Dynamic Dual-Energy CT Iterative Reconstruction Method, Device, Electronic Device and Storage Medium

Abstract

This application relates to the field of CT reconstruction technology, and particularly to a dynamic dual-energy CT iterative reconstruction method, device, electronic device, and storage medium. The method includes: performing CT scanning through a dynamic dual-energy CT detector to obtain dynamic dual-energy CT projection data; calculating multi-energy CT projections based on the dynamic dual-energy CT projection data; reconstructing the multi-energy CT projections to obtain multi-energy CT images, calculating new multi-energy CT projections based on the multi-energy CT images, and replacing the multi-energy CT projections with the new multi-energy CT projections until the iteration end condition is met to obtain the final multi-energy CT image, and calculating the final multi-energy CT projection based on the final multi-energy CT image. Thus, the problem that the regularization term artificially constructed in the related art is difficult to truly reflect the inherent characteristics of multi-energy CT images is solved. For multi-energy CT images obtained under different types and different scanning parameters, the noise can be effectively reduced, and no additional artifacts will be generated in the images.

Description

Technical Field

[0001] This application relates to the technical field of CT (Computed Tomography) reconstruction, and particularly relates to a dynamic dual-energy CT iterative reconstruction method, device, electronic device, and storage medium. Background Technique

[0002] In recent years, with the rapid development of photon-counting detectors, multi-energy CT imaging based on this type of detector has been increasingly widely used in clinics. By setting multiple voltage thresholds in the photon-counting detector circuit, X-ray photons of different energies can be divided into different energy windows for separate counting, thus enabling multi-energy CT imaging. The photon-counting detector can divide photons of different energies into multiple different energy windows, so while achieving multi-energy imaging, it can also avoid energy spectrum aliasing of data in different energy regions. Therefore, the multi-energy spectrum based on the photon-counting detector has better energy resolution; it can play a better role in applications such as material decomposition, virtual mono-energy imaging, multi-contrast agent imaging, and quantitative imaging.

[0003] Although multi-energy CT has the aforementioned advantages, it still faces some problems at the present stage. The two main ones are as follows:

[0004] 1. The statistical noise increases significantly. On the premise that the scanning dose remains unchanged, the total number of X-ray photons incident on the detector within the full energy range is constant; when the energy dimension increases, the photon count in each energy window will decrease, and the statistical noise of its data will thus increase. The quality of the multi-energy CT reconstructed image will drop significantly.

[0005] 2. The design complexity and cost of the detector increase. Each pixel crystal of the photon-counting detector is connected to a separate corresponding processing circuit. When the energy dimension increases, the number of comparators in the processing circuit of each pixel crystal will also increase accordingly. This will increase the design complexity of the circuit, and the cost of the detector will also increase significantly. Therefore, among existing photon-counting detectors, detectors with a threshold number of 2 still account for the majority, and a small number of detectors with multiple thresholds are also achieved at the cost of sacrificing spatial resolution.

[0006] In the related art, iterative image reconstruction is mainly used to solve the problem of increased statistical noise in multi - energy CT images. In the iterative image reconstruction method, the prior knowledge of multi - energy CT images is added to the iterative framework in the form of a regularization term, so as to achieve the purpose of image noise reduction. The prior knowledge of multi - energy CT images comes from its physical characteristics: for the transmitted photons of different energies in the same ray, the paths they pass through are the same, so CT images of different energies have extremely high correlation; for different pixels in a CT image of a certain energy, since the CT image reflects the change information of the object structure, the pixel values between adjacent pixels should not always change irregularly and should have the characteristic of piece - wise smoothness. The above two prior knowledge about multi - energy CT images can be added to the iterative reconstruction framework in the form of a regularization term. Its iterative framework can be expressed by the following formula:

[0007]

[0008] In the above formula, the first term is the data fidelity term, and its principle is to restore the reconstructed image based on the collected data. However, since the collected multi - energy CT data itself has relatively large noise, the multi - energy CT reconstructed image obtained only by minimizing the first term also contains relatively large noise. Therefore, it is necessary to suppress the noise through the second term, that is, the regularization term. There are many construction methods for the regularization term; the basis of its construction all comes from the above - mentioned two prior knowledge about multi - energy CT images. Among many construction methods of the regularization term, relatively typical regularization terms include the TV regularization term and the low - rank regularization term, and their expressions are as follows:

[0009]

[0010] ||X|| Low-Rank =σ M (X)

[0011] The first term in the above formula is the TV regularization term, which first calculates the norm of the gradient of the reconstructed images of different energy windows and then sums them; the second term is the low - rank norm, which is the sum of the largest M singular values after performing singular value decomposition on the reconstructed image.

[0012] Regarding the problem of complex detector design and high cost, the related art does not have a good solution, so most existing photon - counting detectors only have two energy thresholds.

[0013] However, the prior art iterative image reconstruction method reduces noise through prior knowledge of the image, which is reflected by artificially constructed regularization terms. However, the prior knowledge contained in CT images with different structures, different noise levels, and different scanning parameters varies greatly. Therefore, when using the same artificially constructed regularization term to denoise multi-energy CT images, it is difficult to achieve good denoising effects on different types of multi-energy CT images. When the weight of the regularization term is too small, its ability to suppress image noise is often weak; when the weight of the regularization term is too large, it is very easy to generate additional image artifacts in multi-energy CT images. This problem is difficult to be fundamentally solved. The core reason is that the artificially constructed regularization term is difficult to truly reflect the internal characteristics of multi-energy CT images. Summary of the Invention

[0014] The present application provides a dynamic dual-energy CT iterative reconstruction method, device, electronic device, and storage medium to solve the problem in the prior art that the artificially constructed regularization term is difficult to truly reflect the internal characteristics of multi-energy CT images, and can effectively reduce noise for multi-energy CT images obtained under different types and different scanning parameters without generating additional artifacts in the images.

[0015] The first aspect embodiment of the present application provides a dynamic dual-energy CT iterative reconstruction method, including the following steps:

[0016] Performing CT scanning through a dynamic dual-energy CT detector to obtain dynamic dual-energy CT projection data;

[0017] Calculating a multi-energy CT projection based on the dynamic dual-energy CT projection data; and

[0018] Reconstructing the multi-energy CT projection to obtain a multi-energy CT image, calculating a new multi-energy CT projection based on the multi-energy CT image, and replacing the multi-energy CT projection with the new multi-energy CT projection until the iteration end condition is satisfied to obtain a final multi-energy CT image, and calculating a final multi-energy CT projection based on the final multi-energy CT image.

[0019] Optionally, the calculating a multi-energy CT projection based on the dynamic dual-energy CT projection data includes:

[0020] Calculating a multi-energy CT projection based on the dynamic dual-energy CT projection data based on a projection calculation formula, where the projection calculation formula is:

[0021]

[0022] where I k，j is the number of transmitted photons of the k-th energy window and the j-th ray, and t k，jis the proportionality coefficient of the number of transmitted photons in different energy windows of each ray to the number of transmitted photons in the full energy window, I low，j and I high，j are the low-energy projection and high-energy projection in the dynamic dual-energy CT projection data of the j-th ray respectively, N k is the number of all energy windows, N th，j is the ordinal number of the dynamic energy threshold corresponding to the j-th ray, N th，j+1 is the ordinal number of the dynamic energy threshold corresponding to the (j + 1)-th ray, l represents the serial number of the energy window, and the value range is 1 ∽ N k , t l，j represents the proportionality coefficient of the number of transmitted photons in the l-th energy window corresponding to the j-th ray to the number of transmitted photons in the full energy window.

[0023] Optionally, the proportionality coefficient is obtained by the following formula:

[0024]

[0025] where A represents the system matrix, X k is the spectral CT image of the k-th energy window to be reconstructed, X l represents the spectral CT image of the l-th energy window.

[0026] Optionally, the calculation formulas for the low-energy projection and the high-energy projection are:

[0027]

[0028]

[0029] Optionally, the reconstruction of the multi-energy CT projection to obtain a multi-energy CT image includes:

[0030] Based on a reconstruction algorithm, reconstructing the multi-energy CT projection to obtain a multi-energy CT image;

[0031] where the reconstruction algorithm is the SART or SIRT or ART iterative reconstruction algorithm, and the initial value of the multi-energy CT image is the CT image reconstructed after superimposing the high- and low-energy window projections.

[0032] The second aspect of the embodiments of the present application provides a dynamic dual-energy CT iterative reconstruction device, including:

[0033] An acquisition module, configured to obtain dynamic dual-energy CT projection data by performing CT scanning through a dynamic dual-energy CT detector;

[0034] A first calculation module, configured to calculate a multi-energy CT projection according to the dynamic dual-energy CT projection data; and

[0035] A second calculation module, configured to reconstruct the multi-energy CT projections to obtain a multi-energy CT image, calculate a new multi-energy CT projection based on the multi-energy CT image, and replace the multi-energy CT projection with the new multi-energy CT projection until an iteration end condition is met, so as to obtain a final multi-energy CT image, and calculate a final multi-energy CT projection based on the final multi-energy CT image.

[0036] Optionally, the calculating the multi-energy CT projection based on the dynamic dual-energy CT projection data includes:

[0037] Calculating the multi-energy CT projection based on the dynamic dual-energy CT projection data according to a projection calculation formula, where the projection calculation formula is:

[0038]

[0039] where I k，j is the number of transmitted photons of the k-th energy window and the j-th ray, and t k，j is the proportionality coefficient of the number of transmitted photons of each ray in different energy windows to the number of transmitted photons of the full energy window, I low，j and I high，j are the low-energy projection and the high-energy projection in the dynamic dual-energy CT projection data of the j-th ray respectively, N k is the number of all energy windows, N th，j is the ordinal number of the dynamic energy threshold corresponding to the j-th ray, N th，j+1 is the ordinal number of the dynamic energy threshold corresponding to the (j + 1)-th ray, l represents the serial number of the energy window, and its value range is 1 to N k , and t l，j represents the proportionality coefficient of the number of transmitted photons of the l-th energy window corresponding to the j-th ray to the number of transmitted photons of the full energy window.

[0040] Optionally, the proportionality coefficient is obtained by the following formula:

[0041]

[0042] where A represents the system matrix, and X k is the spectral CT image of the k-th energy window to be reconstructed, and X l represents the spectral CT image of the l-th energy window.

[0043] Optionally, the calculation formulas for the low-energy projection and the high-energy projection are:

[0044]

[0045]

[0046] Optionally, the reconstructing the multi-energy CT projections to obtain a multi-energy CT image includes:

[0047] Based on a reconstruction algorithm, perform multi-energy CT projection reconstruction on the multi-energy CT projections to obtain multi-energy CT images;

[0048] Among them, the reconstruction algorithm is the SART or SIRT or ART iterative reconstruction algorithm, and the initial value of the multi-energy CT image is the CT image reconstructed after superimposing the high- and low-energy window projections.

[0049] An embodiment of the third aspect of the present application provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the dynamic dual-energy CT iterative reconstruction method as described in the above embodiments.

[0050] An embodiment of the fourth aspect of the present application provides a computer-readable storage medium, on which a computer program is stored, and the program is executed by a processor to be used to implement the dynamic dual-energy CT iterative reconstruction method as described in the above embodiments.

[0051] Thus, perform CT scanning through a dynamic dual-energy CT detector to obtain dynamic dual-energy CT projection data, calculate multi-energy CT projections based on the dynamic dual-energy CT projection data, perform reconstruction on the multi-energy CT projections to obtain multi-energy CT images, calculate new multi-energy CT projections based on the multi-energy CT images, and replace the multi-energy CT projections with the new multi-energy CT projections until the iteration end condition is satisfied to obtain the final multi-energy CT image, and calculate the final multi-energy CT projection based on the final multi-energy CT image. Thus, multi-energy CT projections with lower statistical noise are recovered from the dynamic dual-energy CT data, and then multi-energy CT images with lower noise are reconstructed, solving the problem that the artificially constructed regularization term in the related art is difficult to truly reflect the inherent characteristics of the multi-energy CT image. For multi-energy CT images obtained under different types and different scanning parameters, the noise can be effectively reduced, and no additional artifacts will be generated in the images.

[0052] Additional aspects and advantages of the present application will be given in part in the following description, become apparent in part from the following description, or be learned through the practice of the present application. Description of the Drawings

[0053] The above and / or additional aspects and advantages of the present application will become apparent and easy to understand from the following description of the embodiments in conjunction with the drawings, where:

[0054] Figure 1 is a flowchart of a dynamic dual-energy CT iterative reconstruction method according to an embodiment of the present application;

[0055] Figure 2 is a schematic diagram of a signal transmission model of a dynamic dual-energy CT detector according to an embodiment of the present application;

[0056] Figure 3 Schematic diagram of a dynamic dual - energy CT iterative method provided according to an embodiment of the present application;

[0057] Figure 4 Schematic diagram of the static dual - energy CT iterative reconstruction result and the dynamic dual - energy CT iterative reconstruction result provided according to an embodiment of the present application;

[0058] Figure 5 Block diagram of a dynamic dual - energy CT iterative reconstruction device provided according to an embodiment of the present application;

[0059] Figure 6 Schematic diagram of an electronic device provided according to an embodiment of the present application. Detailed implementation manners

[0060] The embodiments of the present application will be described in detail below. The examples of the embodiments are shown in the accompanying drawings, where the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described by referring to the accompanying drawings are exemplary and are intended to explain the present application, and should not be construed as a limitation to the present application.

[0061] The dynamic dual - energy CT iterative reconstruction method, device, electronic device, and storage medium of the embodiments of the present application will be described below with reference to the accompanying drawings. In view of the problem in the related art mentioned in the above - mentioned background art that the artificially constructed regularization term is difficult to truly reflect the internal characteristics of the multi - energy CT image, the present application provides a dynamic dual - energy CT iterative reconstruction method. In this method, CT scanning is performed by a dynamic dual - energy CT detector to obtain dynamic dual - energy CT projection data, and multi - energy CT projections are calculated based on the dynamic dual - energy CT projection data, and multi - energy CT images are reconstructed from the multi - energy CT projections, and new multi - energy CT projections are calculated based on the multi - energy CT images, and the new multi - energy CT projections are used to replace the multi - energy CT projections until the iteration end condition is satisfied to obtain the final multi - energy CT image, and the final multi - energy CT projection is calculated based on the final multi - energy CT image. Thus, the problem that the artificially constructed regularization term in the related art is difficult to truly reflect the internal characteristics of the multi - energy CT image is solved. For multi - energy CT images obtained under different types and different scanning parameters, noise can be effectively reduced, and no additional artifacts will be generated in the images.

[0062] Specifically, Figure 1 Flow chart of a dynamic dual - energy CT iterative reconstruction method provided by an embodiment of the present application.

[0063] As Figure 1 shown, the dynamic dual - energy CT iterative reconstruction method includes the following steps:

[0064] In step S101, CT scanning is performed by a dynamic dual-energy CT detector to obtain dynamic dual-energy CT projection data.

[0065] It can be understood that the dynamic dual-energy CT detector of the embodiment of the present application can accurately estimate the proportional coefficient of the transmitted photon numbers in each energy window from the dynamic dual-energy CT data through an iterative update method, and then calculate multi-energy CT projection data with lower statistical noise, and reconstruct a multi-energy CT image with less noise.

[0066] It should be noted that during the CT scanning process, dynamic dual-energy CT projection data can be obtained by dynamically changing the energy thresholds of high and low energies at different scanning angles and different detector pixel positions. Although the energy dimension of the dynamic dual-energy CT projection data is only 2, due to the existence of the dynamic energy threshold, the energy window division between the projection data of these two energies is also dynamically changing, including the energy spectrum information of multi-energy CT, realizing energy spectrum CT imaging. For the convenience of hardware implementation, an easier way to implement is to uniformly adjust the energy thresholds of all detectors along with the angle scanning, and in order to ensure the stability of the thresholds, the threshold sizes can be adjusted sequentially, which will not have a great impact on the dynamic dual-energy CT results.

[0067] Specifically, to implement the dynamic dual-energy CT iterative reconstruction algorithm in the embodiment of the present application, first, CT scanning needs to be performed by a dynamic dual-energy CT detector to obtain dynamic dual-energy CT data. The signal transmission model of the dynamic dual-energy CT detector can be as Figure 2 shown. The dynamic dual-energy CT detector can obtain dynamic dual-energy CT projection data by dynamically changing the energy thresholds at different scanning angles and different detector pixel positions during the scanning process. Although the energy dimension of the dynamic dual-energy CT projection data is only 2, due to the existence of the dynamic energy threshold, the energy window division between the projection data of these two energies is also dynamically changing. Therefore, the dynamic dual-energy CT projection data contains the energy spectrum information of multi-energy CT, and since there are higher photon counts in each energy window compared to the traditional multi-energy CT acquisition method, the dynamic dual-energy CT data has lower statistical noise.

[0068] In step S102, a multi-energy CT projection is calculated based on the dynamic dual-energy CT projection data.

[0069] Specifically, in the dynamic dual-energy CT detector model of the embodiment of the present application, the low-energy projection and high-energy projection data of its j-th ray can be expressed as:

[0070]

[0071]

[0072] where, I k，jis the number of transmitted photons of the k-th energy window and the j-th ray, I low，j and I high，j are the low-energy projection and high-energy projection in the dynamic dual-energy CT projection data of the j-th ray respectively, which are the actually collected data and equal to the sum of the corresponding multiple energy window projections. N k is the number of all energy windows. N th，j is the ordinal number of the dynamic energy threshold corresponding to the j-th ray.

[0073] When performing multi-energy CT reconstruction in the embodiments of the present application, multi-energy CT projection data I k，j is required. However, in a dynamic dual-energy CT detector, I k，j cannot be directly obtained, and only I low，j and I high，j can be indirectly obtained. Therefore, it is necessary to calculate the multi-energy CT projection from the dynamic dual-energy CT projection data:

[0074]

[0075] where, I k，j is the number of transmitted photons of the k-th energy window and the j-th ray, t k，j is the proportionality coefficient of the transmitted photons of each ray in different energy windows to the transmitted photons of the full energy window, I low，j and I high，j are the low-energy projection and high-energy projection in the dynamic dual-energy CT projection data of the j-th ray respectively, N k is the number of all energy windows, N th，j is the ordinal number of the dynamic energy threshold corresponding to the j-th ray, N th，j+1 is the ordinal number of the dynamic energy threshold corresponding to the (j + 1)-th ray, l represents the serial number of the energy window, and the value range is 1 to N k , t l，j represents the proportionality coefficient of the transmitted photons of the l-th energy window corresponding to the j-th ray to the transmitted photons of the full energy window. t k，j is the proportionality coefficient of the transmitted photons of each ray in different energy windows to the transmitted photons of the full energy window, and is specifically equal to:

[0076]

[0077] However, the above formula is the theoretical calculated value of t k，j . In a dynamic dual-energy CT detector, t k，j is also unknown. If it is desired to reconstruct a multi-energy CT image from the dynamic dual-energy CT data, t k，j must be known. If it is desired to reconstruct a multi-energy CT image, the proportionality coefficient t k，j can be calculated by an iterative update method, so as to calculate the multi-energy CT projection and further reconstruct the multi-energy CT image.

[0078] In step S103, multi-energy CT projections are reconstructed to obtain multi-energy CT images, and new multi-energy CT projections are calculated based on the multi-energy CT images. The new multi-energy CT projections replace the original multi-energy CT projections until the iteration end condition is met, obtaining the final multi-energy CT images, and the final multi-energy CT projections are calculated based on the final multi-energy CT images.

[0079] Optionally, in some embodiments, reconstructing multi-energy CT projections to obtain multi-energy CT images includes: based on a reconstruction algorithm, reconstructing multi-energy CT projections to obtain multi-energy CT images; wherein, the reconstruction algorithm can be an iterative reconstruction algorithm such as SART or SIRT or ART, and the initial value of the multi-energy CT image is a CT image reconstructed after superimposing high- and low-energy window projections.

[0080] Specifically, the dynamic dual-energy CT iterative reconstruction algorithm proposed in the embodiments of the present application can calculate the proportionality coefficient t k，j , thereby calculating multi-energy CT projections, and then reconstructing multi-energy CT images. As Figure 3 shown, the algorithm mainly includes the following two steps:

[0081] Step A: According to the reconstructed images X of each energy window reconstructed in Step B k .

[0082] Step B: According to the proportionality coefficient t calculated in Step A k，j to calculate the transmitted photon numbers I of each energy window k，j , and then reconstruct the reconstructed images X of each energy window k .

[0083] In Step A, t k，j can be calculated by the following formula:

[0084]

[0085] In Step B, then according to the proportionality coefficient t calculated in Step A k，j , the multi-energy CT projection I can be calculated from the dynamic dual-energy CT data according to formula (2) k，j , and then the multi-energy CT image X is reconstructed k . Through the repeated iterative calculations of Step A and Step B, the proportionality coefficient t k，j and the reconstructed image X k can be alternately updated to make the two continuously approach their true values. The number of iteration steps of the algorithm can be determined according to the actual situation, usually based on the convergence of the algorithm and the reconstruction image no longer changing.

[0086] When actually applying this algorithm, the reconstruction in step B can select iterative reconstruction algorithms such as SART, SIRT, or ART. The initial value of the multi-energy CT image is a CT image reconstructed after the superposition of high- and low-energy window projections.

[0087] The reason why the dynamic dual-energy CT iterative reconstruction method proposed in the embodiments of this application can reduce noise is that the originally acquired dynamic dual-energy CT data has relatively low statistical noise. Moreover, due to the existence of the dynamic energy threshold, the dynamic dual-energy CT projection data contains multi-spectrum information. Therefore, through the dynamic dual-energy CT iterative reconstruction algorithm proposed in the embodiments of this application, multi-energy projection data with lower statistical noise can be restored, and then a multi-energy CT image with less noise can be reconstructed.

[0088] As Figure 4 shown, Figure 4 This is a comparison chart of the reconstruction results of the dynamic dual-energy CT iterative reconstruction method in the embodiments of this application and the dynamic dual-energy reconstruction results in the related art. Figure 4 (a) in it is the reconstruction result of the static multi-energy CT in the related art. Figure 4 (b) in it is the reconstruction result of the dynamic dual-energy CT iterative reconstruction method in the embodiments of this application.

[0089] Compared with the multi-energy CT noise reduction method in the related art, the core noise reduction principle of the embodiments of this application is to increase the photon count of the projection data. The reason why the multi-energy CT image has greater noise is that the photon count in each energy window projection data decreases. Therefore, the dynamic dual-energy CT iterative reconstruction method proposed in the embodiments of this application can essentially solve the problem of high image noise. The finally reconstructed multi-energy CT image not only has low noise, but also does not introduce any additional artifacts or lose any image details. At the same time, when applying this algorithm, there is no need to specifically adjust the algorithm parameters for different imaging objects; the versatility and universality of this algorithm are better than previous methods.

[0090] It should be noted that the reconstruction step in the iteration of the embodiments of this application can adopt other CT reconstruction algorithms except SART, SIRT, or ART. Its reconstruction parameters and the number of iterations can also be adjusted. Moreover, the initial value of the algorithm does not have to be a CT image reconstructed after the superposition of high- and low-energy window projections. It can be a CT image of the imaged object in other energy ranges. And when applying the method of the embodiments of this application, the number of dynamic energy windows does not have to be two. When multiple dynamic energy thresholds are used during CT scanning to form projection data of multiple dynamic energy windows, the method proposed in the embodiments of this application can be used.

[0091] According to the dynamic dual-energy CT iterative reconstruction method proposed in the embodiments of the present application, CT scanning is performed by a dynamic dual-energy CT detector to obtain dynamic dual-energy CT projection data, and multi-energy CT projections are calculated based on the dynamic dual-energy CT projection data, and the multi-energy CT projections are reconstructed to obtain multi-energy CT images, and new multi-energy CT projections are calculated based on the multi-energy CT images, and the new multi-energy CT projections are used to replace the multi-energy CT projections until the iteration end condition is satisfied, and the final multi-energy CT image is obtained, and the final multi-energy CT projection is calculated based on the final multi-energy CT image. Thus, the problem that the regularization term artificially constructed in the related art is difficult to truly reflect the internal characteristics of the multi-energy CT image is solved, and the noise can be effectively reduced for multi-energy CT images obtained under different types and different scanning parameters, and no additional artifacts will be generated in the images.

[0092] Next, a dynamic dual-energy CT iterative reconstruction device according to an embodiment of the present application will be described with reference to the accompanying drawings.

[0093] Figure 5 It is a block diagram of the dynamic dual-energy CT iterative reconstruction device according to an embodiment of the present application.

[0094] As Figure 5 shown, the dynamic dual-energy CT iterative reconstruction device 10 includes: an acquisition module 100, a first calculation module 200, and a second calculation module 300.

[0095] Among them, the acquisition module 100 is configured to perform CT scanning by a dynamic dual-energy CT detector to obtain dynamic dual-energy CT projection data;

[0096] The first calculation module 200 is configured to calculate multi-energy CT projections based on the dynamic dual-energy CT projection data; and

[0097] The second calculation module 300 is configured to reconstruct the multi-energy CT projections to obtain multi-energy CT images, calculate new multi-energy CT projections based on the multi-energy CT images, and replace the multi-energy CT projections with the new multi-energy CT projections until the iteration end condition is satisfied, obtain the final multi-energy CT image, and calculate the final multi-energy CT projection based on the final multi-energy CT image.

[0098] Optionally, calculating the multi-energy CT projections based on the dynamic dual-energy CT projection data includes:

[0099] Based on the projection calculation formula, calculating the multi-energy CT projections based on the dynamic dual-energy CT projection data, where the projection calculation formula is:

[0100]

[0101] Among them, I k，j is the number of transmitted photons of the k-th energy window and the j-th ray, tk，j is the proportionality coefficient of the number of transmitted photons in different energy windows of each ray to the number of transmitted photons in the full energy window, I low，j and I high，j are the low-energy projection and high-energy projection in the dynamic dual-energy CT projection data of the j-th ray respectively, N k is the number of all energy windows, N th，j is the ordinal number of the dynamic energy threshold corresponding to the j-th ray, N th，j+1 is the ordinal number of the dynamic energy threshold corresponding to the (j + 1)-th ray, l represents the serial number of the energy window, and the value range is 1∽N k , t l，j represents the proportionality coefficient of the number of transmitted photons in the l-th energy window corresponding to the j-th ray to the number of transmitted photons in the full energy window.

[0102] Optionally, the proportionality coefficient is obtained by the following formula:

[0103]

[0104] where A represents the system matrix, X k is the spectral CT image of the k-th energy window to be reconstructed, X l represents the spectral CT image of the l-th energy window.

[0105] Optionally, the calculation formulas for the low-energy projection and high-energy projection are:

[0106]

[0107]

[0108] Optionally, reconstructing the multi-energy CT projection to obtain a multi-energy CT image includes:

[0109] Reconstructing the multi-energy CT projection based on a reconstruction algorithm to obtain a multi-energy CT image;

[0110] where the reconstruction algorithm is the SART or SIRT or ART reconstruction algorithm, and the initial value of the multi-energy CT image is the CT image reconstructed after the superposition of the high- and low-energy window projections.

[0111] It should be noted that the foregoing explanations of the embodiments of the dynamic dual-energy CT iterative reconstruction method also apply to the dynamic dual-energy CT iterative reconstruction device of this embodiment, and will not be elaborated here.

[0112] The dynamic dual-energy CT iterative reconstruction device proposed according to the embodiments of the present application performs CT scans through a dynamic dual-energy CT detector to obtain dynamic dual-energy CT projection data, calculates multi-energy CT projections based on the dynamic dual-energy CT projection data, reconstructs the multi-energy CT projections to obtain multi-energy CT images, calculates new multi-energy CT projections based on the multi-energy CT images, and replaces the multi-energy CT projections with the new multi-energy CT projections until the iteration end condition is met, obtaining the final multi-energy CT image, and calculates the final multi-energy CT projection based on the final multi-energy CT image. Thereby, the problem that the regularization term artificially constructed in the related art is difficult to truly reflect the internal characteristics of the multi-energy CT image is solved. For multi-energy CT images obtained under different types and different scanning parameters, the noise can be effectively reduced, and no additional artifacts will be generated in the images.

[0113] Figure 6 The structural schematic diagram of the electronic device provided by the embodiments of the present application. The electronic device may include:

[0114] A memory 601, a processor 602, and a computer program stored on the memory 601 and executable on the processor 602.

[0115] When the processor 602 executes the program, it implements the dynamic dual-energy CT iterative reconstruction method provided in the above embodiments.

[0116] Further, the electronic device further includes:

[0117] A communication interface 603 for communication between the memory 601 and the processor 602.

[0118] The memory 601 is used to store a computer program executable on the processor 602.

[0119] The memory 601 may include a high-speed RAM memory, and may also include non-volatile memory, such as at least one disk memory.

[0120] If the memory 601, the processor 602, and the communication interface 603 are implemented independently, the communication interface 603, the memory 601, and the processor 602 can be interconnected via a bus to complete communication with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 6 only a thick line is used in Figure 6 , but it does not mean that there is only one bus or one type of bus.

[0121] Optionally, in a specific implementation, if the memory 601, the processor 602, and the communication interface 603 are integrated on a single chip, the memory 601, the processor 602, and the communication interface 603 can complete communication with each other through an internal interface.

[0122] The processor 602 may be a Central Processing Unit (CPU), or an Application Specific Integrated Circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0123] The embodiments of the present application also provide a computer-readable storage medium, on which a computer program is stored, and when the program is executed by a processor, the dynamic dual-energy CT iterative reconstruction method as described above is implemented.

[0124] In the description of this specification, the descriptions with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples", etc. mean that the specific features, structures, materials, or characteristics described in connection with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic representations of the above terms do not necessarily refer to the same embodiment or example. Moreover, the specific features, structures, materials, or characteristics described can be combined in any one or N embodiments or examples in a suitable manner. In addition, without conflict, those skilled in the art can combine and combine the different embodiments or examples described in this specification and the features of the different embodiments or examples.

[0125] In addition, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the quantity of the indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include at least one such feature. In the description of this application, the meaning of "N" is at least two, such as two, three, etc., unless otherwise specifically defined.

[0126] Any process or method description represented in a flowchart or described otherwise herein may be understood to represent a module, segment, or portion of code including one or more N executable instructions for implementing a customized logical function or process, and the scope of the preferred embodiments of this application includes additional implementations in which functions may be performed in a substantially simultaneous manner or in a reverse order according to the functions involved, rather than in the order shown or discussed, which should be understood by those skilled in the art to which the embodiments of this application pertain.

[0127] The logic and / or steps represented in a flowchart or described otherwise herein, for example, may be considered a sequenced list of executable instructions for implementing a logical function, and may be embodied specifically in any computer-readable medium for use by or in connection with an instruction execution system, apparatus, or device, such as a computer-based system, a system including a processor, or other systems that can fetch and execute instructions from the instruction execution system, apparatus, or device. For the purposes of this specification, a "computer-readable medium" can be any device that can contain, store, communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of the computer-readable medium include the following: an electrical connection portion having one or N wirings (electronic device), a portable computer diskette (magnetic device), a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber device, and a portable compact disc read-only memory (CDROM). Additionally, the computer-readable medium can even be paper or other suitable medium on which the program can be printed, as the program can be obtained electronically, for example, by optically scanning the paper or other medium, followed by editing, interpretation, or other suitable processing as necessary, and then storing it in a computer memory.

[0128] It should be understood that each part of the present application can be implemented by hardware, software, firmware, or a combination thereof. In the above embodiments, the N 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, any one or a combination of the following techniques well known in the art can be used: discrete logic circuits with logic gate circuits for implementing logical functions on data signals, application specific integrated circuits with suitable combinational logic gate circuits, programmable gate arrays (PGAs), field programmable gate arrays (FPGAs), etc.

[0129] Those of ordinary skill in the art can understand that all or part of the steps carried by the method of implementing the above embodiments can be completed by instructing relevant hardware through a program, and the program can be stored in a computer-readable storage medium. When the program is executed, it includes one or a combination of the steps of the method embodiments.

[0130] In addition, each functional unit in various embodiments of the present application can be integrated into a processing module, or each unit can exist physically alone, or two or more units can be integrated into one module. The above integrated module can be implemented in the form of hardware or in the form of a software functional module. When the above integrated module is implemented in the form of a software functional module and sold or used as an independent product, it can also be stored in a computer-readable storage medium.

[0131] The above-mentioned storage medium can be a read-only memory, a magnetic disk, an optical disc, etc. Although the embodiments of the present application have been shown and described above, it can be understood that the above embodiments are exemplary and should not be construed as limiting the present application. Those of ordinary skill in the art can make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A dynamic dual-energy CT iterative reconstruction method, characterized in that, Including the following steps: Performing CT scanning by a dynamic dual-energy CT detector to obtain dynamic dual-energy CT projection data; Calculating multi-energy CT projections based on the dynamic dual-energy CT projection data; And Reconstructing the multi-energy CT projections to obtain multi-energy CT images, calculating new multi-energy CT projections based on the multi-energy CT images, and replacing the multi-energy CT projections with the new multi-energy CT projections until the iteration end condition is satisfied, obtaining the final multi-energy CT images, and calculating final multi-energy CT projections based on the final multi-energy CT images; The calculating the multi-energy CT projections based on the dynamic dual-energy CT projection data includes: Calculating multi-energy CT projections based on the dynamic dual-energy CT projection data according to a projection calculation formula, wherein the projection calculation formula is: ； Among them, is the th energy window, the number of transmitted photons of the th ray, is the proportionality coefficient of the number of transmitted photons of each ray in different energy windows to the number of transmitted photons of the full energy window, and are respectively the low-energy projection and the high-energy projection in the dynamic dual-energy CT projection data of the th ray, is the number of all energy windows, is the ordinal number of the dynamic energy threshold corresponding to the th ray, is the ordinal number of the dynamic energy threshold corresponding to the th ray, represents the serial number of the energy window, represents the proportionality coefficient of the number of transmitted photons of the th ray in the th energy window to the number of transmitted photons of the full energy window.

2. The method according to claim 1, wherein The reconstructing the multi-energy CT projections to obtain multi-energy CT images includes: Reconstructing the multi-energy CT projections to obtain multi-energy CT images based on a reconstruction algorithm; Wherein, the reconstruction algorithm is the SART or SIRT or ART iterative reconstruction algorithm, and the initial value of the multi-energy CT images is a CT image reconstructed after the superposition of high- and low-energy window projections.

3. A dynamic dual-energy CT iterative reconstruction device, characterized in that, Including: An acquisition module for performing CT scanning by a dynamic dual-energy CT detector to obtain dynamic dual-energy CT projection data; A first calculation module for calculating multi-energy CT projections based on the dynamic dual-energy CT projection data; And A second calculation module for reconstructing the multi-energy CT projections to obtain multi-energy CT images, calculating new multi-energy CT projections based on the multi-energy CT images, and replacing the multi-energy CT projections with the new multi-energy CT projections until the iteration end condition is satisfied, obtaining the final multi-energy CT images, and calculating final multi-energy CT projections based on the final multi-energy CT images; The calculating the multi-energy CT projections based on the dynamic dual-energy CT projection data includes: Calculating multi-energy CT projections based on the dynamic dual-energy CT projection data according to a projection calculation formula, wherein the projection calculation formula is: ； Among them, is the th energy window, and the number of transmitted photons of the th ray. is the proportionality coefficient of the number of transmitted photons of each ray in different energy windows to the number of transmitted photons in the full energy window. and are respectively the low-energy projection and the high-energy projection in the dynamic dual-energy CT projection data of the th ray. is the number of all energy windows. is the ordinal number of the dynamic energy threshold corresponding to the th ray. is the ordinal number of the dynamic energy threshold corresponding to the th ray. represents the serial number of the energy window. represents the proportionality coefficient of the number of transmitted photons of the th ray in the th energy window to the number of transmitted photons in the full energy window.

4. An electronic device, characterized in that, Including: A memory, a processor, and a computer program stored on the memory and executable on the processor, and the processor executes the program to implement the dynamic dual-energy CT iterative reconstruction method according to any one of claims 1-2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to be used for implementing the dynamic dual-energy CT iterative reconstruction method according to any one of claims 1-2.

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