Method and apparatus for substance decomposition based on physical parameters, electronic device and medium

Abstract

The present application relates to the technical field of spectral CT imaging, and particularly relates to a physical parameter-based material decomposition method and device, electronic equipment and medium, the method comprising: calculating at least two groups of different equivalent energy CT projection correction parameters and spectral correction parameters based on known phantom spectral CT pre-scan data and initial equivalent spectrum; correcting a material decomposition model according to the parameters to establish a mapping relationship between CT projection data and base material thickness under corresponding equivalent energy. The corresponding projection data of the to-be-measured phantom is corrected using the projection correction parameters, and the base material thickness data is obtained in combination with the mapping relationship; the base material image is generated through analysis or iterative reconstruction, and the virtual single-energy image is obtained through linear combination, thereby solving the problem of low spectral CT material quantitative analysis precision in the related art and improving the precision of spectral CT material quantitative analysis.

Description

Technical Field

[0001] This invention relates to the field of energy spectrum CT imaging technology, and in particular to a method, apparatus, electronic device and medium for the decomposition of matter based on physical parameters. Background Technology

[0002] Computed tomography (CT) is an imaging technique that uses spectral projections of different energy levels to decompose the energy-related linear decay coefficients of matter, thereby enabling the differentiation of materials. Compared to traditional single-energy CT, spectral CT not only provides quantitative information such as effective atomic numbers, electron density, or material composition, but also effectively suppresses beam hardening artifacts and metal artifacts commonly found in single-energy CT imaging. Therefore, this technology has demonstrated significant value in clinical applications and has been widely used in recent years in fields such as gout detection, stroke screening, and angiography, achieving rapid development. Among the related technologies, there are mainly X-ray source-based solutions (such as dual-source dual-kVp or kV switching methods), detector-based solutions (such as dual-layer detectors or photon counting detectors), and filter-based solutions (such as beam splitting filters or spectral filters / modulators).

[0003] However, in actual CT systems, there are non-ideal factors such as scattering contamination, which cause deviations between the measured projection data and the ideal energy spectrum projection model. At the same time, the equivalent energy spectrum on which the decomposition of matter depends is often difficult to estimate accurately, resulting in low accuracy of quantitative analysis of matter in energy spectrum CT, which urgently needs to be solved. Summary of the Invention

[0004] This invention provides a method, apparatus, electronic device, and medium for material decomposition based on physical parameters, in order to solve the problem of low accuracy in energy-dispersive CT (EDCT) quantitative analysis of materials and improve the accuracy of ECT quantitative analysis of materials.

[0005] To achieve the above objectives, a first aspect of the present invention proposes a material decomposition method based on physical parameters, comprising the following steps: calculating at least two sets of projection correction parameters and energy spectrum correction parameters at different equivalent energies based on the energy spectrum CT pre-scan data of a known information phantom and a pre-calibrated initial equivalent energy spectrum; correcting the material decomposition model based on the projection correction parameters and the energy spectrum correction parameters, and establishing a mapping relationship between the CT projection data at at least two sets of different equivalent energies and the thickness of the base material; correcting the CT projection data of the phantom under test at at least two sets of different equivalent energies using the projection correction parameters to obtain corrected CT projection data; decomposing the phantom under test using a preset decomposition strategy based on the corrected scan data and the corrected equivalent energy spectrum to obtain the material decomposition result of the phantom under test, and obtaining the base material thickness data according to the mapping relationship and the corrected CT projection data at least two sets of different equivalent energies; generating a base material image based on reconstruction methods including but not limited to analytical, iterative, or data-driven methods, and obtaining a virtual monoenergetic image through linear combination.

[0006] Furthermore, in some embodiments, based on the energy spectrum CT pre-scan data of the known information phantom and the pre-calibrated initial equivalent energy spectrum, at least two sets of projection correction parameters and energy spectrum correction parameters under different equivalent energies are calculated, including: performing an energy spectrum CT pre-scan on the known information phantom, and using the pre-scan projection data to perform material decomposition and image reconstruction to obtain a base material image; obtaining a base material true value image based on the known information and the base material image, and reprojecting the base material true value image to obtain the base material thickness true value; optimizing the base material thickness true value based on a physically parameterized material decomposition model and a joint parameter optimization method to obtain a set of projection correction parameters and energy spectrum correction parameters that minimize the material decomposition deviation, wherein the known information is the distribution of the base material density true value of the phantom.

[0007] Furthermore, in some embodiments, the preset joint parameter optimization strategy is as follows: ; in, For projection correction parameters, For energy spectrum correction parameters, These are projected measurements. This is the integral measurement value of the base material line. For the true value of the thickness of the base material, Let the data consistency loss function be... For parameters regular terms, For parameters The regular term.

[0008] Furthermore, in some embodiments, the pre-constructed physically parameterized matter decomposition model is: ; in, The first measurement obtained from the actual system d The detector pixel, the v Each scanning angle 、 No. k Projected values ​​under the equivalent energy spectrum For projection correction terms, For the first A normalized equivalent energy spectrum This is a correction term for the energy spectrum. Indicates the first Individual base material linear attenuation coefficient .

[0009] Furthermore, in some embodiments, the establishment of the mapping relationship between the projection data and the thickness of the substrate can be achieved by lookup tables or polynomial fitting.

[0010] Furthermore, in some embodiments, generating the base material image based on the analytical or iterative reconstruction method includes: directly obtaining the base material image through a one-step iterative method using a physically parameterized material decomposition model.

[0011] The physical parameter-based material decomposition method proposed in this invention constructs a physical parameterized material decomposition model for energy spectrum CT, including projection correction parameters and energy spectrum correction parameters, through pre-scanning and joint parameter optimization. After acquiring the equivalent energy spectrum and the scan data of the phantom to be tested, the model is used to correct the scan data and the equivalent energy spectrum. Then, the material decomposition of the phantom to be tested is completed using a preset strategy to obtain the results. This solves the problem of low accuracy in quantitative material analysis of energy spectrum CT in related technologies and improves the accuracy of quantitative material analysis of energy spectrum CT.

[0012] To achieve the above objectives, a second aspect of the present invention proposes a material decomposition device based on physical parameters, comprising: a construction module, configured to calculate at least two sets of projection correction parameters and energy spectrum correction parameters under different equivalent energies based on the energy spectrum CT pre-scan data of a known information phantom and a pre-calibrated initial equivalent energy spectrum; to correct the material decomposition model based on the projection correction parameters and the energy spectrum correction parameters, and to establish a mapping relationship between the CT projection data under at least two sets of different equivalent energies and the thickness of the base material; a correction module, configured to correct the CT projection data of the phantom under test under at least two sets of different equivalent energies using the projection correction parameters, to obtain corrected CT projection data; a decomposition module, configured to decompose the phantom under test using a preset decomposition strategy based on the corrected scan data and the corrected equivalent energy spectrum, to obtain the material decomposition result of the phantom under test, and to obtain base material thickness data according to the mapping relationship and the corrected CT projection data under at least two sets of different equivalent energies; and a generation module, configured to generate a base material image based on reconstruction methods including but not limited to analytical, iterative, or data-driven methods, and to obtain a virtual monoenergetic image through linear combination.

[0013] Further, in some embodiments, the construction module is specifically used for: calculating at least two sets of projection correction parameters and energy spectrum correction parameters under different equivalent energies based on the energy spectrum CT pre-scan data of the known information phantom and the pre-calibrated initial equivalent energy spectrum, including: performing energy spectrum CT pre-scan on the known information phantom, and using the pre-scan projection data to perform material decomposition and image reconstruction to obtain a base material image; obtaining a base material true value image based on the known information and the base material image, and reprojecting the base material true value image to obtain the base material thickness true value; optimizing the parameters of the base material thickness true value based on a physically parameterized material decomposition model and a joint parameter optimization method to obtain a set of projection correction parameters and energy spectrum correction parameters that minimize the material decomposition deviation, wherein the known information is the distribution of the base material density true value of the phantom.

[0014] Furthermore, in some embodiments, the preset joint parameter optimization strategy is as follows: ; in, For projection correction parameters, For energy spectrum correction parameters, These are projected measurements. This is the integral measurement value of the base material line. The true value of the line integral of the basic matter is given. Let the data consistency loss function be... For parameters regular terms, For parameters The regular term.

[0015] Furthermore, in some embodiments, the physically parameterized matter decomposition model is: ; in, The first measurement obtained from the actual system d The detector pixel, the v Each scanning angle 、 No. k Projected values ​​under the equivalent energy spectrum For projection correction terms, For the first A normalized equivalent energy spectrum This is a correction term for the energy spectrum. Indicates the first Individual base material linear attenuation coefficient .

[0016] Furthermore, in some embodiments, the establishment of the mapping relationship between the projection data and the thickness of the substrate can be achieved by lookup tables or polynomial fitting.

[0017] Furthermore, in some embodiments, the base generation module is also used to: directly obtain a base material image by using a physically parameterized material decomposition model through a one-step iteration.

[0018] The physical parameter-based material decomposition device proposed in this embodiment of the invention constructs a physical parameterized material decomposition model for energy spectrum CT, including projection correction parameters and energy spectrum correction parameters, through pre-scanning and joint parameter optimization. After acquiring the equivalent energy spectrum and the scan data of the phantom to be tested, the model is used to correct the scan data and the equivalent energy spectrum. Then, the material decomposition of the phantom to be tested is completed and the results are obtained using a preset strategy. This solves the problem of low accuracy in quantitative material analysis of energy spectrum CT in related technologies and improves the accuracy of quantitative material analysis of energy spectrum CT.

[0019] To achieve the above objectives, a third aspect of the present invention provides an electronic device, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the physical parameter-based material decomposition method as described in the above embodiments.

[0020] To achieve the above objectives, a fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which is executed by a processor to implement the physical parameter-based material decomposition method as described in the above embodiments.

[0021] To achieve the above objectives, a fifth aspect of the present invention provides a computer program product, including a computer program that is executed to implement the physical parameter-based material decomposition method as described in the above embodiments.

[0022] Additional aspects and advantages of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0023] The above and / or additional aspects and advantages of the present invention will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein: Figure 1 A flowchart of material decomposition based on physical parameters provided according to an embodiment of the present invention; Figure 2 A schematic diagram of Monte Carlo CT projection simulation results of a water-iodine cylindrical phantom under X-ray energy spectra of 80kVp and 140kVp according to a specific embodiment of the present invention; Figure 3 A parameter optimization method and a schematic diagram of substance decomposition provided according to a specific embodiment of the present invention; Figure 4 This is a schematic diagram comparing the images of base materials (water, iodine) under dual-layer detector energy spectral CT with parametric material decomposition and conventional material decomposition according to a specific embodiment of the present invention. Figure 5 This is a block diagram of a physical parameter-based material decomposition device provided according to an embodiment of the present invention; Figure 6 This is a schematic diagram of the structure of an electronic device provided according to an embodiment of the present invention. Detailed Implementation

[0024] Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain the present invention, and should not be construed as limiting the present invention.

[0025] The following description, with reference to the accompanying drawings, describes a physical parameter-based material decomposition method, apparatus, electronic device, and medium according to embodiments of the present invention. Addressing the issue of low accuracy in quantitative material analysis using energy-dispersive CT (EDCT) as mentioned in the background section, the present invention provides a physical parameter-based material decomposition method. After obtaining the equivalent energy spectrum of an ECT system and performing a pre-scan of the phantom under test using ECT, the scan data and the equivalent energy spectrum are corrected based on a pre-constructed physical parameterized material decomposition model. Then, a preset decomposition strategy is used to decompose the phantom under test, ultimately obtaining the material decomposition result. This method solves the problem of low accuracy in quantitative material analysis using ECT in related technologies and improves the accuracy of quantitative material analysis using ECT.

[0026] Before describing the physical parameter-based matter decomposition method of the present invention, a prior art matter decomposition method is first introduced, wherein the traditional energy spectrum CT projection model used for base matter decomposition can be expressed as:

[0027]

[0028] in Indicates the first A normalized equivalent energy spectrum; Indicates the first Normalized equivalent energy spectrum and ray path Corresponding projection; This indicates that the m-th base material follows the ray path. The line integral; Indicates the first Individual base material linear attenuation coefficient Represents position vector The first The density of each basic substance. Among them... ) , Indicates the number of times the object decays. Normalized equivalent energy spectrum and ray path The corresponding detector signal, Indicates the first case when there is no object Normalized equivalent energy spectrum and ray path The corresponding detector signal. Considering the differences in normalized equivalent energy spectra between different detector pixels and the ray paths under multiple viewpoints in CT imaging, equation (1) can be discretized to obtain the detector pixel d, viewpoint v, and energy spectrum. The corresponding projection is:

[0029] in, It is the discrete step size of photon energy. According to this model, the image of the base matter can be obtained by either projection domain matter decomposition and reconstruction or a one-step method. These two traditional matter decomposition methods can be simply represented as follows:

[0030]

[0031] in, Image representing the base material. Represents projection data, , , , , and These represent the number of image rows, the number of image columns, the number of detectors, the number of projection angles, the number of base materials, and the number of equivalent energy spectra, respectively. For the projective domain matter decomposition operator, Operators for CT image reconstruction This is a one-step iterative operator. Wherein, and The normalized equivalent energy spectrum needs to be estimated in advance for each process. , Due to the limited accuracy of energy spectrum estimation methods, the estimated normalized equivalent energy spectrum deviates to some extent from the actual system. This deviation leads to... and The inability to correctly decompose matter, coupled with non-ideal physical factors such as scattering and detector hysteresis in the actual system causing deviations in the measured projection compared to the ideal model, necessitates a matter decomposition method based on physical parameters to eliminate sheet bias.

[0032] The material decomposition method based on physical parameters according to embodiments of the present invention will now be described with reference to the accompanying drawings.

[0033] Specifically Figure 1 This is a flowchart of a substance decomposition based on physical parameters according to an embodiment of the present invention.

[0034] like Figure 1 As shown, this physical parameter-based material decomposition method includes the following steps: In step S101, based on the energy spectrum CT pre-scan data of the known information phantom and the pre-calibrated initial equivalent energy spectrum, at least two sets of projection correction parameters and energy spectrum correction parameters under different equivalent energies are calculated; based on the projection correction parameters and energy spectrum correction parameters, the material decomposition model is corrected, and the mapping relationship between the CT projection data and the thickness of the base material under at least two sets of different equivalent energies is established.

[0035] Among them, the material decomposition model refers to the improved energy spectrum CT projection model that introduces projection bias parameters and energy spectrum bias parameters to correct the non-ideal physical effects in the actual system. The equivalent energy spectrum refers to the equivalent energy distribution characteristics of the X-rays emitted by the energy spectrum CT equipment. The phantom to be tested refers to a sample or model containing the target material.

[0036] Furthermore, in some embodiments, based on the energy spectrum CT pre-scan data of the known information phantom and the pre-calibrated initial equivalent energy spectrum, at least two sets of projection correction parameters and energy spectrum correction parameters under different equivalent energies are calculated, including: performing an energy spectrum CT pre-scan on the known information phantom, and using the pre-scan projection data to perform material decomposition and image reconstruction to obtain a base material image; obtaining a base material true value image based on the known information and the base material image, and reprojecting the base material true value image to obtain the base material thickness true value; and optimizing the base material thickness true value based on a physically parameterized material decomposition model and a joint parameter optimization method to obtain a set of projection correction parameters and energy spectrum correction parameters that minimize the material decomposition deviation, wherein the known information is the distribution of the base material density true value of the phantom.

[0037] In some embodiments, the preset joint parameter optimization strategy is as follows: ; in, For projection correction parameters, For energy spectrum correction parameters, These are projected measurements. This is the integral measurement value of the base material line. For the true value of the thickness of the base material, Let the data consistency loss function be... For parameters regular terms, For parameters The regular term.

[0038] Specifically, in the system's unloaded (phantom-free) state, the X-ray source emits rays, and the detector records photon counts in different energy ranges. Combined with hardware parameters such as tube voltage and filters, an equivalent energy spectrum of energy-intensity distribution is generated through a calibration algorithm. When the phantom to be tested is pre-scanned, it is fixed on the scanning bed, and parameters such as scanning range and energy channels are set. The system drives the X-ray source and detector to rotate and scan around the phantom. The detector receives the attenuated signal and converts it into multi-energy channel projection data, thus obtaining the scanning data of the phantom to be tested.

[0039] As one possible approach, projection correction and energy spectrum correction are expressed as follows: ; ; in The corrected projection value. The corrected energy spectrum is used to decompose matter using the corrected projection value and energy spectrum.

[0040] In step S102, the CT projection data of the phantom under test at least two sets of different equivalent energies are corrected using projection correction parameters to obtain corrected CT projection data.

[0041] Specifically, based on a pre-constructed physical parameterized material decomposition model, the scan data (i.e., actual projection value) of the test phantom is first superimposed with a polynomial projection correction term using the optimized projection correction parameters in the model to obtain the corrected scan data. Then, by combining the optimized energy spectrum correction parameters in the model with the attenuation coefficients of aluminum, detector crystal materials, etc., the equivalent energy spectrum of the energy spectrum CT system is adjusted through the energy spectrum fine-tuning formula to obtain the corrected equivalent energy spectrum.

[0042] Specifically, firstly, a pre-scan of a pre-defined standard phantom with known composition is performed using an energy-dispersive CT system. Based on the obtained pre-scan data, material decomposition and image reconstruction are performed using traditional methods such as projection domain material decomposition or one-step methods to obtain the base material image. Next, combining the prior information that the standard phantom's composition is known, the density and other parameters of the corresponding material in the base material image are filled with true values ​​to obtain the base material true image. Then, a forward reprojection operation is performed on this true image to generate the true value of the base material line integral. Finally, based on a pre-defined parameter optimization strategy consisting of a data consistency loss function, projection correction parameter regularization, and energy-dispersive CT correction parameter regularization, the base material image is further optimized. The parameters are optimized by solving the true value of the linear integral of the base matter to obtain the projection correction parameters and energy spectrum correction parameters that minimize the deviation of matter decomposition. Finally, the optimized projection correction parameters and energy spectrum correction parameters are substituted into the pre-constructed model framework containing projection correction terms and energy spectrum correction terms to obtain the pre-constructed physical parameterized matter decomposition model.

[0043] In step S103, based on the corrected scan data and the corrected equivalent energy spectrum, the material of the test phantom is decomposed using a preset decomposition strategy to obtain the material decomposition result of the test phantom, and the thickness data of the base material is obtained according to the mapping relationship and at least two sets of CT projection data under different equivalent energies after correction.

[0044] Furthermore, in some embodiments, the pre-constructed physically parameterized matter decomposition model is as follows: ; in, ; ; ; in, The first measurement obtained from the actual system d The detector pixel, the v Each scanning angle 、 No. k Projected values ​​under the equivalent energy spectrum For projection correction terms, For the first A normalized equivalent energy spectrum for First-order projection correction parameters, For the projected polynomial power product term, This is a correction term for the energy spectrum. For energy spectrum correction parameters, Indicates the first Individual base material linear attenuation coefficient , The attenuation coefficient of the nth material used for energy spectrum correction. It should be noted that the projection correction parameters It is a global parameter, meaning that this parameter is shared across different energy spectra and detector pixels, while This is a local parameter, meaning that the parameter differs across different energy spectra and pixels.

[0045] Furthermore, regarding the energy spectrum correction term, This is equivalent to using energy spectrum filtering to normalize the estimated equivalent energy spectrum. For fine-tuning of energy spectrum filtering, materials such as aluminum, water, and detector crystals are typically selected. This represents the equivalent thickness of the corresponding material. The projection correction term is mainly affected by factors such as scattering and detector offset variations. In CT imaging systems with severe scattering contamination, hardware or software methods are used for scattering correction. Hardware correction methods (such as backscattering grids) usually retain small-angle scattering, while software correction methods can accurately calculate the scattering distribution, but the scattering amplitude needs to be linearly scaled to suit the actual scattering intensity; that is, software correction methods also retain slight scattering. Assuming this slight scattering and the combined measurement offset caused by detector offset variations... Repeatable, weakly correlated with the object and relative to the main beam signal If the value is small, the deviation caused by the projection can be expressed as: ; It can be further approximated as: ; in It is a constant. This represents the projected value measured by the actual system, i.e. Therefore, the projection deviation caused by scattering can be expressed as a constant scaling of the natural exponent of the measured projection value of the actual system. Considering the errors introduced by the above assumptions and approximations, and the property that the exponential function of the natural constant can be approximated by a polynomial, it can be further directly expressed as the projection polynomial corresponding to each energy spectrum: To demonstrate the rationality of the above assumptions and approximations, Monte Carlo simulations were used to compare the relationship between the proposed projection deviation and the scaling of the natural exponential constant of the projection value as a function of material variations.

[0046] Figure 2 This is a schematic diagram of Monte Carlo CT projection simulation results of a water-iodine cylindrical phantom under X-ray energy spectra of 80kVp and 140kVp according to a specific embodiment of the present invention, as shown in the figure. Figure 2 As shown, For projection bias, the constant scaling of the projection natural index The results are basically consistent under the same correction parameters but different perspectives, indicating that the correction parameters are weakly correlated with the material thickness.

[0047] In step S104, a base material image is generated based on reconstruction methods including but not limited to analytical, iterative, or data-driven methods, and a virtual monoenergetic image is obtained through linear combination.

[0048] Among them, the matrix material image refers to the CT image that quantitatively characterizes the spatial distribution and content of various matrix materials inside an object, generated based on multi-energy projection data acquired by energy spectrum CT, while the virtual single-energy image refers to the CT image that simulates the imaging effect of single-energy X-rays, generated based on multi-energy projection data acquired by energy spectrum CT.

[0049] Furthermore, in some embodiments, generating a base material image based on an analytical or iterative reconstruction method includes replacing it with a base material image obtained directly through a one-step iterative method using a physically parameterized material decomposition model.

[0050] Specifically, the core of the projection domain material decomposition strategy is to first decompose and then reconstruct. First, the raw projection data obtained from energy spectrum CT scans is processed. During the projection stage, the line integrals of the matrix material are decomposed from these multi-energy projection data. Then, CT image reconstruction methods are used to convert these decomposed matrix material line integrals into a spatial domain matrix material image, thereby achieving quantitative analysis of the material composition. The core of the one-step material decomposition strategy is to perform decomposition and reconstruction simultaneously. It directly processes the raw projection data through an iterative algorithm. During the iteration process, the characteristics of the energy spectrum and the physical relationship between the projection data and the matrix material are considered simultaneously, solving for the spatial domain matrix material image in one step.

[0051] To enable those skilled in the art to better understand the physical parameter-based material decomposition method of the present invention, the following explanation will be provided in conjunction with specific embodiments.

[0052] Figure 3 This invention provides a parameter optimization method and a schematic diagram of material decomposition according to a specific embodiment of the present invention, combined with... Figure 3 As shown, in the pre-scanning and processing stage, the standard phantom is first pre-scanned to obtain multi-energy projections. After pre-decomposition and reconstruction, the base material image is obtained, and then the ground truth image is obtained by combining prior information. Subsequently, forward projection is used to generate the ground truth line integral of the base material. Then, in the parameter optimization stage, the optimal projection correction parameters are solved by minimizing the objective function that includes the data consistency loss function and the regularization term. and energy spectrum correction parameters Finally, in the material decomposition stage, projection correction parameters are used. Projection correction is performed on the projected data using energy spectrum correction parameters. The energy spectrum is corrected, and then the material decomposition of the test model is completed by projection domain decomposition or one-step strategy.

[0053] To verify the effectiveness of the physical parameter-based material decomposition method of this invention, physical experiments were conducted in a dual-layer detector energy dispersive CT imaging scenario to verify the improvement effect of the method on the quantitative accuracy of water iodine in the Gammex phantom. Figure 4 This is a schematic diagram comparing the images of base materials (water, iodine) under dual-layer detector energy-spectral CT with parametric material decomposition and conventional material decomposition according to a specific embodiment of the present invention. Figure 4 As shown, the matrix material images obtained by the physical parameter-based material decomposition method are clearer and more accurate in characterizing material distribution. It can effectively reduce artifacts or gray-scale inhomogeneity problems that may exist in traditional methods, and more accurately distinguish the spatial distribution of water and iodine. Compared with traditional material decomposition methods, the physical parameter-based material decomposition method significantly reduces the quantitative error of water and iodine in Gammex phantoms, which not only improves the quantitative accuracy of matrix materials, but also ensures the stability of the results. At the same time, it does not amplify the decomposition noise, which confirms the effectiveness of the method of the present invention in quantitative material analysis.

[0054] The physical parameter-based material decomposition method proposed in this invention constructs a physical parameterized material decomposition model for energy spectrum CT, including projection correction parameters and energy spectrum correction parameters, through pre-scanning and joint parameter optimization. After acquiring the equivalent energy spectrum and the scan data of the phantom to be tested, the model is used to correct the scan data and the equivalent energy spectrum. Then, the material decomposition of the phantom to be tested is completed using a preset strategy to obtain the results. This solves the problem of low accuracy in quantitative material analysis of energy spectrum CT in related technologies and improves the accuracy of quantitative material analysis of energy spectrum CT.

[0055] Next, a material decomposition apparatus based on physical parameters according to an embodiment of the present invention is described with reference to the accompanying drawings.

[0056] Figure 5 This is a block diagram of a physical parameter-based material decomposition device according to an embodiment of the present invention.

[0057] like Figure 5 As shown, the physical parameter-based material decomposition device 10 includes: a construction module 100, a correction module 200, a decomposition module 300, and a generation module 400.

[0058] The system comprises the following modules: a construction module 100, which calculates at least two sets of projection correction parameters and energy spectrum correction parameters at different equivalent energies based on the energy spectrum CT pre-scan data of the known information phantom and the pre-calibrated initial equivalent energy spectrum; a material decomposition model is corrected based on the projection correction parameters and the energy spectrum correction parameters to establish a mapping relationship between the CT projection data at at least two sets of different equivalent energies and the thickness of the base material; a correction module 200, which corrects the CT projection data of the phantom under test at at least two sets of different equivalent energies using the projection correction parameters to obtain corrected CT projection data; a decomposition module 300, which decomposes the phantom under test using a preset decomposition strategy based on the corrected scan data and the corrected equivalent energy spectrum to obtain the material decomposition result of the phantom under test, and obtains the base material thickness data according to the mapping relationship and the corrected CT projection data at least two sets of different equivalent energies; and a generation module 400, which generates a base material image based on reconstruction methods including but not limited to analytical, iterative, or data-driven methods, and obtains a virtual monoenergetic image through linear combination.

[0059] Furthermore, in some embodiments, the construction module 100 is specifically used for: performing energy-spectral CT pre-scan on the phantom with known information, and using the pre-scan projection data to perform material decomposition and image reconstruction to obtain a base material image; obtaining a base material true value image based on the known information and the base material image, and reprojecting the base material true value image to obtain the base material thickness true value; optimizing the parameters of the base material thickness true value based on a physical parameterized material decomposition model and a joint parameter optimization method to obtain a set of projection correction parameters and energy spectrum correction parameters that minimize the material decomposition deviation, wherein the known information is the distribution of the base material density true value of the phantom.

[0060] Furthermore, in some embodiments, the preset joint parameter optimization strategy is as follows: ; in, For projection correction parameters, For energy spectrum correction parameters, These are projected measurements. This is the integral measurement value of the base material line. The true value of the line integral of the basic matter is given. Let the data consistency loss function be... For parameters regular terms, For parameters The regular term.

[0061] Furthermore, in some embodiments, the pre-constructed physically parameterized matter decomposition model is as follows: ; in, The first measurement obtained from the actual system d The detector pixel, the v Each scanning angle 、 No. k Projected values ​​under the equivalent energy spectrum For projection correction terms, For the first A normalized equivalent energy spectrum This is a correction term for the energy spectrum. Indicates the first Individual base material linear attenuation coefficient .

[0062] Furthermore, in some embodiments, the preset decomposition strategy is a projection domain material decomposition strategy or a one-step material decomposition strategy.

[0063] It should be noted that the foregoing explanation of the embodiment of the material decomposition method based on physical parameters also applies to the material decomposition device based on physical parameters in this embodiment, and will not be repeated here.

[0064] The physical parameter-based material decomposition device proposed in this embodiment of the invention constructs a physical parameterized material decomposition model for energy spectrum CT, including projection correction parameters and energy spectrum correction parameters, through pre-scanning and joint parameter optimization. After acquiring the equivalent energy spectrum and the scan data of the phantom to be tested, the model is used to correct the scan data and the equivalent energy spectrum. Then, the material decomposition of the phantom to be tested is completed and the results are obtained using a preset strategy. This solves the problem of low accuracy in quantitative material analysis of energy spectrum CT in related technologies and improves the accuracy of quantitative material analysis of energy spectrum CT.

[0065] Figure 6 This is a schematic diagram of an electronic device provided according to an embodiment of the present invention. The electronic device may include: The memory 601, the processor 602, and the computer program stored on the memory 601 and capable of running on the processor 602.

[0066] When the processor 602 executes the program, it implements the material decomposition method based on physical parameters provided in the above embodiments.

[0067] Furthermore, electronic devices also include: Communication interface 603 is used for communication between memory 601 and processor 602.

[0068] The memory 601 is used to store computer programs that can run on the processor 602.

[0069] The memory 601 may include high-speed RAM (Random Access Memory) memory, and may also include non-volatile memory, such as at least one disk storage.

[0070] If the memory 601, processor 602, and communication interface 603 are implemented independently, then the communication interface 603, memory 601, and processor 602 can be interconnected via a bus to complete communication between them. The bus can be an ISA (Industry Standard Architecture) bus, a PCI (Peripheral Component Interconnect) bus, or an EISA (Extended Industry Standard Architecture) bus, etc. The bus can be divided into address bus, data bus, control bus, etc. For ease of representation, Figure 6 The bus is represented by a single thick line, but this does not mean that there is only one bus or one type of bus.

[0071] Optionally, in a specific implementation, if the memory 601, processor 602, and communication interface 603 are integrated on a single chip, then the memory 601, processor 602, and communication interface 603 can communicate with each other through an internal interface.

[0072] Processor 602 may be a CPU (Central Processing Unit), an ASIC (Application Specific Integrated Circuit), or one or more integrated circuits configured to implement embodiments of the present invention.

[0073] In addition, embodiments of the present invention also provide a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described material decomposition method based on physical parameters.

[0074] In addition, embodiments of the present invention also provide a computer program product, including a computer program, which is executed to implement the above-described physical parameter-based material decomposition method.

[0075] Furthermore, 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 number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this invention, "a plurality of" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0076] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the present invention. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0077] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of the present invention.

Claims

1. A method for decomposing matter based on physical parameters, characterized in that, Includes the following steps: Based on the energy spectrum CT pre-scan data of the known information phantom and the pre-calibrated initial equivalent energy spectrum, calculate at least two sets of projection correction parameters and energy spectrum correction parameters under different equivalent energies; Based on the projection correction parameters and the energy spectrum correction parameters, the material decomposition model is corrected, and at least two sets of mapping relationships between CT projection data and base material thickness under different equivalent energies are established. The projection correction parameters are used to correct at least two sets of CT projection data of the phantom under different equivalent energies to obtain the corrected CT projection data. Based on the corrected scan data and the corrected equivalent energy spectrum, the material of the test phantom is decomposed using a preset decomposition strategy to obtain the material decomposition result of the test phantom, and the thickness data of the base material is obtained according to the mapping relationship and at least two sets of CT projection data under different equivalent energies after correction. Based on analytical, iterative, or data-driven reconstruction methods, images of the base material are generated, and virtual monoenergetic images are obtained through linear combination. The process involves calculating at least two sets of projection correction parameters and energy spectrum correction parameters at different equivalent energies based on the pre-scan data of the known information phantom using spectral CT and a pre-calibrated initial equivalent energy spectrum. This includes: performing a pre-scan of the known information phantom using spectral CT, and using the pre-scan projection data for material decomposition and image reconstruction to obtain a base material image; obtaining a true base material image based on the known information and the base material image, and reprojecting the true base material image to obtain the true base material thickness; and optimizing the true base material thickness based on a physically parameterized material decomposition model and a joint parameter optimization method to obtain a set of projection correction parameters and energy spectrum correction parameters that minimize the material decomposition deviation. The known information refers to the distribution of the true base material density of the phantom. The physical parameterized matter decomposition model is as follows: ； in, The first measurement obtained from the actual system d The detector pixel, the v Each scanning angle 、 No. k Projected values ​​under the equivalent energy spectrum For projection correction terms, For the first A normalized equivalent energy spectrum This is a correction term for the energy spectrum. Indicates the first Individual base material linear attenuation coefficient .

2. The method according to claim 1, characterized in that, The preset joint parameter optimization strategy is as follows: ； in, These are projection correction parameters. For energy spectrum correction parameters, These are projected measurements. This is the integral measurement value of the base material line. For the true value of the thickness of the base material, Let be the data consistency loss function. For parameters regular terms, For parameters The regular term.

3. The method according to claim 1, characterized in that, The mapping relationship between the projection data and the thickness of the substrate can be established by lookup table or polynomial fitting.

4. The method according to claim 1, characterized in that, The reconstruction method based on analysis, iteration, or data-driven methods generates a base material image, including replacing it with a base material image obtained directly through a one-step iterative method using a physically parameterized material decomposition model.

5. A substance decomposition apparatus based on physical parameters, used to implement the substance decomposition method based on physical parameters as described in any one of claims 1-4, characterized in that, The device includes: The module is used to calculate at least two sets of projection correction parameters and energy spectrum correction parameters under different equivalent energies based on the energy spectrum CT pre-scan data of the known information phantom and the pre-calibrated initial equivalent energy spectrum; and to correct the material decomposition model based on the projection correction parameters and the energy spectrum correction parameters, and establish the mapping relationship between CT projection data and the thickness of the base material under at least two sets of different equivalent energies. The correction module is used to correct at least two sets of CT projection data of the phantom under different equivalent energies using the projection correction parameters to obtain the corrected CT projection data. The decomposition module is used to decompose the material of the test phantom based on the corrected scan data and the corrected equivalent energy spectrum using a preset decomposition strategy, to obtain the material decomposition result of the test phantom, and to obtain the base material thickness data according to the mapping relationship and at least two sets of CT projection data under different equivalent energies after correction. The generation module is used to generate images of the base material based on analytical, iterative, or data-driven reconstruction methods, and obtain virtual monoenergetic images through linear combination.

6. An electronic device, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, the processor executing the program to implement the physical parameter-based material decomposition method as described in any one of claims 1-4.

7. A computer-readable storage medium having a computer program stored thereon, characterized in that, The program is executed by the processor to implement the physical parameter-based material decomposition method as described in any one of claims 1-4.

8. A computer program product, comprising a computer program, characterized in that, When the computer program is executed by the processor, it implements the physical parameter-based material decomposition method as described in any one of claims 1-4.

Images