CT mean value image generation method, device, system and computer equipment

Abstract

The application relates to the field of medical image processing, in particular to a CT mean image generation method, device and system and computer equipment, the method comprising the following steps: determining the number of each scanning angle in a multi-circle scanning process based on scan raw data obtained through multi-circle scanning; determining the weight of each scan raw data corresponding to the each scanning angle in a reconstruction process based on the number of the each scanning angle; and reconstructing and generating a CT mean image by using an image reconstruction algorithm based on the weight of the each scan raw data and the scan raw data. The application does not need to reconstruct and generate an image for multiple times, simplifies the reconstruction process, and utilizes all scan raw data in the reconstruction process.

Description

Technical Field

[0001] This application relates to the field of medical image processing, and in particular to a method, apparatus, system and computer device for generating CT mean images. Background Technology

[0002] Mean value CT (Computed Tomography) is a reconstruction method used in cinema CT, primarily applied in PET (Positron Emission Tomography) / CT to generate attenuation maps for attenuation correction. The main scanning method of mean value CT involves continuous, low-dose scans in areas prone to motion artifacts, such as the heart or lungs, to generate a mean CT image, which is then used for attenuation correction of the PET / CT image. The advantage of mean value CT images is that the generated image, obtained through long, uninterrupted scans over multiple cycles, reduces the risk of mismatches in attenuation caused by the short scan time of images reconstructed from conventional CT, which fails to capture the patient's respiratory or cardiac motion information.

[0003] The existing method for reconstructing the average CT image involves reconstructing the raw data for each scan circle separately to generate an image for that circle, and then averaging all the images to produce the final average CT image. This reconstruction method is quite complex, requiring multiple reconstructions to generate the image. Furthermore, if the raw scan data is not a complete scan circle, that portion of the data is discarded, potentially leading to excessive dosage for the user. Summary of the Invention

[0004] Therefore, it is necessary to provide a method, apparatus, system, and computer device for generating CT mean images to address the aforementioned technical problems.

[0005] In a first aspect, embodiments of the present invention provide a method for generating CT mean images, the method comprising: Based on the scan data obtained from multi-circle scanning, the number of each scanning angle during the multi-circle scanning process is determined; Based on the number of each scanning angle, the weight of each scan data corresponding to each scanning angle in the reconstruction process is determined; Based on the weights of each scan data point and the scan data itself, an image reconstruction algorithm is used to reconstruct and generate a CT mean image.

[0006] In one embodiment, the step of reconstructing and generating a CT mean image using an image reconstruction algorithm based on the weights of each scan dataset and the scan dataset includes: Based on the weights of each scan data point and the scan data points themselves, the mean data point corresponding to each scan angle is determined. The mean data is reconstructed using a first image reconstruction algorithm to generate a CT mean image.

[0007] In one embodiment, the step of reconstructing and generating a CT mean image using an image reconstruction algorithm based on the weights of each scan dataset and the scan dataset includes: Based on the weights of each scanned data point, a second image reconstruction algorithm is used to reconstruct the scanned data and generate a CT mean image.

[0008] In one embodiment, reconstructing the mean data using a first image reconstruction algorithm to generate a CT mean image includes: Based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, and the mean value data, a CT mean image is reconstructed using the first image reconstruction algorithm.

[0009] In one embodiment, the step of reconstructing and generating a CT mean image using a first image reconstruction algorithm based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, and the mean data includes: Based on the location of the point, determine the first value of the corresponding position in the mean data; The product of the first value and the weight of the corresponding pixel is added to the corresponding pixel to reconstruct the CT mean image.

[0010] In one embodiment, the step of reconstructing a CT mean image from the scanned raw data using a second image reconstruction algorithm based on the weights of each scanned raw data includes: Based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, the weight of each scan data, and the scan data, a CT mean image is reconstructed using a second image reconstruction algorithm.

[0011] In one embodiment, the step of reconstructing and generating a CT mean image using a second image reconstruction algorithm based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, the weight of each scan data point, and the scan data point itself includes: Based on the location of the point, determine the second value of the corresponding position in the scanned data; The second value, the weight of the corresponding pixel, and the weight of the corresponding scanned data are multiplied and accumulated and added to the corresponding pixel to reconstruct the CT mean image.

[0012] Secondly, embodiments of the present invention provide a CT mean image generation device, the device comprising: The first determining module is used to determine the number of each scanning angle during the multi-circle scanning process based on the scan data obtained from the multi-circle scanning. The second determining module is used to determine the weight of each scan data corresponding to each scan angle in the reconstruction process based on the number of each scan angle; The image reconstruction module is used to reconstruct and generate a CT mean image based on the weights of each scanned data and the scanned data, using an image reconstruction algorithm.

[0013] Thirdly, embodiments of the present invention provide an image scanning system, the system comprising a scanning device for obtaining scan data through multi-circle scanning, and a CT mean image generation device as described in the second aspect connected to the scanning device.

[0014] Fourthly, embodiments of the present invention provide a computer device including a memory and a processor, wherein the memory stores a computer program and the processor executes the steps described in the first aspect.

[0015] Compared to existing technologies, the aforementioned methods, apparatus, systems, and computer equipment, based on the scan data obtained from multi-round scanning, determine the number of each scanning angle during the multi-round scanning process. Based on the number of each scanning angle, they determine the weight of each scan data point corresponding to each scanning angle in the reconstruction process. Based on the weights of each scan data point and the scan data itself, they use an image reconstruction algorithm to reconstruct and generate a CT mean image. This invention eliminates the need for multiple image reconstructions, simplifying the reconstruction process, and utilizes all scan data during reconstruction. Attached Figure Description

[0016] Figure 1 This is a schematic diagram of the structure of an image scanning system in one embodiment; Figure 2 This is a flowchart illustrating a method for generating CT mean images in one embodiment; Figure 3 This is a schematic diagram of the specific process of a CT mean image generation method in one embodiment; Figure 4 This is a flowchart illustrating the first image reconstruction method in one embodiment; Figure 5 This is a schematic diagram illustrating the reconstruction principle of the first image reconstruction method in one embodiment; Figure 6 This is a schematic diagram of reconstruction from different perspectives in one embodiment; Figure 7 This is a schematic diagram of the specific process of the CT mean image generation method in another embodiment; Figure 8 This is a schematic diagram of the module connections of a CT mean image generation device in one embodiment; Figure 9 This is a schematic diagram of the structure of a computer device in one embodiment. Detailed Implementation

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are merely some examples or embodiments of the present invention. For those skilled in the art, the present invention can be applied to other similar scenarios based on these drawings without creative effort. Unless obvious from the context or otherwise specified, the same reference numerals in the drawings represent the same structures or operations.

[0018] As indicated in this invention and the claims, unless the context clearly indicates otherwise, the words "a," "an," "an," and / or "the" do not specifically refer to the singular and may also include the plural. Generally speaking, the terms "comprising" and "including" only indicate the inclusion of explicitly identified steps and elements, which do not constitute an exclusive list, and the method or apparatus may also include other steps or elements.

[0019] While this invention makes various references to certain modules in systems according to embodiments of the invention, any number of different modules can be used and run on computing devices and / or processors. Modules are merely illustrative, and different aspects of the system and method may use different modules.

[0020] It should be understood that when a unit or module is described as "connected" or "coupled" to other units, modules, or blocks, it may refer to a direct connection or coupling, or communication with other units, modules, or blocks, or the presence of intermediate units, modules, or blocks, unless the context explicitly indicates otherwise. The term "and / or" as used herein may include any and all combinations of one or more of the related listed items.

[0021] like Figure 1 As shown, the image scanning system 100 may include a scanning device 110, a CT average image generation device 120, a storage device 140, and a display device 150. The devices in the image scanning system 100 can be interconnected or communicate with each other via a network 130.

[0022] The scanning device 110 can scan an object. The object can be a physical object, human body, organ, tissue, etc. The scanning device can be a medical imaging device. In some embodiments, the scanning device 110 can be a magnetic resonance imaging (MRI) scanner, a computed tomography (CT) scanner, a positron emission tomography (PET) scanner, a b-scan ultrasonography (B-scan ultrasonography) scanner, a thermal texture map (TTM) scanner, a medical electronic endoscope (MEE), etc. In some embodiments, the scanning device 110 can also be a combination of multiple devices, such as a PET-CT scanner, a PET-MRI scanner, etc. The scanning device 110 can generate scan data corresponding to the object after scanning. Further, the scanning device 110 can send the acquired scan data via network 130 to a CT average image generation device 120, a storage device 140, or a display device 150.

[0023] The CT mean image generation device 120 can process scanned raw data. This scanned raw data can be obtained through scanning by the scanning device 110 or from the storage device 140. In some embodiments, the scanned raw data can be two-dimensional or three-dimensional scanned raw data representing anatomical and / or functional information of the scanned object. The processing can include reconstructing the scanned raw data to generate an image. The reconstruction method can include, but is not limited to, one or more combinations of filtered back projection (FBP), iterative reconstruction, deep learning reconstruction, multiplanar reconstruction (MPR), volume rendering (VR), multiplanar volume reformation (MPVR), curved planar reformatting (CPR), maximum intensity projection (MIP), and shaded surface display (SSD). The processing can also include reconstructing a CT mean image from the scanned raw data. The CT mean image generation device 120 can send the CT mean image to the storage device 140 for storage.

[0024] Network 130 can be any connection method that connects two or more devices. For example, network 130 can be a wired network or a wireless network. In some embodiments, network 130 can be a single network or a combination of multiple networks. For example, network 130 may include one or more of the following: local area network (LAN), wide area network (WAN), public network, private network, wireless LAN, virtual network, public telephone network, etc. The modules in the image correction system 100 can interact with each other by connecting to network 130.

[0025] Storage device 140 can store data and / or information. For example, storage device 140 can store scan data generated by scanning device 110, images obtained by CT average image generation device 120, and user input or instructions received by display device 150. In some embodiments, storage device 140 can be local storage, external storage, cloud storage, etc.

[0026] Display device 150 can be used to display images. Display device 150 may include a display screen, touch screen, etc. In some embodiments, display device 150 may include an interactive interface that can receive input from a user or doctor. In some embodiments, display device 150 may include an input device, such as a touchpad, touch screen, mouse, keyboard, microphone, etc. Display device 150 can send user input to CT average image generation device 120 for processing or to storage device 140 for storage.

[0027] Figure 2 This is a flowchart of a CT mean image generation method according to an embodiment of the present invention. In some embodiments, the process can be implemented by a CT mean image generation device 120. In one embodiment, such as Figure 2 As shown, a method for generating CT mean images is proposed, including the following steps: S201: Based on the scan data obtained from multi-turn scanning, determine the number of each scanning angle during the multi-turn scanning process.

[0028] Multiple scans are performed on a specific part of the object to obtain scan data, such as scanning a part of the human body. It should be noted that multiple scans here do not necessarily mean that each scan is a complete circle; for example, there may be cases where only half a circle is scanned.

[0029] In step 201, the scanned data can be obtained from the scanning device 110 or from the storage device 140.

[0030] Since not every scan is a complete circle, the number of each scanning angle is not necessarily the number of scan circles.

[0031] In one embodiment, the number of each scanning angle is determined using the following formula (1): j ∈[1, ViewPerRev (1) Where N represents the total number of scan data generated in multiple scans. ViewPerRev This indicates the number of data generated in one scan cycle; Indicates the first j Each scan data point corresponds to a scanning angle.

[0032] S202: Based on the number of each scanning angle, determine the weight of each scan data corresponding to each scanning angle in the reconstruction process.

[0033] After determining the number of scanning angles, the weight of each scan data point in the reconstruction process can be determined. For example, if the number of scanning angles is 20, then the weight of each scan data point corresponding to that scanning angle in the reconstruction process is 1 / 20.

[0034] In one embodiment, the weights of each scanned data point in the reconstruction process are determined using the following formula (2): (2) S203: Based on the weights of each scanned data and the scanned data, a CT mean image is reconstructed using an image reconstruction algorithm.

[0035] When using image reconstruction algorithms for reconstruction, multiple scan data from different scanning angles are fused according to the weights of each scan data to generate a CT mean image.

[0036] Based on steps S201-S203, using the scan data obtained from multiple scans, the number of each scanning angle during the multiple scans is determined. Based on the number of each scanning angle, the weight of each scan data point corresponding to each scanning angle in the reconstruction process is determined. Based on the weights of each scan data point and the scan data itself, an image reconstruction algorithm is used to reconstruct and generate a CT mean image. This invention eliminates the need for multiple image reconstructions, simplifying the reconstruction process, and utilizes all scan data during reconstruction.

[0037] In one embodiment, such as Figure 3 As shown, the process of reconstructing and generating a CT mean image using an image reconstruction algorithm based on the weights of each scan dataset and the scan dataset includes the following steps: S301: Based on the weight of each scan data and the scan data, determine the mean data corresponding to each scan angle; Based on the scan data, all scan data corresponding to each scan angle can be obtained. Multiplying these data by the corresponding weights will yield the corresponding mean scan data.

[0038] S302: The mean data is reconstructed using the first image reconstruction algorithm to generate a CT mean image.

[0039] In step S302, a CT mean image is reconstructed using a first image reconstruction algorithm based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, and the mean data.

[0040] like Figure 4 As shown, step S302 specifically includes the following steps: S401: Determine the position of each pixel corresponding to each scanning angle.

[0041] The location of the point is determined based on the distance from the intersection of the ray from the light source point to the reconstruction point and the detector plane to the rotation plane of the light source, as well as the angle between the ray from the light source point to the reconstruction point and the ray from the light source point to the rotation center.

[0042] S402: Based on the location of the points, determine the weight of each pixel and the first value of the corresponding position in the corresponding mean data; S403: The product of the first value and the weight of the corresponding pixel is added to the corresponding pixel to reconstruct and generate the CT mean image.

[0043] The specific reconstruction process includes the following steps: a. Select the first [number]th ... j The mean value of the data corresponds to the scanning angle as follows: ; b. Select scanning angle The corresponding pixel point, the position of the pixel point is ; c. Calculate the values ​​of q, γ, and L of the pixel in the scanned data according to the reconstruction formulas (3), (4), and (5); (3) (4) (5) in, This represents the distance from the point where the ray from the source point to the reconstruction point intersects with the detector plane to the plane of rotation of the source point; This represents the angle between the ray from the source point to the reconstructed point and the ray from the source point to the center of rotation; R represents the length from the light source point to the reconstruction point; R represents the length from the light source point to the center of rotation; x, y, and z are the coordinate axes of the reconstructed image (CT mean image).

[0044] Reconstruction schematic diagram as follows Figure 5 As shown in the diagram, different perspectives on the reconstruction logic are illustrated below. Figure 6 As shown, a ray emitted from a source point is used to project pixels onto the CT mean image.

[0045] d. Determine the position of the pixel based on the distance q and the included angle γ; e. Use the formula based on length R and length L. Calculate the weight W of the point; determine the first value V1 of the corresponding position in the mean data based on the point's location; f. Using the reconstruction formula (6), the product of the first value V1 and the weight W is added to the corresponding pixel. (6) in, This represents the mean of the data.

[0046] g. Select the next pixel. If all pixels have been selected, select the next mean data point. If all mean data points have been selected, the reconstruction is complete, and a CT mean image is generated.

[0047] The reasoning process for reconstructing formula (6) is as follows: Assume the number of scanned data is exactly n. Let viewPerRev be the number of views, n be the number of scan cycles, and viewPerRev be the number of data generated in one scan cycle.

[0048] In this embodiment, the mean value of each scanning angle is determined based on the weight of each scanned data and the scanned data itself. This can be understood as fusing the scanned data obtained from multiple scans into a mean value of one scan using the weight of each scanned data, and then using the first image reconstruction algorithm to reconstruct the mean value of one scan. This reconstruction method requires only one reconstruction and utilizes all the scanned data.

[0049] In one embodiment, the mean image data can be generated by the scanning device 110 and stored in the storage device 140. The CT mean image generation device 120 can obtain the mean image data directly from the scanning device 110 or from the storage device 140.

[0050] Specifically, a memory cache can be added to the scanning device 110 to accumulate the sum of the scanned data for the corresponding projection angle, and a counter group can be added to count for each scanning angle. After scanning is completed, the average value of the scanned data in the cache is calculated.

[0051] In another embodiment, a CT mean image is reconstructed from the scanned data based on the weights of each scanned data.

[0052] Unlike the above embodiments, in this embodiment, a second image reconstruction algorithm is used to reconstruct the CT mean image from the scanned raw data, instead of the mean raw data.

[0053] Specifically, based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, the weight of each scan data, and the scan data, a second image reconstruction algorithm is used to reconstruct and generate a CT mean image.

[0054] like Figure 7 As shown, the step of reconstructing a CT mean image from the scanned raw data using a second image reconstruction algorithm based on the weights of each scanned raw data includes the following steps: S701: Determine the position of each pixel corresponding to each scanning angle.

[0055] S702: Based on the location of the point, determine the weight of each pixel and the second value of the corresponding position in the corresponding scanned data; S703: The product of the second value, the weight of the corresponding pixel, and the weight of the corresponding scanned data is added to the corresponding pixel to reconstruct and generate the CT mean image.

[0056] It should be noted that the methods for determining the weight, position, and second value of each pixel are the same as those used in the above embodiments, and therefore will not be repeated here.

[0057] The second value, the weight of the corresponding pixel, and the weight of the corresponding scanned data are multiplied and accumulated on the corresponding pixel using the reconstruction formula (8) to reconstruct the CT mean image.

[0058] (8) Where P represents the collected raw data.

[0059] The reasoning process for reconstructing formula (8) is as follows: Assuming the number of scanned data cycles is not an integer multiple, i.e., the number of scanned data cycles is not an integer multiple of ViewPerRev, then:

[0060] According to equation (9), the reconstruction formula is obtained: (8) In this embodiment, based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, the weight of each scan data, and the scan data, a second image reconstruction algorithm is used to reconstruct and generate a CT mean image. The difference from the first image reconstruction algorithm is that it does not need to generate mean data. In the second image reconstruction algorithm, the weight of each scan data is considered to reconstruct all the acquired data. This reconstruction method only requires one reconstruction and utilizes all the scan data.

[0061] It should be understood that although the steps in the flowchart above are shown sequentially as indicated by the arrows, these steps are not necessarily executed in the order indicated by the arrows. Unless explicitly stated herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least some steps in the flowchart above may include multiple steps or multiple stages. These steps or stages are not necessarily completed at the same time, but can be executed at different times. The execution order of these steps or stages is not necessarily sequential, but can be performed alternately or in turn with other steps or at least some of the steps or stages in other steps.

[0062] In some embodiments, such as Figure 8 As shown, the present invention provides a CT mean image generation device, the device comprising: The first determining module 810 is used to determine the number of each scanning angle during the multi-circle scanning process based on the scanning data obtained from the multi-circle scanning. The second determining module 820 is used to determine the weight of each scan data corresponding to each scan angle in the reconstruction process based on the number of each scan angle; The image reconstruction module 830 is used to reconstruct and generate a CT mean image based on the weights of each scan data and the scan data using an image reconstruction algorithm.

[0063] In this embodiment, the CT mean image generation device does not require multiple reconstructions to generate images, simplifying the reconstruction process, and utilizes all scan data during the reconstruction process.

[0064] In some embodiments, the image reconstruction module includes: The first determining submodule is used to determine the mean data corresponding to each scanning angle based on the weight of each scan data and the scan data. The first image reconstruction submodule is used to reconstruct the mean data using a first image reconstruction algorithm to generate a CT mean image.

[0065] In some embodiments, the image reconstruction module includes: The second image reconstruction submodule is used to reconstruct a CT mean image from the scanned data based on the weights of each scanned data and using a second image reconstruction algorithm.

[0066] In some embodiments, the first image reconstruction submodule reconstructs and generates a CT mean image based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, and the mean data using a first image reconstruction algorithm.

[0067] In some embodiments, the first image reconstruction submodule determines a first value at the corresponding position in the mean data based on the point location; the first value is multiplied by the weight of the corresponding pixel and added to the corresponding pixel to reconstruct and generate a CT mean image.

[0068] In some embodiments, the second image reconstruction submodule reconstructs and generates a CT mean image based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, the weight of each scan data, and the scan data, using a second image reconstruction algorithm.

[0069] In some embodiments, the second image reconstruction submodule determines a second value at the corresponding position in the scanned data based on the point location; the second value, the weight of the corresponding pixel, and the weight of the corresponding scanned data are multiplied and accumulated onto the corresponding pixel to reconstruct and generate a CT mean image.

[0070] Specific limitations regarding the CT mean image generation device can be found in the limitations of the CT mean image generation method described above, and will not be repeated here. Each module in the aforementioned device can be implemented entirely or partially through software, hardware, or a combination thereof. These modules can be embedded in or independent of the processor in a computer device in hardware form, or stored in the memory of a computer device in software form, so that the processor can call and execute the corresponding operations of each module.

[0071] In one embodiment, a computer device is provided, which may be a server, and its internal structure diagram may be as follows: Figure 9As shown. The computer device includes a processor, memory, and network interface connected via a system bus. The processor provides computing and control capabilities. The memory includes a non-volatile storage medium and internal memory. The non-volatile storage medium stores an operating system, computer programs, and a database. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The database stores motion detection data. The network interface communicates with external terminals via a network connection. When the computer program is executed by the processor, it implements the steps in any of the above embodiments of the CT mean image generation method.

[0072] Those skilled in the art will understand that Figure 9 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0073] In one embodiment, a computer device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps in any of the above embodiments of the CT mean image generation method.

[0074] 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 in this application can include at least one of non-volatile and volatile memory. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical storage, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0075] The technical features of the above embodiments can be combined in any way. 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.

[0076] 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 invention patent. 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 protection scope of this patent application should be determined by the appended claims.

Claims

1. A method for generating CT mean images, characterized in that, The method includes: Based on the scan data obtained from multi-circle scanning, the number of each scanning angle during the multi-circle scanning process is determined; Based on the number of each scanning angle, the weight of each scan data corresponding to each scanning angle in the reconstruction process is determined; Based on the weights of each scan data point and the scan data itself, an image reconstruction algorithm is used to reconstruct and generate a CT mean image. When reconstructing using the image reconstruction algorithm, multiple scan data points of the scanning angle are fused according to the weights of each scan data point to generate the CT mean image.

2. The method according to claim 1, characterized in that, The step of reconstructing and generating a CT mean image using an image reconstruction algorithm based on the weights of each scan dataset and the scan dataset includes: Based on the weights of each scan data point and the scan data points themselves, the mean data points corresponding to each scan angle are determined; the scan data points are then multiplied by their corresponding weights to obtain the corresponding mean data points. The mean data is reconstructed using a first image reconstruction algorithm to generate a CT mean image.

3. The method according to claim 1, characterized in that, The step of reconstructing and generating a CT mean image using an image reconstruction algorithm based on the weights of each scan dataset and the scan dataset includes: Based on the weights of each scanned data point, a second image reconstruction algorithm is used to reconstruct the scanned data and generate a CT mean image.

4. The method according to claim 2, characterized in that, The step of reconstructing the mean data using the first image reconstruction algorithm to generate a CT mean image includes: Based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, and the mean value data, a CT mean image is reconstructed using the first image reconstruction algorithm.

5. The method according to claim 4, characterized in that, The step of reconstructing and generating a CT mean image using a first image reconstruction algorithm based on the pixel position corresponding to each scanning angle, the weight of each pixel, and the mean data includes: Based on the location of the point, determine the first value of the corresponding position in the mean data; The product of the first value and the weight of the corresponding pixel is added to the corresponding pixel to reconstruct the CT mean image.

6. The method according to claim 3, characterized in that, The step of reconstructing a CT mean image from the scan data using a second image reconstruction algorithm based on the weights of each scan data point includes: Based on the position of each pixel corresponding to each scanning angle, the weight of each pixel, the weight of each scan data, and the scan data, a CT mean image is reconstructed using a second image reconstruction algorithm.

7. The method according to claim 6, characterized in that, The process of reconstructing and generating a CT mean image using a second image reconstruction algorithm based on the pixel position corresponding to each scanning angle, the weight of each pixel, the weight of each scan data point, and the scan data point itself includes: Based on the location of the point, determine the second value of the corresponding position in the scanned data; The second value, the weight of the corresponding pixel, and the weight of the corresponding scanned data are multiplied and accumulated and added to the corresponding pixel to reconstruct the CT mean image.

8. A CT mean image generation device, characterized in that, The device includes: The first determining module is used to determine the number of each scanning angle during the multi-circle scanning process based on the scan data obtained from the multi-circle scanning. The second determining module is used to determine the weight of each scan data corresponding to each scan angle in the reconstruction process based on the number of each scan angle; The image reconstruction module is used to reconstruct and generate a CT mean image based on the weights of each scan data and the scan data itself, using an image reconstruction algorithm. When reconstructing using the image reconstruction algorithm, multiple scan data of the scanning angle are fused according to the weights of each scan data to generate the CT mean image.

9. An image scanning system, characterized in that, The system includes a scanning device for obtaining scan data through multi-circle scanning, and a CT mean image generation device as described in claim 8 connected to the scanning device.

10. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method according to any one of claims 1 to 7.

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