Tomographic image reconstruction method, system, equipment and medium

Abstract

The invention discloses a tomography image reconstruction method, system, device and medium wherein an image reconstruction model in the tomography image reconstruction method is obtained by the following steps: obtaining full-angle projection data, and preprocessing the full-angle projection data to obtain a back projection image; the back projection image comprises a full-angle back projection image, a sparse-angle back projection image and a limited-angle back projection image; performing constraint extraction on the back projection image to obtain a constraint image; performing double-domain image constraint reconstruction on the constraint image to obtain a double-domain constraint reconstruction result; and performing parameter constraint updating on the initialized image reconstruction model according to the constraint image and the double-domain constraint reconstruction result to obtain the trained image reconstruction model. According to the method, the difficulty of CT image reconstruction and required computing resources can be effectively reduced, and the efficiency of CT image reconstruction is improved. The invention relates to the technical field of CT image reconstruction.

Description

Technical Field

[0001] The present invention relates to the technical field of CT image reconstruction, and in particular to a method, system, device and medium for reconstructing a tomographic image. Background Art

[0002] With the development of science and technology, low-dose CT (LDCT) imaging technology has become one of the hot research topics to reduce patients' X-ray radiation loss.

[0003] At present, the traditional CT image reconstruction method mainly obtains the CT reconstructed image by performing multiple forward or back projection iterative reconstruction updates on the scanning data. This method requires a lot of computing resources and is time-consuming. In addition, there are some CT image reconstruction methods based on deep learning. These methods need to make detailed assumptions about noise artifacts during the imaging process. CT image reconstruction is difficult, requires a lot of computing resources, and has low reconstruction efficiency.

[0004] Therefore, the problems existing in the prior art still need to be solved and optimized. Summary of the invention

[0005] The purpose of the present invention is to solve one of the technical problems existing in the related art to at least a certain extent.

[0006] To this end, an object of an embodiment of the present invention is to provide a method, system, device and medium for reconstructing a tomographic image, which can effectively reduce the difficulty and required computing resources of CT image reconstruction and improve the efficiency of CT image reconstruction.

[0007] Another object of an embodiment of the present application is to provide a tomographic image reconstruction system.

[0008] In order to achieve the above technical objectives, the technical solutions adopted by the embodiments of the present application include:

[0009] In a first aspect, an embodiment of the present application provides a method for reconstructing a tomographic image, comprising:

[0010] Acquire target projection data, wherein the target projection data is any one of full-angle chord diagram data, sparse-angle chord diagram data, or limited-angle chord diagram data;

[0011] Inputting the target projection data into a trained image reconstruction model to obtain an image reconstruction result;

[0012] The trained image reconstruction model is obtained by the following steps:

[0013] Acquire full-angle projection data, and pre-process the full-angle projection data to obtain a back-projection image, wherein the back-projection image includes a full-angle back-projection image, a sparse-angle back-projection image, and a limited-angle back-projection image;

[0014] Performing constraint extraction on the back-projection image to obtain a constrained image, wherein the constrained image includes a full-angle constrained image, a sparse angle constrained image, and a limited-angle constrained image having a mutual constraint relationship;

[0015] Performing dual-domain image constrained reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result;

[0016] According to the constrained image and the dual-domain constrained reconstruction result, parameter constraints are updated on the initialized image reconstruction model to obtain the trained image reconstruction model.

[0017] In addition, the tomographic image reconstruction method according to the above embodiment of the present application may also have the following additional technical features:

[0018] Further, in one embodiment of the present application, the preprocessing of the full-angle projection data to obtain a back-projection image includes:

[0019] Performing a first filtered back-projection on the full-angle projection data to obtain the full-angle back-projection image;

[0020] According to a preset sparse scanning multiple, sparse feature extraction is performed on the full-angle projection data to obtain sparse angle projection data, and, according to a preset limited angle scanning range, limited angle feature extraction is performed on the full-angle projection data to obtain limited angle projection data;

[0021] Performing a second filtered back-projection on the sparse angular projection data to obtain the sparse angular back-projection image;

[0022] A third filtered back-projection is performed on the limited-angle projection data to obtain the limited-angle back-projection image.

[0023] Further, in one embodiment of the present application, performing constraint extraction on the back-projection image to obtain a constrained image includes:

[0024] Extracting prior information from the back-projected image to obtain a prior image;

[0025] The prior image is subjected to chord diagram compensation to obtain the constrained image.

[0026] Further, in one embodiment of the present application, the prior image includes a full-angle prior image, a sparse angle prior image, and a limited-angle prior image, and the prior information extraction of the back-projection image to obtain the prior image includes:

[0027] Performing first information extraction on the full-angle back-projection image to obtain the full-angle priori image;

[0028] Performing second information extraction on the sparse angle back-projection image to obtain the sparse angle prior image;

[0029] Perform third information extraction on the angle-limited back-projection image to obtain the angle-limited priori image.

[0030] Further, in one embodiment of the present application, performing chord diagram compensation on the prior image to obtain the constrained image includes:

[0031] Acquire a first information mask and a second information mask, wherein the first information mask is used to characterize a sparse angle information mask where the sparse angle projection data is missing relative to the full angle projection data, and the second information mask is used to characterize a limited angle information mask where the limited angle projection data is missing relative to the full angle projection data;

[0032] The priori image is mask reconstructed according to the first information mask and the second information mask to obtain the constrained image.

[0033] Further, in one embodiment of the present application, performing mask reconstruction on the prior image according to the first information mask and the second information mask to obtain the constrained image includes:

[0034] According to the full-angle projection data, performing a first front projection compensation on the full-angle priori image to obtain the full-angle constrained image;

[0035] According to the sparse angle projection data and the first information mask, performing a second front projection compensation on the sparse angle prior image to obtain the sparse angle constrained image;

[0036] According to the angle-limited projection data and the second information mask, a third front projection compensation is performed on the angle-limited a priori image to obtain the angle-limited a priori image.

[0037] Further, in one embodiment of the present application, performing dual-domain image constrained reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result includes:

[0038] Performing chord diagram domain restoration on the constrained image to obtain a chord diagram restored image;

[0039] Perform image domain restoration on the chord diagram restoration image to obtain the dual-domain constrained reconstruction result.

[0040] In a second aspect, an embodiment of the present application provides a tomographic image reconstruction system, comprising:

[0041] An acquisition module, used for acquiring target projection data, wherein the target projection data is any one of full-angle chord diagram data, sparse-angle chord diagram data or limited-angle chord diagram data;

[0042] A processing module, used for inputting the target projection data into a trained image reconstruction model to obtain an image reconstruction result;

[0043] The trained image reconstruction model is obtained by the following steps:

[0044] Acquire full-angle projection data, and pre-process the full-angle projection data to obtain a back-projection image, wherein the back-projection image includes a full-angle back-projection image, a sparse-angle back-projection image, and a limited-angle back-projection image;

[0045] Performing constraint extraction on the back-projection image to obtain a constrained image, wherein the constrained image includes a full-angle constrained image, a sparse angle constrained image, and a limited-angle constrained image having a mutual constraint relationship;

[0046] Performing dual-domain image constrained reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result;

[0047] According to the constrained image and the dual-domain constrained reconstruction result, parameter constraints are updated on the initialized image reconstruction model to obtain the trained image reconstruction model.

[0048] In a third aspect, an embodiment of the present application further provides an electronic device, including:

[0049] at least one processor;

[0050] at least one memory for storing at least one program;

[0051] When the at least one program is executed by the at least one processor, the at least one processor implements the method of the first aspect described above.

[0052] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, which stores a program executable by a processor, and the program executable by the processor is used to implement the method of the first aspect when executed by the processor.

[0053] The advantages and benefits of the present application will be partially given in the following description, and partially become apparent from the following description, or be understood through the practice of the present application:

[0054] The embodiments of the present application disclose a method, system, device and medium for reconstructing a tomographic image, wherein the method for reconstructing a tomographic image obtains target projection data, wherein the target projection data is any one of full-angle chord diagram data, sparse angle chord diagram data or limited-angle chord diagram data; the target projection data is input into a trained image reconstruction model to obtain an image reconstruction result; wherein the trained image reconstruction model is obtained by the following steps: obtaining full-angle projection data, and preprocessing the full-angle projection data to obtain a back-projection image, wherein the back-projection image includes a full-angle back-projection image, a sparse angle back-projection image and a limited-angle back-projection image; performing constraint extraction on the back-projection image to obtain a constrained image, wherein the constrained image includes a full-angle constrained image, a sparse angle constrained image and a limited-angle constrained image having a mutual constraint relationship; performing dual-domain image constraint reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result; and performing parameter constraint update on the initialized image reconstruction model according to the constrained image and the dual-domain constrained reconstruction result to obtain the trained image reconstruction model. This method is based on the constraint extraction of the back-projection image to obtain a full-angle constrained image, a sparse angle constrained image and a limited-angle constrained image with mutual constraint relationships. It does not require assumptions about noise artifacts, effectively reduces the difficulty of CT image reconstruction and the required computing resources, and improves the efficiency of CT image reconstruction. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the embodiments of the present application or the drawings of the related technical solutions in the prior art are introduced below. It should be understood that the drawings introduced below are only for the convenience of clearly expressing some embodiments of the technical solutions of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without creative work.

[0056] Figure 1 A schematic diagram of a flow chart of a method for reconstructing a tomographic image provided in an embodiment of the present application;

[0057] Figure 2 A schematic diagram of a training process of an image reconstruction model provided in an embodiment of the present application;

[0058] Figure 3 A schematic diagram of the structure of a tomography image reconstruction system provided in an embodiment of the present application;

[0059] Figure 4 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0060] 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 represent 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 are not to be construed as limitations on the present application. For the step numbers in the following embodiments, they are only provided for the convenience of explanation, and the order between the steps is not limited in any way, and the execution order of each step in the embodiment can be adaptively adjusted according to the understanding of those skilled in the art.

[0061] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art to which this application belongs. The terms used herein are only for the purpose of describing the embodiments of this application and are not intended to limit this application.

[0062] At present, the traditional CT image reconstruction method mainly reconstructs and updates the scan data through multiple forward or back projection iterations to obtain the CT reconstructed image. Although it can improve the instructions for reconstructing images to a certain extent, it has the disadvantages of requiring more computing resources and long operation time.

[0063] In addition, there are some CT image reconstruction methods based on deep learning. Specifically, supervised CT reconstruction methods usually require generating a pseudo-paired data on a low-dose chord diagram or image before performing paired data training; self-supervised CT reconstruction methods usually require making detailed assumptions about noise artifacts during the imaging process. These methods are more difficult to reconstruct CT images, require more computing resources, and have low reconstruction efficiency.

[0064] In view of this, an embodiment of the present invention provides a method for reconstructing a tomographic image. The method is based on the constraint extraction of the back-projection image to obtain a full-angle constrained image, a sparse angle constrained image, and a limited-angle constrained image with a mutual constraint relationship. It does not require assumptions about noise artifacts, nor does it require pairing of chord data or image data. It can effectively reduce the difficulty of CT image reconstruction and the required computing resources, and improve the efficiency of CT image reconstruction.

[0065] Reference Figure 1 and Figure 2 In an embodiment of the present application, a method for reconstructing a tomographic image includes:

[0066] Step 110, obtaining target projection data, wherein the target projection data is any one of full-angle chord diagram data, sparse angle chord diagram data, or limited angle chord diagram data;

[0067] Step 120: input the target projection data into the trained image reconstruction model to obtain an image reconstruction result;

[0068] In an embodiment of the present application, any one of the full-angle chord diagram data, sparse angle chord diagram data or limited angle chord diagram data to be identified can be input into a trained image reconstruction model for image reconstruction. The trained image reconstruction model is used to complete the CT reconstruction task corresponding to the input chord diagram data, so as to obtain an image reconstruction result corresponding to the input chord diagram data.

[0069] The trained image reconstruction model is obtained by the following steps:

[0070] Step 130: acquiring full-angle projection data, and preprocessing the full-angle projection data to obtain a back-projection image, wherein the back-projection image includes a full-angle back-projection image, a sparse-angle back-projection image, and a limited-angle back-projection image;

[0071] In some embodiments, the step 130 of preprocessing the full-angle projection data to obtain a back-projection image includes:

[0072] A1, performing a first filtered back-projection on the full-angle projection data to obtain the full-angle back-projection image;

[0073] A2. According to a preset sparse scanning multiple, performing sparse feature extraction on the full-angle projection data to obtain sparse angle projection data, and, according to a preset limited angle scanning range, performing limited angle feature extraction on the full-angle projection data to obtain limited angle projection data;

[0074] A3, performing a second filtered back-projection on the sparse angular projection data to obtain the sparse angular back-projection image;

[0075] A4. Perform a third filtered back-projection on the limited-angle projection data to obtain the limited-angle back-projection image.

[0076] In an embodiment of the present application, the full-angle CT scan projection data set publicly available on the Internet can be used as the full-angle projection data in the embodiment of the present application, or, with the user's consent, the full-angle computed tomography data of several users can be used as the full-angle tomography in the embodiment of the present application.

[0077] It can be understood that the first filtered back-projection in step A1 can reconstruct the full-angle projection data directly acquired based on the filtered back-projection (Filter Back-Projection, FBP) algorithm. Specifically, step A1 can be to convert the full-angle projection data to the frequency domain through a one-dimensional Fourier transform, and then enhance the high-frequency signal and suppress noise based on a filter, and convert the filtered data back to the spatial domain through an inverse Fourier transform to obtain the filtered full-angle projection data; then, the filtered full-angle projection data is reversely projected according to the geometric information and all the back-projection results are normalized to obtain a full-angle back-projection image.

[0078] It should be noted that the sparse feature extraction of step A2 can be based on the CT sparse scanning multiples, extracting the full-angle projection data to obtain sparse angle CT projection data (i.e., sparse angle projection data); the limited angle feature extraction of step A3 can be based on the CT limited angle scanning range, extracting the full-angle projection data to obtain limited angle CT projection data. In addition, steps A3 and A4 are similar to the contents of the aforementioned step A1, and can be simply deduced by analogy, so this application will not repeat them here.

[0079] Step 140: performing constraint extraction on the back-projection image to obtain a constraint image, wherein the constraint image includes a full-angle constraint image, a sparse angle constraint image, and a limited-angle constraint image having a mutual constraint relationship;

[0080] In some embodiments, the step 140 of performing constraint extraction on the back-projection image to obtain a constrained image includes:

[0081] B1, extracting prior information from the back-projection image to obtain a prior image;

[0082] Furthermore, the prior image includes a full-angle prior image, a sparse angle prior image and a limited-angle prior image, and the step B1, extracting prior information from the back-projected image to obtain the prior image, includes:

[0083] B11, extracting first information from the full-angle back-projection image to obtain the full-angle priori image;

[0084] B12, performing second information extraction on the sparse angle back-projection image to obtain the sparse angle prior image;

[0085] B13. Perform third information extraction on the angle-limited back-projection image to obtain the angle-limited prior image.

[0086] In an embodiment of the present application, the image reconstruction model includes a priori neural module, a chord diagram compensation module and a dual-domain neural module, wherein the priori neural module includes three parallel priori neural network units, each priori neural network unit corresponding to a back-projection image of an angle type. Specifically, the network structure of the three parallel priori neural network units in the embodiment of the present application can be the same, which can be a five-layer Unet network with residual connections, or a Transformer network built based on an attention mechanism, etc. The present application does not limit the specific network structure of the priori neural network unit. While the priori neural module provides priori information for the subsequent chord diagram compensation module and the dual-domain neural module through mutual learning, the three parallel priori neural network units are mutually constrained, guided and learned based on the loss function, so that the obtained full-angle constrained image, sparse angle constrained image and limited angle constrained image have a mutual constraint relationship, thereby achieving the effect of removing noise artifacts.

[0087] It can be understood that step B11 can be to use the full-angle back-projection image as the input of the first prior neural network unit, and use the output image of the first prior neural network unit as the full-angle prior image; step B12 and step B13 are similar to the contents of the aforementioned step B11 and can be simply deduced by analogy.

[0088] Specifically, the loss function of the prior neural module in the embodiment of the present application can be expressed as:

[0089]

[0090] in, is the loss function of the prior neural module, is the full-angle prior image; is a sparse angle prior image; is the restricted angle prior image.

[0091] B2. Perform chord diagram compensation on the prior image to obtain the constrained image.

[0092] Furthermore, the step B2, performing chord diagram compensation on the prior image to obtain the constrained image, includes:

[0093] Step B21, obtaining a first information mask and a second information mask, wherein the first information mask is used to characterize a sparse angle information mask in which the sparse angle projection data is missing relative to the full-angle projection data, and the second information mask is used to characterize a limited angle information mask in which the limited angle projection data is missing relative to the full-angle projection data;

[0094] Step B22: Perform mask reconstruction on the prior image according to the first information mask and the second information mask to obtain the constrained image.

[0095] Furthermore, the step B22, performing mask reconstruction on the prior image according to the first information mask and the second information mask to obtain the constrained image, includes:

[0096] Step B221, performing a first front projection compensation on the full-angle priori image according to the full-angle projection data to obtain the full-angle constrained image;

[0097] Step B222: performing a second front projection compensation on the sparse angle prior image according to the sparse angle projection data and the first information mask to obtain the sparse angle constrained image;

[0098] Step B223: Perform a third front projection compensation on the limited-angle priori image according to the limited-angle projection data and the second information mask to obtain the limited-angle priori image.

[0099] In an embodiment of the present application, after the prior neural module outputs three parallel full-angle prior images, a sparse angle prior image and a limited-angle prior image, a first information mask corresponding to the sparse angle prior image and a second information mask corresponding to the limited-angle prior image can be obtained first; then, step B221 can be a forward projection (FP) of the full-angle prior image, which can be specifically implemented based on the first pre-customized FPLayer network layer in the chord diagram compensation module to obtain full-angle chord diagram data corresponding to the full-angle prior image; and then, based on the original full-angle projection data, the obtained full-angle chord diagram data is compensated, which can be specifically implemented by performing a concatenation operation on the full-angle chord diagram data and the full-angle projection data to obtain a full-angle constrained image.

[0100] It can be understood that step B222 can be based on the second pre-defined FPLayer network layer in the chord diagram compensation module, forward projecting the sparse angle prior image, obtaining the sparse angle chord diagram data, and then using the sparse angle projection data and the first information mask to perform a substitution operation on the sparse angle chord diagram data to improve the validity of the chord diagram data and obtain a sparse angle constrained image. Specifically, the equivalent expression of the second forward projection compensation of step B222 can be:

[0101]

[0102] Where p is a sparse angle-constrained image; A is a forward projection operation that can be back-propagated; is a sparse angle prior image; p sv is the sparse angle projection data; M svIt is a first information mask, that is, a sparse angle information mask in which the sparse angle projection data is missing relative to the full angle projection data.

[0103] It should be noted that, for step B223, the contents of the other aforementioned steps B222 are similar and can be simply deduced by analogy, so this application will not go into details here.

[0104] Step 150, performing dual-domain image constrained reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result;

[0105] In some embodiments, the step 150 of performing dual-domain image constrained reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result includes:

[0106] C1. Perform chord diagram domain restoration on the constrained image to obtain a chord diagram restored image;

[0107] C2. Perform image domain restoration on the chord diagram restoration image to obtain the dual-domain constrained reconstruction result.

[0108] In an embodiment of the present application, the dual-domain constrained reconstruction result includes a full-angle reconstruction result, a sparse angle reconstruction result, and a limited-angle reconstruction result. The dual-domain neural module includes three parallel dual-domain network units, each of which includes a chord diagram domain network unit and an image domain network unit, and each dual-domain network unit corresponds to a constrained image of an angle type. Specifically, the chord diagram domain network unit and the image domain network unit can both be five-layer Unet networks with residual connections. The dual-domain neural module constrains, guides, and learns the three parallel dual-domain network units based on the loss function, so that the obtained full-angle reconstruction result, sparse angle reconstruction result, and limited-angle reconstruction result have a mutually constrained relationship.

[0109] It can be understood that, for the full-angle constrained image in the constrained image, step 150 can be to input the full-angle constrained image into the corresponding dual-domain network unit. Specifically, the full-angle constrained image can be first input into the chord diagram domain network unit to obtain the chord diagram domain network unit output; then, the chord diagram domain network unit output is FBP reconstructed based on a pre-customized FBPLayer network layer to obtain the chord diagram restored image; then, the chord diagram restored image is input into the image domain network unit to obtain the dual-domain constrained reconstruction result corresponding to the full-angle projection data.

[0110] It should be noted that for sparse angle constrained images and limited angle constrained images, their contents are similar to those of the aforementioned full angle constrained images and can be simply deduced by analogy, so this application will not go into details. It is worth mentioning that for the pre-customized FPLayer network layer and FBPLayer network layer, they can be deep learning custom network layers. Specifically, the FPLayer network layer is used to implement the front projection operation, and the FBPLayer network layer is used to implement the FBP reconstruction operation. It can be specifically constructed based on the deep learning framework used, and this application will not go into details here.

[0111] Specifically, the loss function of the dual-domain neural module in the embodiment of the present application can be expressed as:

[0112]

[0113] in, is the loss function of the dual-domain neural module, Reconstruction results for all angles; Reconstruction results for sparse angles; This is the limited angle reconstruction result.

[0114] Step 160: According to the constrained image and the dual-domain constrained reconstruction result, the initialized image reconstruction model is updated with parameter constraints to obtain the trained image reconstruction model.

[0115] In the embodiment of the present application, step 160 may be to calculate a loss function regarding image consistency based on the constrained image and the dual-domain constrained reconstruction result, which may be specifically expressed as:

[0116]

[0117] Among them, L rc is the loss function for image consistency; μ ld It is the full-angle projection data.

[0118] It can be understood that the parameter constraint update can be based on the target loss function obtained by summing the loss function of the prior neural module, the loss function of the dual-domain neural module and the loss function about image consistency, determining the loss value of the model training, and using the back propagation algorithm to update the initialized image reconstruction model. After several rounds of iterations, the trained image reconstruction model can be obtained. The specific iteration discussion can be set in advance, or the training is considered to be completed when the model accuracy meets the requirements.

[0119] A tomographic image reconstruction system proposed according to an embodiment of the present application is described in detail below with reference to the accompanying drawings.

[0120] Reference Figure 3, a tomography image reconstruction system proposed in an embodiment of the present application includes:

[0121] An acquisition module 101 is used to acquire target projection data, wherein the target projection data is any one of full-angle chord diagram data, sparse-angle chord diagram data, or limited-angle chord diagram data;

[0122] A processing module 102 is used to input the target projection data into a trained image reconstruction model to obtain an image reconstruction result;

[0123] The trained image reconstruction model is obtained by the following steps:

[0124] Acquire full-angle projection data, and pre-process the full-angle projection data to obtain a back-projection image, wherein the back-projection image includes a full-angle back-projection image, a sparse-angle back-projection image, and a limited-angle back-projection image;

[0125] Performing constraint extraction on the back-projection image to obtain a constrained image, wherein the constrained image includes a full-angle constrained image, a sparse angle constrained image, and a limited-angle constrained image having a mutual constraint relationship;

[0126] Performing dual-domain image constrained reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result;

[0127] According to the constrained image and the dual-domain constrained reconstruction result, parameter constraints are updated on the initialized image reconstruction model to obtain the trained image reconstruction model.

[0128] It can be understood that the contents of the above method embodiments are all applicable to the present system embodiments, the functions specifically implemented by the present system embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0129] Reference Figure 4 , the embodiment of the present application further provides an electronic device, including:

[0130] at least one processor 201;

[0131] At least one memory 202, used to store at least one program;

[0132] When the at least one program is executed by the at least one processor 201, the at least one processor 201 implements the above method embodiment.

[0133] Similarly, it can be understood that the contents of the above method embodiments are all applicable to the present device embodiments, the functions specifically implemented by the present device embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0134] The embodiment of the present application further provides a computer-readable storage medium, in which a program executable by the processor 201 is stored. The program executable by the processor 201 is used to implement the above-mentioned method embodiment when executed by the processor 201.

[0135] Similarly, the contents of the above method embodiments are all applicable to the computer-readable storage medium embodiments. The functions specifically implemented by the computer-readable storage medium embodiments are the same as those of the above method embodiments, and the beneficial effects achieved are also the same as those achieved by the above method embodiments.

[0136] In some selectable embodiments, the function / operation mentioned in the block diagram may not occur in the order mentioned in the operation diagram. For example, depending on the function / operation involved, the two boxes shown in succession can actually be executed substantially simultaneously or the boxes can sometimes be executed in reverse order. In addition, the embodiment presented and described in the flow chart of the application is provided by way of example, for the purpose of providing a more comprehensive understanding of technology. The disclosed method is not limited to the operation and logic flow presented herein. Selectable embodiments are expected, wherein the order of various operations is changed and the sub-operation of a part described as a larger operation is performed independently.

[0137] In addition, although the present application is described in the context of functional modules, it should be understood that, unless otherwise specified, one or more of the functions and / or features can be integrated into a single physical device and / or software module, or one or more functions and / or features can be implemented in a separate physical device or software module. It is also understood that a detailed discussion of the actual implementation of each module is unnecessary for understanding the present application. More specifically, in view of the properties, functions, and internal relationships of the various functional modules in the device disclosed herein, the actual implementation of the module will be understood within the conventional techniques of the engineer. Therefore, those skilled in the art can implement the present application set forth in the claims without excessive experimentation using ordinary techniques. It is also understood that the specific concepts disclosed are merely illustrative and are not intended to limit the scope of the present application, which is determined by the full scope of the attached claims and their equivalents.

[0138] If the function is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application, or the part that contributes to the prior art or the part of the technical solution, can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method of the embodiment of the present application. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program codes.

[0139] The logic and / or steps represented in the flowchart or otherwise described herein, for example, can be considered as an ordered list of executable instructions for implementing logical functions, and can be embodied in any computer-readable medium for use by an instruction execution system, device or apparatus (such as a computer-based system, a system including a processor, or other system that can fetch instructions from an instruction execution system, device or apparatus and execute instructions), or in conjunction with such instruction execution systems, devices or apparatuses. For the purposes of this specification, "computer-readable medium" can be any device that can contain, store, communicate, propagate or transmit a program for use by an instruction execution system, device or apparatus, or in conjunction with such instruction execution systems, devices or apparatuses.

[0140] More specific examples of computer-readable media (a non-exhaustive list) include the following: an electrical connection with one or more wires (electronic device), a portable computer disk case (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable and programmable read-only memory (EPROM or flash memory), a fiber optic device, and a portable compact disk read-only memory (CDROM). In addition, the computer-readable medium may even be a paper or other suitable medium on which the program is printed, since the program may be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, deciphering or, if necessary, processing in another suitable manner, and then stored in a computer memory.

[0141] It should be understood that the various parts of the present application can be implemented by hardware, software, firmware or a combination thereof. In the above-mentioned embodiments, multiple steps or methods can be implemented by software or firmware stored in a memory and executed by a suitable instruction execution system. For example, if implemented by hardware, as in another embodiment, it can be implemented by any one of the following technologies known in the art or their combination: a discrete logic circuit having a logic gate circuit for implementing a logic function for a data signal, a dedicated integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0142] In the above description of this specification, the description with reference to the terms "one embodiment / example", "another embodiment / example" or "certain embodiments / examples" etc. means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representation of the above terms does not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials or characteristics described may be combined in any one or more embodiments or examples in a suitable manner.

[0143] Although the embodiments of the present application have been shown and described, those skilled in the art will appreciate that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present application, and that the scope of the present application is defined by the claims and their equivalents.

[0144] The above is a specific description of the preferred implementation of the present application, but the present application is not limited to the embodiments. Technical personnel familiar with the field can make various equivalent modifications or substitutions without violating the spirit of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the present application.

Claims

1. A method for reconstructing a tomographic image, characterized in that: include: Acquire target projection data, wherein the target projection data is any one of full-angle chord diagram data, sparse-angle chord diagram data, or limited-angle chord diagram data; Inputting the target projection data into a trained image reconstruction model to obtain an image reconstruction result; The trained image reconstruction model is obtained by the following steps: Acquire full-angle projection data, and pre-process the full-angle projection data to obtain a back-projection image, wherein the back-projection image includes a full-angle back-projection image, a sparse-angle back-projection image, and a limited-angle back-projection image; Performing constraint extraction on the back-projection image to obtain a constrained image, wherein the constrained image includes a full-angle constrained image, a sparse angle constrained image, and a limited-angle constrained image having a mutual constraint relationship; Performing dual-domain image constrained reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result; According to the constrained image and the dual-domain constrained reconstruction result, parameter constraints are updated on the initialized image reconstruction model to obtain the trained image reconstruction model.

2. The method for reconstructing a tomographic image according to claim 1, characterized in that: The preprocessing of the full-angle projection data to obtain a back-projection image includes: Performing a first filtered back-projection on the full-angle projection data to obtain the full-angle back-projection image; According to a preset sparse scanning multiple, sparse feature extraction is performed on the full-angle projection data to obtain sparse angle projection data, and, according to a preset limited angle scanning range, limited angle feature extraction is performed on the full-angle projection data to obtain limited angle projection data; Performing a second filtered back-projection on the sparse angular projection data to obtain the sparse angular back-projection image; A third filtered back-projection is performed on the limited-angle projection data to obtain the limited-angle back-projection image.

3. The method for reconstructing a tomographic image according to claim 1, characterized in that: The step of performing constraint extraction on the back-projection image to obtain a constrained image comprises: Extracting prior information from the back-projected image to obtain a prior image; The prior image is subjected to chord diagram compensation to obtain the constrained image.

4. The method for reconstructing a tomographic image according to claim 3, characterized in that: The prior image includes a full-angle prior image, a sparse angle prior image and a limited-angle prior image, and the prior information extraction of the back-projection image to obtain the prior image includes: Performing first information extraction on the full-angle back-projection image to obtain the full-angle priori image; Performing second information extraction on the sparse angle back-projection image to obtain the sparse angle prior image; A third information extraction is performed on the angle-limited back-projection image to obtain the angle-limited priori image.

5. The method for reconstructing a tomographic image according to claim 4, characterized in that: The performing chord diagram compensation on the prior image to obtain the constrained image includes: Acquire a first information mask and a second information mask, wherein the first information mask is used to characterize a sparse angle information mask where the sparse angle projection data is missing relative to the full angle projection data, and the second information mask is used to characterize a limited angle information mask where the limited angle projection data is missing relative to the full angle projection data; The priori image is mask reconstructed according to the first information mask and the second information mask to obtain the constrained image.

6. The method for reconstructing a tomographic image according to claim 5, characterized in that: The step of performing mask reconstruction on the prior image according to the first information mask and the second information mask to obtain the constrained image includes: According to the full-angle projection data, performing a first front projection compensation on the full-angle priori image to obtain the full-angle constrained image; According to the sparse angle projection data and the first information mask, performing a second front projection compensation on the sparse angle prior image to obtain the sparse angle constrained image; According to the angle-limited projection data and the second information mask, a third front projection compensation is performed on the angle-limited a priori image to obtain the angle-limited a priori image.

7. The method for reconstructing a tomographic image according to claim 1, characterized in that: The performing dual-domain image constrained reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result includes: Performing chord diagram domain restoration on the constrained image to obtain a chord diagram restored image; Perform image domain restoration on the chord diagram restoration image to obtain the dual-domain constrained reconstruction result.

8. A tomographic image reconstruction system, characterized in that: include: An acquisition module, used for acquiring target projection data, wherein the target projection data is any one of full-angle chord diagram data, sparse-angle chord diagram data or limited-angle chord diagram data; A processing module, used for inputting the target projection data into a trained image reconstruction model to obtain an image reconstruction result; The trained image reconstruction model is obtained by the following steps: Acquire full-angle projection data, and pre-process the full-angle projection data to obtain a back-projection image, wherein the back-projection image includes a full-angle back-projection image, a sparse-angle back-projection image, and a limited-angle back-projection image; Performing constraint extraction on the back-projection image to obtain a constrained image, wherein the constrained image includes a full-angle constrained image, a sparse angle constrained image, and a limited-angle constrained image having a mutual constraint relationship; Performing dual-domain image constrained reconstruction on the constrained image to obtain a dual-domain constrained reconstruction result; According to the constrained image and the dual-domain constrained reconstruction result, parameter constraints are updated on the initialized image reconstruction model to obtain the trained image reconstruction model.

9. An electronic device, characterized in that: include: at least one processor; at least one memory for storing at least one program; When the at least one program is executed by the at least one processor, the at least one processor implements the method according to any one of claims 1 to 7.

10. A computer-readable storage medium storing a program executable by a processor, characterized in that: The program executable by the processor is used to implement the method according to any one of claims 1 to 7 when executed by the processor.

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