Metal artifact correction method and device based on prior projection

Abstract

The invention provides a prior projection-based metal artifact correction method and device. The prior projection-based metal artifact correction method comprises the following steps of: segmenting a to-be-processed original reconstructed image into a metal image and a non-metal image; determining prior projection data based on the non-metal image, and obtaining metal area projection data based on projection of the metal image; repairing the metal area projection data based on the prior projection data to obtain repaired metal area projection data; a corrected image is determined based on the repaired metal region projection data. According to the method, the prior projection data is determined by using the non-metal image, and the metal area projection data is repaired based on the prior projection data, so that new metal artifacts are prevented from being introduced, meanwhile, the calculated amount is relatively small, and the accuracy and efficiency of metal artifact correction are improved.

Description

Technical Field

[0001] The present invention relates to the field of computer imaging technology, and particularly to a metal artifact correction method and device based on prior projection. Background Art

[0002] Metal Artifacts Reduction (MAR) algorithms can reduce or even eliminate streak artifacts during image reconstruction and improve the quality of the reconstructed image. For example, computed tomography (CT) has been increasingly widely used in stomatology, orthopedics, industrial non-destructive testing, etc. due to its advantages such as small size and high spatial resolution. However, metals carried in the object to be detected, such as metal dentures and bone nails in a patient's body, metal components in the object to be detected, etc., will bring serious streak artifacts, i.e., metal artifacts, to the reconstructed image, which greatly affects the accuracy of the corrected image.

[0003] Currently, metal artifact correction algorithms are mainly divided into three categories: interpolation method, iterative method, and hybrid method. The interpolation method first reconstructs the original projection data using the filtered back-projection algorithm, and segments the metal area from the reconstructed image; then projects the segmented metal area at each angle to determine the range of the metal area on the projection image; then replaces the projection of the metal area with the interpolation of the projection data of the surrounding non-metal area, and reconstructs the interpolated projection image using the filtered back-projection algorithm again; finally, superimposes the metal area on the reconstructed image as the final reconstruction result. The interpolation method compensates for the local projection values, can largely eliminate metal artifacts, and uses the filtered back-projection algorithm for reconstruction, so the calculation speed is relatively fast; however, this type of algorithm is prone to introducing other artifacts, and its overall correction effect is still not ideal. The iterative method first compares the theoretical calculated value with the observed value, and then corrects the difference between the theoretical calculated value and the observed value, repeating this process continuously until the error is zero or within the allowable error range. In the CT reconstruction problem with metal artifacts, the projection data at some positions are missing (or have large errors), which can also be considered that the confidence levels of some linear equations are relatively low, but as long as the number of equations is large enough (or additional constraint conditions are added), the image can still be reconstructed. The iterative method usually has a good effect on metal artifact correction, but the computational amount of this type of algorithm is very large, and the running speed is too slow for CT reconstruction with a large amount of data. The hybrid method first determines the metal projection area and the non-metal projection area; then reconstructs the metal area using the iterative method, interpolates the projection of the metal area using the non-metal projection area, and then reconstructs the non-metal area of the entire projection image using the filtered back-projection algorithm; finally, superimposes the metal area and the non-metal area as the final reconstruction result. When the hybrid method removes metal artifacts, it also results in a low efficiency of artifact correction.

[0004] Therefore, the existing technology has defects and needs to be improved and developed. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide a metal artifact correction method and device based on prior projection, aiming to solve the problem of low accuracy and efficiency of metal artifact correction in the prior art, in view of the above-mentioned defects of the prior art.

[0006] The technical solution adopted by the present invention to solve the technical problem is as follows:

[0007] A metal artifact correction method based on prior projection, wherein the method includes:

[0008] Segment the original reconstructed image to be processed into a metal image and a non-metal image;

[0009] Determine prior projection data based on the non-metal image, and obtain metal region projection data based on the projection of the metal image;

[0010] Repair the metal region projection data based on the prior projection data to obtain repaired metal region projection data;

[0011] Determine a corrected image based on the repaired metal region projection data.

[0012] In an implementation manner of the present application, segmenting the original reconstructed image to be processed into a metal image and a non-metal image includes:

[0013] Reconstruct the original projection data to be processed using a filtered back-projection algorithm to obtain an original reconstructed image;

[0014] Segment the original reconstructed image into a metal image and a non-metal image through an image segmentation algorithm.

[0015] In an implementation manner of the present application, determining prior projection data based on the non-metal image includes:

[0016] Remove noise and metal artifacts from the non-metal image to obtain a preprocessed non-metal image;

[0017] Use the projection of the preprocessed non-metal image as prior projection data.

[0018] In an implementation manner of the present application, removing noise and metal artifacts from the non-metal image to obtain a preprocessed non-metal image includes:

[0019] Use a smoothing filtering technique to remove noise and metal artifacts from the non-metal image while retaining the edge information of the non-metal image to obtain a preprocessed non-metal image.

[0020] In an implementation manner of the present application, repairing the metal region projection data based on the prior projection data to obtain the repaired metal region projection data includes:

[0021] Obtaining the basis of the prior projection data, and repairing the metal region projection data based on the basis of the prior projection data to obtain the repaired metal region projection data.

[0022] In an implementation manner of the present application, obtaining the basis of the prior projection data, and repairing the metal region projection data based on the basis of the prior projection data to obtain the repaired metal region projection data includes:

[0023] Obtaining the basis of the prior projection data through singular value decomposition;

[0024] Based on the basis of the prior projection data, repairing the metal region projection data through the orthogonal projection algorithm to obtain the repaired metal region projection data.

[0025] In an implementation manner of the present application, determining a corrected image based on the repaired metal region projection data includes:

[0026] Reconstructing the repaired metal region projection data to obtain an intermediate reconstructed image;

[0027] Fusing the intermediate reconstructed image with the metal image to obtain a corrected image.

[0028] The present application further provides a metal artifact correction device based on prior projection, wherein the device includes:

[0029] A segmentation module, configured to segment the to-be-processed original reconstructed image into a metal image and a non-metal image;

[0030] A projection determination module, configured to determine prior projection data based on the non-metal image, and obtain metal region projection data based on the projection of the metal image;

[0031] A repair module, configured to repair the metal region projection data based on the prior projection data to obtain the repaired metal region projection data;

[0032] An image determination module, configured to determine a corrected image based on the repaired metal region projection data.

[0033] The present application further provides a terminal, which includes: a memory, a processor, and a metal artifact correction program based on prior projection stored on the memory and executable on the processor. When the metal artifact correction program based on prior projection is executed by the processor, the steps of the metal artifact correction method based on prior projection as described above are implemented.

[0034] The present application also provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the metal artifact correction method based on prior projection as described above.

[0035] A metal artifact correction method and apparatus based on prior projection provided by the present invention, the metal artifact correction method based on prior projection includes: segmenting an original reconstructed image to be processed into a metal image and a non-metal image; determining prior projection data based on the non-metal image, and obtaining metal region projection data based on the projection of the metal image; repairing the metal region projection data based on the prior projection data to obtain repaired metal region projection data; determining a corrected image based on the repaired metal region projection data. The present application determines prior projection data by using a non-metal image, repairs the metal region projection data based on the prior projection data, thereby avoiding introducing new metal artifacts, and at the same time reducing the amount of calculation, improving the accuracy and efficiency of metal artifact correction. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 is a flowchart of a preferred embodiment of the metal artifact correction method based on prior projection in the present invention;

[0037] Figure 2 is an image with metal artifacts.

[0038] Figure 3 is an image after metal artifact correction using the method of the present application.

[0039] Figure 4 is a functional principle block diagram of a preferred embodiment of the metal artifact correction apparatus based on prior projection in the present invention;

[0040] Figure 5 is a functional principle block diagram of a preferred embodiment of the terminal in the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0041] To make the objectives, technical solutions and advantages of the present invention clearer and more explicit, the following further describes the present invention in detail with reference to the accompanying drawings and by way of examples. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0042] Existing interpolation-based metal artifact removal algorithms usually use interpolation of projection data in the surrounding non-metal regions to replace the projection in the metal region. In this process, the characteristics of the projection data itself are not considered, which is likely to introduce new artifacts in the corrected image. In this application, prior projection data is first obtained; then, the metal projection region of the original projection data is repaired using the prior projection data; finally, the repaired projection data and the metal region image are comprehensively utilized to obtain the final corrected image. This algorithm can effectively remove metal artifacts while avoiding the introduction of new artifacts and has a relatively small computational load.

[0043] Please refer to Figure 1 , Figure 1 which is a flowchart of the metal artifact correction method based on prior projection in the present invention. As Figure 1 shown, the metal artifact correction method based on prior projection described in the embodiments of the present invention includes:

[0044] Step S100: Segment the original reconstructed image to be processed into a metal image and a non-metal image.

[0045] In the embodiments of this application, step S100 specifically includes:

[0046] Step S110: Reconstruct the original projection data to be processed using the filtered back-projection algorithm to obtain the original reconstructed image;

[0047] Step S120: Segment the original reconstructed image into a metal image and a non-metal image through an image segmentation algorithm.

[0048] Specifically, in the embodiments of this application, the original reconstructed image is segmented into a metal image corresponding to the metal region and a non-metal image corresponding to the non-metal region through an image segmentation algorithm. The filtered back-projection algorithm first projects rays through the object at different angles to collect a series of projection data; then, these projection data are filtered to eliminate artifacts and noise; finally, the filtered projection data are back-projected into the object space, that is, distributed evenly along the paths of the rays passing through the object at each projection angle, so as to obtain a preliminary reconstructed image; through multiple back-projections and superpositions, the original reconstructed image can be inferred.

[0049] Image segmentation algorithms can include threshold segmentation algorithms, k-means algorithms, etc. The threshold segmentation algorithm is used to divide an image into a foreground and a background. It works by selecting one or more gray values (or color values) as thresholds and classifying the pixels in the image according to these thresholds. If the gray value (or color value) of a certain pixel is higher than or equal to the threshold, it is classified as the foreground; if it is lower than the threshold, it is classified as the background. The k-means algorithm aims to divide a number of data objects into several clusters, such that the objects within the same cluster have a high degree of similarity, while the objects in different clusters have a low degree of similarity.

[0050] In the embodiments of the present application, the original reconstructed image is segmented into a metal image and a non-metal image to facilitate the acquisition of prior projection data, thereby improving the accuracy of metal artifact correction.

[0051] As Figure 1 shown, the metal artifact correction method based on prior projection according to the embodiments of the present invention further includes:

[0052] Step S200: Determine prior projection data based on the non-metal image, and obtain metal region projection data based on the projection of the metal image.

[0053] In the embodiments of the present application, determining prior projection data based on the non-metal image includes: removing noise and metal artifacts from the non-metal image to obtain a preprocessed non-metal image; using the projection of the preprocessed non-metal image as the prior projection data.

[0054] In the embodiments of the present application, by removing noise and metal artifacts from the non-metal image and using the projection of the non-metal image after removing noise and metal artifacts as the prior projection data, new noise and metal artifacts will not be introduced during correction, thereby improving the accuracy of metal artifact correction.

[0055] In one embodiment, when obtaining prior projection data, for example, in industrial CT, projection data can be obtained by scanning a customized phantom (the phantom has the same structure and size as the object to be detected but does not contain metal parts), and then registering it with the current projection data (the customized phantom and the object to be detected can be registered through a rigid body transformation, so that the projection data of the customized phantom and the current projection data can be registered through a translation transformation) and using it as the prior projection data.

[0056] In one embodiment of the present application, removing noise and metal artifacts from the non-metal image to obtain a preprocessed non-metal image includes:

[0057] Using a smoothing filtering technique to remove noise and metal artifacts from the non-metal image while retaining the edge information of the non-metal image to obtain a preprocessed non-metal image.

[0058] Specifically, the smoothing filtering techniques include L0 smoothing filtering technique and median filtering technique, etc. The L0 smoothing filtering technique reduces noise or details in an image by optimizing a predetermined sparsity metric while preserving the main structure and edge information of the image. Median filtering is a non-linear smoothing filtering technique. The basic principle is to replace the gray value of each pixel point in the image with the median of the gray values of all pixel points within a certain neighborhood window of this point. Median filtering can make the surrounding pixel values close to the true values, thereby eliminating isolated noise points. Median filtering has a good filtering effect on impulse noise (such as salt-and-pepper noise), and can better protect the edge information of the signal without blurring it while filtering the noise.

[0059] In the embodiments of the present application, the reconstructed non-metal image is preprocessed by the smoothing filtering technique, and while removing noise and metal artifacts, most of the edge information of the image is retained, and the projection of this image is used as the prior projection data, improving the accuracy of metal artifact correction.

[0060] As Figure 1 shown, the metal artifact correction method based on prior projection described in the embodiments of the present invention further includes:

[0061] Step S300, repairing the metal region projection data based on the prior projection data to obtain the repaired metal region projection data.

[0062] In the embodiments of the present application, step S300 specifically includes: obtaining the basis of the prior projection data, and repairing the metal region projection data based on the basis of the prior projection data to obtain the repaired metal region projection data.

[0063] Specifically, in a vector space, a basis is a set of linearly independent vectors, and they can linearly combine to generate all vectors in this space. In projection, the basis usually refers to an orthonormal vector group, that is, a vector group that is perpendicular to each other and has a modulus of 1. Such a basis can conveniently represent and calculate the projection of a vector in each direction.

[0064] In the present application, the metal region projection data is repaired based on the basis of the prior projection data, avoiding the introduction of new metal artifacts and improving the accuracy of metal artifact correction. As Figure 2 and Figure 3 shown, Figure 2 is an image with metal artifacts, and the image display gray window is [0 0.05]; Figure 3 is the image after metal artifact correction using the method of the present application, and the image display gray window is [0 0.05].

[0065] In one embodiment of the present application, obtaining the basis of the prior projection data, and repairing the metal region projection data based on the basis of the prior projection data to obtain the repaired metal region projection data, includes:

[0066] Obtaining the basis of the prior projection data through singular value decomposition;

[0067] Based on the basis of the prior projection data, repairing the metal region projection data through the orthogonal projection algorithm to obtain the repaired metal region projection data.

[0068] In the embodiment of the present application, the basis of the prior projection data is obtained through singular value decomposition (Singular Value Decomposition), and the metal region projection data is repaired through the orthogonal projection algorithm. Singular value decomposition is an important matrix decomposition in linear algebra, which is a generalization of eigenvalue decomposition to any matrix, and this decomposition has important applications in fields such as signal processing and statistics. The basis of the prior projection data generally consists of several rank-one matrices. The orthogonal projection algorithm of the present application can be the least squares method. The least squares method defines the residual between the observed value (true value or observed response variable) and the predicted value (response variable calculated through the model) (the residual is the difference between the observed value and the fitted value provided by the model), and seeks the parameter values that minimize the sum of the squares of these residuals.

[0069] Specifically, the singular value decomposition of the prior projection data P is: P = USV T , where P is a matrix of size M×N, U = (u1, u2,..., u M ) is a matrix of size M×M, and u1, u2,..., u M are column vectors of size M×1; V = (v1, v2,..., v N ) is a matrix of size N×N, and v1, v2,..., v N are column vectors of size N×1, T is the transpose symbol; S is a non-negative diagonal matrix of size M×N.

[0070] The basis of the prior projection data is: P1, P2,..., P K , where, P i = u i v i T , i = 1, 2,..., K, and K is the number of bases.

[0071] The original projection data is Proj, and the combination coefficients are determined by solving the following optimization problem to repair the projection data:

[0072]

[0073] Among them, λ1, λ2,..., λ K are combination coefficients, λi represents the weight of each basis, L represents the projection path, and Ω Metal is the set of projection paths passing through the metal region.

[0074] The projected data of the repaired metal region is:

[0075]

[0076] In the embodiment of the present application, the basis of the prior projection data is obtained by singular value decomposition, and then the projected data of the metal region is repaired by the orthogonal projection algorithm. That is, first, the projection data space is approximated as a certain low-dimensional linear space by the prior projection data, and then the projected data of the non-metal region of the original projection data is projected onto this linear space by the orthogonal projection algorithm to repair the projected data of the metal region.

[0077] As Figure 1 shown, the method for correcting metal artifacts based on prior projection described in the embodiment of the present invention further includes:

[0078] Step S400: Determine a corrected image based on the projected data of the repaired metal region.

[0079] In the embodiment of the present application, step S400 specifically includes:

[0080] Step S410: Reconstruct the projected data of the repaired metal region to obtain an intermediate reconstructed image;

[0081] Step S420: Fuse the intermediate reconstructed image with the metal image to obtain a corrected image.

[0082] Specifically, the repaired projection data is reconstructed using an analytical reconstruction algorithm (such as the filtered back projection algorithm, etc.) to obtain an intermediate reconstructed image, which is then fused with the metal image to obtain the final corrected image.

[0083] The reconstructed image is:

[0084] Image = FBP(Proj)

[0085] Image repair = FBP(Proj repair )

[0086] Among them, FBP is the filtered back projection reconstruction algorithm, Image is the original reconstructed image, and Image repair is the intermediate reconstructed image.

[0087] The final corrected image is:

[0088]

[0089] Among them, X Metal is a metal image, and x represents the corresponding pixel position.

[0090] In the embodiment of the present application, by reconstructing the projection data of the repaired metal region, an intermediate reconstructed image is obtained, and the intermediate reconstructed image is fused with the metal image to obtain a corrected image, with a relatively small amount of calculation, improving the efficiency of metal artifact correction.

[0091] In one embodiment, as Figure 4 shown, based on the above metal artifact correction method based on prior projection, the present invention also correspondingly provides a metal artifact correction device based on prior projection, including:

[0092] A segmentation module 100, configured to segment the to-be-processed original reconstructed image into a metal image and a non-metal image;

[0093] A projection determination module 200, configured to determine prior projection data based on the non-metal image, and obtain metal region projection data based on the projection of the metal image;

[0094] A repair module 300, configured to repair the metal region projection data based on the prior projection data to obtain repaired metal region projection data;

[0095] An image determination module 400, configured to determine a corrected image based on the repaired metal region projection data.

[0096] In an embodiment of the present application, the segmentation module includes:

[0097] A reconstruction unit, configured to reconstruct the to-be-processed original projection data by using a filtered back-projection algorithm to obtain an original reconstructed image;

[0098] A segmentation unit, configured to segment the original reconstructed image into a metal image and a non-metal image through an image segmentation algorithm.

[0099] In an embodiment of the present application, the projection determination module is further configured to remove noise and metal artifacts from the non-metal image to obtain a preprocessed non-metal image; and use the projection of the preprocessed non-metal image as the prior projection data.

[0100] In an embodiment of the present application, the projection determination module is further configured to use a smoothing filtering technique to remove noise and metal artifacts from the non-metal image to retain the edge information of the non-metal image, and obtain a preprocessed non-metal image.

[0101] In one embodiment of the present application, the repair module is further configured to obtain the basis of the prior projection data, and repair the metal area projection data based on the basis of the prior projection data to obtain the repaired metal area projection data.

[0102] In one embodiment of the present application, the repair module includes:

[0103] A basis acquisition unit, configured to obtain the basis of the prior projection data through singular value decomposition;

[0104] A data repair unit, configured to repair the metal area projection data based on the basis of the prior projection data through an orthogonal projection algorithm to obtain the repaired metal area projection data.

[0105] In one embodiment of the present application, the image determination module includes:

[0106] A repaired data reconstruction unit, configured to reconstruct the repaired metal area projection data to obtain an intermediate reconstructed image;

[0107] A fusion unit, configured to fuse the intermediate reconstructed image with the metal image to obtain a corrected image.

[0108] Figure 5 It is a schematic structural diagram of a terminal provided by an embodiment of the present application. The terminal may include:

[0109] A memory 501, a processor 502, and a computer program stored on the memory 501 and executable on the processor 502.

[0110] When the processor 502 executes the program, it implements the metal artifact correction method based on prior projection provided in the above embodiment.

[0111] Further, the terminal further includes:

[0112] A communication interface 503, configured for communication in the memory 501 and the processor 502.

[0113] The memory 501 is used to store a computer program executable on the processor 502.

[0114] The memory 501 may include a high-speed RAM memory, and may also include a non-volatile memory, such as at least one disk memory.

[0115] If the memory 501, the processor 502, and the communication interface 503 are implemented independently, the communication interface 503, the memory 501, and the processor 502 can be interconnected via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, only one line is shown in the figure, but it does not mean that there is only one bus or one type of bus.

[0116] Optionally, in a specific implementation, if the memory 501, the processor 502, and the communication interface 503 are integrated on a single chip, the memory 501, the processor 502, and the communication interface 503 can communicate with each other via an internal interface.

[0117] The processor 502 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0118] This embodiment also provides a computer-readable storage medium, on which a computer program is stored. When the program is executed by a processor, the above-described metal artifact correction method based on prior projection is implemented.

[0119] In the description of this specification, the description with reference to terms such as "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0120] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0121] Any process or method description represented in a flowchart or described otherwise herein can be understood to represent a module, segment, or portion of code including one or N executable instructions for implementing a customized logical function or process. The scope of the preferred embodiments of this application includes additional implementations, where functions may be executed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the technical field to which the embodiments of this application pertain.

[0122] The logic and / or steps represented in a flowchart or described otherwise herein, for example, can be considered as an ordered list of executable instructions for implementing a logical function, and can be specifically implemented in any computer-readable medium for use by an instruction execution system, apparatus, or device (such as a computer-based system, a system including a processor, or other systems that can read and execute instructions from the instruction execution system, apparatus, or device), or in conjunction with such instruction execution systems, apparatus, or devices. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in conjunction with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion with one or N wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable media on which a program can be printed, since the program can be obtained electronically by optically scanning the paper or other media, followed by editing, interpretation, or other appropriate processing as necessary, and then stored in a computer memory.

[0123] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented by hardware, as in another embodiment, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits having logic gate circuits for implementing logic functions on data signals, application specific integrated circuits having appropriate combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0124] Those of ordinary skill in the art can understand that all or part of the steps carried by the method of the above embodiments can be completed by instructing relevant hardware through a program, and this program can be stored in a computer-readable storage medium. When this program is executed, it includes one or a combination of the steps of the method embodiments.

[0125] In addition, in each embodiment of the present application, each functional unit can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0126] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

[0127] In summary, a metal artifact correction method and device based on prior projection disclosed in the present invention, the metal artifact correction method based on prior projection includes: segmenting an original reconstructed image to be processed into a metal image and a non-metal image; determining prior projection data based on the non-metal image, and obtaining metal region projection data based on the projection of the metal image; repairing the metal region projection data based on the prior projection data to obtain repaired metal region projection data; determining a corrected image based on the repaired metal region projection data. The present application avoids introducing new metal artifacts by using the non-metal image to determine prior projection data and repairing the metal region projection data based on the prior projection data, and at the same time has a small amount of calculation, improving the accuracy and efficiency of metal artifact correction.

[0128] It should be understood that the application of the present invention is not limited to the above examples. For those of ordinary skill in the art, improvements or changes can be made according to the above description, and all such improvements and changes should fall within the protection scope of the appended claims of the present invention.

Claims

1. A metal artifact correction method based on prior projection, characterized in that, The method includes: Segmenting the original reconstructed image to be processed into a metal image and a non-metal image; Determining prior projection data based on the non-metal image, and obtaining metal region projection data based on the projection of the metal image; Repairing the metal region projection data based on the prior projection data to obtain repaired metal region projection data; Determining a corrected image based on the repaired metal region projection data.

2. The metal artifact correction method based on prior projection according to claim 1, wherein Segmenting the original reconstructed image to be processed into a metal image and a non-metal image, including: Reconstructing the original projection data to be processed using a filtered back-projection algorithm to obtain an original reconstructed image; Segmenting the original reconstructed image into a metal image and a non-metal image through an image segmentation algorithm.

3. The method for correcting metal artifacts based on prior projection according to claim 1, wherein Determining prior projection data based on the non-metal image, including: Removing noise and metal artifacts from the non-metal image to obtain a preprocessed non-metal image; Taking the projection of the preprocessed non-metal image as the prior projection data.

4. The metal artifact correction method based on prior projection according to claim 3, wherein Removing noise and metal artifacts from the non-metal image to obtain a preprocessed non-metal image, including: Using a smoothing filtering technique to remove noise and metal artifacts from the non-metal image to retain the edge information of the non-metal image, obtaining a preprocessed non-metal image.

5. The metal artifact correction method based on prior projection according to claim 1, wherein Repairing the metal region projection data based on the prior projection data to obtain repaired metal region projection data, including: Obtaining a basis of the prior projection data, and repairing the metal region projection data based on the basis of the prior projection data to obtain repaired metal region projection data.

6. The method for correcting metal artifacts based on prior projection according to claim 5, wherein Obtaining a basis of the prior projection data, and repairing the metal region projection data based on the basis of the prior projection data to obtain repaired metal region projection data, including: Obtaining the basis of the prior projection data through singular value decomposition; Based on the basis of the prior projection data, repairing the metal region projection data through an orthogonal projection algorithm to obtain repaired metal region projection data.

7. The method for correcting metal artifacts based on prior projection according to claim 1, wherein Determining a corrected image based on the repaired metal region projection data, including: Reconstructing the repaired metal region projection data to obtain an intermediate reconstructed image; Fusing the intermediate reconstructed image with the metal image to obtain a corrected image.

8. A metal artifact correction device based on prior projection, characterized in that, The device includes: A segmentation module for segmenting the original reconstructed image to be processed into a metal image and a non-metal image; A projection determination module for determining prior projection data based on the non-metal image and obtaining metal region projection data based on the projection of the metal image; A repair module for repairing the metal region projection data based on the prior projection data to obtain repaired metal region projection data; An image determination module for determining a corrected image based on the repaired metal region projection data.

9. A terminal, characterized in that, Including: A memory, a processor, and a prior-projection-based metal artifact correction program stored on the memory and executable on the processor. When the prior-projection-based metal artifact correction program is executed by the processor, it implements the steps of the prior-projection-based metal artifact correction method according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program, and the computer program can be executed to implement the steps of the prior-projection-based metal artifact correction method according to any one of claims 1 to 7.

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