Slice image processing method and device and network equipment

Abstract

The embodiment of the invention provides a slice image processing method and device and network equipment, and the method comprises the steps: obtaining scanning data which comprises information corresponding to multiple layers of slices of a scanning object; initializing the scanning data to obtain a plurality of initialized images corresponding to the multiple layers of slices; performing iterative processing according to the plurality of initialized images to obtain a plurality of slice images, each iterative processing including analyzing noise data of the input image and processing the input image according to the noise data to obtain an output image, the noise data conforming to self-consistency, and the noise data conforming to self-consistency. The self-consistency of the noise data is consistent between data obtained by mixing the noise data corresponding to the plurality of initialized images and then re-splitting the noise data and the noise data; according to the scheme, the multi-layer excitation technology is utilized to reduce the nuclear magnetic resonance data acquisition time, the sampling efficiency is improved, and the used method can improve the reconstruction quality.

Description

Technical Field

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

[0002] Magnetic resonance imaging is the process of acquiring slice scan data at each slice position of a target site (such as the heart) and then reconstructing it into a slice image.

[0003] The existing solutions usually require scanning each slice to obtain slice images of each slice, and data acquisition is slow. Summary of the invention

[0004] The embodiments of the present application provide a slice image processing method, device and network device, which can generate multiple slice data using the data of a single scan, thereby reducing the number of scans and improving data collection efficiency. The technical solution is as follows:

[0005] In a first aspect, the present application provides a slice image processing method, the method comprising: acquiring scan data, the scan data comprising information corresponding to multiple layers of slices of a scanned object; initializing the scan data to obtain multiple initialized images corresponding to the multiple layers of slices; performing iterative processing based on the multiple initialized images to obtain multiple slice images, each iterative processing comprising analyzing noise data of an input image, and processing the input image based on the noise data to obtain an output image, the noise data being self-consistent, and the self-consistency of the noise data is that the noise data corresponding to the multiple initialized images are mixed and then re-split to obtain data that is consistent with the noise data.

[0006] Furthermore, the iterative processing is performed based on multiple initialization images to obtain multiple slice images, including: determining the time step and multiple input images of this iterative processing, the multiple input images are multiple output images or multiple initialization images obtained in the previous iterative processing; inputting the multiple input images and the iterative time step into a pre-trained processing model to obtain noise data that meets self-consistency, the processing model is used to analyze the noise contained in the image based on the multiple input images; completing the iterative processing based on the multiple input images and the noise data, and determining multiple output images; performing the next round of iterative processing based on the multiple output images until multiple slice images are obtained.

[0007] Furthermore, the iterative processing is completed based on multiple input images and noise data to determine multiple output images, including: generating randomized noise and performing self-consistency processing on the randomized noise to obtain random noise data; the iterative processing is completed based on multiple input images, noise data and random noise data to determine multiple output images.

[0008] Furthermore, the iterative processing is completed based on multiple input images, noise data and random noise data to determine multiple output images, including: determining the round data corresponding to the input image, and analyzing the difference data between the round data and the scan data; the iterative processing is completed based on multiple input images, noise data, random noise data and difference data to determine multiple output images.

[0009] Furthermore, the multiple input images are self-consistent, and the self-consistency of the multiple input images means that the image obtained by re-split after mixing the multiple input images is consistent with the multiple input images; the iterative processing is completed based on the multiple input images, noise data, random noise data and difference data to determine the multiple output images, including: based on the multiple input images, determining the fusion ratio corresponding to the multiple input images to determine the fusion data corresponding to the multiple input images; completing the iterative processing based on the multiple input images, noise data, random noise data, difference data and fusion data to determine the multiple output images.

[0010] Furthermore, the method also includes the step of training a processing model: obtaining multiple training images, and iteratively training the multiple training images to determine multiple training noise images; wherein each iterative training includes analyzing training noise data of a training input image, and processing the training input image according to the noise data to obtain a training output image; the training noise data is self-consistent, and the self-consistency of the training noise data is that the data obtained by mixing and re-split the training noise data corresponding to multiple training input images is consistent with the training noise data.

[0011] Furthermore, the iterative training of the multiple training images includes: determining multiple training input images for this iterative training, the multiple training input images being the training output data or multiple training images of the previous iterative training; generating training noise data corresponding to the multiple training input images of this iterative round; fusing the multiple training input images with the training noise of this iterative round to perform the next round of iterative training until multiple training noise images are determined; and training a processing model based on the multiple training noise images, the multiple training images and the training noise data of each iterative training until a trained training processing model is obtained.

[0012] In a second aspect, the present application provides a slice image processing device, which includes: a scanning data acquisition module, used to acquire scanning data, the scanning data including information corresponding to multiple layers of slices of the scanned object; a scanning data initialization module, used to initialize the scanning data to obtain multiple initialization images corresponding to the multiple layers of slices; a slice image generation module, used to perform iterative processing based on the multiple initialization images to obtain multiple slice images, each iterative processing includes analyzing noise data of an input image, and processing the input image based on the noise data to obtain an output image, the noise data is self-consistent, and the self-consistency of the noise data is that the data obtained by re-split after mixing the noise data corresponding to the multiple initialization images is consistent with the noise data.

[0013] In a third aspect, the present application provides a network device comprising: a memory, a transceiver, and a processor; wherein the memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; and the processor is used to read the computer program in the memory and execute the method described in the first aspect.

[0014] In a fourth aspect, the present application provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the method described in the first aspect.

[0015] The beneficial effects of the technical solution provided by this application are:

[0016] The scheme of the present application can be applied in the scene of nuclear magnetic resonance image acquisition, which can reduce the number of nuclear magnetic resonance data acquisition and improve sampling efficiency. Specifically, in order to avoid the noise generated by simultaneously acquiring the data of each slice, the existing scheme usually uses nuclear magnetic resonance technology to scan each slice of the scanned object separately, and then obtains the data of each slice for reconstruction. This scheme can obtain scan data, and the scan data contains overlapping and confusing information of multi-layer slices of the scanned object. After acquiring the scan data, the scan data can be initialized to an initialization image corresponding to the multi-layer slice of the scanned object, and iterative processing can be used to maintain the consistency of the image in the process of removing image noise, and then obtain a multi-layer slice image corresponding to the multi-layer slice of the scanned object, so as to facilitate subsequent three-dimensional reconstruction and other processing. This scheme can use the method of simultaneous multi-slice scanning to obtain the scan data of the multi-layer slice of the scanned object for separation, denoising and other processing, reducing the number of sampling times and improving sampling efficiency. This scheme uses multi-layer excitation technology to reduce the time of nuclear magnetic resonance data acquisition, improve sampling efficiency, and the method used can improve the reconstruction quality. Specifically, this scheme can pre-train a processing model, and the processing model is used to analyze the noise contained in the image based on the input multiple images for subsequent denoising processing. This scheme can obtain scanning data, which includes information corresponding to multi-layer slices of the scanned object; initialize the scanning data to obtain multiple initialization images corresponding to the multi-layer slices; then, perform multiple iterative processing based on the multiple initialization images and a pre-trained processing model to obtain multiple slice images, wherein each iterative processing includes analyzing noise data of an input image based on a pre-trained processing model, and processing the input image based on the noise data to obtain an output image, and the noise data is self-consistent, and the self-consistency of the noise data is that the noise data corresponding to the multiple initialization images are mixed and then re-split to obtain data that is consistent with the noise data. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required for use in describing the embodiments of the present application are briefly introduced below.

[0018] Figure 1 It is a flowchart of a slice image processing method according to an embodiment of the present application;

[0019] Figure 2 is a structural schematic diagram of a slice image processing device according to an embodiment of the present application;

[0020] Figure 3 It is a structural block diagram of a network device according to an embodiment of the present application;

[0021] Figure 4 This is a structural block diagram of a user device according to an embodiment of the present application. DETAILED DESCRIPTION

[0022] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout identify the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0023] It will be understood by those skilled in the art that, unless specifically stated, the singular forms "one", "said", and "the" used herein may also include plural forms, and "multiple" refers to two or more, and other quantifiers are similar. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or their groups. It should be understood that when we say that an element is "connected" or "coupled" to another element, it can be directly connected or coupled to other elements, or there may be intermediate elements. In addition, the "connection" or "coupling" used here may include wireless connection or wireless coupling. The term "and / or" used here describes the association relationship of associated objects, indicating that there may be three relationships, for example, A and / or B, which can represent: A exists alone, A and B exist at the same time, and B exists alone. The character " / " generally indicates that the objects associated before and after are in an "or" relationship.

[0024] The solution of the present application can be applied in the scenario of nuclear magnetic resonance image acquisition, which can reduce the number of times nuclear magnetic resonance data is collected and improve sampling efficiency. Specifically, in order to avoid the noise generated by simultaneously acquiring the data of each slice, the existing solution usually uses nuclear magnetic resonance technology to scan each slice of the scanned object separately, and then obtains the data of each slice for reconstruction. This solution can obtain scanning data, and the scanning data contains overlapping and confusing information of multiple layers of slices of the scanned object. After acquiring the scanning data, the scanning data can be initialized to an initialization image corresponding to the multiple layers of slices of the scanned object, and iterative processing can be used to maintain the consistency of the image in the process of removing image noise, thereby obtaining a multi-layer slice image corresponding to the multi-layer slices of the scanned object, so as to facilitate subsequent three-dimensional reconstruction and other processing.

[0025] Specifically, the present application provides a slice image processing method, such as Figure 1 As shown, the method includes:

[0026] Step 102: acquiring scan data, where the scan data includes information corresponding to multi-layer slices of the scanned object.

[0027] Step 104: Initialize the scan data to obtain a plurality of initialized images corresponding to the multi-layer slices.

[0028] Step 106, iterative processing is performed based on the multiple initialization images to obtain multiple slice images, each iterative processing includes analyzing the noise data of the input image, and processing the input image based on the noise data to obtain an output image, the noise data is self-consistent, and the self-consistency of the noise data is that the noise data corresponding to the multiple initialization images are mixed and then the data obtained by re-split are consistent with the noise data.

[0029] The scheme of the present application can be applied in the scene of nuclear magnetic resonance image acquisition, which can reduce the number of nuclear magnetic resonance data acquisition and improve sampling efficiency. Specifically, in order to avoid the noise generated by simultaneously acquiring the data of each slice, the existing scheme usually uses nuclear magnetic resonance technology to scan each slice of the scanned object separately, and then obtains the data of each slice for reconstruction. This scheme can obtain scan data, and the scan data contains overlapping and confusing information of multi-layer slices of the scanned object. After acquiring the scan data, the scan data can be initialized to an initialization image corresponding to the multi-layer slice of the scanned object, and iterative processing can be used to maintain the consistency of the image in the process of removing image noise, and then obtain a multi-layer slice image corresponding to the multi-layer slice of the scanned object, so as to facilitate subsequent three-dimensional reconstruction and other processing. This scheme can use the method of simultaneous multi-slice scanning to obtain the scan data of the multi-layer slice of the scanned object for separation, denoising and other processing, which reduces the number of sampling times, reduces the scanning time, improves the sampling efficiency, and improves the reconstruction effect. Magnetic resonance rapid imaging technology can also be called multi-layer excitation technology and simultaneous multi-slice imaging, which is a magnetic resonance accelerated imaging technology based on parallel imaging. In simultaneous multi-slice imaging, multi-frequency excitation pulses are used to simultaneously excite multiple slices to reduce the scanning time and achieve rapid imaging. Among the fast magnetic resonance imaging methods, simultaneous multi-slice imaging is a method of inter-slice acceleration. Compared with the intra-slice acceleration method, its biggest advantage is that the signal-to-noise ratio only increases by the square root of the number of slices.

[0030] This scheme combines multi-layer excitation technology with a diffusion model to obtain better reconstruction effects. The diffusion model can also be called a processing model. The diffusion model is used to analyze the noise contained in the image based on multiple input images for subsequent denoising. In the model training stage, this scheme can train the processing model by iteratively adding noise to the training image, where the added noise is self-consistent. After the subsequent diffusion model training is completed, the noise that is self-consistent will be learned. In the process of initializing the image processing, the corresponding self-consistent noise can be analyzed and denoising can be performed according to the corresponding weights. After multiple iterations, multiple images corresponding to different slice positions are obtained.

[0031] Specifically, the present scheme can acquire scanning data, which includes information corresponding to multi-layer slices of the scanned object; initialize the scanning data to obtain multiple initialization images corresponding to the multi-layer slices; then, perform multiple iterative processing based on the multiple initialization images and a pre-trained processing model to obtain multiple slice images, wherein each iterative processing includes analyzing noise data of an input image, and obtaining an output image after processing the input image based on the noise data, and the noise data is self-consistent, and the self-consistency of the noise data is that the noise data corresponding to the multiple initialization images are mixed and then re-split to obtain data that is consistent with the noise data.

[0032] This solution can pre-train a diffusion model (processing model), and the processing model is used to analyze the noise contained in the image based on multiple input images for subsequent denoising. Specifically, as an optional embodiment, the iterative processing based on multiple initialization images to obtain multiple slice images includes: determining the time step and multiple input images of this iterative processing, the multiple input images are multiple output images or multiple initialization images obtained by the previous iterative processing; inputting the multiple input images and the iterative time step into the pre-trained processing model to obtain noise data that meets self-consistency, the processing model is used to analyze the noise contained in the image based on the multiple input images; completing the iterative processing based on the multiple input images and the noise data, and determining multiple output images; performing the next round of iterative processing based on the multiple output images until multiple slice images are obtained.

[0033] The model used in this solution is the diffusion model, which is a generative model used to generate data similar to the training data. The diffusion model works by corrupting the training data by continuously adding Gaussian noise, and then recovering the data by learning to invert this noise process. After training, the diffusion model can be used to generate data by simply passing randomly sampled noise through the learned denoising process.

[0034] In the model training stage, this scheme can train the diffusion model by iteratively adding noise to the training image, wherein the added noise is self-consistent. After the subsequent diffusion model training is completed, the self-consistent noise will be learned. In the process of initializing the image processing, the corresponding self-consistent noise can be analyzed and denoising can be performed according to the corresponding weights. After multiple iterations, multiple images corresponding to different slice positions are obtained.

[0035] In the present solution, the process of using the diffusion model to generate slice images is a process of splitting the input image and continuously iteratively denoising it. The diffusion model training process is a process of continuously adding noise to data to form training data, and using the diffusion model to learn the noise in the image. Specifically, as an optional embodiment, the method also includes the step of training a processing model: obtaining multiple training images, and iteratively training the multiple training images to determine multiple training noise images; wherein each iterative training includes analyzing the training noise data of the training input image, and processing the training input image according to the noise data to obtain a training output image; the training noise data is self-consistent, and the self-consistency of the training noise data is that the data obtained by mixing the training noise data corresponding to the multiple training input images and then splitting them again are consistent with the training noise data. Specifically, as an optional embodiment, the iterative training of multiple training images includes: determining multiple training input images for this iterative training, the multiple training input images are training output data or multiple training images of the previous iterative training; generating training noise data corresponding to the multiple training input images of this iterative round; fusing the multiple training input images with the training noise of this iterative round to perform the next round of iterative training until multiple training noise images are determined; and training a processing model based on the multiple training noise images, the multiple training images and the training noise data of each iterative training until a trained training processing model is obtained.

[0036] This solution uses a processing model to analyze noise that meets self-consistency, but only taking this noise will have limitations in the denoising process (such as small differences, and it is impossible to obtain a slice image that meets the requirements later). Therefore, in order to better perform denoising, this solution can also randomly generate noise and perform denoising according to corresponding weights. Specifically, as an optional embodiment, the iterative processing is completed based on multiple input images and noise data to determine multiple output images, including: generating randomized noise, and performing self-consistency processing on the randomized noise to obtain random noise data; completing the iterative processing based on multiple input images, noise data and random noise data to determine multiple output images. This solution can use noise data and random noise data for denoising, wherein the weight corresponding to the noise data and the weight corresponding to the random noise data can have an associated relationship, such as the weight corresponding to the noise data is the square of the weight corresponding to the random noise data.

[0037] In order to avoid the difference between the output image formed after multiple iterations and the original data exceeding the limit, the present solution can also convert the input image into round data (k-space data) of the same type as the scan data, and then analyze the difference between the round data and the scan data to process the input image of this iterative processing according to the difference amount. Specifically, as an optional embodiment, the iterative processing is completed based on multiple input images, noise data and random noise data to determine multiple output images, including: determining the round data corresponding to the input image, and analyzing the difference data between the round data and the scan data; completing the iterative processing based on multiple input images, noise data, random noise data and difference data to determine multiple output images. The difference data can be fused with the input image according to the corresponding weights.

[0038] This solution can also perform self-consistency processing on the input image, and form fusion data according to the corresponding fusion ratio, so as to fuse the fusion data with the input image. Specifically, as an optional embodiment, multiple input images are self-consistent, and the self-consistency of multiple input images means that the images obtained by re-splitting after mixing the multiple input images are consistent with the multiple input images; the iterative processing is completed based on the multiple input images, noise data, random noise data and difference data to determine the multiple output images, including: determining the fusion ratio corresponding to the multiple input images based on the multiple input images to determine the fusion data corresponding to the multiple input images; completing the iterative processing based on the multiple input images, noise data, random noise data, difference data and fusion data to determine the multiple output images. In the process of a single iterative processing, this solution can perform a comprehensive analysis based on the noise data, random noise data, difference data and fusion data, and then process the input image to form the input image of the next iteration. After multiple iterations, multiple slice images corresponding to the multi-layer slices are formed.

[0039] Based on the above embodiments, the present application also provides a slice image processing device, such as Figure 2 As shown, the device comprises:

[0040] The scanning data acquisition module 202 is used to acquire scanning data, where the scanning data includes information corresponding to multi-layer slices of the scanned object.

[0041] The scanning data initialization module 204 is used to initialize the scanning data to obtain a plurality of initialization images corresponding to the multi-layer slices.

[0042] The slice image generation module 206 is used to perform iterative processing based on multiple initialization images to obtain multiple slice images. Each iterative processing includes analyzing the noise data of the input image, and obtaining an output image after processing the input image based on the noise data. The noise data is self-consistent. The self-consistency of the noise data is that the noise data corresponding to the multiple initialization images are mixed and then re-split to obtain the data and the noise data.

[0043] The implementation of the embodiment of the present application is similar to the implementation of the above-mentioned method embodiment. The specific implementation can refer to the specific implementation of the above-mentioned method embodiment, which will not be repeated here.

[0044] The solution of the present application can be applied in the scenario of nuclear magnetic resonance image acquisition, which can reduce the number of nuclear magnetic resonance data acquisition and improve sampling efficiency. Specifically, in order to avoid the noise generated by simultaneously acquiring the data of each slice, the existing solution usually uses nuclear magnetic resonance technology to scan each slice of the scanned object separately, and then obtains the data of each slice for reconstruction. This solution can obtain scan data, and the scan data contains overlapping and confused information of multiple layers of slices of the scanned object. After acquiring the scan data, the scan data can be initialized to an initialization image corresponding to the multiple layers of slices of the scanned object, and iterative processing can be used to maintain the consistency of the image in the process of removing image noise, thereby obtaining a multi-layer slice image corresponding to the multi-layer slice of the scanned object, so as to facilitate subsequent three-dimensional reconstruction and other processing. This solution can use simultaneous multi-slice scanning to obtain the scan data of multiple layers of slices of the scanned object for separation, denoising and other processing, reducing the number of sampling times and improving sampling efficiency. Specifically, the present scheme can acquire scanning data, which includes information corresponding to multi-layer slices of the scanned object; initialize the scanning data to obtain multiple initialization images corresponding to the multi-layer slices; then, perform multiple iterative processing based on the multiple initialization images to obtain multiple slice images, wherein each iterative processing includes analyzing the noise data of the input image based on a pre-trained processing model, and processing the input image based on the noise data to obtain an output image, wherein the noise data is self-consistent, and the self-consistency of the noise data is that the noise data corresponding to the multiple initialization images are mixed and then the data obtained by re-split are consistent with the noise data.

[0045] It should be noted that the division of units and / or modules in the embodiments of the present application is schematic and is only a logical function division. There may be other division methods in actual implementation. In addition, each functional unit and / or module in each embodiment of the present application may be integrated into a processing unit and / or module, or each unit and / or module may exist physically separately, or two or more units and / or modules may be integrated into one unit and / or module. The above-mentioned integrated units and / or modules may be implemented in the form of hardware or in the form of software functional units and / or modules.

[0046] If the integrated units and / or modules are implemented in the form of software functional units and / or modules and sold or used as independent products, they can be stored in a processor-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the relevant technology or all or part of the technical solution can be embodied in the form of a software product, and the computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) or a processor (processor) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (Read-Only Memory, ROM), random access memory (Random Access Memory, RAM), disk or optical disk and other media that can store program codes.

[0047] In addition, the data transmission device and the data transmission method provided in the above embodiments are based on the same application concept. Since the principles of solving problems by the method and the device are similar, the implementation of the device and the method can refer to each other, and the repeated parts will not be repeated.

[0048] Figure 3 A structural block diagram of a network device is shown according to an exemplary embodiment.

[0049] like Figure 3 As shown, the network device 1100 includes at least: a processor 1110 , a memory 1120 , and a transceiver 1130 .

[0050] The transceiver 1130 is used to receive and send data under the control of the processor 1110 .

[0051] exist Figure 3In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically linking together various circuits of one or more processors represented by processor 1110 and memory represented by memory 1120. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 1130 may be a plurality of components, namely, a transmitter and a receiver, providing units and / or modules for communicating with various other devices on a transmission medium, which may include a wireless channel, a wired channel, an optical cable, and other transmission media.

[0052] The processor 1110 is responsible for managing the bus architecture and general processing, and the memory 1120 can store data used by the processor 1110 when performing operations.

[0053] Optionally, the processor 1110 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), a field programmable gate array (FPGA) or a complex programmable logic device (CPLD), and the processor 1110 may also adopt a multi-core architecture. The processor 1110 and the memory 1120 may also be physically arranged separately.

[0054] The processor 1110 calls the computer program stored in the memory 1120 to execute any one of the methods for allocating a cell radio network temporary identifier provided in the above embodiments of the present application according to the obtained executable instructions.

[0055] Figure 4 A structural block diagram of a user equipment is shown according to an exemplary embodiment.

[0056] like Figure 4 As shown, the user equipment 1300 includes at least: a processor 1310 , a memory 1320 , and a transceiver 1330 .

[0057] The transceiver 1330 is used to receive and send data under the control of the processor 1310 .

[0058] exist Figure 4In the embodiment, the bus architecture may include any number of interconnected buses and bridges, specifically one or more processors represented by processor 1310 and various circuits of memory represented by memory 1320 are linked together. The bus architecture may also link together various other circuits such as peripherals, voltage regulators, and power management circuits, which are well known in the art and are therefore not further described herein. The bus interface provides an interface. The transceiver 1330 may be a plurality of components, namely, a transmitter and a receiver, providing units and / or modules for communicating with various other devices on a transmission medium, including transmission media such as wireless channels, wired channels, and optical cables. For different user devices, the user interface 1340 may also be an interface capable of externally and internally connecting required devices, and the connected devices include but are not limited to a keypad, a display, a speaker, a microphone, a joystick, and the like.

[0059] The processor 1310 is responsible for managing the bus architecture and general processing, and the memory 1320 can store data used by the processor 1310 when performing operations.

[0060] Optionally, the processor 1310 may be a CPU (central processing unit), an ASIC (Application Specific Integrated Circuit), an FPGA (Field Programmable Gate Array) or a CPLD (Complex Programmable Logic Device), and the processor 1310 may also adopt a multi-core architecture. The processor 1310 and the memory 1320 may also be physically arranged separately.

[0061] The processor 1310 calls the computer program stored in the memory 1320 to execute any one of the methods for allocating a cell radio network temporary identifier provided in the above embodiments of the present application according to the obtained executable instructions.

[0062] It should be noted here that the above-mentioned device provided in the embodiment of the present application can implement all the method steps implemented in the above-mentioned method embodiment, and can achieve the same technical effect. The parts and beneficial effects of this embodiment that are the same as those in the method embodiment will not be described in detail here.

[0063] In addition, a storage medium is provided in an embodiment of the present application, on which a computer program is stored, and when the computer program is executed by the processor, the data transmission method in the above embodiments is implemented. The storage medium can be any available medium or data storage device that the processor can access, including but not limited to magnetic storage (such as floppy disk, hard disk, tape, magneto-optical disk (MO), etc.), optical storage (such as CD, DVD, BD, HVD, etc.), and semiconductor storage (such as ROM, EPROM, EEPROM, non-volatile memory (NAND FLASH), solid state drive (SSD)), etc.

[0064] In an embodiment of the present application, a program product is provided, for example, the program product is an FPGA chip or a DSP chip, and the program product includes executable instructions, and the executable instructions are stored in a storage medium. The processor reads the executable instructions from the storage medium, so that when the executable instructions are executed by the processor, the data transmission method in each of the above embodiments is implemented.

[0065] The solution of the present application can be applied in the scene of nuclear magnetic resonance image acquisition, which can reduce the number of nuclear magnetic resonance data acquisition and improve sampling efficiency. Specifically, in order to avoid the noise generated by simultaneously acquiring the data of each slice, the existing solution usually uses nuclear magnetic resonance technology to scan each slice of the scanned object separately, and then obtains the data of each slice for reconstruction. This solution can obtain scan data, and the scan data contains overlapping and confusing information of multi-layer slices of the scanned object. After acquiring the scan data, the scan data can be initialized to an initialization image corresponding to the multi-layer slices of the scanned object, and iterative processing can be used to maintain the consistency of the image in the process of removing image noise, and then obtain a multi-layer slice image corresponding to the multi-layer slice of the scanned object, so as to facilitate subsequent three-dimensional reconstruction and other processing. This solution can use the method of simultaneous multi-slice scanning to obtain the scan data of the multi-layer slices of the scanned object for separation, denoising and other processing, reducing the number of sampling and improving sampling efficiency. Specifically, this solution can pre-train a processing model, and the processing model is used to analyze the noise contained in the image based on the input multiple images for subsequent denoising processing. This scheme can obtain scanning data, which includes information corresponding to multi-layer slices of the scanned object; initialize the scanning data to obtain multiple initialization images corresponding to the multi-layer slices; then, perform multiple iterative processing based on the multiple initialization images and a pre-trained processing model to obtain multiple slice images, wherein each iterative processing includes analyzing noise data of an input image based on a pre-trained processing model, and processing the input image based on the noise data to obtain an output image, and the noise data is self-consistent, and the self-consistency of the noise data is that the noise data corresponding to the multiple initialization images are mixed and then re-split to obtain data that is consistent with the noise data.

[0066] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage and optical storage, etc.) that contain computer-usable program code.

[0067] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer executable instructions. These computer executable instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0068] These processor executable instructions may also be stored in a processor readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the processor readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0069] These processor-executable instructions may also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for executing on the computer or other programmable device to implement the process. Figure 1 A process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0070] It should be understood that, although the steps in the flowchart of the accompanying drawings are displayed in sequence as indicated by the arrows, these steps are not necessarily executed in sequence in the order indicated by the arrows. Unless otherwise specified herein, there is no strict order restriction on the execution of these steps, and they can be executed in other orders. Moreover, at least a part of the steps in the flowchart of the accompanying drawings may include multiple sub-steps or multiple stages, and these sub-steps or stages are not necessarily executed at the same time, but can be executed at different times, and their execution order is not necessarily sequential, but can be executed in turn or alternately with other steps or at least a part of the sub-steps or stages of other steps.

[0071] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A slice image processing method, characterized in that: The method comprises: Acquire scanning data, the scanning data including information corresponding to multiple layers of slices of the scanned object; Initializing the scan data to obtain multiple initialization images corresponding to the multi-layer slices; Iterative processing is performed based on multiple initialization images to obtain multiple slice images. Each iterative processing includes analyzing noise data of an input image, and processing the input image based on the noise data to obtain an output image. The noise data is self-consistent. The self-consistency of the noise data is that the noise data corresponding to the multiple initialization images are mixed and then re-split to obtain data that is consistent with the noise data.

2. The method according to claim 1, characterized in that The iterative processing is performed based on the multiple initialization images to obtain multiple slice images, including: Determine the time step and multiple input images of this iterative processing, where the multiple input images are multiple output images or multiple initialization images obtained in the previous iterative processing; Inputting a plurality of input images and iteration time steps into a pre-trained processing model to obtain noise data that meets self-consistency, wherein the processing model is used to analyze noise contained in the image based on the input plurality of images; This iterative processing is completed based on multiple input images and noise data to determine multiple output images; The next round of iterative processing is performed based on the multiple output images until multiple slice images are obtained.

3. The method according to claim 2, characterized in that The iterative processing is completed according to the multiple input images and the noise data to determine the multiple output images, including: Generate randomized noise, and perform self-consistency processing on the randomized noise to obtain randomized noise data; This iterative processing is completed based on multiple input images, noise data and random noise data to determine multiple output images.

4. The method according to claim 3, characterized in that The iterative processing is completed according to the multiple input images, the noise data and the random noise data to determine the multiple output images, including: Determine the round data corresponding to the input image, and analyze the difference data between the round data and the scan data; This iterative processing is completed based on multiple input images, noise data, random noise data and difference data to determine multiple output images.

5. The method according to claim 4, characterized in that The multiple input images are self-consistent. The self-consistency of the multiple input images means that the images obtained by re-split after mixing the multiple input images are consistent with the multiple input images. The iterative processing is completed according to the multiple input images, the noise data, the random noise data and the difference data to determine the multiple output images, including: Determining fusion ratios corresponding to the multiple input images according to the multiple input images to determine fusion data corresponding to the multiple input images; This iterative processing is completed based on multiple input images, noise data, random noise data, difference data and fusion data to determine multiple output images.

6. The method according to claim 2, characterized in that The method also includes the step of training the processing model: Acquire multiple training images, and perform iterative training on the multiple training images to determine multiple training noise images; Each iterative training includes analyzing the training noise data of the training input image, and processing the training input image according to the noise data to obtain a training output image; the training noise data is self-consistent, and the self-consistency of the training noise data is that the training noise data corresponding to multiple training input images are mixed and then re-split to obtain data that is consistent with the training noise data.

7. The method according to claim 6, characterized in that The iterative training of the multiple training images comprises: Determine multiple training input images for this iterative training, where the multiple training input images are training output data or multiple training images from the previous iterative training; Generate training noise data corresponding to multiple training input images of this iteration round; Fusing multiple training input images with the training noise of this iteration round to perform the next round of iterative training until multiple training noise images are determined; The processing model is trained based on the multiple training noise images, the multiple training images and the training noise data of each iterative training until a trained training processing model is obtained.

8. A slice image processing device, characterized in that: The device comprises: A scanning data acquisition module, used to acquire scanning data, the scanning data including information corresponding to multi-layer slices of the scanned object; A scanning data initialization module is used to initialize the scanning data to obtain a plurality of initialization images corresponding to the multi-layer slices; The slice image generation module is used to perform iterative processing based on multiple initialization images to obtain multiple slice images. Each iterative processing includes analyzing the noise data of the input image, and obtaining an output image after processing the input image based on the noise data. The noise data is self-consistent. The self-consistency of the noise data is that the noise data corresponding to the multiple initialization images are mixed and then re-split to obtain the data and the noise data.

9. A network device, characterized in that: include: A memory, a transceiver, and a processor; wherein the memory is used to store a computer program; the transceiver is used to send and receive data under the control of the processor; The processor is used to read the computer program in the memory and execute the method according to claims 1-7.

10. A storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the method according to claims 1-7 is implemented.

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