subsequent extraction of shear group velocity and extraction of mid-to-high frequency phase velocity dispersion curves. In some embodiments, the data preprocessing module can also introduce digital processing operators such as bandpass digital filters, moving average filters, and two-dimensional Hanning windows to improve the signal-to-noise ratio and resolution of the spatiotemporal image sequence data.

[0071] In this embodiment, the time-domain wave velocity tracking module is used to extract shear group velocities based on the spatiotemporal image sequence data using the time-of-flight method, and to equate the shear group velocities to phase velocity anchor points under zero-frequency limiting conditions, thereby determining the static shear modulus of the tested soft tissue. The function of the time-domain wave velocity tracking module is to extract physical quantities related to the low-frequency limiting response from the spatiotemporal image sequence data, and to further convert these physical quantities into a static shear modulus that can be used to constrain the rheological model. Therefore, the time-domain wave velocity tracking module not only performs velocity extraction but also converts the time-domain analysis results into model constraint information, thus providing a low-frequency constraint basis for subsequent viscoelastic parameter inversion. For a detailed description of the implementation of the time-domain wave velocity tracking module, please refer to the relevant description in Embodiment 2, which will not be repeated here.

[0072] In this embodiment, the frequency domain dispersion extraction module is used to extract the mid-to-high frequency phase velocity dispersion curve based on the spatiotemporal image sequence data using a two-dimensional fast Fourier transform. The function of the frequency domain dispersion extraction module is to extract the dynamic response information of the tested soft tissue in the mid-to-high frequency range from the spatiotemporal image sequence data to form the mid-to-high frequency phase velocity dispersion curve. The mid-to-high frequency phase velocity dispersion curve can reflect the propagation characteristics of the tested soft tissue under different frequency conditions and provide dispersion information for subsequent single-parameter fitting of the viscosity coefficient. For the specific implementation of the frequency domain dispersion extraction module, please refer to the relevant description in Embodiment 3, which will not be repeated here.

[0073] In this embodiment, the joint inversion module is used to constrain the rheological model based on the static shear modulus and perform single-parameter fitting of the viscosity coefficient in conjunction with the mid-to-high frequency phase velocity dispersion curve to obtain the viscoelastic parameters of the tested soft tissue. The function of the joint inversion module is to jointly utilize the low-frequency constraint information from the time-domain wave velocity tracking module and the mid-to-high frequency dispersion information from the frequency-domain dispersion extraction module, and complete the viscoelastic parameter inversion within a unified rheological model framework. Therefore, the joint inversion module does not simply perform calculations on the input data, but rather performs single-parameter fitting of the viscosity coefficient under the condition that the static shear modulus is already determined, so that the inversion of viscoelastic parameters is transformed from multi-parameter optimization to single-parameter optimization for the viscosity coefficient. For the specific implementation of the joint inversion module, please refer to the relevant description in Embodiment 4, which will not be repeated here.

[0074] In this embodiment, there are clear data flow relationships and functional cooperation relationships among the data preprocessing module, the time-domain wave velocity tracking module, the frequency-domain dispersion extraction module, and the joint inversion module. The spatiotemporal image sequence data output by the data preprocessing module serves as the input to the time-domain wave velocity tracking module and the frequency-domain dispersion extraction module, respectively. The static shear modulus output by the time-domain wave velocity tracking module and the mid-to-high frequency phase velocity dispersion curve output by the frequency-domain dispersion extraction module serve as the input to the joint inversion module. Based on the aforementioned inputs, the joint inversion module performs single-parameter fitting of the viscosity coefficient and outputs the viscoelastic parameters of the tested soft tissue. Through the cooperation between the above modules, the joint utilization of low-frequency constraint information and mid-to-high frequency dispersion information can be achieved at the system level, thereby improving the stability and accuracy of viscoelastic parameter inversion.

[0075] In this embodiment, the viscoelastic parameter inversion system can be integrated into the image processing system of a diagnostic device or deployed in a medical computing device containing a processor and memory. The data preprocessing module, the time-domain wave velocity tracking module, the frequency-domain dispersion extraction module, and the joint inversion module can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be integrated into the medical computing device in hardware form or stored in memory as software programs, to be called and executed by the processor to achieve the various functions of the viscoelastic parameter inversion system described in this embodiment.

[0076] Therefore, the viscoelastic parameter inversion system provided in this embodiment achieves a complete system flow from wavefield signal reconstruction to viscoelastic parameter output through the organic cooperation between the data preprocessing module, the time-domain wave velocity tracking module, the frequency-domain dispersion extraction module, and the joint inversion module. The data preprocessing module provides a unified data foundation for the system, the time-domain wave velocity tracking module provides static shear modulus constraints, the frequency-domain dispersion extraction module provides mid-to-high frequency phase velocity dispersion curves, and the joint inversion module completes single-parameter fitting of the viscosity coefficient under rheological model constraints. These modules support and cooperate with each other, enabling the viscoelastic parameter inversion system to maintain good physical constraint capabilities and parameter inversion performance even when low-frequency information is insufficient.

[0077] Example 7: This invention also provides a computer device in specific embodiments. Specifically, the computer device includes a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or it may be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), or field-programmable gate arrays (FPGAs). The processor, described in this embodiment, is a programmable logic device (FPGA) or other programmable logic device, discrete gate or transistor logic device, discrete hardware component, etc., which is the computing core and control core of the terminal. It is suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to realize the corresponding method flow or corresponding function. The processor can be used to acquire spatiotemporal image sequence data of the tested soft tissue after excitation. Based on the spatiotemporal image sequence data, the shear group velocity is extracted using the time-of-flight method, and the shear group velocity is equivalent to the phase velocity anchor point under the zero-frequency limit condition to determine the static shear modulus of the tested soft tissue. Based on the spatiotemporal image sequence data, the phase velocity dispersion curve in the mid-to-high frequency band is extracted using two-dimensional fast Fourier transform. The rheological model of the tested soft tissue is constrained based on the static shear modulus, and the viscosity coefficient is fitted with a single parameter in combination with the phase velocity dispersion curve in the mid-to-high frequency band to obtain the viscoelastic parameters of the tested soft tissue.

[0078] Example 8: This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device used to store programs and data. It is understood that the computer-readable storage medium here can include both the built-in storage medium in the terminal device and extended storage media supported by the terminal device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, this storage space also stores one or more instructions suitable for loading and execution by a processor. These instructions can be one or more computer programs (including program code). It should be noted that the computer-readable storage medium here can be high-speed RAM or non-volatile memory, such as at least one disk storage device. One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the methods in the above embodiments. One or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: acquiring spatiotemporal image sequence data of the tested soft tissue after excitation; extracting shear group velocity using the time-of-flight method based on the spatiotemporal image sequence data, and equating the shear group velocity to the phase velocity anchor point under the zero-frequency limit condition to determine the static shear modulus of the tested soft tissue; extracting the mid-to-high frequency phase velocity dispersion curve using a two-dimensional fast Fourier transform based on the spatiotemporal image sequence data; constraining the rheological model of the tested soft tissue based on the static shear modulus, and performing single-parameter fitting of the viscosity coefficient in combination with the mid-to-high frequency phase velocity dispersion curve to obtain the viscoelastic parameters of the tested soft tissue.

[0079] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0080] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0081] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0082] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0083] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above, and that the invention can be implemented in other specific forms without departing from its spirit or essential characteristics. Therefore, the embodiments should be considered illustrative and non-limiting in all respects, and the scope of the invention is not limited by the foregoing description. Thus, all changes falling within the meaning and scope of equivalents are intended to be included within the scope of the invention. No reference numerals in the drawings should be considered limiting.

[0084] Furthermore, it should be understood that although this specification describes embodiments, not every embodiment contains only one independent technical solution. This narrative style is merely for clarity. Those skilled in the art should consider the specification as a whole, and the technical solutions in each embodiment can be appropriately combined to form other embodiments that can be understood by those skilled in the art. The above content is only for illustrating the technical concept of the present invention and should not be used to limit the scope of protection of the present invention. Any modifications made to the technical solutions based on the technical concept proposed in this invention fall within the scope of protection of this invention.

Claims

1. A method for inverting viscoelastic parameters, characterized in that, include: Acquire spatiotemporal image sequence data of the tested soft tissue after stimulation; Based on the spatiotemporal image sequence data, the time-of-flight method is used to extract the shear group velocity, and the shear group velocity is equivalent to the phase velocity anchor point under the zero-frequency limit condition to determine the static shear modulus of the tested soft tissue. Based on the spatiotemporal image sequence data, a two-dimensional fast Fourier transform is used to extract the phase velocity dispersion curve in the mid-to-high frequency band. The rheological model of the tested soft tissue is constrained based on the static shear modulus, and the viscosity coefficient is fitted with a single parameter using the mid-to-high frequency phase velocity dispersion curve to obtain the viscoelastic parameters of the tested soft tissue.

2. The viscoelastic parameter inversion method according to claim 1, characterized in that, The extraction of shear group velocity based on the spatiotemporal image sequence data using the time-of-flight method includes: Determine the arrival time of the shear wave at different spatial locations along the propagation direction; Establish a distance-time correspondence between each spatial location and the corresponding shear wave arrival time, and determine the shear wave group velocity based on the distance-time correspondence.

3. The viscoelastic parameter inversion method according to claim 2, characterized in that, Determining the arrival time of the shear wave at different spatial locations along the propagation direction includes: The target for tracking is determined to be either a positive wave peak or a negative wave trough based on the initial wave field energy distribution. Under the polarity locking condition of the tracked target, the arrival time of shear waves at different spatial locations of the same wavefront is extracted.

4. The viscoelastic parameter inversion method according to claim 3, characterized in that, The extraction of shear wave arrival times at different spatial locations on the same wavefront includes: Parabolic interpolation is used to achieve subpixel-level shear wave arrival time extraction.

5. The viscoelastic parameter inversion method according to claim 2, characterized in that, The shear wave group velocity is determined by performing robust linear regression on the distance-time correspondence using a random sampling consensus algorithm and removing outliers.

6. The viscoelastic parameter inversion method according to claim 1, characterized in that, The extraction of mid-to-high frequency phase velocity dispersion curves based on the spatiotemporal image sequence data using two-dimensional fast Fourier transform includes: A Hanning window is applied to the wavefield time rate of change matrix corresponding to the spatiotemporal image sequence data; A two-dimensional fast Fourier transform is performed on the wavefield time rate of change matrix after processing with the Hanning window to obtain the frequency-wavenumber spectrum. Search for energy ridges in the frequency-wavenumber spectrum and extract the mid-to-high frequency phase velocity dispersion curves based on the energy ridges.

7. The viscoelastic parameter inversion method according to claim 1, characterized in that, The rheological model of the tested soft tissue was the Kelvin-Voigt rheological model; The static shear modulus is determined based on the density of the tested soft tissue and the shear group velocity. When performing single-parameter fitting on the viscosity coefficient, the static shear modulus is substituted as a known constant into the Kelvin-Voigt rheological model, and the viscosity coefficient is fitted in conjunction with the mid-to-high frequency phase velocity dispersion curve, so that the inversion of the viscoelastic parameters is transformed from multi-parameter optimization to single-parameter optimization for the viscosity coefficient.

8. A viscoelastic parameter inversion system, characterized in that, include: The data preprocessing module is used to reconstruct the acquired wave field signal to obtain the spatiotemporal image sequence data of the tested soft tissue after excitation; The time-domain wave velocity tracking module is used to extract the shear wave group velocity based on the spatiotemporal image sequence data using the time-of-flight method, and to convert the shear wave group velocity into a phase velocity anchor point under the zero-frequency limiting condition in order to determine the static shear modulus of the tested soft tissue. The frequency domain dispersion extraction module is used to extract the mid-to-high frequency phase velocity dispersion curves based on the spatiotemporal image sequence data using a two-dimensional fast Fourier transform. The joint inversion module is used to constrain the rheological model of the tested soft tissue based on the static shear modulus, and to perform single-parameter fitting of the viscosity coefficient by combining the mid-to-high frequency phase velocity dispersion curve to obtain the viscoelastic parameters of the tested soft tissue.

9. A computer device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the viscoelastic parameter inversion method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by a processor, it implements the steps of the viscoelastic parameter inversion method as described in any one of claims 1 to 7.

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