Scanning image generation method and device and computer equipment

Abstract

The invention relates to a scanning image generation method and device and computer equipment. The method comprises the following steps: acquiring original scanning data of a to-be-detected object, and acquiring a plurality of preset image reconstruction modes; obtaining reconstructed images obtained by performing image reconstruction based on each image reconstruction mode; the reconstructed image is obtained according to original scanning data; acquiring area selection information input for the plurality of reconstructed images, and acquiring image fusion areas corresponding to the plurality of reconstructed images according to the area selection information; performing image fusion processing on the plurality of image fusion regions to obtain a fused image, and taking the fused image as a target scanning image corresponding to the scanning imaging equipment; the target scanning image is used for performing health analysis on the to-be-detected object. By adopting the method, the diversity of the scanning images generated by the scanning equipment can be improved.

Description

Technical Field

[0001] The present application relates to the field of image processing technology, and in particular to a method, device and computer equipment for generating a scanned image. Background Art

[0002] With the development of medical technology, imaging scanning is becoming more and more important in the medical field. Scanned images can usually be used in the medical field as a medical auxiliary means. In the medical field, there are many scanning devices and image reconstruction methods, and the application scenarios are different. For example, medical scanning equipment includes positron emission computed tomography (PET), computed tomography (CT), magnetic resonance imaging (MRI), etc.; image reconstruction methods include analytical algorithms, iterative algorithms, etc.

[0003] In actual clinical applications, the application scenarios of the equipment and the expected image effects of each medical institution are also different. In general use, each medical institution will individually select the scanning equipment and image reconstruction method suitable for its institution.

[0004] In the prior art, if the object to be tested has multiple requirements, multiple scans and imaging are required to obtain the required diagnostic auxiliary results. This consumes a lot of imaging scanning time and cannot meet the result requirements at the same time. In the prior art, the scanned images generated by the scanning device cannot meet the diverse image requirements. Summary of the invention

[0005] Based on this, it is necessary to provide a scanning image generation method, device and computer equipment that can improve the diversity of scanning images in response to the above technical problems.

[0006] In a first aspect, the present application provides a method for generating a scanned image, the method comprising:

[0007] Acquire original scan data of the object to be detected, and acquire multiple pre-set image reconstruction modes;

[0008] Acquire a reconstructed image obtained by performing image reconstruction based on each image reconstruction mode; the reconstructed image is obtained according to the original scanning data;

[0009] Obtaining region selection information for a plurality of reconstructed image inputs, and obtaining image fusion regions corresponding to the plurality of reconstructed images according to the region selection information;

[0010] Image fusion processing is performed on multiple image fusion areas to obtain a fused image, and the fused image is used as a target scanning image corresponding to the scanning imaging device; the target scanning image is used to perform health analysis on the object to be detected.

[0011] In one embodiment, a plurality of pre-set image reconstruction modes are obtained, including:

[0012] Acquire a current image reconstruction strategy from a plurality of image reconstruction strategies, and acquire a plurality of image reconstruction parameter types pre-set by the current image reconstruction strategy; each image reconstruction parameter type is pre-associated with a plurality of image reconstruction parameters;

[0013] Obtaining a target image reconstruction parameter from image reconstruction parameters associated with each image reconstruction parameter type, respectively, and forming a current image reconstruction parameter set using each target image reconstruction parameter;

[0014] According to each current image reconstruction parameter set, a plurality of image reconstruction modes corresponding to the current image reconstruction strategy are obtained;

[0015] The multiple image reconstruction modes corresponding to each current image reconstruction strategy are used as the pre-set multiple image reconstruction modes.

[0016] In one embodiment, obtaining a reconstructed image obtained by performing image reconstruction based on each image reconstruction mode includes:

[0017] The original scan data and a plurality of pre-set image reconstruction modes are sent to a cloud server; the cloud server is used to reconstruct the original scan data using each image reconstruction mode to obtain a reconstructed image corresponding to each image reconstruction mode;

[0018] Receive multiple reconstructed images sent by the cloud server.

[0019] In one embodiment, obtaining the image fusion region corresponding to the plurality of reconstructed images according to the region selection information includes:

[0020] Filtering a target reconstructed image from a plurality of reconstructed images;

[0021] Extracting keyword information associated with the reconstructed image from the semantic information input by the target user;

[0022] Based on the keyword information associated with the reconstructed image, an image fusion region matching the keyword information is obtained from the target reconstructed image.

[0023] In one embodiment, screening a target reconstructed image from a plurality of reconstructed images comprises:

[0024] Obtaining a preset standard image and a pixel mean value of the preset standard image; the preset standard image is a standard image used for health analysis;

[0025] Obtaining a current reconstructed image and a pixel mean value of the current reconstructed image from a plurality of reconstructed images;

[0026] Determine the difference between the pixel mean of the current reconstructed image and the pixel mean of the preset standard image;

[0027] If the difference meets a preset difference threshold condition, the current reconstructed image is used as a candidate reconstructed image, and a target reconstructed image is determined based on the candidate reconstructed image.

[0028] In one embodiment, performing image fusion processing on multiple image fusion areas to obtain a fused image includes:

[0029] Get the weight corresponding to each image fusion area;

[0030] According to each weight, multiple image fusion areas are spliced ​​to obtain a fused image.

[0031] In one embodiment, after using the fused image as a target scan image corresponding to the scanning imaging device, the method further includes:

[0032] Using the target scan image as a sample scan image;

[0033] Input the original scan data into the image generation model to be trained, and output the predicted scan image through the image generation model;

[0034] The sample scanned images and the predicted scanned images are used to train the scanned image generation model to obtain a trained image generation model.

[0035] In one embodiment, after obtaining the trained image generation model, the following steps are included:

[0036] Acquiring detection data of the object to be detected by an imaging scanning device;

[0037] The detection data is input into the trained image generation model, and the target detection image is obtained through the trained image generation model. The target detection image is used to perform health analysis on the object to be detected.

[0038] In a second aspect, the present application further provides a scanning image generating device, the device comprising:

[0039] A reconstruction data acquisition module is used to acquire the original scan data of the object to be detected and to acquire a plurality of pre-set image reconstruction modes;

[0040] A reconstructed image acquisition module is used to acquire a reconstructed image obtained by performing image reconstruction based on each image reconstruction mode; the reconstructed image is obtained based on the original scanning data;

[0041] A fusion region acquisition module is used to acquire region selection information for a plurality of reconstructed images input, and acquire image fusion regions corresponding to the plurality of reconstructed images according to the region selection information;

[0042] The image fusion processing module is used to perform image fusion processing on multiple image fusion areas to obtain a fused image, and use the fused image as a target scanning image corresponding to the scanning imaging device; the target scanning image is used to perform health analysis on the object to be detected.

[0043] In a third aspect, the present application further provides a computer device, which includes a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of the above method when executing the computer program.

[0044] The above-mentioned scanning image generation method, device and computer equipment obtain the original scanning data of the object to be detected, and obtain multiple pre-set image reconstruction modes; obtain reconstructed images obtained by performing image reconstruction based on each image reconstruction mode; the reconstructed image is obtained according to the original scanning data; obtain the region selection information input for multiple reconstructed images, and obtain the image fusion region corresponding to the multiple reconstructed images according to the region selection information; perform image fusion processing on the multiple image fusion regions to obtain a fusion image, and use the fusion image as the target scanning image corresponding to the scanning imaging device; the target scanning image is used for health analysis of the object to be detected. Compared with the traditional technology, the present application obtains the corresponding reconstructed image through multiple image reconstruction modes, obtains the image fusion region corresponding to the multiple reconstructed images according to the region selection information, and can perform fusion processing on the multiple image fusion regions corresponding to the multiple image reconstruction modes to obtain a fusion image that fuses the reconstructed images corresponding to the multiple reconstruction modes, and can scan the target scanning image corresponding to the imaging device, thereby improving the diversity of the scanning images generated by the scanning device. BRIEF DESCRIPTION OF THE DRAWINGS

[0045] In order to more clearly illustrate the technical solutions in the embodiments of the present application or the related technologies, the drawings required for use in the embodiments or the related technical descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0046] Figure 1 A diagram showing an application environment of a scanning image generation method in an embodiment;

[0047] Figure 2 is a schematic flow chart of a scan image generating method in one embodiment;

[0048] Figure 3 A schematic flow chart of a step of obtaining image fusion regions corresponding to a plurality of reconstructed images according to region selection information in one embodiment;

[0049] Figure 4 A schematic flow chart of a step of selecting a target reconstructed image from a plurality of reconstructed images in another embodiment;

[0050] Figure 5 is a flow chart of the steps of processing the fused target scan image in one embodiment;

[0051] Figure 6 is a flowchart of a cloud-based medical image reconstruction, storage, screening, display and diagnosis method in one embodiment;

[0052] Figure 7 is a schematic flow chart of an image fusion step in one embodiment;

[0053] Figure 8 is a schematic flow chart of an image display step in one embodiment;

[0054] Fig. 9 is a structural block diagram of a scanning image generating device in one embodiment;

[0055] Fig.10 FIG. 4 is a diagram showing the internal structure of a computer device in one embodiment. DETAILED DESCRIPTION

[0056] In order to make the purpose, technical solution and advantages of the present application more clearly understood, the present application is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0057] The scanning image generation method provided in the embodiment of the present application can be applied to Figure 1 In the application environment shown. Among them, the terminal 102 communicates with the server 104 through the network. The data storage system can store the data that the server 104 needs to process. The data storage system can be integrated on the server 104, or it can be placed on the cloud or other network servers. The terminal 102 can obtain the original scan data, and can transmit the original scan data to the server 104, and the data storage system can store the original scan data. Among them, the terminal 102 can be but not limited to various personal computers, laptops, smart phones, tablet computers, Internet of Things devices and portable wearable devices. The server 104 can be implemented with an independent server or a server cluster consisting of multiple servers.

[0058] In an exemplary embodiment, Figure 2 As shown, a scanning image generation method is provided, and the method is applied to Figure 1 The terminal 102 in the example is used as an example to illustrate, and the steps include S202 to S208. Among them:

[0059] S202, obtaining original scan data of the object to be detected, and obtaining a plurality of pre-set image reconstruction modes.

[0060] The raw scan data may be raw data obtained by scanning the object to be detected by an imaging scanning device, for example, the raw scan data may be raw scan data acquired by an imaging scanning device. The object to be detected may be an object for generating a scanned image, and a scanned image of the object to be detected may be generated based on the scanned data of the object to be detected. The image reconstruction mode may correspond to different image reconstruction algorithms. The image reconstruction mode may be a mode for performing image reconstruction, for example, in a certain image reconstruction mode, the raw scan data may be reconstructed according to the reconstruction algorithm and reconstruction parameters corresponding to the image reconstruction mode.

[0061] Exemplarily, the terminal may be an imaging scanning device, or a terminal for data processing that is in communication with the imaging scanning device. The terminal for data processing may perform imaging scanning on the object to be detected through the imaging scanning device to obtain raw scan data of the object to be detected. The terminal may determine a plurality of pre-set image reconstruction modes for image reconstruction of the raw scan data.

[0062] S204, obtaining a reconstructed image obtained by performing image reconstruction based on each image reconstruction mode; the reconstructed image is obtained according to the original scanning data.

[0063] The reconstructed image may be an image obtained by reconstructing the original scan data according to an image reconstruction mode.

[0064] Exemplarily, the terminal can send the image reconstruction mode set by the user on the terminal and the original scanning data of the object to be detected to the cloud server. Through the cloud server, the original scanning data can be reconstructed according to a variety of different image reconstruction modes to obtain reconstructed images corresponding to the various different image reconstruction modes.

[0065] S206, obtaining region selection information input for the multiple reconstructed images, and obtaining image fusion regions corresponding to the multiple reconstructed images according to the region selection information.

[0066] The region selection information may be used to select information of a partial region in the reconstructed image. For example, the region selection information may be in the form of voice or text as an information carrier.

[0067] Exemplarily, the terminal can obtain the region selection information for dividing the image region by manually selecting a local region, manually outlining a region, automatically outlining a region using a numerical or morphological method, identifying a local region using an artificial intelligence image recognition method, obtaining through big data analysis, combining voice and text description with artificial intelligence to obtain relevant local regions, etc. Further, the terminal can determine the image fusion regions that need to be fused in multiple reconstructed images according to the region selection information.

[0068] S208, performing image fusion processing on multiple image fusion areas to obtain a fused image, and using the fused image as a target scanning image corresponding to the scanning imaging device; the target scanning image is used to perform health analysis on the object to be detected.

[0069] The fused image may be an image obtained by fusing image fusion regions corresponding to a plurality of different image reconstruction modes. The target scanning image may be an image used to analyze whether the object to be detected is abnormal.

[0070] Exemplarily, image fusion regions obtained in reconstructed images corresponding to a plurality of different image reconstruction modes may be subjected to image fusion to obtain a fused image. The fused image is composed of selected image regions of reconstructed images corresponding to a plurality of different image reconstruction modes, so that the diversity of image information of the fused image can be achieved. The image information obtained by a plurality of different image reconstruction modes may be fused into one scanned image, which may reduce the number of times the object to be detected is scanned and detected, and improve the efficiency of the scanned image analysis.

[0071] In this embodiment, the original scanning data of the object to be detected is obtained, and multiple pre-set image reconstruction modes are obtained; reconstructed images obtained by performing image reconstruction based on each image reconstruction mode are obtained; the reconstructed image is obtained according to the original scanning data; the region selection information input for multiple reconstructed images is obtained, and the image fusion region corresponding to the multiple reconstructed images is obtained according to the region selection information; the multiple image fusion regions are subjected to image fusion processing to obtain a fusion image, and the fusion image is used as the target scanning image corresponding to the scanning imaging device; the target scanning image is used for health analysis of the object to be detected. Compared with the conventional technology, the present embodiment obtains corresponding reconstructed images through multiple image reconstruction modes, obtains image fusion regions corresponding to multiple reconstructed images according to the region selection information, and can perform fusion processing on multiple image fusion regions corresponding to multiple image reconstruction modes to obtain a fusion image that fuses the reconstructed images corresponding to the multiple reconstruction modes, and can scan the target scanning image corresponding to the imaging device, thereby improving the diversity of the scanning images generated by the scanning device. At the same time, it can reduce the number of scanning detections of the object to be detected and improve the efficiency of scanning image analysis.

[0072] In an exemplary embodiment, a plurality of preset image reconstruction modes are obtained, including:

[0073] Acquire a current image reconstruction strategy from a plurality of image reconstruction strategies, and acquire a plurality of image reconstruction parameter types pre-set by the current image reconstruction strategy; each image reconstruction parameter type is pre-associated with a plurality of image reconstruction parameters;

[0074] Obtaining a target image reconstruction parameter from image reconstruction parameters associated with each image reconstruction parameter type, respectively, and forming a current image reconstruction parameter set using each target image reconstruction parameter;

[0075] According to each current image reconstruction parameter set, a plurality of image reconstruction modes corresponding to the current image reconstruction strategy are obtained;

[0076] The multiple image reconstruction modes corresponding to each current image reconstruction strategy are used as the pre-set multiple image reconstruction modes.

[0077] Among them, the current image reconstruction strategy can be any one of a plurality of image reconstruction strategies. For example, the image reconstruction strategy can be an analytical reconstruction method, an iterative reconstruction method, a regularized reconstruction method, an AI reconstruction method, etc. The image reconstruction parameter type can refer to the type of the image reconstruction parameter, for example, the image reconstruction parameter type can be a subset, number of iterations, post-filtering, correction method, resolution, time cutting method, motion correction scheme, etc. The image reconstruction parameter can be a parameter for image reconstruction, for example, the number of iterations can be any number from 1 to 15, etc. The target image reconstruction parameter can be a selected image reconstruction parameter. The current image reconstruction parameter set can include multiple image reconstruction parameters.

[0078] Exemplarily, any one image reconstruction strategy can be confirmed from a plurality of image reconstruction strategies for processing. For each image reconstruction strategy, a plurality of image reconstruction parameter types pre-set for the image reconstruction strategy can be obtained, and a target image reconstruction parameter is selected from each image reconstruction parameter type, and a target image reconstruction parameter corresponding to each image reconstruction parameter type is used to obtain an image reconstruction parameter set for the selected round. The target image reconstruction parameter can be repeatedly selected multiple times to obtain multiple image reconstruction parameter sets. Based on the multiple image reconstruction parameter sets corresponding to the current image reconstruction strategy, a plurality of image reconstruction modes corresponding to the current image reconstruction strategy can be obtained, and the k image reconstruction modes corresponding to the image reconstruction strategy can be used as the pre-set multiple image reconstruction modes. Furthermore, the m image reconstruction modes corresponding to the n image reconstruction strategies can be used as the pre-set multiple image reconstruction modes. Wherein, m>n, and m can be equal to n×k.

[0079] In this embodiment, a target image reconstruction parameter is obtained from each image reconstruction parameter associated with each image reconstruction parameter type, and each target image reconstruction parameter is used to form a current image reconstruction parameter set; based on each current image reconstruction parameter set, a plurality of image reconstruction modes corresponding to the current image reconstruction strategy are obtained, and a plurality of pre-set image reconstruction modes can be obtained, thereby improving the diversity of reconstruction of scanned images.

[0080] In an exemplary specific embodiment, for an image reconstruction method, corresponding image reconstruction parameters are used, and the image reconstruction parameter settings can be set according to equal spacing and traversal settings. For example, the OSEM (Ordered Subset Expectation Maximum) reconstruction method can be selected, and its reconstruction parameters include: different subsets, such as 1, 2, 4, 5, 10, 20 subsets; different numbers of iterations, such as 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15 iterations; different post-filters, such as Gaussian post-filters, NLM (Non-Local Mean) filtering; the reconstruction parameters corresponding to the post-filter can also be set, such as the Gaussian post-filter half-height width (FWHM) is 1, 2, 3, 4, 5, 6, 7, 8, 9, 10mm; d) different correction methods, such as attenuation correction method, normalization correction method, scattering correction method, random correction method; different resolutions, such as selecting reconstruction FOV and reconstruction matrix, there are many corresponding options. For example, for 600FOV reconstruction, the reconstruction matrix can be 128*128, 192*192, 256*256, 360*360, 600*600; different time cutting methods, such as cutting time of 120s and repeated 10 times, cutting time of 5mins and repeated 10 times; different motion correction schemes, such as head motion correction and body motion correction.

[0081] By traversing all reconstruction parameters, for the OSEM reconstruction algorithm, there are at least 7 types of image reconstruction parameters that need to be selected. Each image reconstruction parameter type corresponds to 5-20 groups of different values, so at least 3,000 groups of reconstructions need to be issued. These 3,000 groups of reconstructions can be distributed to cloud computers for reconstruction, and at least 3,000 groups of images can be output after reconstruction. Different reconstruction algorithms can be selected, and at least 10,000 groups of images may be reconstructed and output.

[0082] In this embodiment, multiple image reconstruction strategies are obtained through multiple image reconstruction parameter types and different image reconstruction parameters corresponding to each image reconstruction parameter type, thereby improving the diversity of scanned images.

[0083] In an exemplary embodiment, obtaining a reconstructed image obtained by performing image reconstruction based on each image reconstruction mode includes:

[0084] The original scan data and a plurality of pre-set image reconstruction modes are sent to a cloud server; the cloud server is used to reconstruct the original scan data using each image reconstruction mode to obtain a reconstructed image corresponding to each image reconstruction mode;

[0085] Receive multiple reconstructed images sent by the cloud server.

[0086] Among them, the server 104 can be a cloud server, and the cloud server can be a cloud architecture server that coordinates computing resources.

[0087] Exemplarily, the terminal may send the original scan data and a plurality of pre-set image reconstruction modes to the cloud server. The cloud server may reconstruct the original scan data according to the plurality of image reconstruction modes, obtain the reconstructed images corresponding to the respective image reconstruction modes, and obtain the reconstructed images of a plurality of different image reconstruction modes. The cloud server may return the reconstructed image to the terminal, and the terminal may receive the plurality of reconstructed images sent by the cloud server. In this way, the efficiency of image reconstruction may be improved by performing image reconstruction in a plurality of image reconstruction modes through the cloud server.

[0088] In this embodiment, the original scan data and the pre-set multiple image reconstruction modes are sent to the cloud server; the cloud server uses each image reconstruction mode to reconstruct the original scan data, and the reconstructed image corresponding to each image reconstruction mode can be obtained. In this way, the computing resources of the cloud server can be effectively utilized, the computing power of the terminal can be reduced, and the efficiency of reconstructing the image can be improved.

[0089] In an exemplary specific embodiment, the original scan data and reconstruction parameters are distributed based on the cloud server architecture: For example, the cloud server architecture coordinates computer resources and can be divided into: the acquisition interface end is responsible for monitoring the acquisition status of new data and starting the overall process of image reconstruction and image fusion. The central command end is responsible for querying data, sending reconstruction commands, sending fusion commands, including real-time query data, specifying reconstruction parameters, specifying fusion parameters, etc., and a database and network connection settings can be established. The command distribution end is responsible for distributing reconstruction commands, coordinating resources, and coordinating data transmission and computer allocation. The data storage end is responsible for storing the original scan data and intermediate image data. The computing end is responsible for image reconstruction. The image storage end is responsible for storing all reconstructed images generated and the final selected reconstructed image. The cloud server reconstructs and generates images, reconstructs them in the computing end, receives all the contents transmitted by the central command distribution end before reconstruction, and starts reconstruction according to the transmitted parameters. The image is transmitted to the target machine and the image is transmitted to the image storage end. This process can be transmitted through the intranet or the Internet.

[0090] In this embodiment, the efficiency of image reconstruction can be improved by performing image reconstruction through multi-terminal coordination.

[0091] In an exemplary embodiment, Figure 3 As shown, the image fusion region corresponding to the multiple reconstructed images is obtained according to the region selection information, including:

[0092] S302, selecting a target reconstructed image from a plurality of reconstructed images;

[0093] S304, extracting keyword information associated with the reconstructed image from the semantic information input by the target user;

[0094] S306: Based on the keyword information associated with the reconstructed image, an image fusion region matching the keyword information is acquired from the target reconstructed image.

[0095] The target reconstructed image may be a target image that needs to be fused. The semantic information may be information input by a user, for example, the semantic information may be voice information or text information. The keyword information may be information related to image fusion, for example, it may be information of a region that needs to be fused, or it may be feature information of an image.

[0096] Exemplarily, a reconstructed image that meets a pre-condition can be screened from a plurality of reconstructed images as a target reconstructed image. In response to an input operation of the target user, semantic information corresponding to the target user can be obtained, and keyword information associated with the reconstructed image can be extracted from the semantic information input by the target user. Further, according to the keyword information associated with the reconstructed image, an image fusion region matching the keyword information can be divided from the target reconstructed image, and the image fusion region matching the keyword information can be segmented to perform image fusion based on the image fusion region. In this way, the image region to be fused can be accurately obtained based on the semantic information input by the user, thereby improving the accuracy of image fusion.

[0097] In an exemplary specific embodiment, obtaining the image fusion area may include: obtaining the image fusion area according to clinical needs. Since the local area of ​​the image may be the focus of attention, the image fusion software can be used to select the designated area for fusion. This area can be the entire image, a certain organ area, or a certain lesion area. The selection of the image fusion area can be taken as an example of the lesion area: using the naked eye to observe and select the local area; manually outlining the area; automatically outlining the area using numerical or morphological methods; using artificial intelligence image recognition methods to identify the local area; obtaining through big data analysis; obtaining the relevant local area through voice and text description combined with artificial intelligence. In this way, the image area that needs to be fused can be accurately obtained.

[0098] In an exemplary specific embodiment, obtaining the image fusion region corresponding to the multiple reconstructed images according to the region selection information includes:

[0099] The input from the control end may include button selection of the image, description of the voice sentence, or text description. Use artificial intelligence methods to perform semantic analysis on the input. For example, in practical applications, through spoken expression: "Select images with low noise in the liver and high lesion pixel values", natural language processing is used to obtain the keywords "liver", "low noise", "high lesion pixel value", and "image" as key information for the next step of image processing. Extract keywords related to fusion from the above key information as information and parameters for image fusion. This information and parameters can correspond one-to-one with the information parameters in the image screening process. Select the fusion method, and the traditional fusion method and the artificial intelligence fusion method can be used for fusion.

[0100] In this embodiment, manual input work is reduced. For example, in ultrasound examination, voice description is used to obtain a screenshot of the required image portion and generate a text report, which can accurately fuse the required image area, thereby improving the accuracy of image fusion.

[0101] In an exemplary embodiment, Figure 4 As shown, a target reconstructed image is obtained by screening from multiple reconstructed images, including:

[0102] S402, obtaining a preset standard image and a pixel mean value of the preset standard image; the preset standard image is a standard image used for health analysis;

[0103] S404, obtaining a current reconstructed image and a pixel mean value of the current reconstructed image from a plurality of reconstructed images;

[0104] S406, determining the difference between the pixel mean of the current reconstructed image and the pixel mean of the preset standard image;

[0105] S408: If the difference satisfies a preset difference threshold condition, the current reconstructed image is used as a candidate reconstructed image, and a target reconstructed image is determined based on the candidate reconstructed image.

[0106] Among them, the preset standard image may be a gold standard image. The pixel mean may be a local mean index of the image. The current reconstructed image may be any one of the multiple reconstructed images. The difference threshold condition may be a condition set according to the difference threshold, or may be a judgment condition set for the difference. For example, the difference threshold condition may indicate whether the difference is greater than the difference threshold. The difference threshold may be a threshold set for the difference. The candidate reconstructed image may be a reconstructed image obtained by screening based on the difference threshold.

[0107] Exemplarily, a preset standard image for reconstructed image screening can be obtained, and the pixel mean of the preset standard image can be obtained. Any reconstructed image can be determined from multiple reconstructed images, and for any reconstructed image, the pixel mean of the reconstructed image can be obtained, and the difference between the pixel mean of the reconstructed image and the pixel mean of the preset standard image can be further determined, and the difference can be compared with a preset difference threshold. If the difference satisfies the difference threshold condition, the reconstructed image can be used as a candidate reconstructed image. In this way, at least one candidate reconstructed image can be obtained, and further based on at least one candidate reconstructed image, a target reconstructed image can be obtained. For example, it can be determined according to actual conditions whether images with large differences from the preset standard image need to be screened or reconstructed images with small differences from the preset standard image need to be screened. As an example, in the case where images with large differences from the preset standard image need to be screened, the difference threshold condition can be set to a difference greater than a preset difference threshold. If the difference is greater than the preset difference threshold, the reconstructed image can be used as a candidate reconstructed image. As an example, when it is necessary to screen images with small differences from a preset standard image, the difference threshold condition can be set to a difference value less than or equal to a preset difference threshold value, and if the difference value is less than or equal to the preset difference threshold value, the reconstructed image can be used as a candidate reconstructed image. The difference threshold condition can be set according to the reconstructed image actually required.

[0108] In this embodiment, by determining the difference between the pixel mean of the current reconstructed image and the pixel mean of the preset standard image; and if the difference satisfies the preset difference threshold condition, the current reconstructed image is used as a candidate reconstructed image, and an accurate target reconstructed image can be obtained. In this way, by comparing the pixel means, the target reconstructed image can be effectively and accurately determined, thereby improving the accuracy of image fusion.

[0109] In an exemplary specific embodiment, if the original scan data of the object to be detected is reconstructed in a multi-image reconstruction mode, it is assumed that 3000 groups of images can be obtained. The target reconstructed image can be screened from multiple reconstructed images, which can include the following steps: selecting a gold standard image, which can be defined by oneself and can be obtained by big data search. Extracting the mean index of a certain local area of ​​all reconstructed images, and classifying the images according to the clustering method. In each classification, the small differences, large differences and huge differences of all images relative to the gold standard image are identified by big data technology, and grouped, or more specifically, the difference value of each reconstructed image relative to the gold standard image is calculated. By setting image screening parameters, such as a difference value greater than 0.5, images with a difference greater than 0.5 are screened out from all images, saved and marked. The marking content can be in the form of an image, a stylistic form or a voice form. In this way, different groups of images in different classes after screening can be obtained, and the amount of images can be greatly reduced. The screened target image can be used for further image display.

[0110] In this embodiment, the reconstructed image can be screened by using the gold standard image, thereby improving the accuracy of the screened target reconstructed image.

[0111] In an exemplary embodiment, performing image fusion processing on multiple image fusion areas to obtain a fused image includes:

[0112] Get the weight corresponding to each image fusion area;

[0113] According to each weight, multiple image fusion areas are spliced ​​to obtain a fused image.

[0114] The weight may be a weight for fusing the image fusion area.

[0115] Exemplarily, the terminal may determine the weight corresponding to each image fusion region, and may perform fusion and splicing processing on multiple image fusion regions according to the weight corresponding to each image fusion region to obtain a fused image. For example, the pixel values ​​of each image fusion region may be processed respectively according to the weight corresponding to each image fusion region, and each image fusion region after pixel processing may be spliced ​​and fused to obtain an accurate fused image.

[0116] In this embodiment, by obtaining the weights corresponding to the image fusion regions and performing splicing processing on the multiple image fusion regions according to the weights, a fused image can be obtained, thereby improving the accuracy of the fused image.

[0117] In an exemplary specific embodiment, image fusion processing can be performed on multiple image fusion regions through fusion region indicators. The fusion region indicators are obtained as needed, and the indicators may include: average value in the region; noise in the region; contrast between the average value in the region and the surrounding area; weighted mean value based on features in the region (the weight for each point is different).

[0118] Select the fusion method: Based on the needs, the fusion method can be linear ratio fusion; image feature fusion; pathological feature fusion.

[0119] The fusion process may include: for the fusion process, different methods may be used to make the fused image close to the medical image; for example, Gaussian post-filter blur may be added before image fusion, and weighting coefficients may be used during the image fusion process.

[0120] In this embodiment, fusion is performed by fusion region indicators and a fusion method is provided for selection, so that different reconstructed images under different actual situations can be effectively and accurately fused. At the same time, by adding Gaussian post-filter blur before fusion and using weighting coefficients during the image fusion process, an accurate fused image can be obtained, thereby improving the quality of the fused image.

[0121] In an exemplary embodiment, after the fused image is used as a target scan image corresponding to the scanning imaging device, the method further includes:

[0122] Using the target scan image as a sample scan image;

[0123] Input the original scan data into the image generation model to be trained, and output the predicted scan image through the image generation model;

[0124] The sample scanned images and the predicted scanned images are used to train the scanned image generation model to obtain a trained image generation model.

[0125] The target scan image may be an image used to analyze whether the object to be detected is abnormal. The sample scan image may be a sample image used for model training. The predicted scan image may be an image predicted and generated by the image generation model.

[0126] Exemplarily, the target scan image can be used as a sample image for training the image generation model, and the original scan data can be input into the image generation model to be trained, and the predicted scan image can be generated by the image generation model prediction. The scan image generation model can be trained based on the difference between the sample scan image and the predicted scan image, and a trained image generation model can be obtained. Furthermore, the trained image generation model can be used to generate target scan images from the original scan data of other objects to be detected, thereby improving the efficiency of generating target scan images.

[0127] In this embodiment, the original scanning data is input into the image generation model to be trained, the predicted scanning image is predicted, and the image generation model is trained using the target scanning image and the predicted scanning image, so that a trained image generation model can be obtained, thereby improving the accuracy of the image generation model training and further improving the efficiency of the target scanning image generation.

[0128] In an exemplary embodiment, if the fused target scan image is determined to be useful for assisting clinical medical diagnosis, further processing may be performed. The fused target scan image may be used as a training set to improve the quality and accuracy of the reconstructed image by learning the features of the fused image. Figure 5 As shown, the following steps may be included:

[0129] S501, obtain fused image; S502, obtain original scan data or intermediate reconstructed image; S503, establish artificial intelligence network; S504, train to obtain network parameters; S505, network application, image generation; S506, image display.

[0130] Obtaining original scan data or intermediate reconstructed images includes: scanning to obtain original scan data, which can be used as network input; rearranging or reconstructing the original scan data, and obtaining the rearrangement result or reconstruction intermediate result to obtain a target scan image, which can be used as network input.

[0131] Through the target scan image, the artificial intelligence network is established and trained, including: selecting a suitable artificial intelligence network and preparing for training. For example: AUTOMAP network, using multiple fully connected layers, connecting sparse encoder-decoders, learning the mapping relationship from the original scan data chord diagram to the image space; DirectPET network, using the designed Radon inverse transform network, learning the mapping from the original scan data and attenuation map to the multi-layer image; FastPET network, using Radon inverse transform layer, encoder-decoder layer, from the original scan data chord diagram and the original scan data rearrangement intermediate image histo image to generate the final image; CNN: learning the mapping from the intermediate result of the reconstructed image to the final image; training to obtain network parameters; through training the network, obtaining appropriate network parameters.

[0132] Network application and image generation, including: applying artificial intelligence network in a single image reconstruction process to generate the required predicted scan image.

[0133] In this embodiment, the quality and accuracy of the reconstructed image can be improved by using the fused target scanned image as a training set and learning the features of the fused image.

[0134] In an exemplary embodiment, after obtaining the trained image generation model, the following steps are included:

[0135] Acquiring detection data of the object to be detected by an imaging scanning device;

[0136] The detection data is input into the trained image generation model, and the target detection image is obtained through the trained image generation model. The target detection image is used to perform health analysis on the object to be detected.

[0137] The detection data may be original scan data or may be original scan data.

[0138] Exemplarily, the terminal can obtain detection data of the object to be detected through an imaging scanning device, and can input the detection data into a trained image generation model. Through the trained image generation model, a target detection image is predicted, and the target detection image is used to assist in health analysis of the object to be detected.

[0139] In this embodiment, the target detection image is generated by using a pre-trained image generation model, which can improve the efficiency of generating the target detection image and ensure the quality of the target detection image.

[0140] In an exemplary embodiment, the scanning image generation method may further include: raw scanning data quality control, image storage and screening, image display and image-assisted diagnosis.

[0141] Get raw scan data from imaging scanners, complete quality control and store, including:

[0142] Set the parameters that need to be quality controlled: compatibility check parameters between data and reconstruction environment; data defects, such as whether there is incomplete data recorded; detector-related data loss, such as whether there is detector damage, energy drift; data volume; data noise level, etc.

[0143] Generate quality control related reports, which can be obtained by analyzing and comparing big data: absolute quantitative reports; relative reports after comparing with all other data or gold standard data, which can be defined by the gold standard data. Store the original scan data.

[0144] The steps of image storage and screening include:

[0145] Use blockchain to store data: The blockchain has N nodes, and each node is connected in sequence. Different storage spaces and importance levels are assigned to different nodes. For example, according to the storage duration, data is divided into permanent data, long-term data, and temporary data. According to the importance, data is divided into important data, generally important data, and unimportant data, and accessed in blocks. Data storage expansion can be achieved by adding blockchains.

[0146] Analyze images using big data analysis methods: use data mining to refine image information; perform distributed data processing to increase speed; and conduct information image interoperability comparison based on the Internet.

[0147] In this way, with the promotion of cloud computing, big data analysis has become more important, and blockchain data storage can also become a data storage solution. In the reconstruction process, after obtaining the original scan data and a large number of images, data storage and image analysis become difficult. We can link the storage and analysis of a large amount of original scan data and a large number of images in medicine with big data and blockchain, which is convenient for the preservation and optimization of medical resources.

[0148] The step of image display includes: obtaining the reconstructed image in the cloud from the acquisition interface end and displaying it. Special image fusion software can be designed for display to facilitate image fusion operation. The acquisition interface end obtains the image display. The image storage end and the acquisition interface end perform high-speed image set transmission and display, so that the image can be directly pulled and viewed at the acquisition interface end.

[0149] The steps of image-assisted diagnosis include: When image fusion reaches the expected demand standard, the automatic diagnosis method will become an effective tool to assist doctors in reading films. The diagnosis method can combine the advantages of big data to give corresponding judgments. Obtain diagnostic parameters, such as disease type, disease classification, treatment plan, probability of treatment success, life expectancy, etc. Obtain a diagnostic report, for example, by comparing the similarity of the reconstructed image with previous cases, combining the imaging performance analysis of different detectors and the patient's physiological and pathological records, to obtain a simulated diagnosis result.

[0150] In this embodiment, through the steps of raw scan data quality control, image storage and screening, image display and image-assisted diagnosis, the reconstructed image can meet different needs and improve the diversity of the reconstructed image.

[0151] In an exemplary embodiment, Figure 6 As shown, a cloud-based medical image reconstruction, storage, screening, display and diagnosis method is provided, including:

[0152] S601, start the transmission of CT scan; S602, obtain the original scan data; S603, set the quality control analysis parameters of the original scan data; S604, perform quality control analysis on the original scan data and obtain a report; S605, store the original scan data; S606, set the reconstruction parameter information; S607, distribute the original scan data and reconstruction parameters based on the cloud architecture; S608, reconstruct and generate images in the cloud; S609, set image storage rules; S610; image storage; S611, set image screening conditions; S612, image screening; S613, set image display conditions; S614, image display; S615, set image diagnosis conditions; S616, obtain image diagnosis report.

[0153] like Figure 7 As shown, the image fusion steps include: S701, image screening is successful; S702, control end input, including options, voice or text description; S703, semantic analysis of the input through artificial intelligence methods; S704, obtaining image fusion information and parameters; S705, selecting a fusion method; S706, performing fusion; S707, image display.

[0154] In this embodiment, the batch image information processing capabilities of the cloud and big data, personalized image fusion display, and automated auxiliary diagnosis are combined to provide a solution that meets diverse clinical needs. Medical reconstruction distributes tasks based on the cloud to achieve rapid batch reconstruction of different images. Batch image screening and processing and personalized fusion display and diagnosis meet different clinical needs. The choice of medical image processing is opened to a wider group, stimulating the optimal performance of medical images for diagnosis and treatment.

[0155] In an exemplary embodiment, a medical processing software application can be used in communication devices such as mobile phones, tablets and computers. The entire system can be connected via a network, so an application can be designed to make image processing operations more convenient. The application can provide a graphical interface for users to use. The application design can provide different processing of medical images, allowing users to perform remote image reading and diagnosis anytime and anywhere. The specific process is as follows Figure 8 As shown: S801, cloud image storage; S802, mobile phone, tablet and other communication devices browse images; S803, the communication device sends commands; S804, cloud image processing; S805, communication device image display.

[0156] It should be understood that, although the various steps in the flowcharts involved in the above-mentioned embodiments are displayed in sequence according to the indication of the arrows, these steps are not necessarily executed in sequence according to the order indicated by the arrows. Unless there is a clear explanation in this article, the execution of these steps does not have a strict order restriction, and these steps can be executed in other orders. Moreover, at least a part of the steps in the flowcharts involved in the above-mentioned embodiments can include multiple steps or multiple stages, and these steps or stages are not necessarily executed at the same time, but can be executed at different times, and the execution order of these steps or stages is not necessarily to be carried out in sequence, but can be executed in turn or alternately with other steps or at least a part of the steps or stages in other steps.

[0157] Based on the same inventive concept, the embodiment of the present application also provides a scanning image generation device for implementing the scanning image generation method involved above. The implementation solution provided by the device to solve the problem is similar to the implementation solution recorded in the above method, so the specific limitations in one or more scanning image generation device embodiments provided below can refer to the limitations of the scanning image generation method above, and will not be repeated here.

[0158] In an exemplary embodiment, Fig. 9 As shown, a scanning image generating device 900 is provided, comprising: a reconstruction data acquisition module 910, a reconstruction image acquisition module 920, a fusion region acquisition module 930 and an image fusion processing module 940, wherein:

[0159] The reconstruction data acquisition module 910 is used to acquire the original scan data of the object to be detected, and to acquire a plurality of preset image reconstruction modes.

[0160] The reconstructed image acquisition module 920 is used to acquire a reconstructed image obtained by performing image reconstruction based on each image reconstruction mode; the reconstructed image is obtained based on the original scanning data.

[0161] The fusion region acquisition module 930 is used to obtain region selection information input for multiple reconstructed images, and obtain image fusion regions corresponding to the multiple reconstructed images according to the region selection information.

[0162] The image fusion processing module 940 is used to perform image fusion processing on multiple image fusion areas to obtain a fused image, and use the fused image as a target scanning image corresponding to the scanning imaging device; the target scanning image is used to perform health analysis on the object to be detected.

[0163] In an exemplary embodiment, the reconstruction data acquisition module includes a reconstruction strategy acquisition unit, a reconstruction parameter set determination unit, a reconstruction mode determination unit and a preset mode determination unit.

[0164] The reconstruction strategy acquisition unit is used to acquire the current image reconstruction strategy from a plurality of image reconstruction strategies, and to acquire a plurality of image reconstruction parameter types pre-set for the current image reconstruction strategy; each image reconstruction parameter type is pre-associated with a plurality of image reconstruction parameters. The reconstruction parameter set determination unit is used to acquire a target image reconstruction parameter from the image reconstruction parameters associated with each image reconstruction parameter type, respectively, and to form the current image reconstruction parameter set using each target image reconstruction parameter. The reconstruction mode determination unit is used to acquire a plurality of image reconstruction modes corresponding to the current image reconstruction strategy according to each current image reconstruction parameter set. The preset mode determination unit is used to use the plurality of image reconstruction modes corresponding to each current image reconstruction strategy as a plurality of preset image reconstruction modes.

[0165] In an exemplary embodiment, the reconstructed image acquisition module includes a data sending unit and an image receiving unit.

[0166] The data sending unit is used to send the original scan data and a plurality of pre-set image reconstruction modes to the cloud server; the cloud server is used to reconstruct the original scan data using each image reconstruction mode to obtain a reconstructed image corresponding to each image reconstruction mode. The image receiving unit is used to receive the plurality of reconstructed images sent by the cloud server.

[0167] In an exemplary embodiment, the fusion region acquisition module includes a target image screening unit, a keyword extraction unit and a fusion region determination unit.

[0168] The target image screening unit is used to screen a target reconstructed image from a plurality of reconstructed images. The keyword extraction unit is used to extract keyword information associated with the reconstructed image from the semantic information input by the target user. The fusion region determination unit is used to obtain an image fusion region matching the keyword information from the target reconstructed image based on the keyword information associated with the reconstructed image.

[0169] In an exemplary embodiment, the target image screening unit includes a standard mean value acquisition unit, a current mean value acquisition unit, a difference value acquisition unit and a candidate image unit.

[0170] The standard mean acquisition unit is used to acquire a preset standard image and a pixel mean of the preset standard image; the preset standard image is a standard image used for health analysis. The current mean acquisition unit is used to acquire a current reconstructed image and a pixel mean of the current reconstructed image from a plurality of reconstructed images. The difference acquisition unit is used to determine the difference between the pixel mean of the current reconstructed image and the pixel mean of the preset standard image. The candidate image unit is used to use the current reconstructed image as a candidate reconstructed image if the difference meets a preset difference threshold condition, and determine a target reconstructed image based on the candidate reconstructed image.

[0171] In an exemplary embodiment, the image fusion processing module includes a weight acquisition unit and a fusion image determination unit.

[0172] The weight acquisition unit is used to acquire the weight corresponding to each image fusion region. The fusion image determination unit is used to perform splicing processing on multiple image fusion regions according to each weight to obtain a fusion image.

[0173] In an exemplary embodiment, the apparatus further includes a sample scan image determination unit, a predicted scan image acquisition unit, and a model training unit.

[0174] The sample scan image determination unit is used to use the target scan image as the sample scan image. The predicted scan image acquisition unit is used to input the original scan data into the image generation model to be trained, and output the predicted scan image through the image generation model. The model training unit is used to train the scan image generation model using the sample scan image and the predicted scan image to obtain a trained image generation model.

[0175] In an exemplary embodiment, the device further includes a detection data acquisition unit and a target detection image acquisition unit.

[0176] The detection data acquisition unit is used to acquire detection data of the object to be detected through an imaging scanning device. The target detection image acquisition unit is used to input the detection data into the trained image generation model, and acquire the target detection image through the trained image generation model. The target detection image is used to perform health analysis on the object to be detected.

[0177] Each module in the above-mentioned scanning image generation device can be implemented in whole or in part by software, hardware or a combination thereof. Each module can be embedded in or independent of a processor in a computer device in the form of hardware, or can be stored in a memory in a computer device in the form of software, so that the processor can call and execute the operations corresponding to each module.

[0178] In an exemplary embodiment, a computer device is provided. The computer device may be a server, and its internal structure diagram may be as shown in FIG. Fig.10 As shown. The computer device includes a processor, a memory, an input / output interface (Input / Output, referred to as I / O) and a communication interface. The processor, the memory and the input / output interface are connected through a system bus, and the communication interface is connected to the system bus through the input / output interface. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, a computer program and a database. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The database of the computer device is used to store original scan data. The input / output interface of the computer device is used to exchange information between the processor and an external device. The communication interface of the computer device is used to communicate with an external terminal through a network connection. When the computer program is executed by the processor, a method for generating a scanned image is implemented.

[0179] Those skilled in the art will understand that Fig.10 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0180] In one embodiment, a computer device is further provided, including a memory and a processor, wherein a computer program is stored in the memory, and the processor implements the steps in the above method embodiments when executing the computer program.

[0181] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored. When the computer program is executed by a processor, the steps in the above-mentioned method embodiments are implemented.

[0182] In one embodiment, a computer program product is provided, including a computer program, which implements the steps in the above method embodiments when executed by a processor.

[0183] It should be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for analysis, stored data, displayed data, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties, and the collection, use and processing of relevant data must comply with relevant regulations.

[0184] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to the memory, database or other medium used in the embodiments provided in the present 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, optical memory, high-density embedded non-volatile memory, resistive random access memory (ReRAM), magnetoresistive random access memory (MRAM), ferroelectric random access memory (FRAM), phase change memory (PCM), graphene memory, etc. Volatile memory can include random access memory (RAM) or external cache memory, etc. As an illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM). The database involved in each embodiment provided in this application may include at least one of a relational database and a non-relational database. Non-relational databases may include distributed databases based on blockchains, etc., but are not limited to this. The processor involved in each embodiment provided in this application may be a general-purpose processor, a central processing unit, a graphics processor, a digital signal processor, a programmable logic device, a data processing logic device based on quantum computing, etc., but are not limited to this.

[0185] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, 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.

[0186] The above-described embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the present application. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the present application shall be subject to the attached claims.

Claims

1. A method for generating a scanned image, characterized in that: The method comprises: Acquire original scan data of the object to be detected, and acquire multiple pre-set image reconstruction modes; Acquire a reconstructed image obtained by performing image reconstruction based on each of the image reconstruction modes; the reconstructed image is obtained based on the original scanning data; Acquire region selection information input for the plurality of reconstructed images, and acquire image fusion regions corresponding to the plurality of reconstructed images according to the region selection information; Image fusion processing is performed on the multiple image fusion areas to obtain a fused image, and the fused image is used as a target scanning image corresponding to the scanning imaging device.

2. The method according to claim 1, characterized in that The obtaining of a plurality of preset image reconstruction modes includes: Acquire a current image reconstruction strategy from a plurality of image reconstruction strategies, and acquire a plurality of image reconstruction parameter types pre-set by the current image reconstruction strategy; each of the image reconstruction parameter types is pre-associated with a plurality of image reconstruction parameters; Obtaining a target image reconstruction parameter from the image reconstruction parameters associated with each image reconstruction parameter type, respectively, and forming a current image reconstruction parameter set using each target image reconstruction parameter; According to each of the current image reconstruction parameter sets, obtaining multiple image reconstruction modes corresponding to the current image reconstruction strategy; The multiple image reconstruction modes corresponding to each of the current image reconstruction strategies are used as the multiple preset image reconstruction modes.

3. The method according to claim 2, characterized in that The obtaining of reconstructed images obtained by performing image reconstruction based on each of the image reconstruction modes comprises: The original scanning data and a plurality of pre-set image reconstruction modes are sent to a cloud server; the cloud server is used to reconstruct the original scanning data using each of the image reconstruction modes to obtain a reconstructed image corresponding to each of the image reconstruction modes.

4. The method according to claim 1, characterized in that: The acquiring, according to the region selection information, image fusion regions corresponding to the plurality of reconstructed images comprises: Screening and obtaining a target reconstructed image from the plurality of reconstructed images; Extracting keyword information associated with the reconstructed image from the semantic information input by the target user; Based on the keyword information associated with the reconstructed image, an image fusion region matching the keyword information is acquired from the target reconstructed image.

5. The method according to claim 4, characterized in that The step of screening a target reconstructed image from the plurality of reconstructed images comprises: Acquire a preset standard image and a pixel mean value of the preset standard image; Acquire a current reconstructed image and a pixel mean value of the current reconstructed image from the plurality of reconstructed images; Determine a difference between a pixel mean of the current reconstructed image and a pixel mean of the preset standard image; If the difference satisfies a preset difference threshold condition, the current reconstructed image is used as a candidate reconstructed image, and the target reconstructed image is determined based on the candidate reconstructed image.

6. The method according to claim 1, characterized in that The performing image fusion processing on the plurality of image fusion areas to obtain a fused image includes: Obtaining weights corresponding to each of the image fusion regions; According to each of the weights, a plurality of the image fusion regions are spliced ​​to obtain the fused image.

7. The method according to claim 1, characterized in that After the fused image is used as a target scan image corresponding to the scanning imaging device, the method further includes: Using the target scan image as a sample scan image; Inputting the original scan data into an image generation model to be trained, and outputting a predicted scan image through the image generation model; The sample scanned image and the predicted scanned image are used to train the scanned image generation model to obtain a trained image generation model.

8. The method according to claim 7, characterized in that After obtaining the trained image generation model, the method includes: Acquiring detection data of the object to be detected by the imaging scanning device; The detection data is input into the trained image generation model, and a target detection image is obtained through the trained image generation model.

9. A scanning image generating device, characterized in that: The device comprises: A reconstruction data acquisition module is used to acquire the original scan data of the object to be detected and to acquire a plurality of pre-set image reconstruction modes; A reconstructed image acquisition module, used to acquire a reconstructed image obtained by performing image reconstruction based on each of the image reconstruction modes; the reconstructed image is obtained based on the original scanning data; A fusion region acquisition module, used to acquire region selection information input for the plurality of reconstructed images, and acquire image fusion regions corresponding to the plurality of reconstructed images according to the region selection information; The image fusion processing module is used to perform image fusion processing on the multiple image fusion areas to obtain a fused image, and use the fused image as a target scanning image corresponding to the scanning imaging 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, the steps of the method according to any one of claims 1 to 8 are implemented.

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