Cone-beam CT Scattering Correction Method, Device, Electronic Device and Storage Medium

Abstract

The present application provides a cone-beam CT scattering correction method, apparatus, electronic device, and storage medium. The method includes: acquiring DR projections of two sets of workpieces collected by an acquisition device, and a single DR projection of a scattering correction plate; generating a first DR projection set and a second DR projection set based on the DR projections of the two sets of workpieces; obtaining a discrete scattering signal set based on the first DR projection set and the second DR projection set; performing image processing on the single DR projection to obtain a hole region; establishing an L1 norm constraint model based on the discrete scattering signal set and the hole region, and solving it through the ADMM algorithm to reconstruct a complete scattering signal; and subtracting the complete scattering signal from the first DR projection set to obtain a corrected DR projection. This method can improve the accuracy of scattering correction.

Description

Technical Field

[0001] This application relates to the field of CT imaging technology, and particularly to a cone-beam CT scatter correction method, apparatus, electronic device, and storage medium. Background Art

[0002] In the process of X-ray imaging, when using computed tomography (CT) to detect workpieces such as aero-engine turbine blades, due to their high density and complex geometry, the turbine blades are prone to noise problems during scanning, resulting in artifacts in the reconstructed image. The superposition of scatter artifacts and projections is an important factor restricting the imaging quality. Most of the artifacts are caused by the Compton effect, resulting in the photons received by the detector containing both the initial photons and the scattered photons, causing a deviation between the measurement result and the actual intensity measurement result, reducing the contrast of the image, affecting the image quality, and blurring the boundary and internal structure information. Therefore, in order to improve the image quality and obtain clearer image details, it is necessary to perform scatter correction on cone-beam CT images.

[0003] When using a scatter correction plate to perform scatter correction on digital radiography (DR) projection images, there are currently methods such as the beam stop array method ((BSA)), the beam hole array method ((BHA)), and the beam stop grid method ((BSG)). Taking the BSA method as an example, the correction plate is composed of a plexiglass plate, and evenly arranged lead blocks are embedded therein. The correction plate is placed in front of the detector. The light intensity detected at the positions corresponding to the lead blocks on the detector is all generated by scattered rays. By performing two-dimensional interpolation on the signal values at the centroids of the corresponding lead blocks and extending it to the entire plane, a scatter intensity distribution map is obtained. The disadvantage of this method is that due to the occlusion of the correction plate, the data received by the detector is less, and at the same time, only the signal interpolation at the centroid is selected, resulting in a decrease in the accuracy of the fitted scatter signal. Summary of the Invention

[0004] In view of this, this application provides a cone-beam CT scatter correction method, apparatus, electronic device, and storage medium, which can improve the accuracy of scatter correction.

[0005] To solve the above technical problems, the technical solution of this application is implemented as follows:

[0006] In one embodiment, a cone-beam CT scatter correction method is provided, and the method includes:

[0007] Obtain two sets of DR projections of the workpiece collected by the acquisition device, and a single DR projection of the scatter correction plate;

[0008] Generate a first DR projection set and a second DR projection set based on the DR projections of the two sets of workpieces; and obtain a discrete scattering signal set based on the first DR projection set and the second DR projection set; wherein, the first DR projection set is a set of DR projections obtained by scanning the workpieces without adding the scatter correction plate; the second DR projection set is a set of DR projections obtained by scanning the workpieces with the scatter correction plate added;

[0009] Perform image processing on the single DR projection to obtain a hole region;

[0010] Based on the discrete scattering signal set and the hole region, establish an L1-norm constraint model in combination with CS theory, and solve it by the ADMM algorithm to reconstruct the complete scattering signal;

[0011] Deduct the complete scattering signal from the first DR projection set to obtain a corrected DR projection.

[0012] Wherein, the acquisition device acquires the DR projections of the two sets of workpieces without relative displacement at the workpiece position and with consistent scanning parameters.

[0013] Wherein, the performing image processing on the single DR projection includes:

[0014] Binarization, contour extraction, contour tracking, and circle fitting processing.

[0015] Wherein, the L1-norm constraint model promotes sparsity by introducing the L1-norm constraint, such that the gradients of the complete scattering signal in the horizontal and vertical directions are respectively equal to the corresponding sparse vectors.

[0016] Wherein, the L1-norm constraint model is expressed as:

[0017]

[0018] s.t D h ·x - p = 0, D v ·x - q = 0

[0019] Wherein, x is a scattering signal in the complete scattering signal, x * is a scattering signal in the discrete scattering signal set, W = diag{ω i} ∈ R n×n is a diagonal weighted matrix, ω i is a position matrix, and the position correction is used to represent the position information of the scatter correction plate; ω i is 1 when in the hole region and 0 when outside the hole region; D h and D vwhere \(H\) and \(V\) are the horizontal difference matrix and the vertical difference matrix respectively, \(\lambda\) is the regularization parameter, and \(p\) and \(q\) are the sparse representation coefficients, representing the sparse representations of the gradients in the horizontal and vertical directions respectively.

[0020] After obtaining the corrected DR projection, the method further includes:

[0021] Reconstructing the corrected DR projection using the FDK algorithm to obtain a three-dimensional CT reconstruction model after scatter correction.

[0022] After obtaining the three-dimensional CT reconstruction model after scatter correction, the method further includes:

[0023] Slicing the three-dimensional CT reconstruction model to obtain slice images;

[0024] Analyzing the scatter correction effect based on the slice images.

[0025] In another embodiment, a cone-beam CT scatter correction device is provided, and the device includes:

[0026] An acquisition unit configured to acquire DR projections of two groups of workpieces collected by an acquisition device and a single DR projection of a scatter correction plate;

[0027] A determination unit configured to generate a first DR projection set and a second DR projection set based on the DR projections of the two groups of workpieces; and obtain a discrete scatter signal set based on the first DR projection set and the second DR projection set; wherein, the first DR projection set is a set of DR projections obtained by scanning the workpieces without adding the scatter correction plate; the second DR projection set is a set of DR projections obtained by scanning the workpieces with the scatter correction plate added;

[0028] A processing unit configured to perform image processing on the single DR projection to obtain a hole region;

[0029] A reconstruction unit configured to reconstruct a complete scatter signal by establishing an L1-norm constraint model based on the discrete scatter signal set and the hole region and solving it through the ADMM algorithm;

[0030] A calculation unit configured to subtract the complete scatter signal from the first DR projection set to obtain a corrected DR projection.

[0031] In another embodiment, an electronic device is provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor, and when the processor executes the program, it implements the cone-beam CT scatter correction method as described above.

[0032] In another embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned cone-beam CT scatter correction method is implemented.

[0033] As can be seen from the above technical solutions, in the above embodiments, a discrete scatter signal set is obtained by projecting two groups of workpieces collected, and a hole region is obtained by a single DR projection of the scatter correction plate of the rubbing agent; based on the discrete scatter signal set and the hole region, an L1-norm constraint model is established in combination with the CS theory, and solved by the ADMM algorithm to reconstruct the complete scatter signal; the complete scatter signal is subtracted from the first DR projection set to obtain the corrected DR projection. This solution can improve the accuracy of scatter correction. Brief Description of the Drawings

[0034] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the following will briefly introduce the drawings required for the description of the embodiments. Obviously, the drawings in the following description are only some embodiments of the present application. For those of ordinary skill in the art, without creative efforts, other drawings can also be obtained based on these drawings.

[0035] Figure 1 It is a schematic diagram of a cone-beam CT scatter correction process in an embodiment of the present application;

[0036] Figure 2 It is a schematic diagram of a three-dimensional CT reconstruction model generation process in an embodiment of the present application;

[0037] Figure 3 It is a schematic diagram of an analysis scatter correction effect process in an embodiment of the present application;

[0038] Figure 4 It is a schematic diagram of the structure of a cone-beam CT scatter correction device in an embodiment of the present application;

[0039] Figure 5 It is a schematic diagram of the physical structure of an electronic device provided in an embodiment of the present invention. Detailed Embodiments

[0040] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only some embodiments of the present application, rather than all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative efforts belong to the scope of protection of the present application.

[0041] In the description and claims of the present invention and the above-mentioned drawings, the terms "first", "second", "third", "fourth", etc. (if any) are used to distinguish similar objects and do not necessarily describe the order or sequence of the objects. It should be understood that the data used in this way can be interchanged under appropriate circumstances so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "comprising" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device comprising a series of steps or units need not be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0042] The technical solutions of the present invention will be described in detail below with specific embodiments. The following several specific embodiments can be combined with each other, and the same or similar concepts or processes may not be described again in some embodiments.

[0043] In the related art, a scattering correction plate is used for scattering correction of DR projection images. Currently, there are methods such as the beam stop array method (BSA), the beam hole array method (BHA), and the beam stop grid method (BSG). Taking the BSA method as an example, the correction plate is made of a plexiglass plate and uniformly arranged lead blocks are embedded therein. The correction plate is placed in front of the detector. The light intensity detected at the positions corresponding to the lead blocks on the detector is all generated by scattered rays. By performing two-dimensional interpolation on the signal values at the centroids of the corresponding lead blocks and extending it to the entire plane, a scattering intensity distribution map is obtained.

[0044] For example, bicubic interpolation is used to fit the scattering field in the related art. During the interpolation process, the pixel value of the target point (x, y) = (i + u, j + v) is obtained by weighted averaging of the 16 points closest to the origin in the matrix grid, where: i is the abscissa of the target point; j is the ordinate of the target point; u is the distance from the target point in the abscissa direction; v is the distance from the target point in the ordinate direction. At this time, it is necessary to calculate the corresponding weights using the Bicubic function along the x and y directions respectively, and then perform the required interpolation calculation. When calculating, interpolation is first performed in the vertical direction, and after obtaining the values in the vertical direction, these values are used for interpolation in the horizontal direction.

[0045] The disadvantage of this method is that due to the occlusion of the correction plate, the detector receives less data. At the same time, only the signal interpolation at the centroid is selected, and the data utilization rate is low, resulting in a reduction in the accuracy of the fitted scattering signal.

[0046] In view of the above problems, a cone-beam CT scatter correction method is disclosed in the embodiments of the present application. A discrete scatter signal set is obtained from the projections of two groups of workpieces collected, and a hole region is obtained from a single DR projection of a scatter correction plate collected; based on the discrete scatter signal set and the hole region, an L1-norm constraint model is established in combination with the Compressed Sensing (CS) theory, and solved by the Alternating Direction Method of Multipliers (ADMM) algorithm to reconstruct the complete scatter signal; the complete scatter signal is deducted from the first DR projection set to obtain the corrected DR projection. This solution can improve the accuracy of scatter correction.

[0047] The following combines with the accompanying drawings to present the process of realizing cone-beam CT scatter correction in the embodiments of the present application.

[0048] See Figure 1 , Figure 1 which is a schematic diagram of a cone-beam CT scatter correction process in the embodiments of the present application. The specific steps are as follows:

[0049] Step 101, obtain the DR projections of two groups of workpieces collected by the acquisition device, and a single DR projection of the scatter correction plate.

[0050] The acquisition device collects the DR projections of two groups of workpieces without relative displacement at the workpiece position and with consistent scanning parameters. The first group of DR projections collected is the DR projections collected without adding the scatter correction plate, and the second group of DR projections collected is the DR projections collected with the scatter correction plate added.

[0051] Step 102, generate a first DR projection set and a second DR projection set based on the DR projections of the two groups of workpieces; and obtain a discrete scatter signal set based on the first DR projection set and the second DR projection set; where the first DR projection set is the set of DR projections obtained by scanning the workpiece without adding the scatter correction plate; the second DR projection set is the set of DR projections obtained by scanning the workpiece with the scatter correction plate added.

[0052] Step 103, perform image processing on the single DR projection to obtain the hole region.

[0053] Performing image processing on the single DR projection includes:

[0054] Performing binarization, contour extraction, contour tracking, and circle fitting processing on the single DR projection.

[0055] Step 104, based on the discrete scatter signal set and the hole region, establish an L1-norm constraint model in combination with the CS theory, and solve it by the ADMM algorithm to reconstruct the complete scatter signal.

[0056] The complete scattering signal here refers to the scattering signal of the complete image.

[0057] Step 105: Deduct the complete scattering signal from the first set of DR projections to obtain the corrected DR projections.

[0058] In this embodiment, a discrete scattering signal set is obtained by projecting two groups of workpieces collected, and a hole area is obtained through a single DR projection of the scattering correction plate of the rubbing agent. Based on the discrete scattering signal set and the hole area, an L1-norm constraint model is established in combination with the CS theory and solved by the ADMM algorithm to reconstruct the complete scattering signal. The complete scattering signal is deducted from the first set of DR projections to obtain the corrected DR projections. This solution can improve the accuracy of scattering correction.

[0059] In another example of this embodiment, the L1-norm constraint model promotes sparsity by introducing the L1-norm constraint, such that the gradients of the complete scattering signal in the horizontal and vertical directions are respectively equal to the corresponding sparse vectors.

[0060] Among them, the L1-norm constraint model is expressed as:

[0061]

[0062] s.t D h ·x - p = 0, D v ·x - q = 0

[0063] Among them, x is a scattering signal in the complete scattering signal, and x * is a scattering signal in the discrete scattering signal set. W = diag{ω i} ∈ R n×n is a diagonal weighting matrix, and R n×n represents a real number matrix. n is the number of sampling points of the x signal, ω i is a position matrix, and position correction is used to represent the position information of the scattering correction plate; ω i is 1 when in the hole area and 0 when outside the hole area; D h and D v are the horizontal difference matrix and the vertical difference matrix respectively. λ is a regularization parameter, and p and q are sparse representation coefficients, representing the sparse representations of the gradients in the horizontal and vertical directions respectively.

[0064] See Figure 2 , Figure 2 is a schematic diagram of the generation process of a three-dimensional CT reconstruction model in an embodiment of this application. The specific steps are as follows:

[0065] Step 201: Obtain the DR projections of two groups of workpieces collected by the acquisition device and a single DR projection of the scattering correction plate.

[0066] Before the acquisition device acquires the DR projection, the scanning parameters of the acquisition device need to be set:

[0067] According to the scanning target, such as a workpiece, select an effective imaging range according to its size. The acquisition DR projection gray range can be set to 0 - 65535, the scanning voltage V can be set to 430 KV, the scanning current I can be set to 1600 μA, the source-to-detector distance FDD, for example, can be set to 1158 mm, and the source-to-rotating table center distance, that is, the source-to-workpiece distance FDD, for example, can be 500 mm. The setting of the above specific parameters is an example. In actual implementation, it is restricted according to the specific scanning device and the relevant situation of the workpiece on the stage. In the embodiments of the present application, no such restrictions are imposed.

[0068] When performing two complete scans on the workpiece, it is necessary to ensure that there is no relative displacement of the workpiece position, and the above-set scanning parameters are the same. For the first time, directly place the workpiece to be measured on the stage, and do not add any equipment between the ray source, the workpiece to be measured, and the detector, that is, do not add a scatter correction plate, to obtain a set of DR projections. This set of projections is a DR projection containing scatter signals and real signals. Add a scatter correction plate between the workpiece to be measured and the detector, and perform the second scan to obtain a set of projected DR projections.

[0069] In actual implementation, the scatter correction plate can use a BHA scatter correction plate. The BHA scatter correction plate is processed from a lead plate with uniform texture. The lead plate is perforated at specific intervals, and the rays pass through the holes to scan the workpiece to be measured, and the remaining signals are absorbed by the lead plate.

[0070] Step 202, generate a first DR projection set and a second DR projection set based on the DR projections of the two groups of workpieces; and obtain a discrete scatter signal set based on the first DR projection set and the second DR projection set; wherein, the first DR projection set is a set of DR projections obtained by scanning the workpiece without adding a scatter correction plate; the second DR projection set is a set of DR projections obtained by scanning the workpiece when adding a scatter correction plate.

[0071] When performing DR projection acquisition, 360° DR projections can be acquired at equal intervals and directly form the first DR projection set and the second DR projection set; or 360° DR projections can be continuously acquired, and then DR projections with a preset value are sampled at equal intervals to form the first DR projection set and the second DR projection set.

[0072] When obtaining the discrete scatter signal set based on the first DR projection set and the second DR projection set, subtract the corresponding projections in the first DR projection set and the second DR projection set to obtain the discrete scatter signal set. The specific implementation is as follows:

[0073] In the first set of DR projections, the DR projections in which the DR projections are aliased with scattered signals and true signals. According to Lambert-Beer's law, we have:

[0074] I1 = I0exp(-μl) (1)

[0075] The initial intensity of the X-ray is I0, and the intensity becomes I1 after passing through the workpiece. μ represents the attenuation coefficient of the workpiece, and l is the thickness of the workpiece through which the ray passes. At the detector position, due to the presence of the scattered signal S, the actually received signal intensity C1 is:

[0076] C1 = I1 + S1 (2)

[0077] For the second scan, since the lead plate has a certain thickness, the scattered rays entering the hole will be absorbed by the hole wall of the lead plate. Therefore, the signal intensity at the hole position on the detector is regarded as a discrete distribution of the true signal intensity, that is, C2 = I1. By subtracting the signal intensities collected in the two scans, the corresponding discrete scattered signal can be obtained:

[0078] S1 = C1 - C2 (3)

[0079] The set of discrete scattered signals consists of multiple obtained discrete scattered signals.

[0080] Step 203: Perform image processing on a single DR projection to obtain the hole region.

[0081] Performing image processing on a single DR projection specifically means:

[0082] Performing binarization, contour extraction, contour tracking, circle fitting processing, etc. on a single DR projection.

[0083] Step 204: Based on the set of discrete scattered signals and the hole region, establish an L1-norm constraint model in combination with CS theory, and solve it through the ADMM algorithm to reconstruct the complete scattered signal.

[0084] For discrete scattered signals, introduce CS theory to replace the traditional interpolation algorithm. By establishing an L1-norm constraint model, use the CS framework to reconstruct the scattered signal.

[0085] The L1-norm constraint model is expressed as:

[0086]

[0087] where x is a scattered signal in the complete scattered signal, that is, the signal to be solved, x * is a scattered signal in the set of discrete scattered signals, that is, S1. Then, for a set of discrete scattered signals, the complete scattered signal can be obtained; W = diag{ω i} ∈ R n×n is the diagonal weighted matrix, Rn×n represents a real number matrix, n is the number of sampling points of the x signal, ω i is a position matrix, and position correction is used to represent the position information of the scatter correction plate; ω i When in the hole area, the value is 1, and when in the area outside the hole, the value is 0; D h and D v are the horizontal difference matrix and the vertical difference matrix, λ is the regularization parameter, p and q are the sparse representation coefficients, representing the sparse representation of the gradients in the horizontal and vertical directions respectively; s.t represents the constraint condition.

[0088] The L1 norm constraint model promotes sparsity by introducing the L1 norm constraint, making the gradients of the complete scatter signal in the horizontal and vertical directions equal to the corresponding sparse vectors respectively, which helps to recover and reconstruct the scatter signal from limited measurements.

[0089] To handle the norm constraint problem introduced in Equation (4), this application adopts the ADMM algorithm to solve it. During the application of the ADMM algorithm, an augmented Lagrangian function is introduced to formalize the objective function. Equation (4) can be expressed as:

[0090]

[0091] where u p , u q are the dual variables corresponding to the equality constraints D h ·x - p = 0, D v ·x - q = 0, ρ is an adjustable parameter, and the algorithm minimizes x, p, q, u p , u q respectively. Specifically, at the k-th iteration, the steps are:

[0092]

[0093] Thus, the solution of Equation (4) is completed, and one scatter signal corresponding to a scatter signal in each discrete scatter signal set can be obtained respectively, and then the complete scatter signal can be obtained.

[0094] Step 205, subtract the complete scatter signal from the first DR projection set to obtain the corrected DR projection.

[0095] Step 206, use the FDK algorithm to reconstruct the corrected DR projection to obtain the three-dimensional CT reconstruction model after scatter correction.

[0096] The FDK (Feldkamp-Davis-Kress) algorithm is an algorithm widely used in 3D image reconstruction. This algorithm is based on the Radon transform and the Fourier slice theorem, and reconstructs images from projection data through the method of filtered backprojection. The FDK algorithm is particularly suitable for cone-beam CT imaging, and can process a large amount of projection data and reconstruct high-quality 3D images.

[0097] In this embodiment, based on the scatter correction plate, combined with the CS theory, by the discrete signals collected by the correction plate, a suitable sampling matrix is designed, and finally the ADMM algorithm is used to solve the established L1-norm constraint model to obtain the complete scatter signal, and a scatter field reconstruction technical process based on the scatter correction plate and the ADMM algorithm under the CS theory is established, which improves the data utilization rate of the DR projections collected by adding the correction plate, improves the accuracy of scatter correction, and further obtains a high-quality 3D CT reconstruction model based on the corrected DR projections.

[0098] See Figure 3 , Figure 3 which is a schematic flow chart for analyzing the scatter correction effect in the embodiment of the present application. The specific steps are as follows:

[0099] Step 301, obtain the DR projections of two groups of workpieces collected by the acquisition device, and a single DR projection of the scatter correction plate.

[0100] The acquisition device collects the DR projections of two groups of workpieces without relative displacement at the workpiece position and with the same scanning parameters.

[0101] Step 302, generate a first DR projection set and a second DR projection set based on the DR projections of the two groups of workpieces; and obtain a discrete scatter signal set based on the first DR projection set and the second DR projection set; wherein, the first DR projection set is a set of DR projections obtained by scanning the workpiece without adding a scatter correction plate; the second DR projection set is a set of DR projections obtained by scanning the workpiece with a scatter correction plate added.

[0102] Step 303, perform image processing on the single DR projection to obtain a hole region.

[0103] Performing image processing on the single DR projection includes:

[0104] Performing binarization, contour extraction, contour tracking, and circle fitting processing on the single DR projection.

[0105] Step 304, based on the discrete scatter signal set and the hole region, establish an L1-norm constraint model in combination with the CS theory, and solve it through the ADMM algorithm to reconstruct the complete scatter signal.

[0106] Step 305, subtract the complete scatter signal from the first DR projection set to obtain the corrected DR projection.

[0107] In step 306, the FDK algorithm is used to reconstruct the corrected DR projection to obtain a three-dimensional CT reconstruction model after scatter correction.

[0108] In step 307, the three-dimensional CT reconstruction model is sliced to obtain slice images.

[0109] In step 308, the scatter correction effect is analyzed based on the slice images.

[0110] Based on the slice images, the scatter correction effect is analyzed. If the correction effect cannot meet the expectation, the parameters involved in the scatter correction process can be readjusted.

[0111] Any combination of the above optional technical solutions can form an optional embodiment of the present disclosure, which will not be elaborated here one by one.

[0112] Based on the same inventive concept, an embodiment of the present application also provides a cone-beam CT scatter correction device. Refer to Figure 4 , Figure 4 which is a schematic structural diagram of the cone-beam CT scatter correction device in the embodiment of the present application. The device includes:

[0113] An acquisition unit 401, configured to acquire DR projections of two groups of workpieces collected by an acquisition device, and a single DR projection of a scatter correction plate;

[0114] A determination unit 402, configured to generate a first DR projection set and a second DR projection set based on the DR projections of the two groups of workpieces; and obtain a discrete scatter signal set based on the first DR projection set and the second DR projection set; wherein, the first DR projection set is a set of DR projections obtained by scanning the workpieces without adding a scatter correction plate; the second DR projection set is a set of DR projections obtained by scanning the workpieces with a scatter correction plate added;

[0115] A processing unit 403, configured to perform image processing on the single DR projection to obtain a hole area;

[0116] A reconstruction unit 404, configured to reconstruct a complete scatter signal by establishing an L1 norm constraint model based on the discrete scatter signal set and the hole area in combination with the CS theory and solving it through the ADMM algorithm;

[0117] A calculation unit 405, configured to subtract the complete scatter signal from the first DR projection set to obtain a corrected DR projection.

[0118] In another embodiment,

[0119] The acquisition device acquires DR projections of two groups of workpieces under the condition that there is no relative displacement at the workpiece position and the scanning parameters are the same.

[0120] In another embodiment,

[0121] The processing unit 403 is specifically configured to perform binarization, contour extraction, contour tracking, and circle fitting on a single DR projection.

[0122] In another embodiment, the L1 norm constraint model promotes sparsity by introducing the L1 norm constraint, such that the gradients of the complete scattering signal in the horizontal and vertical directions are respectively equal to the corresponding sparse vectors.

[0123] In another embodiment, the L1 norm constraint model is expressed as:

[0124]

[0125] s.t D h ·x - p = 0, D v ·x - q = 0

[0126] where x is a scattering signal in the complete scattering signal, and x * is a scattering signal in the discrete scattering signal set, W = diag{ω i} ∈ R n×n is the diagonal weighted matrix, ω i is the position matrix, and the position correction is used to represent the position information of the scattering correction plate; ω i is 1 when in the hole region and 0 when outside the hole region; D h and D v are the horizontal difference matrix and the vertical difference matrix respectively, λ is the regularization parameter, and p and q are the sparse representation coefficients, representing the sparse representations of the gradients in the horizontal and vertical directions respectively.

[0127] In another embodiment, the device further includes:

[0128] The modeling unit 406 is configured to perform reconstruction on the corrected DR projection using the FDK algorithm to obtain a three-dimensional CT reconstruction model after scatter correction.

[0129] In another embodiment, the device further includes:

[0130] The analysis unit 407 is configured to perform slicing on the three-dimensional CT reconstruction model to obtain slice images; and analyze the scatter correction effect based on the slice images.

[0131] The units in the above embodiments can be integrated into one body or separately deployed; they can be combined into one unit or further split into multiple sub-units.

[0132] In another embodiment, an electronic device is further provided, including a memory, a processor, and a computer program stored on the memory and executable on the processor. When the processor executes the program, the cone-beam CT scattering correction method can be implemented.

[0133] In another embodiment, a computer-readable storage medium is further provided, on which computer instructions are stored. When the instructions are executed by a processor, the cone-beam CT scattering correction method can be implemented.

[0134] Figure 5 It is a schematic diagram of the physical structure of the electronic device provided by the embodiment of the present invention. As Figure 5 shown, the electronic device may include: a processor (Processor) 510, a communication interface (Communications Interface) 520, a memory (Memory) 530, and a communication bus 540. Among them, the processor 510, the communication interface 520, and the memory 530 communicate with each other through the communication bus 540. The processor 510 can call the logical instructions in the memory 530 to execute the following method:

[0135] Obtain the DR projections of two groups of workpieces collected by the acquisition device, and the single DR projection of the scatter correction plate;

[0136] Generate a first DR projection set and a second DR projection set based on the DR projections of the two groups of workpieces; and obtain a discrete scatter signal set based on the first DR projection set and the second DR projection set; wherein, the first DR projection set is a set of DR projections obtained by scanning the workpieces without adding the scatter correction plate; the second DR projection set is a set of DR projections obtained by scanning the workpieces with the scatter correction plate added;

[0137] Perform image processing on the single DR projection to obtain a hole area;

[0138] Based on the discrete scatter signal set and the hole area, establish an L1-norm constraint model in combination with the CS theory, and solve it through the ADMM algorithm to reconstruct the complete scatter signal;

[0139] Deduct the complete scatter signal from the first DR projection set to obtain the corrected DR projection.

[0140] In addition, when the logical instructions in the above-mentioned memory 530 can be implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods of various embodiments of the present invention. The aforementioned storage medium includes: various media such as USB flash drives, mobile hard disks, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), magnetic disks, or optical discs that can store program codes.

[0141] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated. The components shown as units may or may not be physical units, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without creative effort.

[0142] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and of course, it can also be implemented by hardware. Based on such an understanding, the technical solution, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. This computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, magnetic disks, optical discs, etc., and includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the methods described in various embodiments or some parts of the embodiments.

[0143] The flowcharts and block diagrams in the accompanying drawings of the present application illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments disclosed in the present application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a portion of code that contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in the order marked in different drawings. For example, two consecutively represented blocks may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, as well as combinations of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system that performs the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0144] Those skilled in the art can understand that the features recited in the various embodiments and / or claims of the present application can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly recited in the present application. In particular, without departing from the spirit and teachings of the present application, the features recited in the various embodiments and / or claims of the present application can be combined and / or combined in various ways, and all such combinations and / or combinations fall within the scope disclosed in the present application.

[0145] Specific embodiments are used herein to illustrate the principles and implementation manners of the present invention. The description of the above embodiments is only used to help understand the method and its core idea of the present invention, and is not used to limit the present application. For those skilled in the art, changes can be made in the specific implementation manners and application scopes according to the ideas, spirits, and principles of the present invention. Any modifications, equivalent replacements, improvements, etc. made by them should be included within the scope protected by the present application.

Claims

1. A cone beam CT scatter correction method, characterized in that, The method includes: Obtaining DR projections of two groups of workpieces collected by a collection device, and a single DR projection of a scatter correction plate; Generating a first DR projection set and a second DR projection set based on the DR projections of the two groups of workpieces; and obtaining a discrete scatter signal set based on the first DR projection set and the second DR projection set; wherein, the first DR projection set is a set of DR projections obtained by scanning the workpieces without adding the scatter correction plate; the second DR projection set is a set of DR projections obtained by scanning the workpieces with the scatter correction plate added; Performing image processing on the single DR projection to obtain a hole region; Based on the discrete scatter signal set and the hole region, establishing an L1 norm constraint model in combination with CS theory, and solving it through the ADMM algorithm to reconstruct a complete scatter signal; Subtracting the complete scatter signal from the first DR projection set to obtain a corrected DR projection; Wherein, the L1 norm constraint model is expressed as: Among them, x is a scattered signal in the complete scattered signal, x * is a scattered signal in the discrete scattered signal set, W = diag{ω i} ∈ R n×n is the diagonal weighted matrix, R n×n represents the real matrix, n is the number of sampling points of the x signal, ω i is the position matrix, and the position matrix is used to represent the position information of the scattering correction plate; ω i is 1 when in the hole area and 0 when in the area outside the hole; D h and D v are the horizontal difference matrix and the vertical difference matrix, λ is the regularization parameter, and p and q are the sparse representation coefficients, respectively representing the sparse representation of the gradients in the horizontal and vertical directions.

2. The method according to claim 1, wherein The collection device collects DR projections of two groups of workpieces without relative displacement at the workpiece position and with consistent scanning parameters.

3. The method according to claim 1, characterized in that, The performing image processing on the single DR projection includes: Binaryzation, contour extraction, contour tracking, and circle fitting processing.

4. The method according to claim 1, wherein The L1 norm constraint model promotes sparsity by introducing an L1 norm constraint, such that the gradients of the complete scatter signal in the horizontal and vertical directions are respectively equal to the corresponding sparse vectors.

5. The method according to any one of claims 1-4, characterized in that, After obtaining the corrected DR projection, the method further includes: Reconstructing the corrected DR projection using the FDK algorithm to obtain a three-dimensional CT reconstruction model after scatter correction.

6. The method according to claim 5, characterized in that, After obtaining the three-dimensional CT reconstruction model after scatter correction, the method further includes: Slicing the three-dimensional CT reconstruction model to obtain slice images; Analyzing the scatter correction effect based on the slice images.

7. A cone-beam CT scatter correction device, characterized in that, The device includes: An obtaining unit configured to obtain DR projections of two groups of workpieces collected by a collection device, and a single DR projection of a scatter correction plate; A determining unit configured to generate a first DR projection set and a second DR projection set based on the DR projections of the two groups of workpieces; and obtain a discrete scatter signal set based on the first DR projection set and the second DR projection set; wherein, the first DR projection set is a set of DR projections obtained by scanning the workpieces without adding the scatter correction plate; the second DR projection set is a set of DR projections obtained by scanning the workpieces with the scatter correction plate added; A processing unit configured to perform image processing on the single DR projection to obtain a hole region; A reconstructing unit configured to establish an L1 norm constraint model in combination with CS theory based on the discrete scatter signal set and the hole region, and solve it through the ADMM algorithm to reconstruct a complete scatter signal; A calculating unit configured to subtract the complete scatter signal from the first DR projection set to obtain a corrected DR projection; Wherein, the reconstructing unit is specifically used for the established L1 norm constraint model to be expressed as: Among them, x is a scattered signal in the complete scattered signal, x * is a scattered signal in the discrete scattered signal set, W = diag{ω i} ∈ R n×n is the diagonal weighted matrix, R n×n represents the real matrix, n is the number of sampling points of the x signal, ω i is the position matrix, and the position matrix is used to represent the position information of the scattering correction plate; ω i is 1 when in the hole area and 0 when in the area outside the hole; D h and D v are the horizontal difference matrix and the vertical difference matrix, λ is the regularization parameter, and p and q are the sparse representation coefficients, respectively representing the sparse representation of the gradients in the horizontal and vertical directions.

8. An electronic device, comprising a memory, a processor, and a computer program stored on the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the method according to any one of claims 1-6.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by a processor, the program implements the method according to any one of claims 1-6.

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