A CT image artifact correction method, system and medium

Abstract

The application discloses a CT image artifact correction method and system and a medium, obtains a projection image set; in the projection image set, selects an arbitrary projection image, processes the projection image through a scattering kernel function, obtains straight-through projection data, processes the straight-through projection data through a hard correction function, obtains mapping projection data, traverses the projection image set, and obtains a plurality of mapping projection data; reconstructs an image; calculates the information entropy of the reconstructed image, takes the minimum information entropy as a target, calculates optimal scattering fitting parameters in the scattering kernel function and optimal hardening fitting parameters in the hard correction function; performs scattering correction on the plurality of shadow data based on the optimal scattering fitting parameters, corrects the straight-through projection data through the optimal hardening fitting parameters, and constructs a CT image of the object to be measured based on the corrected data; the application has the beneficial effect that the CT image with good de-artifact effect is constructed through the accurately obtained correction parameters.

Description

Technical Field

[0001] This invention relates to the field of CT application technology, and more specifically, to a method, system, and medium for CT image artifact correction. Background Technology

[0002] Computed tomography (CT) is a major non-destructive testing technique widely used in medical diagnosis, industrial inspection, geological analysis, and archaeology. Standard CT reconstruction algorithms assume the use of single-energy X-rays. When broad-spectrum X-rays pass through an object, the object's attenuation coefficient for low-energy X-rays is greater. As the object's thickness increases, the proportion of low-energy X-rays penetrating the object decreases, while the proportion of high-energy X-rays increases, resulting in beam hardening. Reconstruction using beam-hardened projection data leads to "cup-shaped" artifacts (bright around the edges, dark in the center) and radial artifacts around metallic objects in the reconstructed attenuation coefficient image. Hardening artifacts reduce image quality, affecting object recognition, segmentation, and density determination in the reconstructed image. Furthermore, the interaction between X-rays and matter involves absorption, scattering, and pair production. CT reconstruction algorithms only consider linear absorption of X-rays by matter. In reality, when X-ray energy is below 300kV, pair production can be ignored, but scattering still contributes significantly. Scattered rays diverge in direction, and when superimposed on penetrating rays, they cause blurring and decreased contrast in the projected image.

[0003] Software-based hardening correction methods mainly include Monte Carlo correction, iterative correction, and deep learning methods. Currently, linear methods are still the primary approach in practical engineering applications, correcting nonlinear projection data into linear projection data. The key to this type of method is accurately obtaining the parameters of the correction function model. Software-based scattering correction methods have a wide range of applications and do not require additional costs, but their actual performance is closely related to the quality of the algorithm.

[0004] In summary, the key to software correction methods for hardening and scattering is obtaining the correct correction parameters. In real physical processes, hardening causes changes in the X-ray energy spectrum, and X-rays of different energies have different scattering parameters. Therefore, the hardening and scattering processes influence each other, and the effect of correcting scattering and hardening separately is unsatisfactory. As a result, in the current technology, it is impossible to accurately obtain the correction parameters, resulting in poor quality CT images.

[0005] In view of the above, this application is hereby submitted. Summary of the Invention

[0006] The technical problem to be solved by the present invention is that in the prior art, the projection image is corrected by hardening software correction method or scattering software correction method. However, if the correction parameters obtained by the method are not ideal, the artifact removal effect of the constructed CT image will be poor. The purpose is to provide a CT image artifact correction method, system and medium that can accurately obtain correction parameters and make the artifact removal effect of the constructed CT image good.

[0007] This invention is achieved through the following technical solution:

[0008] A method for correcting artifacts in CT images, comprising the following steps:

[0009] Acquire a set of projected images, which are projected images of the object under test taken at different angles;

[0010] In the set of projected images, any one projected image is selected, and the projected image is processed by a scattering kernel function to obtain direct projection data. Then, the direct projection data is processed by a hardening correction function to obtain mapped projection data. The set of projected images is traversed to obtain several mapped projection data.

[0011] Based on the aforementioned mapping and projection data, a reconstructed image is constructed;

[0012] Calculate the information entropy of the reconstructed image, and with the goal of minimizing the information entropy, calculate the optimal scattering fitting parameters in the scattering kernel function and the optimal hardening fitting parameters in the hardening correction function;

[0013] The optimal scattering fitting parameters are used to perform scattering correction on several projection data, the optimal hardening fitting parameters are used to correct several through projection data, and the corrected data are used to construct a CT image of the object under test.

[0014] Traditionally, artifact removal in CT images is achieved using two independent methods: hardening software correction and scattering software correction. However, when using this method to remove artifacts from CT images, the hardening process causes changes in the X-ray energy spectrum, and different energies of X-rays have different scattering parameters. Therefore, the hardening and scattering processes interfere with each other, resulting in poor correction effects and a poor final reconstructed CT image. This invention provides a CT image artifact correction method that calculates the optimal parameters during the scattering and hardening processes and uses these optimal parameters to construct the CT image. This achieves the construction of a CT image with good artifact removal effect by accurately obtaining the correction parameters.

[0015] Preferably, the sub-step of obtaining the through projection data includes: constructing a scattering kernel function model, initializing scattering fitting parameters, and performing iterative operations on the projection image to obtain the through projection data.

[0016] Preferably, the sub-step of obtaining the mapping projection data includes:

[0017] A hardening correction function is constructed, and the hardening fitting parameters are initialized. The direct projection data is then mapped using a function to obtain the mapped projection data.

[0018] Preferably, the method for constructing the reconstructed image is as follows:

[0019] The reconstructed image is obtained by sequentially performing weighted processing, filtering processing, and backprojection processing on several of the aforementioned mapping projection data.

[0020] Preferably, the specific sub-steps for calculating the optimal scattering fitting parameters and the optimal hardening fitting parameters are as follows:

[0021] Using information entropy as the objective function, a model for solving the optimal parameters is constructed.

[0022] An iterative optimization method is used to obtain the optimal scattering fitting parameters and the optimal hardening fitting parameters. The convergence condition of the iterative method is: the maximum distance in the parameter space is less than or equal to a set distance value, or the number of iterations reaches a set value.

[0023] Preferably, before traversing the projected image set, the projected image set is downsampled to reduce each image in the projected image set to a low-resolution image.

[0024] The present invention also provides a CT image artifact correction system, including an image acquisition module, a correction module, an image reconstruction module, a parameter optimization module, and an optimized reconstruction module;

[0025] The image acquisition module is used to acquire a projection image set, which consists of projection images of the object under test taken at different angles.

[0026] The correction module is used to select any one projection image from the projection image set, process the selected projection image using a scattering kernel function to obtain direct projection data, process the direct projection data using a hardening correction function to obtain mapped projection data, and traverse the projection image set to obtain several mapped projection data.

[0027] The image reconstruction module is used to construct a reconstructed image based on several of the mapping projection data;

[0028] The parameter optimization module is used to calculate the information entropy of the reconstructed image, and with the goal of minimizing the information entropy, calculate the optimal scattering fitting parameters in the scattering kernel function and the optimal hardening fitting parameters in the hardening correction function.

[0029] The optimized reconstruction module is used to perform scattering correction on several projection data based on the optimal scattering fitting parameters, correct several direct projection data through the optimal hardening fitting parameters, and construct a CT image of the object under test based on the corrected data.

[0030] Preferably, the correction module includes a scattering correction module and a hardening correction module;

[0031] The scattering correction module is used to process the projected image through the scattering kernel function to obtain through-projection data;

[0032] The hardening correction module is used to process the through projection data using the hardening correction function to obtain mapped projection data.

[0033] Preferably, the scattering correction module includes a scattering calculation module and an iteration module;

[0034] The scattering calculation module calculates the scattering ray distribution based on the scattering kernel function model and the current direct-penetrating ray, and subtracts the scattering ray distribution from the current projected image to obtain a new direct-penetrating ray;

[0035] The iterative module is used to perform difference processing on two adjacent scattered ray distributions until the difference between the two scattered ray distributions is less than or equal to a set value.

[0036] The present invention also provides a computer storage medium having a computing program stored thereon, which, when executed by a processor, implements the method described above.

[0037] Compared with the prior art, the present invention has the following advantages and beneficial effects:

[0038] The present invention provides a CT image artifact correction method, system and medium that calculates the optimal parameters in the scattering and hardening process and uses these optimal parameters to construct a CT image, thereby achieving the construction of a CT image with good artifact removal effect by accurately obtaining the correction parameters. Attached Figure Description

[0039] To more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the accompanying drawings used in the embodiments will be briefly described below. It should be understood that the following drawings only show some embodiments of the present invention and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0040] Figure 1 This is a schematic diagram of the calculation process for the correction method;

[0041] Figure 2 This is a CT reconstructed slice of the part after scattering correction;

[0042] Figure 3 This is a CT reconstructed slice of the part after hardening correction;

[0043] Figure 4 This is a CT reconstructed slice of the part after hardening and scattering correction. Detailed Implementation

[0044] To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention will be further described in detail below with reference to the embodiments and accompanying drawings. The illustrative embodiments and descriptions of the present invention are only used to explain the present invention and are not intended to limit the present invention.

[0045] In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other embodiments, well-known structures, circuits, materials, or methods have not been specifically described in order to avoid obscuring the invention.

[0046] Throughout this specification, references to "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the present invention. Therefore, the phrases "an embodiment," "an example," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale. The term "and / or" as used herein includes any and all combinations of one or more of the associated listed items.

[0047] In the description of this invention, the terms "front", "rear", "left", "right", "up", "down", "vertical", "horizontal", "high", "low", "inner", and "outer" indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and 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 limiting the scope of protection of this invention.

[0048] Example 1

[0049] Traditionally, artifact removal in CT images is achieved through two independent methods: hardening software correction and scattering software correction. However, when using this method to remove artifacts from CT images, the hardening process causes changes in the X-ray energy spectrum, and different energies of X-rays have different scattering parameters. Therefore, the hardening and scattering processes affect each other, resulting in poor correction effects and ultimately poor quality reconstructed CT images.

[0050] This embodiment discloses a CT image artifact correction method. By calculating the optimal parameters during the scattering and hardening processes, and using these optimal parameters to construct the CT image, it achieves the construction of a CT image with good artifact removal effect through accurately obtained correction parameters. The specific flowchart of the correction method in this embodiment is shown below. Figure 1 As shown, the method steps include:

[0051] S1: Obtain a projection image set, which consists of projection images of the object under test taken at different angles; before traversing the projection image set, perform downsampling processing on the projection image set, downsampling each image in the projection image set into a low-resolution image.

[0052] In step S1, each of the acquired images is a photograph taken of the object under test at different angles, and all the photographed images can be reconstructed into a three-dimensional image according to a specific algorithm. Specifically, the projection image I(m,n) at each angle is downsampled to obtain a low-resolution image I′(m′,n′), where m,n are the row and column coordinates of the original projection image, and m',n' are the row and column coordinates of the downsampled projection image.

[0053] S2: In the set of projected images, select any one projected image, process the projected image using a scattering kernel function to obtain direct projection data, process the direct projection data using a hardening correction function to obtain mapped projection data, and traverse the set of projected images to obtain several mapped projection data.

[0054] The sub-step for obtaining the direct projection data includes: selecting the scattering kernel function k(I′(m′,n′,u) according to the scattering kernel function model.q This function separates scattered rays and through rays in a projected image through iterative operations. q The parameters are the fitting parameters for the scattering correction function; the scattered ray distribution is calculated based on the current direct-penetrating ray, and the new direct-penetrating ray is obtained by subtracting the scattered ray distribution from the current projected image. This calculation is repeated until the difference between the two scattered ray distributions is less than or equal to a set value, or the number of iterations reaches a set value; the direct-penetrating projection data I is output. p (m′,n′).

[0055] The sub-steps for obtaining the monoenergetic mapping projection data include: constructing a hardening correction function and initializing the hardening fitting parameters, performing function mapping on the projection image, and obtaining monoenergetic mapping projection data.

[0056] Choose an appropriate hardening correction function f(I) p (m′,n′), v q This function maps multicolor projection data to equivalent monochrome projection data. q The fitting parameters for the hardening correction function are defined; the fitting parameters v are initialized. q The monoenergetic mapping projection data I′ is obtained by performing function mapping calculations on the projected image at each angle. p (m′,n′)=f(I p (m′,n′), v q ).

[0057] S3: Based on several projection data that have been scattered and hardened and corrected, a reconstructed image is constructed; the method for constructing the reconstructed image is as follows: the several mapping projection data are sequentially subjected to weighted processing, filtering processing and back projection processing to obtain the reconstructed image.

[0058] S4: Calculate the information entropy of the reconstructed image, and with the goal of minimizing the information entropy, calculate the optimal scattering fitting parameters in the scattering kernel function and the optimal hardening fitting parameters in the hardening correction function;

[0059] The specific sub-steps for calculating the optimal scattering fitting parameters and the optimal hardening fitting parameters are as follows: construct an optimal parameter solution model with information entropy as the objective function; use an iterative optimization solution method to solve for the optimal scattering fitting parameters and the optimal hardening fitting parameters. The convergence condition of the iterative solution method is: the maximum distance in the parameter space is less than or equal to a set distance value, or the number of iterations reaches a set value.

[0060] Calculate the information entropy E of the reconstructed image; using E as the objective function, establish an optimization parameter solution model arg min E(u q v q The optimal parameters are solved using an iterative optimization method. The convergence condition for the iterative solution is that the maximum distance in the parameter space is less than or equal to a set value. i,j are the indexes of the parameter points in the parameter space, or the number of iterations has reached a set value;

[0061] S5: Based on the optimal scattering fitting parameters, perform scattering correction on several projection data; use the optimal hardening fitting parameters to correct several through-projection data; and construct a CT image of the object under test based on the corrected data; utilize the solved optimal parameters... The projection data undergoes scattering and hardening correction, followed by CT reconstruction according to standard procedures. The reconstructed image after simultaneous scattering and hardening correction is shown below. Figure 4 As shown. The reconstructed image obtained by performing only scattering correction is as follows. Figure 2 As shown, the reconstructed image after hardening correction is as follows: Figure 3 As shown in the comparison, it was found that the artifact suppression was better after simultaneous correction of scattering and hardening.

[0062] Specific implementation process:

[0063] An industrial cone-beam CT scanner was used to scan an aero-engine blade workpiece. The voltage of the X-ray source tube was 280kV to obtain the original projected image data. Then, the following steps were performed using the present invention: In order to reduce the amount of computation, the projected sequence image was first downsampled to obtain a low-resolution image I′(m′, n′), where m′ and n′ are the row and column coordinates of the image.

[0064] Choose a double Gaussian scattering kernel function These are the row and column coordinates of the image center, u q (A, B, α, β, R1, R2) are the scattering kernel parameters to be solved; the scattering kernel parameter u is constrained. q The range is determined and the scattering kernel parameters are initialized. Iterative scattering correction is then performed on the projected image to obtain the through-projection image I. p (m′,n′);

[0065] For the through projection image I p Dark field and flat field corrections and logarithmic transformations are performed on (m′, n′); the hardening correction function is selected as the threshold power exponent I′. p (m′,n′)=f(l p (m′, n′), a, b, c) = I p (m′,n′)+a(l p (m′,n′)-b) c v q (a, b, c) are the hardening correction parameters to be solved; the hardening correction parameter v is constrained. qThe range is initialized, and the function mapping is executed to obtain a new set of new projected images I′. p (m′, n′); for the projected sequence image I′ p The reconstructed image is obtained by weighting, filtering and backprojecting (m′, n′), and the information entropy E of the reconstructed image is calculated.

[0066] Take E(u q v q To establish an NM simplex optimization model for the objective value, the vertices of the NM simplex are calculated using different initial values, and the maximum distance between each vertex is calculated. It then checks if the distance is less than a set value. If the distance is greater than the set value, it updates the vertices according to the simplex rule and repeats the steps until the maximum vertex distance is less than the set value or the maximum number of iterations reaches the set value; the optimal parameters are used to terminate the final iteration. Scattering and hardening corrections are performed using the correction function parameters to calculate the final corrected projection image. The corrected projection data is then used to perform standard CT reconstruction procedures such as weighting, filtering, and backprojection to obtain the final CT reconstructed image.

[0067] This embodiment discloses a CT image artifact correction method that can simultaneously correct scattering and hardening artifacts without changing the existing scanning process or adding additional hardware. It has the advantages of wide applicability, low cost, and good correction effect. This method is independent of the CT scanning mode and is applicable to scattering and hardening artifact correction for various CT scanning modes such as circular track, spiral track, and center offset.

[0068] Example 2

[0069] This embodiment discloses a CT image artifact correction system. This embodiment is designed to implement the correction method described in Embodiment 1, and includes an image acquisition module, a correction module, an image reconstruction module, a parameter optimization module, and an optimized reconstruction module.

[0070] The image acquisition module is used to acquire a projection image set, which consists of projection images of the object under test taken at different angles.

[0071] The correction module is used to select any one projection image from the projection image set, process the projection image by using a scattering kernel function and a hardening correction function respectively, and traverse the projection image set to obtain several projection data after scattering and hardening correction.

[0072] The image reconstruction module is used to construct a reconstructed image based on several of the scattered and hardened corrected projection data;

[0073] The parameter optimization module is used to calculate the information entropy of the reconstructed image, and with the goal of minimizing the information entropy, calculate the optimal scattering fitting parameters in the scattering kernel function and the optimal hardening fitting parameters in the hardening correction function.

[0074] The optimized reconstruction module is used to perform scattering correction on several projection data based on the optimal scattering fitting parameters, correct several direct projection data through the optimal hardening fitting parameters, and construct a CT image of the object under test based on the corrected data.

[0075] The correction module includes a scattering correction module and a hardening correction module;

[0076] The scattering correction module is used to process the projected image through the scattering kernel function to obtain scattering projection data;

[0077] The hardening correction module is used to process the through-projection image through the hardening correction function to obtain monoenergetic mapping projection data.

[0078] The scattering correction module includes a scattering calculation module and an iteration module;

[0079] The scattering calculation module calculates the scattering ray distribution based on the scattering kernel function model and the current direct-penetrating ray, and subtracts the scattering ray distribution from the current projected image to obtain a new direct-penetrating ray;

[0080] The iterative module is used to perform difference processing on two adjacent scattered ray distributions until the difference between the two scattered ray distributions is less than or equal to a set value.

[0081] Example 3

[0082] This embodiment discloses a computer storage medium storing a computing program, which, when executed by a processor, implements the method described in Embodiment 1.

[0083] 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.

[0084] 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 issuing instructions. These computer program issuing 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 issuing instructions, which execute via the processor of the computer or other programmable data processing apparatus, produce implementations of the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0085] These computer program issuing instructions may also be stored in a computer-readable storage medium capable of directing a computer or other programmable data processing device to function in a particular manner, such that the issuing instructions stored in the computer-readable storage medium produce an article of manufacture including an issuing instruction means, the issuing instruction means being implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0086] These computer program instructions can 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 the 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.

[0087] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above description is only a specific embodiment of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for correcting artifacts in CT images, characterized in that, The method steps include: Acquire a set of projected images, which are projected images of the object under test taken at different angles; In the set of projected images, any one projected image is selected, and the projected image is processed by a scattering kernel function to obtain direct projection data. Then, the direct projection data is processed by a hardening correction function to obtain mapped projection data. The set of projected images is traversed to obtain several mapped projection data. Based on the aforementioned mapping and projection data, a reconstructed image is constructed; Calculate the information entropy of the reconstructed image, and with the goal of minimizing the information entropy, calculate the optimal scattering fitting parameters in the scattering kernel function and the optimal hardening fitting parameters in the hardening correction function; Based on the optimal scattering fitting parameters, scattering correction is performed on several image data sets. Then, based on the optimal hardening fitting parameters, several through-projection data sets are corrected. Finally, based on the corrected data, a CT image of the object under test is constructed. The optimal scattering fitting parameters and the optimal hardening fitting parameters are obtained in the following ways: Using information entropy as the objective function, a model for solving the optimal parameters is constructed. An iterative optimization method is used to obtain the optimal scattering fitting parameters and the optimal hardening fitting parameters. The convergence condition of this iterative method is that the maximum distance in the parameter space is less than or equal to a set distance value. Where i and j are the indexes of the parameter points in the parameter space. The scattering kernel function is the scattering fitting parameter. is the fitting parameter for the hardening correction function, and d is the set distance value.

2. The CT image artifact correction method according to claim 1, characterized in that, The sub-steps for obtaining the direct projection data include: A scattering kernel function model is constructed, and the scattering fitting parameters are initialized. The projection image is then iterated to obtain the through projection data.

3. The CT image artifact correction method according to claim 2, characterized in that, The sub-steps for obtaining the mapping projection data include: A hardening correction function is constructed, and the hardening fitting parameters are initialized. The direct projection data is then mapped using a function to obtain the mapped projection data.

4. The CT image artifact correction method according to claim 1, characterized in that, The method for constructing the reconstructed image is as follows: The reconstructed image is obtained by sequentially performing weighted processing, filtering processing, and backprojection processing on several of the aforementioned mapping projection data.

5. The CT image artifact correction method according to claim 1, characterized in that, Before traversing the projected image set, the projected image set is downsampled to a low-resolution image.

6. A CT image artifact correction system, characterized in that, It includes an image acquisition module, a correction module, an image reconstruction module, a parameter optimization module, and an optimized reconstruction module; The image acquisition module is used to acquire a projection image set, which consists of projection images of the object under test taken at different angles. The correction module is used to select any projection image in the projection image set, process the projection image through a scattering kernel function to obtain direct projection data, process the direct projection data through a hardening correction function to obtain mapped projection data, and traverse the projection image set to obtain several mapped projection data. The image reconstruction module is used to construct a reconstructed image based on several of the mapping projection data; The parameter optimization module is used to calculate the information entropy of the reconstructed image, and with the goal of minimizing the information entropy, calculate the optimal scattering fitting parameters in the scattering kernel function and the optimal hardening fitting parameters in the hardening correction function. The optimized reconstruction module is used to perform scattering correction on several projection data based on the optimal scattering fitting parameters, correct several direct-penetrating projection data using the optimal hardening fitting parameters, and construct a CT image of the object under test based on the corrected data. The optimal scattering fitting parameters and the optimal hardening fitting parameters are obtained in the following ways: Using information entropy as the objective function, a model for solving the optimal parameters is constructed. An iterative optimization method is used to obtain the optimal scattering fitting parameters and the optimal hardening fitting parameters. The convergence condition of this iterative method is that the maximum distance in the parameter space is less than or equal to a set distance value. Where i and j are the indexes of the parameter points in the parameter space. The fitting parameters for the scattering correction function are: is the fitting parameter for the hardening correction function, and d is the set distance value.

7. A CT image artifact correction system according to claim 6, characterized in that, The correction module includes a scattering correction module and a hardening correction module; The scattering correction module is used to process the projected image through the scattering kernel function to obtain through-projection data; The hardening correction module is used to process the through projection data using the hardening correction function to obtain the mapped projection data.

8. A CT image artifact correction system according to claim 7, characterized in that, The scattering correction module includes a scattering calculation module and an iteration module; The scattering calculation module calculates the scattering ray distribution based on the scattering kernel function model and the current direct-penetrating ray, and subtracts the scattering ray distribution from the current projected image to obtain a new direct-penetrating ray. The iterative module is used to perform difference processing on two adjacent scattered ray distributions until the difference between the two scattered ray distributions is less than or equal to a set value.

9. A computer storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by the processor, it implements the method as described in any one of claims 1 to 5.

Images