A CPWC beamforming method, system, and storage medium based on adaptive parameter zero-subtraction imaging.

Abstract

This invention discloses a CPWC beamforming method based on adaptive parameter zero-subtraction imaging, applied to ultrasound imaging signal processing. It involves transmitting ultrasonic waves to a target object using an ultrasound array element module and acquiring the echo imaging signal dataset; calculating a time-delayed imaging dataset based on the echo imaging signal dataset using a delay processing module; calculating an adaptive offset parameter based on the time-delayed imaging dataset using a model combining the generalized coherence factor and the mean-standard deviation coefficient; and calculating the final imaging beamforming signal using a zero-subtraction imaging beamforming model based on the adaptive offset parameter and the time-delayed imaging dataset. This method effectively solves the contrast-resolution-speckle quality trade-off problem caused by fixed offset in zero-subtraction imaging, improving imaging quality, and is particularly suitable for high-frame-rate ultrasound CPWC imaging.

Description

Technical Field

[0001] This invention belongs to the field of ultrasound imaging signal processing, and particularly relates to a CPWC beamforming method, system and storage medium based on adaptive parameter zero subtraction imaging. Background Technology

[0002] Coherent plane wave composite imaging (CPWC) technology has been widely used in clinical diagnosis due to its ultra-high imaging frame rate and good image quality, in fields such as cardiovascular imaging, liver elastography, and neuroscience research. CPWC technology achieves ultra-fast imaging frame rates by transmitting an unfocused plane wave array and dynamically focusing it at the receiver.

[0003] However, traditional CPWC technology still faces several unresolved issues, primarily limited image quality, the challenge of balancing computational resources with image quality, and the need to balance image resolution and speckle quality. First, the non-focused plane waves emitted by the ultrasound waves contribute to image detail blurring and reduced resolution, thus decreasing the accuracy and reliability of medical diagnosis. Second, while traditional nonlinear and adaptive CPWC beamforming techniques improve image quality, they require significant computational resources and have long processing times. These methods rely on the statistical characteristics of the input signal, the selected model, and preset parameters; improper selection can lead to image artifacts or distortion, further reducing the accuracy and reliability of medical diagnosis. Third, traditional zero-subtraction imaging synthesis methods, relying on preset offset parameters, cannot simultaneously balance image resolution and speckle quality. Specifically, low offsets improve spatial resolution and contrast but significantly reduce speckle quality; high offsets maintain good speckle quality but reduce image resolution and contrast. Summary of the Invention

[0004] This invention provides a CPWC beamforming method, system, and storage medium based on adaptive parameter zero-subtraction imaging. A delay processing module calculates a delayed imaging dataset based on the echo imaging signal dataset, achieving signal alignment at specific times. An adaptive offset parameter is calculated based on the delayed imaging dataset using a model combining the generalized coherence factor and the mean-standard deviation coefficients. This parameter is then applied to construct dynamic positive and negative offset apodization functions. The integration of the adaptive offset parameter into the zero-subtraction imaging beamforming model improves the spatial resolution and contrast of the final beamforming signal while maintaining superior background speckle quality. Furthermore, this invention reduces the requirement for the number of plane waves in ultrasonic CPWC acquisition, increasing the frame rate while maintaining imaging quality and reducing computational complexity.

[0005] The first aspect of this invention provides a CPWC beamforming method based on adaptive parameter zero-subtraction imaging, applied to ultrasound imaging signal processing, comprising the following steps: The ultrasonic array module emits ultrasonic waves to the target object and collects the echo imaging signal dataset of the ultrasonic waves. The time-delayed imaging dataset is calculated by the time-delay processing module based on the echo imaging signal dataset. The adaptive offset parameters are calculated based on the time-lapse imaging dataset using a model that combines the generalized coherence factor and the mean standard deviation coefficient. The final imaging beamforming signal is calculated using the zero-subtraction imaging beamforming model based on the adaptive offset parameters and the time-delay imaging dataset.

[0006] Preferably, the step of emitting ultrasonic waves to the target object through the ultrasonic array element module and acquiring the echo imaging signal dataset of the ultrasonic waves specifically includes: The ultrasonic array element module emits ultrasonic signals of a preset frequency to the target object through several array elements. The ultrasonic array module is configured with a constant angle value so that the ultrasonic waves emitted by each of the plurality of array elements maintain a fixed angle with the ultrasonic array module. The delay time of each element in the ultrasonic array module is calculated based on the angle constant value; The ultrasonic array element module emits ultrasonic waves toward the target object at preset time points, and the ultrasonic array element module receives the echo imaging signal dataset; The echo imaging signal dataset is indexed to distinguish echo imaging signal data from different array elements and at different time points.

[0007] Preferably, the step of indexing the echo imaging signal dataset specifically includes: Each element of the ultrasonic array element module is uniquely identified and indexed with a digital identifier. The echo imaging signal dataset is identified according to the (i,j) rule, where j is the index of the array element corresponding to the emitted ultrasonic wave, and i is incremented by a preset step value based on the emission time point.

[0008] Preferably, the step of calculating the adaptive offset parameters based on the time-lapse imaging dataset using a model combining the generalized coherence factor and the mean standard deviation coefficient specifically includes: The time-delay imaging datasets are accumulated along the time dimension to obtain imaging datasets for different plane waves; The spectral signal sets of different plane waves are calculated based on the imaging datasets of the different plane waves using Fourier transform; An adaptive weighting function is calculated based on the imaging datasets of different plane waves and the spectral signal sets of different plane waves using a model that combines the generalized coherence factor and the mean standard deviation coefficient. Construct an offset function based on the aforementioned adaptive weight function; The adaptive offset parameter is calculated based on the offset function using a cleanup model derived from domain statistics.

[0009] Preferably, the step of calculating the adaptive weighting function based on the imaging datasets of different plane waves and the spectral signal sets of the different plane waves using the model combining the generalized coherence factor and the mean standard deviation coefficient specifically includes: The generalized coherence factor is calculated based on the spectral signal sets of the different plane waves. By using coherence to distinguish different regions in an image, the calculation expression is as follows: in For a spectral signal, M0 is set to 1; The mean standard deviation coefficient is calculated based on the imaging datasets of the different plane waves. This is used to smooth the value distribution within different regions and highlight the value differences between different regions. The calculation expression is: in The image dataset for the different plane waves is given, where M is the number of plane waves. The adaptive weighting function is calculated based on the generalized coherence factor and the mean-standard deviation coefficients through the normalization operation, and the calculation expression is: .

[0010] Preferably, the step of calculating the adaptive offset parameter based on the offset function using the cleanup model derived from the domain statistics specifically includes: Construct a domain window of size odd number × odd number; The proportions of the domain window division values ​​of 10 and 0.1 were calculated respectively, and the calculation expressions are as follows: in For the domain window, the value takes values ​​of 10 and 0.1 respectively; Based on the division values ​​of 10 and 0.1, the adaptive offset parameter is calculated, and the calculation expression is as follows: Where T is a preset threshold, and d(p) is the offset function, the calculation expression of which is: Where α is a preset constant value.

[0011] Preferably, the step of calculating the final imaging beam signal based on the adaptive offset parameter and the time-delay imaging dataset using the zero-subtraction imaging beamforming model specifically includes: Based on the adaptive offset parameters, a zero-mean apodization function is preconstructed for each of the array elements. Positive offset apodization function Negative offset apodization function ; The zero-mean datasets for different plane waves are calculated based on the time-delay imaging dataset using the zero-mean apodization function, the positive offset apodization function, and the negative offset apodization function corresponding to each array element. Positive migration datasets of different plane waves Negative migration datasets of different plane waves The formula for adjusting the amplitude distribution of imaging information is: The final imaging beam signal is calculated by nonlinearly combining the zero-mean dataset, the positive offset dataset, and the negative offset dataset of the different plane waves. The calculation expression is: .

[0012] Preferably, the zero-mean apodization function for each of the array elements is pre-constructed based on the adaptive offset parameters. The positive offset apodization function The negative offset apodization function The specific steps include: Preconstruct the zero-mean apodization function The calculation expression is: In the formula, j is the index value of each array element, and N is the total number of array elements in the array element module; The positive offset apodization function is pre-constructed respectively. The negative offset apodization function The calculation expressions are as follows: in The adaptive offset parameter is...

[0013] A second aspect of this invention provides a CPWC beamforming system based on adaptive parameter zero-subtraction imaging, applied to ultrasound imaging signal processing, comprising: The data acquisition module is used to emit ultrasonic waves to the target object through the ultrasonic array element module and acquire the echo imaging signal dataset of the ultrasonic waves; The delay processing module is used to calculate the delay imaging dataset based on the echo imaging signal dataset. The offset calculation module is used to calculate adaptive offset parameters based on the time-lapse imaging dataset using a model that combines the generalized coherence factor and the mean standard deviation coefficient. The beamforming module is used to calculate the final imaging beamforming signal based on the adaptive offset parameters and the time-delay imaging dataset using a zero-subtraction imaging beamforming model.

[0014] A third aspect of the present invention provides a CPWC beamforming device based on adaptive parameter zero-subtraction imaging, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the CPWC beamforming method based on adaptive parameter zero-subtraction imaging described above.

[0015] Because the present invention adopts the above technical solution, it has the following advantages and positive effects compared with the prior art: A zero-subtraction imaging beamforming model combined with adaptive offset parameters and time-delay imaging datasets is used for calculation. The adaptive offset parameter integration algorithm addresses the contrast-resolution-speckle quality trade-off caused by fixed parameters, improving image resolution and contrast while maintaining excellent speckle quality. The adaptive offset parameters are calculated using a model combining the generalized coherence factor and the mean-standard deviation coefficients, enabling robust region segmentation. During the calculation of these parameters, the application and computation of a cleanup model using normalization functions and neighborhood statistics help eliminate outliers in the offset, further optimizing image quality. Indexing the echo imaging signal dataset facilitates the differentiation of echo imaging signal data from different array elements and time points, making the system's data processing more efficient and accurate. By reducing the number of plane waves required for ultrasound CPWC acquisition, the frame rate is significantly improved while maintaining imaging quality, and computational complexity is reduced. Attached Figure Description

[0016] The specific embodiments of the present invention will be further described in detail below with reference to the accompanying drawings, wherein: Figure 1 This is a schematic flowchart of the CPWC beamforming method based on adaptive zero-subtraction imaging according to the present invention. Figure 2 This is a flowchart illustrating the specific computational process of the CPWC beamforming method based on adaptive zero-subtraction imaging according to the present invention. Figure 3 This is a schematic diagram illustrating the region division using the improved generalized coherence factor (GCF) of this invention. Figure 4 The image shows a comparison of the output images of the CPWC beamforming method based on adaptive parameter zero-subtraction imaging of this invention with those of traditional delay summation and zero-subtraction imaging in a resolution simulation experiment. Figure 5 This is a comparison of the output images of the CPWC beamforming method based on adaptive zero-subtraction imaging of the present invention with those of traditional delay summation and zero-subtraction imaging in a simulated tissue contrast experiment. Figure 6 The image shows the reconstruction results of a tissue phantom experiment using the CPWC beamforming method based on adaptive zero-subtraction imaging of the present invention with different numbers of plane waves. Detailed Implementation

[0017] The present invention will be further described in detail below with reference to the accompanying drawings and specific embodiments. The advantages and features of the present invention will become clearer from the following description and claims. It should be noted that the drawings are all in a very simplified form and use non-precise ratios, and are only used to facilitate and clarify the illustration of the embodiments of the present invention.

[0018] It should be noted that all directional indications (such as up, down, left, right, front, back, etc.) in the embodiments of the present invention are only used to explain the relative positional relationship and movement of each component in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indication will also change accordingly.

[0019] First Embodiment For ease of understanding, the specific process of the embodiments of the present invention is described below. Please refer to [link / reference]. Figure 1 and Figure 2 A CPWC beamforming method based on adaptive parameter zero-subtraction imaging, applied to a multi-device audio system, includes the following steps: S100: Emits ultrasonic waves to the target object through the ultrasonic array element module and collects the ultrasonic echo imaging signal dataset. S200: The time-delayed imaging dataset is calculated based on the echo imaging signal dataset by the time-delay processing module; S300: Adaptive offset parameters are calculated based on time-lapse imaging datasets using a model that combines the generalized coherence factor and the mean standard deviation coefficient. S400: The final imaging beam imaging signal is calculated based on adaptive offset parameters and time-delay imaging dataset using a zero-subtraction imaging beamforming model.

[0020] The ultrasonic array element has at least one element component that converts the acquired echo signal into signal data. This signal data is then amplified, filtered, and sampled to obtain an echo imaging signal dataset, which provides the raw data set for subsequent imaging processing. Based on the characteristics of each array element, the system calculates the delay time of each element and performs delay data processing on the echo imaging signal dataset according to the delay characteristics of the corresponding array element, thereby aligning echo signals from different paths in time. The calculated generalized coherence factor can effectively distinguish different regions in the image based on signal coherence, and the calculated standard deviation coefficient can highlight the differences between different regions of the generalized coherence factor, smoothing the midpoint distribution within the same region. The constructed apodization function, fused with adaptive offset parameters, performs zero-subtraction imaging beamforming processing on the delayed data, realizing an adaptive zero-subtraction imaging beamforming method.

[0021] Preferably, the steps of emitting ultrasonic waves to the target object through the ultrasonic array element module and acquiring the ultrasonic echo imaging signal dataset specifically include: The ultrasonic array element module transmits ultrasonic signals of a preset frequency to the target object through several array elements. The ultrasonic array element module is configured with a constant angle value so that the ultrasonic waves emitted by each of the array elements maintain a fixed angle with the ultrasonic array element module. The delay time of each element in the ultrasonic array module is calculated based on the angle constant value; The ultrasonic array element module emits ultrasonic waves toward the target object at preset time points, and the ultrasonic array element module receives the echo imaging signal dataset. The echo imaging signal dataset is indexed to distinguish echo imaging signal data from different array elements and different time points.

[0022] In this embodiment, the number of array elements can be set to any number greater than 1, without limitation; there is no limitation on the angle constant value. The indexing process classifies and labels the echo imaging signal data according to array elements and time points. The signal received by each array element at different time points is identified as a unique identifier, forming a multi-dimensional data structure. This structured organization enables the data to be quickly located and accessed.

[0023] Preferably, the steps for indexing the echo imaging signal dataset specifically include: Each element of the ultrasonic array element module is uniquely identified and indexed with a digital identifier. The echo imaging signal dataset is labeled according to the (i,j) rule, where j is the index of the array element corresponding to the emitted ultrasonic wave, and i is incremented by a preset step value based on the emission time point.

[0024] In this embodiment, the j-index of the pair element is identified by a unique number. The range of numbers is not limited in this embodiment. The i-index of the time point is incremented according to the time sequence of the transmission and the sequence number is incremented by a step value of 1. In other embodiments, the preset step value can also be 2, 3, etc., and this embodiment does not impose any restrictions.

[0025] Continue reading Figure 2 and Figure 3 Preferably, the step of calculating the adaptive offset parameters based on a time-lapse imaging dataset using a model combining the generalized coherence factor and the mean standard deviation coefficient specifically includes: The time-lapse imaging datasets are accumulated along the time dimension to obtain imaging datasets for different plane waves; The spectral signal sets of different plane waves are calculated using Fourier transform based on imaging datasets of different plane waves. The calculation expression is as follows: in This is a time-lapse imaging dataset; An adaptive weighting function is calculated based on the imaging datasets of the different plane waves and the spectral signal sets of the different plane waves using a model that combines the generalized coherence factor and the mean standard deviation coefficient. Construct an offset function based on an adaptive weighting function; The adaptive offset parameters are calculated based on the offset function using a cleanup model derived from domain statistics.

[0026] Adaptive weights are constructed based on the generalized coherence coefficient (GCF) and the mean-standard-deviation coefficient (SMSF). An adaptive offset parameter is constructed based on the pre-built adaptive weight function and the cleaning algorithm of neighborhood statistics to remove outliers in the imaging process.

[0027] Continue reading Figure 2 and Figure 3 Preferably, the step of calculating the adaptive weighting function based on the spectral signal sets of different plane waves using a normalization function specifically includes: Calculating the generalized coherence factor based on spectral signal sets of different plane waves This is used to improve image details, feature extraction capabilities, and reduce the uncertainty of sidelobe artifacts. The calculation expression is: in For a spectral signal, M0 is set to 1; Calculate the mean standard deviation coefficient based on imaging datasets of different plane waves. This is used to address the issue of inconsistent weighting scales caused by changes in signal-to-noise ratio. The calculation expression is: in This is an imaging dataset of different plane waves, where M is the number of plane waves; The adaptive weighting function is calculated based on the generalized coherence factor and the mean-standard deviation coefficients through normalization operations. The calculation expression is as follows: .

[0028] Continue reading Figure 2 Preferably, the steps for calculating adaptive offset parameters based on the offset function using a domain statistics cleanup model specifically include: Construct a domain window of size odd number × odd number; The proportions of the domain window division values ​​of 10 and 0.1 were calculated respectively, and the calculation expressions are as follows: in For the domain window, the value takes the values ​​10 and 0.1 respectively; Based on the division values ​​of 10 and 0.1, the adaptive offset parameter is calculated, and the calculation expression is as follows: Where T is the preset threshold, and d(p) is the offset function, calculated as follows: Where α is a preset constant value.

[0029] In this embodiment, the size L of the neighborhood window is not limited, and the preset constant value of α is not limited. The adaptive offset parameter is calculated based on the offset parameter through the neighborhood statistics cleanup algorithm. The adaptive offset parameter is applied to the apodization function in the zero-subtraction imaging beamforming model, which can be used to solve the contrast-resolution-image quality trade-off caused by the fixed offset.

[0030] Continue reading Figure 2 Preferably, the step of calculating the final imaging beam signal using the zero-subtraction imaging beamforming model based on adaptive offset parameters and time-delay imaging dataset specifically includes: The zero-mean apodization function for each array element is preconstructed based on the adaptive offset parameters. Positive offset apodization function Negative offset apodization function ; The zero-mean datasets for different plane waves were calculated based on the time-lapse imaging dataset using the zero-mean apodization function, positive offset apodization function, and negative offset apodization function corresponding to each array element. Positive migration datasets of different plane waves Negative migration datasets of different plane waves The formula for adjusting the amplitude distribution of imaging information is: The final imaging beam signal is obtained by nonlinearly combining zero-mean datasets, positive offset datasets, and negative offset datasets of different plane waves. The calculation expression is: .

[0031] A zero-subtraction imaging apodization function is constructed based on adaptive offset parameters. Based on the constructed apodization function, zero-subtraction imaging beamforming processing is performed on the time-lapse imaging dataset to obtain the final imaging beamforming signal. Figure 4 This invention demonstrates that, compared to delay-sum beamforming and zero-subtraction imaging beamforming, the imaging information output by this invention has better image quality. Figure 5 This invention demonstrates that, compared to delay-sum beamforming and zero-subtraction imaging beamforming, the imaging information output by this invention has better contrast. Figure 6 The results of the tissue phantom reconstruction experiment of the present invention under different numbers of plane waves are shown. The results show that the computational complexity is reduced while maintaining a high quality imaging frame rate.

[0032] A comparison of the output images of the CPWC beamforming method based on adaptive parameter zero-subtraction imaging with the delayed summation beamforming method and the zero-subtraction imaging beamforming method in the resolution simulation experiment shows that the image quality of the present invention is improved compared to the delayed summation beamforming method, and the image contrast is improved compared to the zero-subtraction imaging beamforming method.

[0033] Preferably, a zero-mean apodization function for each array element is pre-constructed based on the adaptive offset parameters. Positive offset apodization function Negative offset apodization function The specific steps include: Preconstructed zero-mean apodization function The calculation expression is: In the formula, j is the index value of each array element, and N is the total number of array elements in the array element module; Preconstruct positive offset apodization functions respectively Negative offset apodization function The calculation expressions are as follows: in This is the adaptive offset parameter.

[0034] The working process of the present invention will be further explained below: The ultrasonic array element module acquires echo imaging signal datasets from the radio frequency data of ultrasound waves from the target object. Through delay processing, a time-lapse imaging dataset is obtained based on the spatial location of the imaging region. Adaptive offset parameters are then derived from the time-lapse imaging dataset using a model constructed with the generalized coherence coefficient (GCF) and mean-standard-deviation coefficients. These parameters are used to construct the apodization function in the zero-subtraction imaging beamforming method. Finally, the imaging beam signal is calculated using the zero-subtraction imaging beamforming model based on the time-lapse imaging dataset. The adaptive offset parameters are first obtained from the time-lapse imaging data using the GCF and mean-standard-deviation coefficients. An adaptive weight function is then calculated based on the GCF and mean-standard-deviation coefficients through normalization. An offset function is constructed based on this adaptive weight function. Finally, the adaptive offset parameters are calculated using a neighborhood statistics cleanup model based on the offset function. The delayed imaging dataset is calculated based on the echo imaging signal dataset by the delay processing module, achieving signal alignment at time points. An adaptive offset parameter is calculated based on the delayed imaging dataset using a model combining the generalized coherence factor and the mean-standard deviation coefficients. This parameter is then applied to construct dynamic positive and negative offset apodization functions. The adaptive offset parameter is incorporated into a zero-subtraction imaging beamforming model, resulting in improved spatial resolution and contrast of the final beamforming signal while maintaining superior background speckle quality. Furthermore, this invention reduces the requirement for the number of plane waves in ultrasonic CPWC acquisition, increasing the frame rate while maintaining imaging quality and reducing computational complexity.

[0035] Second Embodiment Based on the same inventive concept, a second aspect of the present invention provides a CPWC beamforming system based on adaptive parameter zero-subtraction imaging, applied to ultrasound imaging signal processing, comprising: The data acquisition module is used to transmit ultrasonic waves to the target object through the ultrasonic array element module and acquire the echo imaging signal dataset of the ultrasonic waves; The delay processing module is used to calculate the delay imaging dataset based on the echo imaging signal dataset. The offset calculation module is used to calculate adaptive offset parameters based on a time-lapse imaging dataset using a model that combines the generalized coherence factor and the mean standard deviation coefficient. The beamforming module is used to calculate the final imaging beam signal based on adaptive offset parameters and time-delay imaging dataset using a zero-subtraction imaging beamforming model.

[0036] Data Acquisition Module Working Principle: Each ultrasonic array element has at least one element component that converts the acquired echo signals into signal data. This signal data is then amplified, filtered, and sampled to obtain the echo imaging signal dataset. Delay Processing Module: This dataset provides the raw data for subsequent imaging processing. Based on the characteristics of each array element, the system calculates the delay time for each element and performs delay data processing on the echo imaging signal dataset according to the corresponding array element's delay characteristics, aligning echo signals from different paths in time. Offset Calculation Module: The calculated generalized coherence factor, applied to ultrasonic imaging, reduces unnecessary signals, optimizes computational complexity, and enhances image quality, significantly improving the effect and efficiency of ultrasonic imaging. The calculated standard deviation coefficient balances the differences in different regions of the generalized coherence factor, smoothing imaging information within the same region. An apodization function is constructed to fuse adaptive offset parameters. Beamforming Module: This module performs zero-subtraction imaging beamforming on the data processed by the delay processing module, implementing an adaptive zero-subtraction imaging beamforming method.

[0037] Third Embodiment Based on the same inventive concept, a third aspect of the present invention provides a CPWC beamforming device based on adaptive parameter zero-subtraction imaging, including a memory, a processor, and a computer program stored in the memory and executable on the processor. When the computer program is executed by the processor, it implements the steps of any of the above-described CPWC beamforming methods based on adaptive parameter zero-subtraction imaging.

[0038] In the description of this application, it should be noted that the terms "inner" and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings, or the orientation or positional relationship commonly used when the product is in use. They are used only for the convenience of describing this application and for simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this application. Furthermore, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0039] It should also be noted that, unless otherwise explicitly specified and limited, the terms "setup" and "connection" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this application based on the specific circumstances.

[0040] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific identification content executed by the system and device described above can be referred to the corresponding process in the foregoing method embodiments.

[0041] The embodiments of the present invention have been described in detail above with reference to the accompanying drawings, but the present invention is not limited to the above embodiments. Even if various changes are made to the present invention, if these changes fall within the scope of the claims of the present invention and their equivalents, they shall still fall within the protection scope of the present invention.

Claims

1. A CPWC beamforming method based on adaptive parameter zero-subtraction imaging, applied to ultrasound imaging signal processing, characterized in that, Includes the following steps: The ultrasonic array module emits ultrasonic waves to the target object and collects the echo imaging signal dataset of the ultrasonic waves. The time-delayed imaging dataset is calculated by the time-delay processing module based on the echo imaging signal dataset. The adaptive offset parameters are calculated based on the time-lapse imaging dataset using a model that combines the generalized coherence factor and the mean standard deviation coefficient. The final imaging beamforming signal is calculated using the zero-subtraction imaging beamforming model based on the adaptive offset parameters and the time-delay imaging dataset.

2. The CPWC beamforming method based on adaptive parameter zero-subtraction imaging according to claim 1, characterized in that, The steps of emitting ultrasonic waves to the target object through the ultrasonic array element module and acquiring the echo imaging signal dataset of the ultrasonic waves specifically include: The ultrasonic array element module emits ultrasonic signals of a preset frequency to the target object through several array elements. The ultrasonic array module is configured with a constant angle value so that the ultrasonic waves emitted by each of the plurality of array elements maintain a fixed angle with the ultrasonic array module. The delay time of each element in the ultrasonic array module is calculated based on the angle constant value; The ultrasonic array element module emits ultrasonic waves toward the target object at preset time points, and the ultrasonic array element module receives the echo imaging signal dataset; The echo imaging signal dataset is indexed to distinguish echo imaging signal data from different array elements and at different time points.

3. The CPWC beamforming method based on adaptive parameter zero-subtraction imaging according to claim 2, characterized in that, The specific steps for indexing the echo imaging signal dataset include: Each element of the ultrasonic array element module is uniquely identified and indexed with a digital identifier. The echo imaging signal dataset is identified according to the (i,j) rule, where j is the index of the array element corresponding to the emitted ultrasonic wave, and i is incremented by a preset step value based on the emission time point.

4. The CPWC beamforming method based on adaptive parameter zero-subtraction imaging according to claim 1, characterized in that, The steps for calculating the adaptive offset parameters based on the time-lapse imaging dataset using the model combining the generalized coherence factor and the mean standard deviation coefficient specifically include: The time-delay imaging datasets are accumulated along the time dimension to obtain imaging datasets for different plane waves; The spectral signal sets of different plane waves are calculated based on the imaging datasets of the different plane waves using Fourier transform; An adaptive weighting function is calculated based on the imaging datasets of different plane waves and the spectral signal sets of different plane waves using a model that combines the generalized coherence factor and the mean standard deviation coefficient. Construct an offset function based on the aforementioned adaptive weight function; The adaptive offset parameter is calculated based on the offset function using a cleanup model derived from domain statistics.

5. The CPWC beamforming method based on adaptive parameter zero-subtraction imaging according to claim 4, characterized in that, The steps for calculating the adaptive weighting function based on the imaging datasets and spectral signal sets of different plane waves using the model combining the generalized coherence factor and the mean standard deviation coefficient specifically include: The generalized coherence factor is calculated based on the spectral signal sets of the different plane waves. By using coherence to distinguish different regions in an image, the calculation expression is as follows: in For a spectral signal, M0 is set to 1; The mean standard deviation coefficient was calculated based on the imaging datasets of the different plane waves. This is used to smooth the value distribution within different regions and highlight the value differences between different regions. The calculation expression is: in The image dataset for the different plane waves is given, where M is the number of plane waves. The adaptive weighting function is calculated based on the generalized coherence factor and the mean-standard deviation coefficients through the normalization operation, and the calculation expression is: 。 6. The CPWC beamforming method based on adaptive parameter zero-subtraction imaging according to claim 4, characterized in that, The steps for calculating the adaptive offset parameters based on the offset function using the cleaning model derived from the domain statistics specifically include: Construct a domain window of size odd number × odd number; The proportions of the domain window division values ​​of 10 and 0.1 were calculated respectively, and the calculation expressions are as follows: in For the domain window, the value takes values ​​of 10 and 0.1 respectively; Based on the division values ​​of 10 and 0.1, the adaptive offset parameter is calculated, and the calculation expression is as follows: Where T is a preset threshold, and d(p) is the offset function, the calculation expression of which is: Where α is a preset constant value.

7. The CPWC beamforming method based on adaptive parameter zero-subtraction imaging according to claim 2, characterized in that, The steps for calculating the final imaging beam signal using the zero-subtraction imaging beamforming model based on the adaptive offset parameters and the time-delay imaging dataset specifically include: Based on the adaptive offset parameters, a zero-mean apodization function is preconstructed for each of the array elements. Positive offset apodization function Negative offset apodization function ; The zero-mean datasets for different plane waves are calculated based on the time-delay imaging dataset using the zero-mean apodization function, the positive offset apodization function, and the negative offset apodization function corresponding to each array element. Positive migration datasets of different plane waves Negative migration datasets of different plane waves The formula for adjusting the amplitude distribution of imaging information is: The final imaging beam signal is calculated by nonlinearly combining the zero-mean dataset, the positive offset dataset, and the negative offset dataset of the different plane waves. The calculation expression is: 。 8. The CPWC beamforming method based on adaptive parameter zero-subtraction imaging according to claim 7, characterized in that... Based on the adaptive offset parameters, a zero-mean apodization function is preconstructed for each of the array elements. Positive offset apodization function Negative offset apodization function The specific steps include: Preconstruct the zero-mean apodization function The calculation expression is: In the formula, j is the index value of each array element, and N is the total number of array elements in the array element module; The positive offset apodization function is pre-constructed respectively. The negative offset apodization function The calculation expressions are as follows: in The adaptive offset parameter is...

9. A CPWC beamforming system based on adaptive parameter zero-subtraction imaging, applied to ultrasound imaging signal processing, characterized in that, include: The data acquisition module is used to emit ultrasonic waves to the target object through the ultrasonic array element module and acquire the echo imaging signal dataset of the ultrasonic waves; The delay processing module is used to calculate the delay imaging dataset based on the echo imaging signal dataset. The offset calculation module is used to calculate adaptive offset parameters based on the time-lapse imaging dataset using a model that combines the generalized coherence factor and the mean standard deviation coefficient. The beamforming module is used to calculate the final imaging beamforming signal based on the adaptive offset parameters and the time-delay imaging dataset using a zero-subtraction imaging beamforming model.

10. A CPWC beamforming device based on adaptive parameter zero-subtraction imaging, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, when executed by the processor, the computer program implements the steps of the CPWC beamforming method based on adaptive parameter zero-subtraction imaging as described in any one of claims 1-8.

Images