Method for reconstructing multi-spectrum CT image based on fast oblique projection correction algorithm

Abstract

This application relates to the field of multi-energy spectral CT imaging technology. This application discloses a method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm, which can reduce the total computational cost of multiple iterations and improve the overall image reconstruction efficiency. The method includes acquiring a multi-energy spectral CT image as the current image and a preset residual value; when the preset residual value is greater than or equal to a preset threshold, the following steps are executed iteratively: when the current iteration is the first iteration of the current cycle, the direction of the current cycle is calculated using the fast oblique projection correction algorithm to obtain the target direction of the current cycle; a target reconstructed image is generated based on the target direction and the current image; a target value is generated based on the target reconstructed image; the value corresponding to the preset residual value is modified to the value corresponding to the target value; the image corresponding to the current image is modified to the image corresponding to the target reconstructed image; when the preset residual value is less than a preset threshold, the target reconstructed image is used as the final reconstructed image.

Description

Technical Field

[0001] This application relates to the field of multi-energy spectral CT imaging technology. More specifically, this application relates to a method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm. Background Technology

[0002] Traditional multi-energy spectral CT image reconstruction methods employ oblique projection correction techniques to process multi-energy spectral CT images and obtain reconstructed images. However, while oblique projection correction techniques can address the geometric inconsistencies between different energy spectral projection data and accelerate convergence, they require complex derivative calculations in each iteration to update the iteration direction, resulting in a large total computational cost across multiple iterations and reducing the overall image reconstruction efficiency. Summary of the Invention

[0003] The purpose of this application is to provide a method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm, which can reduce the total computational cost of multiple iterations and improve the overall image reconstruction efficiency. This application is mainly achieved through the following technical solutions: A first aspect of this application provides a method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm, comprising: Acquire a multi-energy spectral CT image and a preset residual value, and use the multi-energy spectral CT image as the current image; If the preset residual value is greater than or equal to a preset threshold, the following steps are executed cyclically: if the current iteration is the first iteration of the current period, the direction of the current period is calculated using a fast oblique projection correction algorithm to obtain the target direction of the current period; a target reconstructed image of the multi-energy spectral CT image is generated based on the target direction of the current period and the current image; a target value is generated based on the target reconstructed image; the value corresponding to the preset residual value is modified to the value corresponding to the target value; and the image corresponding to the current image is modified to the image corresponding to the target reconstructed image. If the preset residual value is less than the preset threshold, the target reconstructed image is used as the final reconstructed image of the multi-energy spectral CT image.

[0004] According to one embodiment of this application, the calculation formula for the step of calculating the direction of the current period using a fast oblique projection correction algorithm to obtain the target direction of the current period is as follows: ; in, It is the target direction of the current cycle; It is the first proportionality coefficient for adjusting the contributions of the orthogonal direction and the oblique projection correction direction; These are the oblique projection correction directions obtained from energy spectra other than the known energy spectra; It is the second proportionality coefficient that adjusts the contribution of the orthogonal direction and the oblique projection correction direction; It is the unit vector of the current iteration point of the current image along the orthogonal direction of the next projective hyperplane.

[0005] According to one embodiment of this application, the method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm further includes a step of obtaining the oblique projection correction direction obtained from energy spectra other than the known energy spectrum. This step of obtaining the oblique projection correction direction from energy spectra other than the known energy spectrum includes: Obtain the first normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space, and the second normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space; The first normal direction and the second normal direction of the hyperplane are calculated and processed using the first preset algorithm to obtain the oblique projection correction direction of the energy spectrum other than the known energy spectrum.

[0006] According to one embodiment of this application, the calculation formula for the step of obtaining the oblique projection correction direction obtained from the energy spectrum other than the known energy spectrum by calculating the first normal direction and the second normal direction of the hyperplane using a first preset algorithm is as follows: ; in, It is the first normal direction of the hyperplane formed by the normal directions of N-1 pre-defined equations in N-dimensional space; It is the second normal direction of the hyperplane formed by the normal directions of N-1 pre-defined equations in N-dimensional space; It is a norm operator; Represents conditional judgment; represent and The dot product; represent and The dot product.

[0007] According to one embodiment of this application, the method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm further includes a step of obtaining the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane. This step includes: Obtain the orthogonal direction of the current iteration point of the current image along the next projection hyperplane; The second preset algorithm is used to calculate the current iteration point of the current image along the orthogonal direction of the next projection hyperplane to obtain the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane.

[0008] According to one embodiment of this application, the calculation formula for the step of using a second preset algorithm to calculate the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane is as follows: ; in, It is the orthogonal direction of the current iteration point of the current image along the next projection hyperplane; It is a norm operator.

[0009] According to one embodiment of this application, the step of generating a target value based on the target reconstructed image includes: Obtain real CT images; The target value is obtained by performing residual calculation processing on the target reconstructed image and the real CT image.

[0010] A second aspect of this application provides a reconstruction apparatus for multi-energy spectral CT images based on a fast oblique projection correction algorithm, comprising: The first acquisition module is used to acquire a multi-energy spectral CT image and a preset residual value, and to use the multi-energy spectral CT image as the current image; The loop module is used to repeatedly execute the following steps when the preset residual value is greater than or equal to a preset threshold: when the current iteration is the first iteration of the current period, calculate the direction of the current period using a fast oblique projection correction algorithm to obtain the target direction of the current period; generate a target reconstructed image of the multi-energy spectral CT image based on the target direction of the current period and the current image; generate a target value based on the target reconstructed image; modify the value corresponding to the preset residual value to the value corresponding to the target value; and modify the image corresponding to the current image to the image corresponding to the target reconstructed image. The final reconstructed image generation module is used to use the target reconstructed image as the final reconstructed image of the multi-energy spectral CT image when the preset residual value is less than the preset threshold.

[0011] A third aspect of this application provides a terminal device, including a processor and a memory, the memory being used to store a computer program, and the processor being used to call and run the computer program stored in the memory to perform the steps of the multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm provided in the first aspect of this application.

[0012] A fourth aspect of this application provides a computer-readable storage medium for storing a computer program that causes a computer to perform the steps of the multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm provided in the first aspect of this application.

[0013] The beneficial effects of the embodiments of this application include: This application proposes a fast oblique projection correction algorithm to change the mode of updating the direction by performing derivative calculations in each iteration to a mode of periodically updating the iterative direction with a fixed direction within one cycle. Specifically, this application acquires a multi-energy spectral CT image and a preset residual value, and uses the multi-energy spectral CT image as the current image. When the preset residual value is greater than or equal to a preset threshold, the following steps are executed iteratively: when the current iteration is the first iteration of the current cycle, the fast oblique projection correction algorithm is used to calculate the direction of the current cycle to obtain the target direction of the current cycle; a target reconstructed image of the multi-energy spectral CT image is generated based on the target direction of the current cycle and the current image; a target value is generated based on the target reconstructed image; the value corresponding to the preset residual value is modified to the value corresponding to the target value; and the image corresponding to the current image is modified to the image corresponding to the target reconstructed image; when the preset residual value is less than the preset threshold, the target reconstructed image is used as the final reconstructed image of the multi-energy spectral CT image. Compared with the prior art, the embodiments of this application can calculate the direction (i.e., the iteration direction) only in the first iteration of each cycle, without having to calculate the method in each iteration. Therefore, the embodiments of this application can reduce the total computational cost of multiple iterations and improve the overall image reconstruction efficiency. Attached Figure Description

[0014] To more clearly illustrate the technical solutions in the embodiments of this application or the conventional technology, the drawings used in the description of the embodiments or the conventional technology will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0015] Figure 1 The flowcharts for some embodiments of the multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm of this application are shown below. Figure 2 The flowcharts are shown in some other embodiments of the multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm of this application; Figure 3The final reconstructed image of this application is a reference image in some embodiments; Figure 4 Reference image for the final reconstructed image of this application in some other embodiments; Figure 5 This is a block diagram illustrating the principle of the multi-energy spectral CT image reconstruction device based on the fast oblique projection correction algorithm in some embodiments of this application. Figure 6 This is a block diagram illustrating the principle of the multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm in some embodiments of this application. Detailed Implementation

[0016] To make the above-mentioned objectives, features, and advantages of this application more apparent and understandable, the specific embodiments of this application are described in detail below with reference to the accompanying drawings. Many specific details are set forth in the following description to provide a thorough understanding of this application. However, this application can be implemented in many other ways different from those described herein, and those skilled in the art can make similar modifications without departing from the spirit of this application. Therefore, this application is not limited to the specific embodiments disclosed below.

[0017] It should be noted that 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 number of technical features indicated. Therefore, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0018] The terms "exemplary" or "for example" are used to indicate that something is an example, illustration, or illustration. Any embodiment or design described as "exemplary" or "for example" in the embodiments of this application should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of terms such as "exemplary" or "for example" is intended to present the relevant concepts in a specific manner.

[0019] The terms “comprising,” “including,” or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, system, product, or apparatus that includes a series of steps or units is not necessarily limited to those steps or units that are expressly listed, but may include other steps or units that are not expressly listed or that are inherent to such process, method, product, or apparatus.

[0020] Unless otherwise defined, all technical and scientific terms used in this application have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used in this application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. The term "and / or" as used in this application includes any and all combinations of one or more of the associated listed items.

[0021] Traditional multi-energy spectral CT image reconstruction methods employ oblique projection correction techniques to process and reconstruct the images. However, while oblique projection correction can address the geometric inconsistencies between different energy spectral projection data and accelerate convergence, it requires complex derivative calculations in each iteration to update the iteration direction, resulting in a large total computational cost across multiple iterations and reducing the overall image reconstruction efficiency. To overcome these problems, this application proposes a multi-energy spectral CT image reconstruction method based on a fast oblique projection correction algorithm.

[0022] The specific embodiments of this application will be further described below with reference to the accompanying drawings.

[0023] refer to Figure 1 The diagram shows a flowchart of a method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm, provided in the first aspect of an embodiment of this application. This method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm can be applied to a terminal device or a server. The terminal device can be a computer or other device, and the specific configuration can be determined by those skilled in the art according to actual needs. Figure 1 The reconstruction method of multi-energy spectral CT images based on the fast oblique projection correction algorithm includes the following steps S1, S2 and S3.

[0024] S1. Acquire a multi-energy spectral CT image and a preset residual value, and use the multi-energy spectral CT image as the current image.

[0025] Multi-spectral CT images, also known as spectral CT images, are advanced medical images that utilize the attenuation information of different energy components after X-rays pass through an object and are reconstructed through advanced algorithms to reveal the composition and physical properties of a material.

[0026] In step S1, the preset residual value can be understood as the initial preset residual value, which can be set by those skilled in the art according to actual needs.

[0027] S2. If the preset residual value is greater than or equal to a preset threshold, the following steps are executed repeatedly: If the current iteration is the first iteration of the current period, the direction of the current period is calculated using a fast oblique projection correction algorithm to obtain the target direction of the current period (refer to...). Figure 2 The process includes the steps of "first iteration within the cycle?" and "updating oblique projection correction direction"); generating a target reconstructed image of the multi-energy spectral CT image based on the target direction of the current cycle and the current image; generating a target value based on the target reconstructed image; modifying the value corresponding to the preset residual value to the value corresponding to the target value; and modifying the image corresponding to the current image to the image corresponding to the target reconstructed image.

[0028] It should be noted that before the loop, the specific value of the preset residual value must be greater than or equal to the preset threshold.

[0029] The specific value of the preset threshold can be set by those skilled in the art according to actual needs.

[0030] The fast oblique projection correction algorithm no longer recalculates the direction in each iteration, but instead uses one direction calculation cycle as the unit. While fully retaining the ability of the original OPMT (Oblique Projection Modification Technique) algorithm to handle geometrically inconsistent data, it reduces the frequency of high-complexity derivative calculations from each iteration to every K iterations.

[0031] The oblique projection correction technique typically refers to the correction of image distortions (such as trapezoidal distortion) caused by the tilt of the projection angle in projection display or image processing, in order to restore the normal geometric shape of the image. This technique is widely used in projectors, remote sensing image processing, multi-energy spectral CT imaging, and computer vision.

[0032] In this embodiment, the Taylor expansion calculation of the oblique projection correction direction is performed only once in the first iteration of the current cycle. The update direction determined in the first iteration is used in the remaining iterations to greatly reduce the computational overhead caused by derivative calculation.

[0033] Furthermore, the calculation formula for obtaining the target direction of the current period by using the fast oblique projection correction algorithm is as follows: ; in, It is the target direction of the current cycle; It is the first proportionality coefficient for adjusting the contributions of the orthogonal direction and the oblique projection correction direction; These are the oblique projection correction directions obtained from energy spectra other than the known energy spectra; It is the second proportionality coefficient that adjusts the contribution of the orthogonal direction and the oblique projection correction direction; It is the unit vector of the current iteration point of the current image along the orthogonal direction of the next projective hyperplane.

[0034] The known energy spectrum is a pre-set physical calibration of the multi-energy spectral CT system for scanning multi-energy spectral CT images.

[0035] Further, the step of obtaining the oblique projection correction direction obtained from energy spectra other than the known energy spectrum includes: obtaining the first normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space and the second normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space; and using a first preset algorithm to calculate and process the first normal direction and the second normal direction of the hyperplane to obtain the oblique projection correction direction obtained from energy spectra other than the known energy spectrum.

[0036] Furthermore, the calculation formula for the step of obtaining the oblique projection correction direction obtained from the energy spectrum other than the known energy spectrum by calculating and processing the first normal direction and the second normal direction of the hyperplane using the first preset algorithm is as follows: ; in, It is the first normal direction of the hyperplane formed by the normal directions of N-1 pre-defined equations in N-dimensional space; It is the second normal direction of the hyperplane formed by the normal directions of N-1 pre-defined equations in N-dimensional space; It is a norm operator; Represents conditional judgment; represent and The dot product; represent and The dot product.

[0037] Furthermore, the formula for calculating the first normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space is as follows: ; in, yes elements The algebraic cofactor; yes elements The algebraic cofactor; yes elements The algebraic cofactor; It is the direction of the normal to the hyperplane in N-dimensional space, which is composed of N-1 dimensional vectors.

[0038] Furthermore, the normal direction of the hyperplane in N-dimensional space is composed of N-1 dimensional vectors. The calculation formula is: ; in, It is an orthonormal basis in N-dimensional space; vector The direction of the normal to the hyperplane; It is the total number of orthonormal bases; yes elements The algebraic cofactor.

[0039] Furthermore, , , and For specific calculation methods, please refer to The calculation formula, that is, using In , In , In or In In the alternative In That's all.

[0040] Furthermore, The calculation formula is: ; in, This is the normal direction of the second hyperplane; This is the normal direction of the third hyperplane; Let be the normal direction of the Nth hyperplane; .

[0041] Furthermore, the formula for calculating the second normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space is as follows: .

[0042] Further, the step of obtaining the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane includes: obtaining the orthogonal direction of the current iteration point of the current image along the next projection hyperplane; and using a second preset algorithm to calculate and process the orthogonal direction of the current iteration point of the current image along the next projection hyperplane to obtain the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane.

[0043] Furthermore, the calculation formula for the step of using the second preset algorithm to calculate the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane is as follows: ; in, It is the orthogonal direction of the current iteration point of the current image along the next projection hyperplane; It is a norm operator.

[0044] Furthermore, the current iteration point of the current image is along the orthogonal direction of the next projection hyperplane. The calculation formula is: ; in, It is a value calculated from the known projection data on the current ray path; ; It is the first of the current image estimates Individual morphology value; It is the normal direction of the first hyperplane; It is the first normal direction of the first hyperplane. One element; It is the first energy spectrum of the measured object. Projection data along a ray path; Is with The corresponding number The projection of the image after the next iteration; It is the first voxel value estimated from the current image relative to the first energy spectrum. The contribution weight of each ray path; It is the second voxel value estimated from the current image relative to the first energy spectrum. The contribution weight of each ray path; It is the first of the current image estimates Individual prime values ​​for the first energy spectrum The contribution weight of each ray path; It is the first of the current image estimates Individual prime values ​​for the first energy spectrum The contribution weight of each ray path; It is the first of the current image estimates Individual prime values ​​for the first energy spectrum The contribution weight of each ray path; This is the normalization factor. The normalization factor can be understood as the effective attenuation coefficient under the current estimate.

[0045] Furthermore, the value calculated from the known projection data along the current ray path. The calculation formula is: ; in, It is the base material in the first The projection contribution vector under the ray path; It is the first of the current image estimates Individual morphology value at the th Image values ​​in the next iteration.

[0046] Furthermore, embodiments of this application can also calculate when the projection data on the current ray path is unknown. unknown values .

[0047] More specifically, The calculation formula is: ; in, It is the first The projection, the first Unknown projection values ​​under the energy spectrum; Also the first The projection of the image after the next iteration; It is the first of the current image estimates Individual primality values ​​for the first The first energy spectrum The contribution weight of each ray path; It is the normalization factor.

[0048] Furthermore, The calculation formula for each element can be found in [reference]. The calculation formula.

[0049] More specifically, The calculation formula is: ; in, It is the first of the current image estimates Individual primality values ​​for the first The first energy spectrum The contribution weight of each ray path; It is the normalization factor.

[0050] Furthermore, with The corresponding number Projection of the image after the next iteration The calculation formula can be referenced. The calculation formula. It can be understood as the first The projection of the image after the next iteration.

[0051] Specifically, the first Projection of the image after the next iteration The calculation formula is: ; in, It is the total number of energy ranges in the discretized continuous X-ray energy spectrum; It is the first The first normalized energy spectrum Jokes can be melodies; It is the first The first normalized energy spectrum The width of the spectrum of the segment; It is an exponential function; It is the first Seed material in the first Linear decay coefficient under the energy spectrum of the segment; It is the first of the current image estimates Individual morphology value at the th The image value in the nth iteration can also be understood as the nth iteration. The image after the next iteration. Furthermore, the normalization factor The calculation formula can be referenced. The calculation formula.

[0052] More specifically, The calculation formula is: .

[0053] Furthermore, , , and The calculation formula can be referenced. The calculation formula.

[0054] More specifically, The calculation formula is: .

[0055] Furthermore, the calculation formula for the step of generating the target reconstruction image of the multi-energy CT image based on the target direction of the current period and the current image is as follows: ; in, It is the first After the nth iteration The image value of the individual pixels, i.e., the current image; It is the first After the nth iteration The image value of the individual pixels, which is the target reconstructed image generated in the previous iteration; The step size is used to calculate the direction vector; yes Calculate the direction vector with step size The dot product.

[0056] Furthermore, the calculation formula for the step of generating the target reconstruction image of the multi-energy spectral CT image based on the target direction of the current period and the current image can refer to the following formula: ; in, It is the first The image value of the first voxel after the next iteration; It is the first The image value of the first voxel after the next iteration; It is the first After the nth iteration Image values ​​of individual pixels; It is the first After the nth iteration Image values ​​of individual pixels; It is the first After the nth iteration Image values ​​of individual pixels; It is the first After the nth iteration Image values ​​of individual pixels.

[0057] Furthermore, the step of generating a target value based on the target reconstructed image includes: acquiring a real CT image; performing residual calculation processing on the target reconstructed image and the real CT image to obtain the target value.

[0058] Furthermore, the formula for calculating the target value by performing residual calculation processing on the target reconstructed image and the real CT image is as follows: ; in, It is the target value; It is the reconstructed image of the target; It is the actual CT image.

[0059] S3. If the preset residual value is less than the preset threshold, the target reconstructed image is used as the final reconstructed image of the multi-energy spectral CT image. Step S3 can be referred to... Figure 2 The "Output final reconstructed image" step in the process.

[0060] The final reconstructed image can be referenced. Figure 3 As shown. Figure 3 This is a reconstructed image of the bone matrix. The image is reconstructed based on geometrically inconsistent multicolor spectral projection data using the multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm. Figure 3 The image clearly displays the anatomical structure of the skeleton, with high contrast, sharp boundaries, and good noise suppression.

[0061] The final reconstructed image can also be referenced. Figure 4 As shown. Figure 4 It is a reconstructed image of water-based materials. Figure 4 and Figure 3 The same projection data is obtained synchronously in the same reconstruction process. Figure 4 The uniform grayscale values ​​of the water-filled model are consistent and the background noise is low, indicating the effectiveness of the method of the present invention.

[0062] In the above embodiments, this application presents a fast oblique projection correction algorithm to change the mode of updating the direction by performing derivative calculations in each iteration to a mode of periodically updating the iterative direction and iterating with a fixed direction within one cycle. This application can calculate the direction (i.e., the iterative direction) only in the first iteration of each cycle, without needing to calculate the direction in each iteration. Therefore, this application can reduce the total computational cost of multiple iterations and improve the overall image reconstruction efficiency.

[0063] In some implementations, this application embodiment performs a complete derivative calculation using a first-order Taylor expansion based on the simplified Newton method at the iteration start point or after each iteration cycle (e.g., K iterations) to obtain the overall iteration direction for the current cycle. This process is consistent with the original OPMT algorithm: linearization is performed at the current image iteration point, the derivative vector of the projection function is calculated, and the optimized oblique projection correction direction is synthesized by combining the precise information of the known energy spectrum with the geometric information of other energy spectra. The specific calculation formula is as follows: ; in, The measured object is in the 1st... The first energy spectrum Projection data along a ray path; It is the first The spatial density function of the seed material; , Is with Related neighborhood set; .

[0064] In some embodiments, this application also provides a multicolor spectral CT imaging model, which can be expressed as the following formula: .

[0065] The parameter explanations in the embodiments of this application can all be understood as explanations made under the assumption that the number of base materials and the number of X-ray energy spectra used are the same.

[0066] In some implementations, the fixed period can be changed to a dynamically adjustable period based on the convergence state. The core idea is: when convergence is rapid, the update period is extended to reduce computational overhead; when convergence is slow or tends to stagnate, the period is shortened to update the direction more frequently, avoiding inefficient iteration.

[0067] In some implementations, embodiments of this application may also employ the secant method to approximate the direction, using the information of the current iteration point and the previous iteration point to approximate the derivative with difference, thereby constructing an updated direction with lower computational cost.

[0068] refer to Figure 5 The diagram shown is a principle block diagram of a multi-energy spectral CT image reconstruction device based on a fast oblique projection correction algorithm, provided in the second aspect of an embodiment of this application. Figure 5 The reconstruction device 100 for multi-energy spectral CT images based on the fast oblique projection correction algorithm includes: The first acquisition module 101 is used to acquire a multi-energy spectral CT image and a preset residual value, and to use the multi-energy spectral CT image as the current image; The loop module 102 is configured to repeatedly execute the following steps when the preset residual value is greater than or equal to a preset threshold: when the current iteration is the first iteration of the current period, calculate the direction of the current period using a fast oblique projection correction algorithm to obtain the target direction of the current period; generate a target reconstructed image of the multi-energy spectral CT image based on the target direction of the current period and the current image; generate a target value based on the target reconstructed image; modify the value corresponding to the preset residual value to the value corresponding to the target value; and modify the image corresponding to the current image to the image corresponding to the target reconstructed image. The final reconstructed image generation module 103 is used to use the target reconstructed image as the final reconstructed image of the multi-energy spectral CT image when the preset residual value is less than the preset threshold.

[0069] In some embodiments, the multi-energy spectral CT image reconstruction device 100 based on the fast oblique projection correction algorithm further includes a second acquisition module and a first calculation module. The second acquisition module is used to acquire the first normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space and the second normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space. The first calculation module is used to perform calculation processing on the first normal direction and the second normal direction of the hyperplane using a first preset algorithm to obtain the oblique projection correction direction obtained from the energy spectra other than the known energy spectra.

[0070] In some embodiments, the multi-energy spectral CT image reconstruction device 100 based on the fast oblique projection correction algorithm further includes a third acquisition module and a second calculation module. The third acquisition module is used to acquire the orthogonal direction of the current iteration point of the current image along the next projection hyperplane. The second calculation module is used to perform calculation processing on the orthogonal direction of the current iteration point of the current image along the next projection hyperplane using a second preset algorithm to obtain the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane.

[0071] In some implementations, the loop module 102 is also used to acquire a real CT image; perform residual calculation processing on the target reconstructed image and the real CT image to obtain the target value.

[0072] A third aspect of this application provides a terminal device, the schematic diagram of which is as follows: Figure 6 As shown. The terminal device includes a processor, memory, network interface, display screen, and temperature sensor connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage media. The network interface of the terminal device is used for communication with external terminals via a network connection. When the computer program is executed by the processor, it implements a method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm. The display screen can be a liquid crystal display screen or an e-ink display screen, and the temperature sensor is pre-installed inside the terminal device to detect the operating temperature of the internal components.

[0073] Those skilled in the art will understand that Figure 6The schematic diagram shown is only a partial structural diagram related to the present invention and does not constitute a limitation on the terminal device to which the present invention is applied. The specific terminal device may include more or fewer components than shown in the figure, or combine certain components, or have different component arrangements.

[0074] In some embodiments, this application provides a terminal device, which includes a processor and a memory. The memory stores a computer program, and the processor calls and runs the computer program stored in the memory to perform the following steps: Acquire a multi-energy spectral CT image and a preset residual value, and use the multi-energy spectral CT image as the current image; If the preset residual value is greater than or equal to a preset threshold, the following steps are executed cyclically: if the current iteration is the first iteration of the current period, the direction of the current period is calculated using a fast oblique projection correction algorithm to obtain the target direction of the current period; a target reconstructed image of the multi-energy spectral CT image is generated based on the target direction of the current period and the current image; a target value is generated based on the target reconstructed image; the value corresponding to the preset residual value is modified to the value corresponding to the target value; and the image corresponding to the current image is modified to the image corresponding to the target reconstructed image. If the preset residual value is less than the preset threshold, the target reconstructed image is used as the final reconstructed image of the multi-energy spectral CT image.

[0075] A fourth aspect of this application provides a computer-readable storage medium for storing a computer program that causes a computer to perform the steps of the multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm provided in the first aspect of this application.

[0076] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided by this invention can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0077] The technical features of the above embodiments can be combined without changing the basic principles of this application. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0078] The above embodiments merely illustrate several implementation methods of this application, and while the descriptions are relatively specific and detailed, they should not be construed as limiting the scope of the patent application. It should be noted that those skilled in the art can make various modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application. Therefore, the patent protection scope of this application should be determined by the appended claims.

Claims

1. A method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm, characterized in that, include: Acquire multi-energy spectral CT images and preset residual values, and use the multi-energy spectral CT images as the current images; If the preset residual value is greater than or equal to the preset threshold, the following steps are executed repeatedly: If the current iteration is the first iteration of the current period, the direction of the current period is calculated using the fast oblique projection correction algorithm to obtain the target direction of the current period; Generate a target reconstruction image of the multi-energy spectral CT image based on the target direction of the current period and the current image; generate target values ​​based on the target reconstruction image; Modify the value corresponding to the preset residual value to the value corresponding to the target value; and modify the image corresponding to the current image to the image corresponding to the target reconstructed image. If the preset residual value is less than the preset threshold, the target reconstructed image is used as the final reconstructed image of the multi-energy spectral CT image.

2. The method for reconstructing multi-energy spectral CT images based on the fast oblique projection correction algorithm according to claim 1, characterized in that, The formula for calculating the target direction of the current period using the fast oblique projection correction algorithm is as follows: ； in, It is the target direction of the current cycle; It is the first proportionality coefficient for adjusting the contributions of the orthogonal direction and the oblique projection correction direction; These are the oblique projection correction directions obtained from energy spectra other than the known energy spectra; It is the second proportionality coefficient that adjusts the contribution of the orthogonal direction and the oblique projection correction direction; It is the unit vector of the current iteration point of the current image along the orthogonal direction of the next projective hyperplane.

3. The method for reconstructing multi-energy spectral CT images based on the fast oblique projection correction algorithm according to claim 2, characterized in that, The method for reconstructing multi-energy spectral CT images based on a fast oblique projection correction algorithm further includes a step of obtaining the oblique projection correction direction from energy spectra other than the known energy spectra. This step of obtaining the oblique projection correction direction from energy spectra other than the known energy spectra includes: Obtain the first normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space, and the second normal direction of the hyperplane formed by the normal directions of N-1 preset equations in N-dimensional space; The first normal direction and the second normal direction of the hyperplane are calculated and processed using the first preset algorithm to obtain the oblique projection correction direction of the energy spectrum other than the known energy spectrum.

4. The method for reconstructing multi-energy spectral CT images based on the fast oblique projection correction algorithm according to claim 3, characterized in that, The calculation formula for obtaining the oblique projection correction direction obtained from the energy spectrum other than the known energy spectrum by calculating the first normal direction and the second normal direction of the hyperplane using the first preset algorithm is as follows: ； in, It is the first normal direction of the hyperplane formed by the normal directions of N-1 pre-defined equations in N-dimensional space; It is the second normal direction of the hyperplane formed by the normal directions of N-1 pre-defined equations in N-dimensional space; It is a norm operator; Represents conditional judgment; represent and The dot product; represent and The dot product.

5. The method for reconstructing multi-energy spectral CT images based on the fast oblique projection correction algorithm according to claim 2, characterized in that, The multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm further includes a step of obtaining the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane. This step includes: Obtain the orthogonal direction of the current iteration point of the current image along the next projection hyperplane; The second preset algorithm is used to calculate the current iteration point of the current image along the orthogonal direction of the next projection hyperplane to obtain the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane.

6. The method for reconstructing multi-energy spectral CT images based on the fast oblique projection correction algorithm according to claim 5, characterized in that, The formula for calculating the unit vector of the current iteration point of the current image along the orthogonal direction of the next projection hyperplane using the second preset algorithm is as follows: ； in, It is the orthogonal direction of the current iteration point of the current image along the next projection hyperplane; It is a norm operator.

7. The method for reconstructing multi-energy spectral CT images based on the fast oblique projection correction algorithm according to claim 1, characterized in that, The steps for generating target values ​​based on the target reconstructed image include: Obtain real CT images; The target value is obtained by performing residual calculation processing on the target reconstructed image and the real CT image.

8. A reconstruction device for multi-energy spectral CT images based on a fast oblique projection correction algorithm, characterized in that, include: The first acquisition module is used to acquire a multi-energy spectral CT image and a preset residual value, and to use the multi-energy spectral CT image as the current image; The loop module is used to repeatedly execute the following steps when the preset residual value is greater than or equal to a preset threshold: when the current iteration is the first iteration of the current period, the direction of the current period is calculated by using a fast oblique projection correction algorithm to obtain the target direction of the current period; Generate a target reconstruction image of the multi-energy spectral CT image based on the target direction of the current period and the current image; generate target values ​​based on the target reconstruction image; Modify the value corresponding to the preset residual value to the value corresponding to the target value; and modify the image corresponding to the current image to the image corresponding to the target reconstructed image. The final reconstructed image generation module is used to use the target reconstructed image as the final reconstructed image of the multi-energy spectral CT image when the preset residual value is less than the preset threshold.

9. A terminal device, characterized in that, include: A processor and a memory, the memory for storing a computer program, the processor for calling and running the computer program stored in the memory to perform the steps of the multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that, Used to store a computer program that causes a computer to perform the steps of the multi-energy spectral CT image reconstruction method based on the fast oblique projection correction algorithm as described in any one of claims 1 to 7.

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