Ultrasonic transmission CT imaging method and system, computer and storage medium

Abstract

The invention provides an ultrasonic transmission CT (Computed Tomography) imaging method and system, a computer and a storage medium, which can be used for quickly reconstructing an ultrasonic transmission image with high resolution and high contrast. The method comprises the following steps: in layer-by-layer scanning of an annular ultrasonic transducer array, calculating a TOF (Time of Flight) matrix containing transmission time of all ultrasonic paths after the annular ultrasonic transducer array rotates by a specific angle each time; after one layer of scanning is completed, the TOF matrixes of different angles are recombined into a three-dimensional matrix according to the spatial relation, and missing data are complemented; discretizing an area to be imaged into two-dimensional grids, making each grid correspond to a pixel point, counting transmission time and path length of all ultrasonic paths penetrating through the pixel point, calculating sound velocity contribution values of the paths, taking an average value as a sound velocity value of the pixel point, and generating a two-dimensional ultrasonic transmission image; a two-dimensional image is converted into a three-dimensional image through a ray casting algorithm, the visual influence of voxels is adjusted by introducing a weight factor based on depth, and the depth perception and semitransparent rendering effect of the image are enhanced.

Description

Technical Field

[0001] The present invention relates to an ultrasonic transmission CT imaging method, system, computer, and storage medium. Background Art

[0002] Capacitive Micromachined Ultrasonic Transducer (CMUT), as a new transducer technology in the field of ultrasonic imaging in recent years, has gradually replaced the traditional Piezoelectric Transducer (PZT) in many applications. CMUT exhibits significant advantages in high-resolution ultrasonic imaging due to its wide bandwidth, high sensitivity, easy miniaturization, and large-scale integration. In addition, the manufacturing of CMUT uses micromachining technology, which can achieve high-density array arrangement and is more suitable for high-precision and all-round imaging applications.

[0003] Transmission Ultrasound Imaging is an imaging technology based on measuring the propagation characteristics of ultrasonic waves in a medium and is widely used in fields such as medical imaging and industrial inspection. In this method, ultrasonic waves are emitted from a transmitting transducer, pass through the medium to be measured, and then reach a receiving transducer. Since the propagation speed and energy attenuation of ultrasonic waves depend on the physical properties of the medium (such as density and sound speed), by analyzing the Time of Flight (TOF) and attenuation characteristics of ultrasonic waves, the sound speed distribution inside the medium can be reconstructed, thereby reflecting the internal structure of the object to be measured. Based on the straight-ray theory, it is assumed that ultrasonic waves propagate along a straight path without considering the influence of refraction and scattering inside the medium. This simplified treatment effectively improves the calculation efficiency. By measuring the flight time and path length between the transmitting and receiving transducers and combining the solution of inverse problems, the average propagation speed of ultrasonic waves in the medium can be calculated, and the sound speed distribution map of the area to be measured can be reconstructed grid by grid.

[0004] However, the existing technology has certain limitations in terms of the perspective and integrity of data acquisition. Especially when imaging the internal structure of complex media, information blind spots or data missing may occur, affecting the imaging accuracy and resolution. Summary of the Invention

[0005] The present invention proposes an ultrasonic transmission CT imaging method, system, computer, and storage medium, which can quickly reconstruct high-resolution and high-contrast ultrasonic transmission images.

[0006] In a first aspect, an ultrasonic transmission CT imaging method is proposed, including: during the layer-by-layer scanning process, after the annular ultrasonic transducer array rotates a specific angle each time, a TOF matrix is calculated, and the TOF matrix contains the transmission times of all ultrasonic paths; after completing one layer of scanning, multiple TOF matrices at different rotation angles are rearranged and combined according to the spatial relationship to generate a reconstructed TOF matrix, and the missing data in the reconstructed TOF matrix is complemented; the area to be imaged is discretized into a two-dimensional matrix composed of several grids, and each grid corresponds to a pixel point. For each pixel point, all ultrasonic paths passing through the pixel point are counted, and the transmission time of each ultrasonic path in the complemented reconstructed TOF matrix is obtained. According to the transmission time of each ultrasonic path and the path length passing through the pixel point, the sound velocity contribution value of each ultrasonic path at the pixel point is calculated, and the average value of the sound velocity contribution values of all ultrasonic paths passing through the pixel point is used as the sound velocity value Q(r, s) of the pixel point. A two-dimensional ultrasonic transmission image is generated based on the sound velocity values Q(r, s) of all pixel points; the two-dimensional ultrasonic transmission image is converted into a three-dimensional image with depth perception and semi-transparent rendering effects.

[0007] In some examples, the calculation formula of the sound velocity value Q(r, s) is:

[0008]

[0009] In the formula, k represents the number of ultrasonic paths passing through the pixel point (r, s), and V i (r, s) represents the sound velocity contribution value of the i-th ultrasonic path at the pixel point (r, s).

[0010] In some examples, the transmission time of each ultrasonic path is calculated as follows: the ultrasonic signal of each ultrasonic path is segmented into multiple signal intervals and multiple statistical models are used to predict the flight time; the AIC value corresponding to each model is calculated through the AIC algorithm, and the model with the smallest AIC value is the optimal model; an exponential weight coefficient is generated based on the AIC value gap between each model and the optimal model, a dynamic fusion strategy is constructed, and the flight times predicted by different models are weighted and averaged according to the weight coefficient. The prediction result with an AIC value closer to the optimal model has a higher weight.

[0011] In some examples, the transmission time of each ultrasonic path is calculated by the following formula:

[0012]

[0013] In the formula, t TOF represents the transmission time of the ultrasonic signal on each path; AIC(j) represents the Akaike information criterion value of the j-th model, which is used to measure the quality of the model; AIC minis the minimum AIC value among all models, identifying the best model; t j represents the flight time predicted by the j-th model; and Δ l = AIC l - AIC min represents the AIC gap between the l-th model and the best model; exp represents the natural exponential function.

[0014] In some examples, the calculation formula for the sound speed contribution value of each ultrasonic path at the pixel point (r, s) is:

[0015]

[0016] In the formula, Δt = t w - t b , representing the difference between the flight time t w when there is only a liquid medium and the flight time t b when there is a measured object; V0 is the sound speed of the liquid medium, and L0 is the length that the ultrasonic signal propagates in the measured object.

[0017] In some examples, the ray casting algorithm is used to convert a two-dimensional ultrasonic transmission image into a three-dimensional image.

[0018] In some examples, during the ray casting process, a weight factor is assigned to each voxel in the three-dimensional data field, and the cumulative calculation of the color and opacity of each voxel along the ray path is adjusted through this weight factor, so that the visual impact of nearby voxels is amplified and the visual impact of distant voxels is weakened.

[0019] In a second aspect, an ultrasonic transmission CT imaging system includes: an annular ultrasonic transducer array, which is coupled to the measured object through a liquid medium; a rotary lifting control platform for controlling the rotation angle and lifting height of the annular ultrasonic transducer array to achieve multi-angle layer-by-layer scanning; and a computer configured to execute the ultrasonic transmission CT imaging method according to any one of claims 1-6.

[0020] In a third aspect, a computer is proposed, including: a processor; a memory including one or more computer program modules; wherein, the one or more computer program modules are stored in the memory and configured to be executed by the processor, and the one or more computer program modules include instructions for implementing the ultrasonic transmission CT imaging method described above.

[0021] In a fourth aspect, a non-transitory computer-readable instruction is proposed, which can implement the ultrasonic transmission CT imaging method when executed by a computer. Description of the Drawings

[0022] Figure 1 Schematic diagram of multi-angle measurement of the annular ultrasonic transducer array according to an embodiment of the present invention.

[0023] Figure 2 Flowchart of the ultrasonic transmission CT imaging method according to an embodiment of the present invention.

[0024] Figure 3 Schematic diagram of the recombination method of four groups of TOF matrix data according to an embodiment of the present invention. Detailed implementation manners

[0025] The ultrasonic transmission CT imaging system is designed to automatically acquire ultrasonic image data of the object to be measured. It includes: a computer, a data acquisition system, an annular CMUT (capacitive micromachined ultrasonic transducer) ultrasonic transducer array, a rotary lifting control platform, and a water tank.

[0026] The computer is the core of the entire system. It not only controls the operations of other hardware components but also undertakes the important task of data processing. The workstation processes a large number of ultrasonic signals in real time and performs complex image reconstruction algorithms. In addition, it supports a user interface, enabling users to define scanning parameters such as transmit frequency, receive gain, rotation angle, lifting height, etc.

[0027] The data acquisition system connects the CMUT ultrasonic transducer array to the computer. It is responsible for converting the physical quantities (such as ultrasonic signals) of the ultrasonic transducers into electrical signals and then providing them to the computer for subsequent analysis.

[0028] The annular design of the CMUT ultrasonic transducer array allows ultrasonic waves to penetrate the object to be measured from multiple directions. During each scanning process, each transducer acts as a transmitter to send ultrasonic pulses in turn, while the remaining transducers act as receivers to record the transmitted ultrasonic signals passing through the object to be measured.

[0029] The rotary lifting control platform precisely controls the position changes of the CMUT array. As Figure 1 shown, according to a predetermined program, the platform rotates the CMUT array at a set angular interval to achieve 360° full coverage, and after completing one circle, it rises a certain distance to continue scanning the next layer. This one-transmit-all-receive data acquisition mode combined with fine position adjustment ensures full coverage in three-dimensional space. For example, the angle of each rotation is one-fourth of the central angle between two CMUT ultrasonic transducers, and 4 groups of different data are acquired; after measuring one layer, it rises 1 mm upward to scan the next layer.

[0030] The coupling between the ultrasonic transducer array and the organ to be measured is achieved through a liquid medium to ensure the minimum energy loss of ultrasonic waves before they propagate to the organ to be measured. For example, the medium can be water. The water is stored in a water tank, and the detection window is located on the water tank and is used to accommodate the organ to be measured. The ultrasonic transducer array is installed on the side or side wall of the water tank (the CMUT array rotates and rises with the water tank). The specific assembly form of the ultrasonic transducer array, the detection window, the water tank, and the rotation and lifting control platform is not the focus of the present invention. For detailed information, reference can be made to the patent document CN118787384A.

[0031] Figure 2 Shows the ultrasonic transmission CT imaging method. The following will introduce the Figure 2 method shown in detail.

[0032] (1) Using the Akaike Information Criterion (AIC algorithm), accurately extract the transmission time of the ultrasonic signal. The specific steps are as follows: First, divide the received ultrasonic signal into multiple stages. For each stage, apply a series of different statistical models (such as linear regression models, autoregressive models, etc.) to analyze the signal. Then, use the AIC algorithm to identify the relatively stable signal intervals before and after the time of flight point, which helps to determine the exact moment when the ultrasonic wave propagates in the medium. By evaluating the performance of these different models and selecting the best model based on their AIC values, the error that may be brought by relying on a single model is avoided. This method enhances the robustness of the time delay estimation by taking the weighted average of the results of multiple models.

[0033] For each ultrasonic path, the present invention uses the AIC algorithm to process the transmission signal to extract the accurate arrival time. This process involves selecting a suitable statistical model, identifying the stable local regions in the received signal, and thus calculating the time when the ultrasonic signal passes through the object to be measured, that is, TOF (Time of Flight). Determine the time window size according to the sound speed of the liquid medium (such as water), where j is each data point within the selected time window, and n is the total number of data points within the time window (j = 1, 2,..., n), and f s is the sampling frequency of the received signal.

[0034] The AIC formula is defined as follows:

[0035]

[0036] Here, AIC(j) is the Akaike Information Criterion value of the j-th model, which is used to select the optimal model. It takes into account the complexity and goodness of fit of the model. is the variance of the data set from 1 to j. The smaller the variance, the better the model fit. (n - j - 1) represents the remaining degrees of freedom, subtracting j and 1 (because estimating one parameter requires losing one degree of freedom). Represents the variance of the data set from j + 1 to n.

[0037] To find the model corresponding to the minimum AIC value, the formula is designed as:

[0038]

[0039] To further improve the robustness and accuracy of the ultrasonic transmission imaging technology, especially in the face of challenges such as noise interference, incomplete data, and differences in model adaptability, the present invention can adopt a robust model fusion strategy based on the weighted average method. This method dynamically adjusts the weights of each model by introducing an exponential weighting mechanism, thereby optimizing the stability and accuracy of the reconstruction results.

[0040] Specifically, the time of flight (TOF) of the signal in each ultrasonic path can be calculated by the following formula:

[0041]

[0042] In the formula, t TOF Represents the time of flight of the ultrasonic signal on each path; AIC(j) represents the Akaike information criterion value of the j-th model, which is used to measure the quality of the model; AIC min Is the minimum AIC value among all models, identifying the best model; t j Represents the time of flight predicted by the j-th model; and Δ l = AIC l - AIC min Represents the AIC gap between the l-th model and the best model; exp represents the natural exponential function.

[0043] In this framework, the present invention uses an exponential function to adjust the weight of each model, so that the model with a lower AIC value (i.e., better performance) obtains a higher weight. This not only emphasizes the influence of more appropriate models on the final result, but also ensures that the sum of all model weights is 1, maintaining the rationality of the calculation process and the consistency of physical meaning. Through this intelligent optimization and allocation method, the instability of the results and the problem of error accumulation caused by differences in model adaptability can be effectively alleviated, thereby improving the overall performance of ultrasonic transmission imaging.

[0044] (2) TOF matrix completion method

[0045] The TOF matrix is composed of the transmission times of ultrasonic signals in the ultrasonic path. During layer-by-layer scanning, the data collected each time after the CMUT ultrasonic transducer array rotates by a certain angle will be used to calculate a TOF matrix. When the scanning of one layer (i.e., one slice) is completed, multiple TOF matrices obtained at different rotation angles will be rearranged and combined to obtain a reconstructed TOF matrix.

[0046] Rearrangement: This may mean adjusting the positions of the elements within a single TOF matrix to match a certain predefined spatial or logical order. For example, if the sensor array is physically rotated, the corresponding TOF values also need to be repositioned according to the new geometric layout to ensure that they correctly reflect the spatial relationships at the current angle. Combination: This means merging multiple independent TOF matrices into a more complete set of matrices. By combining data from different rotation positions, the data deficiencies that may exist from a single perspective can be compensated for. The combination process may include, but is not limited to, techniques such as weighted averaging and interpolation filling, aiming to utilize all available information to construct as accurate and comprehensive a representation of TOF data as possible. Figure 3 It is a schematic diagram of the recombination method for four groups of TOF matrix data.

[0047] Apply the Singular Value Thresholding (SVT) algorithm to process the rows of the reconstructed TOF matrix to identify and repair any missing or incomplete time-of-flight data. Specifically, this process is achieved by solving the following low-rank matrix approximation optimization problem:

[0048] minimize ∥X∥ *

[0049] subject to P Ω (X) = P Ω (TOF)

[0050] where X represents the reconstructed TOF matrix to be completed, and ∥X∥ * represents the nuclear norm of matrix X, which is the sum of all singular values. Minimizing the nuclear norm is a low-rank matrix approximation method because it encourages matrix X to have a low rank. P Ω (.) is a projection operator, indicating that only the element values of the matrix at the known data positions Ω are retained, and the values at the remaining positions are set to zero. The constraint condition P Ω (X) = P Ω (TOF) requires that the values of matrix X at the known positions Ω must be the same as the corresponding positions of the original TOF matrix (the matrix of time-of-flight data directly collected at a single scanning angle), and the values at the unknown positions are filled through the optimization process.

[0051] The iterative optimization formula is as follows:

[0052]

[0053] Among them, the shrink operator is used for the matrix Y m-1 to perform soft-threshold shrinkage on the singular values to generate a new matrix X m , that is, to perform thresholding on the singular values obtained from the singular value decomposition (SVD) of Y m-1 ; τ is the threshold parameter that controls the degree of shrinkage of the singular values; the scalar step sequence {δ m} m≥1 is used to adjust the update amplitude in each iteration. Starting from the initial matrix Y 0 = 0, through continuous iteration until the stopping condition is met:

[0054]

[0055] where ∈ is a fixed tolerance value, such as 10 –4 , which is the threshold for measuring the relative error. When the relative error of the matrix X m at the known positions Ω is less than the preset tolerance ∈, the iteration process terminates. ∥.∥ F represents the Frobenius norm, that is, the square root of the sum of the squares of the matrix elements, which is used to quantify the difference between matrices.

[0056] By this method, the missing data in the reconstructed TOF matrix can be effectively completed, ensuring the integrity and accuracy of imaging, and thus improving the quality of ultrasonic imaging. Thereby improving the accuracy of the subsequent sound speed distribution reconstruction and enhancing the quality of the finally generated image.

[0057] (3) Sound speed tomography image reconstruction algorithm based on straight rays

[0058] This algorithm aims to solve the inverse problem and calculate the average sound speed in the object to be measured by combining the completed time-of-flight matrix (TOF matrix) with the actual length of each ultrasonic propagation path. Subsequently, the imaging area is divided into multiple grids, and the corresponding sound speed value is calculated for each grid to gradually reconstruct the sound speed distribution map of the object to be measured, and finally generate a high-resolution ultrasonic transmission imaging result. The following is a detailed introduction.

[0059] For each directed line segment between the transmitting transducer and the receiving transducer, measure the time of flight t w when there is only water medium, and the time of flight t b when the object to be measured exists.

[0060] Calculate the time difference between the two:

[0061] Δt = t w - t b

[0062]

[0063] L is the propagation path length of ultrasonic waves in only water medium, V0 is the sound speed in water (constant), L0 is the length of ultrasonic waves propagating in the object to be measured, and V i is the average sound speed on the propagation path of ultrasonic waves in the object to be measured.

[0064] Assume there are M transmitting transducers and also M receiving transducers. Then the total number of directed line segments from the transmitting transducers to the receiving transducers is M 2 , and the average sound speed V of ultrasonic waves on the propagation path in the object to be measured i (i = 1, 2, …, M 2 ). The average sound speed V of ultrasonic waves on the propagation path in the object to be measured can be calculated as i :

[0065]

[0066] Discretize the region to be imaged into N×N square grids to form a two-dimensional matrix, where each element represents a pixel point, denoted by coordinates (r, s) (r = 1, 2, …, N, s = 1, 2, …, N). The initial sound speed value of each pixel point is set to 0.

[0067] For each pixel point, count all the ultrasonic wave paths passing through this pixel point, and update the sound speed value of the pixel point according to the average sound speed values on these paths. Specifically, if k paths pass through the pixel point (r, s), then the sound speed value Q(r, s) of this point can be expressed as the weighted average or simple average of the sound speeds on these paths, that is:

[0068]

[0069] In the formula, V i (r, s) represents the sound speed contribution of the i-th path at the pixel point (r, s).

[0070] Finally, based on the sound speed value Q(r, s) of each pixel point, a sound speed distribution map of the entire imaging region can be generated, which is the ultrasonic transmission imaging result of the object to be measured.

[0071] (4) Implement three-dimensional imaging through the ray casting algorithm

[0072] To convert the two-dimensional ultrasonic transmission images obtained in the previous steps into three-dimensional images with depth perception and semi-transparent effects, the present invention employs a ray casting algorithm. This process starts from a three-dimensional data field composed of multiple two-dimensional slices, which are obtained by scanning the object to be measured layer by layer through the aforementioned rotary lifting control platform. After each rotation, the CMUT array rises a certain distance (e.g., 1 mm) and continues to scan the next layer. This series of operations generates a series of continuous two-dimensional images, namely the so-called "slices". All these slices together constitute a three-dimensional matrix data that describes the entire imaging area.

[0073] The ray casting algorithm assumes that each data point in the three-dimensional data field has chromaticity and opacity attributes. Using parallel projection technology, a ray is emitted from each pixel of the projection plane, and this ray passes through the three-dimensional data set along a preset direction vector. During the process of equidistant sampling along the ray path, the color and opacity values of each sampling point are calculated through trilinear interpolation, and these values are accumulated from front to back until the ray is completely absorbed or passes through the data field, thereby determining the final color value of the pixel point.

[0074] Specifically, for each ray, its mathematical expression can be represented as:

[0075] r(t) = r0 + td, t ∈ [t min , t max

[0076] where r0 represents the starting point of the ray, usually corresponding to the pixel position of the projection plane; d is the ray direction vector; t is the parameter along the ray direction, and t min and t max represent the parameter values when the ray enters and leaves the data field, respectively.

[0077] The final color value C is calculated according to the following rules final :

[0078]

[0079] When , the cumulative calculation is stopped, and the C at this time is the final color value of the pixel point. C(t i ) is the color value of the i-th sampling point; α(t i ) is the opacity of the i-th sampling point; is the transmittance at the i-th sampling point, indicating the transmittance of the ray before this point. Among them, (1 - α(t j )) represents the transparency degree of this point; N in the ray casting algorithm represents the total number of sampling points along the ray direction, that is, the number of steps of the ray sampling in the three-dimensional data field.

[0080] ​In addition, during the process of color and opacity accumulation, the present invention introduces a depth-based weight factor ω to enhance the sense of hierarchy and depth perception of the image. This technique is implemented through the following formula:

[0081] C(t i )←C(t i )·(1 + ω·t i )

[0082] Where: t i is a value between t min and t max , representing the position parameter of a point along this ray path. Each t i corresponds to the position of a sampling point or voxel on the ray path. The core idea of this technique is to assign greater weights to the voxels closer to the observer and smaller weights to the voxels farther from the observer, thereby enhancing the three-dimensional sense and depth perception of the image and better displaying the details of the internal structure of the object under test.

[0083] Through the above ray casting algorithm, the present invention can effectively utilize the ray casting algorithm to realize the visualization of the three-dimensional data field, thereby generating three-dimensional ultrasonic transmission imaging results with depth perception and semi-transparent effects, providing intuitive and high-resolution image support for medical diagnosis and other fields.

[0084] The present invention also provides an embodiment of a computer. The computer includes a processor and a memory. The memory is used to store non-transitory computer-readable instructions (such as one or more computer program modules). The processor is used to run the non-transitory computer-readable instructions, and when the non-transitory computer-readable instructions are run by the processor, one or more steps in the ultrasonic transmission CT imaging method described above can be executed. The memory and the processor can be interconnected through a bus system and / or other forms of connection mechanisms.

[0085] For example, the processor can be a central processing unit (CPU), a graphics processing unit (GPU), or other forms of processing units with data processing capabilities and / or program execution capabilities. For example, the central processing unit (CPU) can be of the X86 or ARM architecture, etc. The processor can be a general-purpose processor or a dedicated processor, and can control other components in the computer to execute the desired functions.

[0086] For example, the memory may include any combination of one or more computer program products, and the computer program products may include various forms of computer-readable storage media, such as volatile memory and / or non-volatile memory. Volatile memory may include, for example, random access memory (RAM) and / or cache memory, etc. Non-volatile memory may include, for example, read-only memory (ROM), hard disk, erasable programmable read-only memory (EPROM), compact disc read-only memory (CD-ROM), USB memory, flash memory, etc. One or more computer program modules may be stored on the computer-readable storage media, and the processor may run one or more computer program modules to implement various functions of the computer.

[0087] The present invention also provides a computer-readable storage medium for storing non-transitory computer-readable instructions, which can implement one or more steps in the above ultrasonic transmission CT imaging method when executed by a computer. When the ultrasonic transmission CT imaging method provided by the embodiments of the present invention is implemented in the form of software and sold or used as an independent product, it can be stored in a computer-readable storage medium. For the relevant description of the storage medium, reference can be made to the corresponding description of the memory in the computer system above, and details are not repeated here.

Claims

1. An ultrasonic transmission CT imaging method, characterized in that, Comprising: During the layer-by-layer scanning process, after the annular ultrasonic transducer array rotates a specific angle each time, a TOF matrix is calculated, and the TOF matrix contains the transmission times of all ultrasonic paths; After completing one layer of scanning, multiple TOF matrices at different rotation angles are rearranged and combined according to the spatial relationship to generate a reconstructed TOF matrix, and the missing data in the reconstructed TOF matrix is complemented; The area to be imaged is discretized into a two-dimensional matrix composed of several grids, and each grid corresponds to a pixel point. For each pixel point, all ultrasonic paths passing through the pixel point are counted, and the transmission time of each ultrasonic path in the complemented reconstructed TOF matrix is obtained. According to the transmission time of each ultrasonic path and the path length of the ultrasonic path passing through the pixel point, the sound speed contribution value of each ultrasonic path at the pixel point is calculated, and the average value of the sound speed contribution values of all ultrasonic paths passing through the pixel point is used as the sound speed value Q(r, s) of the pixel point. A two-dimensional ultrasonic transmission image is generated based on the sound speed values Q(r, s) of all pixel points; The two-dimensional ultrasonic transmission image is converted into a three-dimensional image with depth perception and semi-transparent rendering effects.

2. The ultrasonic transmission CT imaging method according to claim 1, wherein The calculation formula for the sound speed value Q(r, s) is: where k represents the number of ultrasonic paths passing through the pixel point (r, s), and V i (r, s) represents the contribution value of the sound velocity of the i-th ultrasonic path at the pixel point (r, s).

3. The ultrasonic transmission CT imaging method according to claim 1, characterized in that The transmission time of each ultrasonic path is calculated as follows: the ultrasonic signal of each ultrasonic path is divided into multiple signal intervals and multiple statistical models are used to predict the flight time; the AIC value corresponding to each model is calculated through the AIC algorithm, and the model with the smallest AIC value is the optimal model; an exponential weight coefficient is generated based on the AIC value gap between each model and the optimal model, and a dynamic fusion strategy is constructed. The flight times predicted by different models are weighted and averaged according to the weight coefficient, and the prediction results with AIC values closer to the optimal model have higher weights.

4. The ultrasonic transmission CT imaging method according to claim 3, characterized in that, The transmission time of each ultrasonic path is calculated by the following formula: where t TOF represents the transmission time of the ultrasonic signal on each path; AIC(j) represents the Akaike information criterion value of the j-th model, which is used to measure the quality of the model; AIC min is the minimum AIC value among all models, identifying the best model; t j represents the flight time predicted by the j-th model; and Δ l = AIC l - AIC min represents the AIC gap between the l-th model and the best model; exp represents the natural exponential function.

5. The ultrasonic transmission CT imaging method according to claim 1, wherein The calculation formula for the sound speed contribution value of each ultrasonic path at the pixel point (r, s) is: where Δt = t w - t b , representing the flight time t of the ultrasonic signal when there is only a liquid medium w and the flight time t when there is a measured object b The difference; V0 is the sound speed of the liquid medium, and L0 is the length that the ultrasonic signal propagates in the measured object.

6. The ultrasonic transmission CT imaging method according to claim 1, wherein, The two-dimensional ultrasonic transmission image is converted into a three-dimensional image using the ray casting algorithm.

7. The ultrasonic transmission CT imaging method according to claim 6, characterized in that, During the ray casting process, a weight factor is assigned to each voxel in the three-dimensional data field, and the cumulative calculation of the color and opacity of each voxel along the ray path is adjusted through the weight factor, so that the visual impact of nearby voxels is amplified and the visual impact of distant voxels is weakened.

8. An ultrasonic transmission CT imaging system, characterized in that, Comprising: An annular ultrasonic transducer array, which is coupled to the object to be measured through a liquid medium; A rotary lifting control platform for controlling the rotation angle and lifting height of the annular ultrasonic transducer array to achieve multi-angle layer-by-layer scanning; and A computer configured to execute the ultrasonic transmission CT imaging method according to any one of claims 1-6.

9. A computer, characterized in that, Comprising: A processor; A memory including one or more computer program modules; Wherein, the one or more computer program modules are stored in the memory and configured to be executed by the processor, and the one or more computer program modules include instructions for implementing the ultrasonic transmission CT imaging method according to any one of claims 1-6.

10. A non-transitory computer-readable instruction storage, characterized in that, When the non-transitory computer-readable instructions are executed by a computer, the ultrasonic transmission CT imaging method according to any one of claims 1-6 can be implemented.

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