Multi-energy CT spectral imaging method and device

Abstract

The present application relates to the field of medical imaging technology, and in particular to a multi-energy CT spectral imaging method and apparatus, wherein the method comprises: obtaining scanning object parameters of a target object; constructing a target optimization criterion, and establishing a high- and low-energy spectral projection model based on the target optimization criterion combined with the scanning object parameters to obtain optimal spatiotemporal filter parameters; using the optimal spatiotemporal filter parameters to produce corresponding spatiotemporal filters to collect high- and low-energy projection data, and using the low-frequency properties of scattering to perform scatter correction on the high- and low-energy projection data to obtain descattered projection data, and performing base material decomposition in the projection domain or performing descattered image reconstruction to perform base material decomposition in the image domain to complete the multi-energy CT spectral imaging task. The present application can select appropriate spatiotemporal filter parameters based on a specific material decomposition task and apply them to the production of spatiotemporal filters, effectively improving the performance of multi-energy CT spectral imaging.

Description

Technical Field

[0001] The present application relates to the field of medical imaging technology, and in particular to a multi-energy CT spectrum imaging method and device. Background Art

[0002] Among related technologies, computed tomography (CT) has been widely used in medical imaging and plays a vital role in medical diagnosis. CT imaging systems utilize the attenuation properties of X-rays to reconstruct an object's internal structure by scanning it from multiple angles. According to the principle of CT imaging, the reconstruction result at a specific location on an object reflects the linear attenuation coefficient at that point. Different materials with different densities may have the same or similar attenuation coefficients at a given energy, which presents difficulties in material identification and disease diagnosis using CT imaging. Furthermore, because the photon energy of an X-ray source is a continuous spectrum and materials have different attenuation capabilities at different X-ray energies, the average energy of X-rays increases after passing through an object. This can cause hardening artifacts in the reconstructed image, making the diagnosis of low-contrast soft tissue lesions even more difficult. Therefore, in 1973, Godfrey Hounsfield proposed that scanning with two energy spectra could effectively distinguish different materials and, in principle, eliminate the hardening artifacts caused by spectral hardening. Research on multi-energy CT has since gradually developed.

[0003] Dual-energy or multi-energy CT imaging is currently a hot topic in the field of CT research. Multi-energy CT exploits the correlation between material attenuation coefficients and X-ray energy, providing more information than conventional CT. Therefore, compared to conventional CT, multi-energy CT offers numerous advantages, including suppressing hardening artifacts, enhancing tissue contrast, improving image quality, and reducing radiation dose. Currently, mainstream dual-energy CT systems include technologies such as dual-source dual-detector, rapid kilovolt switching, and dual-layer detectors. Each of these technologies has its own advantages and disadvantages, and all have been commercially available. Furthermore, with the rise of large-area flat-panel detectors, cone-beam CT imaging, due to its advantages of high integration, high spatial resolution, and flexibility, has become a major academic hotspot in cutting-edge imaging theory and application research, becoming a new and important development direction in X-ray imaging. Cone-beam CT imaging holds broad application prospects in numerous fields, including industry, agriculture, and medicine. It already plays an indispensable role in oral (dental) examinations, image-guided interventional treatments, and radiotherapy. Research on the theory and application of multi-energy cone-beam CT imaging continues to deepen.

[0004] However, in related technologies, multi-energy cone-beam CT faces problems such as ray scattering, low energy spectrum separation and detector hysteresis, which seriously restrict the imaging performance of multi-energy cone-beam CT. How to improve the energy spectrum separation by selecting appropriate or specially designed filters, or to improve the imaging quality of cone-beam CT by performing scattering estimation, and thus improve the imaging performance of the multi-energy cone-beam CT energy spectrum, needs to be solved urgently. Summary of the Invention

[0005] The present application provides a multi-energy CT spectral imaging method and apparatus to address the problems faced by multi-energy cone-beam CT in related technologies, such as ray scattering, low spectral separation, and detector hysteresis, which severely restrict the imaging performance of multi-energy cone-beam CT. The present application also addresses the issues of how to improve the spectral separation by selecting appropriate or specially designed filters, or how to improve the cone-beam CT imaging quality by performing scattering estimation, thereby improving the spectral imaging performance of multi-energy cone-beam CT.

[0006] The first aspect of the present application provides a multi-energy CT spectral imaging method, comprising the following steps: acquiring scanning object parameters of a target object; constructing a target optimization criterion, and establishing a high- and low-energy spectral projection model based on the target optimization criterion in combination with the scanning object parameters to obtain optimal space-time filter parameters; using the optimal space-time filter parameters to produce corresponding space-time filters to collect high- and low-energy projection data, and performing scattering correction on the high- and low-energy projection data using the low-frequency properties of scattering to obtain descattered projection data, and performing base material decomposition of the projection domain or descattered image reconstruction based on the descattered projection data to perform base material decomposition of the image domain to complete the multi-energy CT spectral imaging task.

[0007] Optionally, in one embodiment of the present application, the constructing target optimization criterion includes: obtaining a relationship expression between the noise floor of the base material projection obtained by material decomposition of high- and low-energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter; traversing and searching the decomposition tasks of the target object according to the relationship expression to obtain suitable parameters of the spatiotemporal filter that can achieve the best multi-energy CT energy spectrum imaging performance, so as to determine the target optimization criterion.

[0008] Optionally, in one embodiment of the present application, the parameters of the spatiotemporal filter include at least one of material, thickness and spatial frequency.

[0009] Optionally, in one embodiment of the present application, the formula of the target optimization criterion can be expressed as:

[0010]

[0011] Among them, γ represents the ratio of detector pixels attenuated by the spatiotemporal filter to the total detector pixels in the region, which can be used to determine the spatial structure of the spatiotemporal filter, μ m represents the material of the spatiotemporal filter, T m Represents the thickness of the space-time filter, μ1, μ2, L1, L2 are the base material and corresponding thickness of the space-time filter respectively.

[0012] The second aspect of the present application provides a multi-energy CT spectral imaging device, including: an acquisition module for acquiring scanning object parameters of a target object; a construction module for constructing a target optimization criterion, and based on the target optimization criterion and the scanning object parameters, establishing a high- and low-energy spectral projection model to obtain optimal spatiotemporal filter parameters; an imaging module for using the optimal spatiotemporal filter parameters to produce corresponding spatiotemporal filters to collect high- and low-energy projection data, and using the low-frequency properties of scattering to perform scattering correction on the high- and low-energy projection data to obtain descattered projection data, and performing base material decomposition of the projection domain or descattered image reconstruction based on the descattered projection data to perform base material decomposition of the image domain to complete the multi-energy CT spectral imaging task.

[0013] Optionally, in one embodiment of the present application, the construction module includes: an acquisition unit for obtaining a relationship expression between the noise floor of the base material projection obtained by material decomposition of high and low energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter; a traversal unit for traversing and searching the decomposition tasks of the target object according to the relationship expression, and obtaining suitable parameters of the spatiotemporal filter that can achieve the best multi-energy CT energy spectrum imaging performance, so as to determine the target optimization criterion.

[0014] Optionally, in one embodiment of the present application, the parameters of the spatiotemporal filter include at least one of material, thickness and spatial frequency.

[0015] Optionally, in one embodiment of the present application, the formula of the target optimization criterion can be expressed as:

[0016]

[0017] Among them, γ represents the ratio of detector pixels attenuated by the spatiotemporal filter to the total detector pixels in the region, which can be used to determine the spatial structure of the spatiotemporal filter, μ m represents the material of the spatiotemporal filter, T m Represents the thickness of the space-time filter, μ1, μ2, L1, L2 are the base material and corresponding thickness of the space-time filter respectively.

[0018] The third aspect of the present application 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 multi-energy CT spectral imaging method as described in the above embodiment.

[0019] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, which stores a computer program. When the program is executed by a processor, it implements the above multi-energy CT spectral imaging method.

[0020] The fifth aspect of the present application provides a computer program product, including a computer program, which, when executed, is used to implement the above multi-energy CT spectral imaging method.

[0021] The embodiment of the present application can select appropriate and optimal spatiotemporal filter parameters based on a specific material decomposition task and apply them to the production of the spatiotemporal filter, thereby completing the multi-energy CT energy spectrum imaging task that can improve the multi-energy CT energy spectrum imaging performance. In this way, it is achieved that when the relationship expression between the noise floor of the base material projection obtained by the material decomposition of the high and low energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter is quantitatively derived, the specific material decomposition task is traversed and searched according to the expression, thereby obtaining the optimal spatiotemporal filter parameters and applying them to the production of the spatiotemporal filter to achieve the best multi-energy CT energy spectrum imaging performance. In this way, it solves the problems faced by multi-energy cone-beam CT in the related art, such as ray scattering, low energy spectrum separation and detector hysteresis, which deeply restrict the imaging performance of multi-energy cone-beam CT, how to improve the energy spectrum separation by selecting appropriate or specially designed filters, or improve the cone-beam CT imaging quality by performing scattering estimation, and thus improve the imaging performance of the multi-energy cone-beam CT energy spectrum.

[0022] Additional aspects and advantages of the present application will be given in part in the description below, and in part will become apparent from the description below, or will be learned through practice of the present application. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0024] Figure 1 This is a schematic structural diagram of a multi-energy cone-beam CT spectral imaging system based on a spatiotemporal filter according to an embodiment of the present application;

[0025] Figure 2 Schematic diagram of spatiotemporal filters with different spatial structures according to one embodiment of the present application;

[0026] Figure 3 This is a flow chart of a multi-energy CT spectral imaging method provided according to an embodiment of the present application;

[0027] Figure 4 This is a flow chart of a multi-energy CT spectral imaging method according to one embodiment of the present application;

[0028] Figure 5 A schematic diagram illustrating the effect of a spatiotemporal filter produced using optimal spatiotemporal filter parameters on the signal-to-noise ratio of material decomposition according to an embodiment of the present application;

[0029] Figure 6 A schematic diagram illustrating the effect of a space-time filter produced using optimal space-time filter parameters on energy spectrum imaging performance according to an embodiment of the present application;

[0030] Figure 7 Schematic diagram of the structure of a multi-energy CT spectral imaging device according to an embodiment of the present application;

[0031] Figure 8 Schematic diagram of the structure of an electronic device according to an embodiment of the present application.

[0032] Reference numerals:

[0033] 10-Multi-energy CT spectral imaging device: 100-acquisition module, 200-construction module and 300-imaging module; 801-memory, 802-processor and 803-communication interface. DETAILED DESCRIPTION

[0034] The following describes in detail embodiments of the present application, examples of which are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are intended to be used to explain the present application, and should not be construed as limiting the present application.

[0035] The following describes a multi-energy CT spectral imaging method and apparatus according to an embodiment of the present application with reference to the accompanying drawings. In view of the related art mentioned in the background art, multi-energy cone-beam CT faces problems such as ray scattering, low spectral separation, and detector hysteresis, which severely restrict the imaging performance of multi-energy cone-beam CT. The present application provides a multi-energy CT spectral imaging method, in which appropriate and optimal spatiotemporal filter parameters can be selected based on a specific material decomposition task and applied to the fabrication of the spatiotemporal filter, thereby completing a multi-energy CT spectral imaging task that can improve the performance of multi-energy CT spectral imaging. This method achieves the following: quantitatively deriving an expression for the relationship between the noise floor of the base material projection obtained by material decomposition of high- and low-energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter, traversing and searching for a specific material decomposition task based on the expression, thereby obtaining the optimal spatiotemporal filter parameters and applying them to the fabrication of the spatiotemporal filter, thereby achieving optimal multi-energy CT spectral imaging performance. This solves the problems faced by multi-energy cone-beam CT in related technologies, such as ray scattering, low energy spectrum separation and detector hysteresis, which seriously restrict the imaging performance of multi-energy cone-beam CT. The problem is how to improve the energy spectrum separation by selecting suitable or specially designed filters, or to improve the imaging quality of cone-beam CT by performing scattering estimation, and thus improve the imaging performance of the energy spectrum of multi-energy cone-beam CT.

[0036] Before explaining the multi-energy CT spectral imaging method in the embodiment of the present application, the multi-energy cone-beam CT spectral imaging system and the spatiotemporal filter based on the spatiotemporal filter involved in the embodiment of the present application are first explained.

[0037] Multi-energy CT spectral imaging, also known as multi-energy / spectral CT, is an imaging technology that uses the different absorption of matter at different X-ray energies to provide more image information than conventional CT.

[0038] Figure 1 This is a schematic diagram of the structure of a multi-energy cone-beam CT spectral imaging system based on a spatiotemporal filter according to an embodiment of the present application. Figure 1 As shown, the multi-energy CT spectral imaging system based on a spatiotemporal filter primarily includes an X-ray source module, a flat-panel detector module, a spatiotemporal filter, and mechanical / electrical control and data transmission / processing units. The system's dual energy capabilities can be achieved using dual-energy X-ray sources, such as those using rapid kilovolt switching technology, or energy-resolving detectors, such as dual-layer flat-panel detectors or photon-counting detectors. Furthermore, the multi-energy CT spectral imaging system can also capture both high- and low-energy spectral data.

[0039] The space-time filter is placed between the X-ray tube and the scanned object, and remains relatively stationary with the X-ray tube and the detector during the scanning process. Figure 2 Schematic diagram of space-time filters with different spatial structures according to an embodiment of the present application. Figure 2 As shown, the spatiotemporal filter differs from conventional flat metal filters in that it is a semi-transparent metal filter that selectively attenuates and filters a portion of X-rays in space. This spatiotemporal filter not only spatially alters the distribution of high- and low-energy spectrum separation, generating richer spectral projection data, but also utilizes the low-frequency nature of scattering to perform scatter correction [1,2,3]. Due to the particularity of the spatiotemporal filter's spatial structure, X-rays at different spatial locations are attenuated by metal blocks of varying thicknesses, resulting in non-uniformity in the spatial field of X-ray photons, which in turn leads to non-uniformity in the spatial distribution of noise. Furthermore, parameters such as the filter's material and thickness also affect the final results of spectral imaging. Therefore, parameter optimization of the spatiotemporal filter is crucial for multi-energy CT spectral imaging systems.

[0040] Specifically, Figure 3 This is a flow chart of a multi-energy CT spectral imaging method provided in an embodiment of the present application.

[0041] like Figure 3 As shown, the multi-energy CT spectral imaging method includes the following steps:

[0042] In step S301 , scanning object parameters of the target object are obtained.

[0043] As you can understand, multi-energy CT spectral imaging is an imaging technique that uses the differential absorption of matter at different X-ray energies to provide more information than conventional CT. Imaging requires an object to be imaged. The target object, as used here, is the object to be scanned and ultimately imaged using multi-energy CT spectral imaging, such as the human body, organs, or tissues.

[0044] The object scanning parameters can be understood as a series of technical parameters set for the target object (such as the human body, organs, or tissues) being scanned during a CT scan. For example, a pre-scan roughly determines the material composition and size of the scanned object. Tube voltage (kVp) parameters: In multi-energy CT, two different tube voltages are typically set, such as low kVp (e.g., 80 kVp) and high kVp (e.g., 140 kVp), to generate two sets of X-ray data at different energies. Exposure time: This needs to be adjusted based on the tube voltage and the specific conditions of the scan target to ensure sufficient X-ray intensity and image quality. The energy spectrum curve: A curve that shows the attenuation (i.e., CT value) of a material or structure as it changes with X-ray energy. The energy spectrum curve can be used to obtain the average CT value and standard deviation at each energy point, thereby analyzing the energy attenuation characteristics of the material. CT value: A quantitative indicator of material density. In single-energy images and virtual plain scan images at different energies, the CT value of different tissues can be measured to assess their density and attenuation characteristics.

[0045] Step S302 : constructing a target optimization criterion, and establishing a high- and low-energy spectrum projection model based on the target optimization criterion and the scanned object parameters to obtain the optimal spatiotemporal filter parameters.

[0046] It is understandable that the space-time filter has an important influence on the final result of energy spectrum imaging, and the parameters of the space-time filter such as material and thickness are particularly important for the production of the space-time filter. Therefore, the embodiment of the present application can propose to construct a target optimization criterion to optimize the parameters of the space-time filter, thereby effectively improving the final imaging effect and system performance of the multi-energy CT energy spectrum imaging system.

[0047] In some embodiments, after obtaining the scanning object parameters of the target object, a high- and low-energy spectrum projection model can be established based on the scanning object parameters and combined with the target optimization criteria, that is, the space-time filter parameter optimization method proposed in this application. Then, simulation calculations are performed on space-time filters made of different materials, different thicknesses, and different spatial structures to obtain the optimal space-time filter parameters. These optimal space-time filter parameters can be applied to the production process of the space-time filter to achieve the ultimate excellent use effect.

[0048] Next, the process of constructing target optimization criteria in the embodiment of the present application is further explained.

[0049] Optionally, in one embodiment of the present application, constructing a target optimization criterion includes: obtaining a relationship expression between the noise floor of the base material projection obtained by material decomposition of high- and low-energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter; traversing and searching the decomposition tasks of the target object based on the relationship expression to obtain appropriate parameters of the spatiotemporal filter that can achieve optimal multi-energy CT spectral imaging performance, thereby determining the target optimization criterion. The parameters of the spatiotemporal filter include at least one of material, thickness, and spatial frequency; the formula of the target optimization criterion can be expressed as:

[0050]

[0051] Among them, γ represents the ratio of the detector pixels attenuated by the filter to the total detector pixels in the region, which can be used to determine the spatial structure of the spatiotemporal filter, μ m represents the material of the spatiotemporal filter, T m Represents the thickness of the filter, μ1, μ2, L1, L2 are the base material and corresponding thickness of the spatiotemporal filter respectively.

[0052] Based on the relevant descriptions of other embodiments, it can be understood that the present application can optimize the parameters of the spatiotemporal filter based on the proposed target optimization criterion, and then obtain the optimized parameters.

[0053] During the actual execution process, the embodiment of the present application can first obtain the relationship expression between the noise floor of the base material projection obtained by the material decomposition of the high and low energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter, and then traverse and search the decomposition tasks of the target object according to the relationship expression, thereby obtaining the appropriate parameters of the spatiotemporal filter that can achieve the best multi-energy CT energy spectrum imaging performance, and thereby determine the target optimization criterion.

[0054] Taking low-energy and high-energy spectrum data as an example, assuming that there is no scattering, for a specific base material thickness combination {L1, L2}, the multi-energy projection of multi-energy CT can be expressed as follows:

[0055]

[0056] Among them, I L (r),I H (r) represents the multi-energy projection results of multi-energy CT when the base material thickness is L1 and L2 respectively, S L (E),S H (E) represents the equivalent energy spectrum including the detector response at low energy and high energy, μ f (E),T fThey represent the linear attenuation coefficient and corresponding thickness of the space-time filter respectively, μ1(E), μ2(E) are the two selected base materials, L1, L2 are the corresponding thicknesses, and r represents different spatial positions.

[0057] Since the space-time filter has uneven spatial distribution, that is, T f (r) There is a certain degree of spatial inhomogeneity. Even if the base material with the same thickness is combined to attenuate, I L (r),I H (r) will also be due to T f (r) is different at different spatial positions r, resulting in large differences in the number of photons, which in turn causes the problem of inconsistent noise levels.

[0058] It should be noted that the purpose of multi-energy CT spectral imaging can be understood as solving Equation (1) to obtain the line-integrated thickness {L1, L2} of the base material, and then reconstructing the corresponding base material CT image. The noise, signal-to-noise ratio, and contrast-to-noise ratio of the reconstructed base material image are related to the initial signal statistical noise and the spectral separation between the high- and low-energy spectra.

[0059] Furthermore, in order to improve the performance of multi-energy CT spectral imaging, the embodiment of the present application establishes an optimization analysis method that can quantitatively describe the relationship between the signal-to-noise ratio of the multi-energy CT spectral imaging base material image and the spatiotemporal filter parameters. The optimization analysis method can be based on, but is not limited to, the Cramer-Rao lower bound (CRLB) method, and quantitatively gives the theoretical noise lower bound of the base material projection after material decomposition and the virtual monoenergetic projection under given spatiotemporal filter parameters. According to the CRLB theory, from the high and low energy projections of a single detector pixel {I L ,I H}, the theoretical noise lower bound can be expressed as:

[0060]

[0061]

[0062] in, σ VM are the noises of basis materials 1, 2, and virtual monoenergetic projection, c1 and c2 are the energy-related weight coefficients, F is the Fisher Information Matrix (FIM), a widely used tool in statistics and deep learning that provides important information about the sensitivity and information content of model parameters, and F i,j The Fisher information matrix can be expressed as follows:

[0063]

[0064] It should be noted that the noise lower bound of the base material projection after material decomposition obtained by formulas (2) and (3) is only the noise level of a single detector pixel. For multi-energy cone-beam CT spectral imaging, when the spatiotemporal filter exists, the noise distribution of the area with the same base material combination will be affected by the thickness T of the equivalent filter. f (r) varies with the spatial distribution. For a pixel area composed of the same base material thickness, the total noise can be expressed as:

[0065]

[0066] Where M is the number of detector pixels in the area, j is the detector pixel index in the area, are attenuated by the space-time filter material (T f >0) and unattenuated (T f = 0) after the single detector pixel material decomposition can be expressed as follows:

[0067]

[0068] Where γ represents the ratio of detector pixels attenuated by the filter to the total number of detector pixels in the region, which can be used to determine the spatial structure of the spatiotemporal filter, γ∈(0,1). In formula (5), the first term represents the contribution of high and low energy projections after attenuation by the spatiotemporal filter to the base material projection noise. The second term represents the contribution of high and low energy projections not attenuated by the spatiotemporal filter to the base material projection noise. Furthermore, the effect of the spatiotemporal filter parameters on the base material projection noise can be quantitatively described using the following formula:

[0069]

[0070] Among them, γ represents the ratio of detector pixels attenuated by the spatiotemporal filter to the total detector pixels in the region, which can be used to determine the spatial structure of the spatiotemporal filter, μ m Indicates the material of the filter, T m In summary, when the base material and the corresponding thickness {μ1, μ2, L1, L2…} are determined, the influence of various physical parameters of the spatiotemporal filter on the projection noise of the base material can be expressed as formula (7). At this time, the optimization criterion of multi-energy cone-beam CT spectral imaging becomes to find a suitable spatiotemporal filter parameter combination {γ, μ m ,T m}, so that At least, the formula can be expressed as follows:

[0071]

[0072] Formula (8) can be understood as the target optimization criterion for the spatiotemporal filter parameters. Based on this criterion, the optimal parameters of the spatiotemporal filter that can achieve the best multi-energy CT spectral imaging performance can be obtained.

[0073] It should be noted that the spatiotemporal filter parameters {γ,μ m ,T m During the optimization process, professionals in this field can make corrections or adjustments based on actual conditions. This is only an example and is not a limitation. For example, γ cannot be too small or too large. If it is too small, the modulation effect of the projection will be weakened, which will lead to a weakening of the scatter correction effect; if it is too large, the dose reduction will be too large, and higher system power requirements will be required. For the material μ of the spatiotemporal filter, m Some metal materials may not be suitable for making space-time filters. For example, metallic gadolinium is mostly powdery at room temperature and difficult to make into space-time filters. Metallic tin is relatively soft at room temperature and difficult to process. The thickness of the space-time filter is T. m It should not be too large, otherwise the X-ray attenuation will be too large, requiring higher mAs (Milliampere-seconds), which will require higher power for the actual system; T m If it is too small, there will be difficulties in processing.

[0074] It should also be noted that Formula (8) in the embodiments of the present application is for a given base material combination of material and thickness. For different energy spectrum imaging tasks, the optimal spatiotemporal filter parameters may not necessarily be the same. For example, the spatiotemporal filter parameters that minimize the noise in the virtual monoenergetic image may differ from the spatiotemporal filter parameters that minimize the noise in the base material. Therefore, the optimal parameters for the spatiotemporal filter can be determined by professionals in this field based on the specific energy spectrum imaging task. This is only an illustrative example and does not impose specific limitations.

[0075] It should be further explained that the optimization analysis method provided in the embodiments of the present application, which can quantitatively describe the relationship between the signal-to-noise ratio of the multi-energy CT spectral imaging-based material image and the spatiotemporal filter parameters, the obtained optimal spatiotemporal filter parameters, and the spatiotemporal filter produced using the optimal spatiotemporal filter parameters, can be applied to, but are not limited to, multi-energy CT spectral imaging systems. Professional and technical personnel in this field can also apply the spatiotemporal filter parameter optimization method and multi-energy CT spectral imaging method in the embodiments of the present application to other systems according to actual conditions. This is only an illustrative explanation and is not specifically limited.

[0076] In step S303, the corresponding spatiotemporal filters are produced using the optimal spatiotemporal filter parameters to collect high- and low-energy projection data, and the low-frequency properties of scattering are used to perform scattering correction on the high- and low-energy projection data to obtain descattered projection data. Based on the descattered projection data, the basis material decomposition of the projection domain or the descattered image reconstruction is performed to perform basis material decomposition of the image domain, thereby completing the multi-energy CT spectral imaging task.

[0077] As a possible implementation method, after obtaining the optimal space-time filter parameters, the embodiment of the present application can apply the optimal space-time filter parameters to the production of the space-time filter, and use the space-time filter in the multi-energy CT energy spectrum imaging system to collect multi-energy projection data.

[0078] Furthermore, the embodiments of the present application can also utilize the low-frequency properties of scattering and combine the scattering correction method of the space-time filter to perform scattering correction on high- and low-energy projection data, thereby obtaining descattered projection data, and then performing basis material decomposition on the projection domain, or first perform descattered image reconstruction and then perform basis material decomposition on the image domain.

[0079] After completing the above process, an image formed by the multi-energy CT spectral imaging system of the spatiotemporal filter with the optimal spatiotemporal filter parameters can be obtained, thereby completing the multi-energy CT spectral imaging task.

[0080] Specifically, Figure 4 This is a flow chart of a multi-energy CT spectral imaging method according to one embodiment of the present application. Figure 4 As shown, the process can be expressed as follows:

[0081] Step S401: Pre-scan to roughly determine the material composition and size of the scanned object, scanning voltage parameters, etc.

[0082] Step S402: Based on the estimated scanning object parameters and the proposed spatiotemporal filter parameter optimization method, a high- and low-energy spectrum projection model is established, and simulation calculations are performed on spatiotemporal filters made of different materials, different thicknesses, and different spatial structures to obtain the optimal spatiotemporal filter parameters.

[0083] Step S403: Create the spatiotemporal filter according to the filter parameters obtained by simulation calculation.

[0084] Step S404: Based on the multi-energy CT spectral imaging system, use the spatiotemporal filter to collect multi-energy projection data.

[0085] Step S405: Utilizing the low-frequency properties of scattering and combining the scattering correction method of the spatiotemporal filter, scattering correction is performed on the high and low energy projection data.

[0086] Step S406: Obtain descattered projection data and perform basis material decomposition in the projection domain, or first perform descattered image reconstruction and then perform basis material decomposition in the image domain to complete the multi-energy CT spectral imaging task.

[0087] Figure 5 This is a schematic diagram of the effect of the spatiotemporal filter on the signal-to-noise ratio of material decomposition according to one embodiment of the present application. Figure 5 As shown in FIG, a schematic diagram of the effect of space-time filters of different materials, different thicknesses, and different spatial structures on the signal-to-noise ratio of material decomposition obtained by simulation calculation using 80 / 120 kVp in the embodiment of the present application can be seen. Specifically, Figure 5 The results show the signal-to-noise ratios of different base materials and virtual monoenergetic projections, calculated using the optimization method proposed in this application for special filters of different materials, thicknesses, and spatial structures, at scanning parameters such as 80 / 120 kVp and a base material combination of 20 cm of water and 0.2 mm of iodine. The baseline reference value 1 is the signal-to-noise ratio obtained for material decomposition without the special filter. When γ = 1, the special filter degenerates to a common flat metal filter. It can be seen that among the different base materials, metallic erbium and gadolinium have the greatest effect on improving spectral imaging performance.

[0088] from Figure 5 (a), Figure 5 (b) Figure 5 (c) As can be seen, when γ = 0.5 and the material is erbium metal, the thickness of the space-time filter that maximizes the signal-to-noise ratio for the water-iodine-based material is 0.2 mm. At this time, the thickness of the space-time filter that maximizes the signal-to-noise ratio for the virtual monoenergetic (70 keV) is 0.08 mm. Therefore, choosing the appropriate space-time filter parameters for a specific spectral imaging task is crucial.

[0089] Figure 6 This is a schematic diagram showing the effect of a spatiotemporal filter produced using optimal spatiotemporal filter parameters on energy spectrum imaging performance according to an embodiment of the present application. Specifically, Figure 6 The example of this application shows a material decomposition simulation experiment conducted at 80 / 120 kVp. The scanning phantom is a water-iodine contrast phantom. Dual energy refers to high and low energy spectrum imaging without using special filters, and dual energy-modulation refers to dual energy spectrum imaging using special filters (spectrum modulators). Figure 6 It can be seen that the space-time filter made using the optimal space-time filter parameters has significantly improved the energy spectrum imaging performance.

[0090] According to the multi-energy CT spectral imaging method proposed in the embodiment of the present application, the appropriate optimal spatiotemporal filter parameters can be selected based on a specific material decomposition task and applied to the production of the spatiotemporal filter, thereby completing the multi-energy CT spectral imaging task that can improve the multi-energy CT spectral imaging performance. In this way, it is achieved that when the relationship expression between the noise floor of the base material projection obtained by the material decomposition of the high and low energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter is quantitatively derived, the specific material decomposition task is traversed and searched according to the expression, thereby obtaining the optimal spatiotemporal filter parameters and applying them to the production of the spatiotemporal filter to achieve the best multi-energy CT spectral imaging performance. In this way, the problems faced by multi-energy cone-beam CT in the related art, such as ray scattering, low energy spectrum separation and detector hysteresis, which deeply restrict the imaging performance of multi-energy cone-beam CT, are solved. How to improve the energy spectrum separation by selecting appropriate or specially designed filters, or improve the cone-beam CT imaging quality by performing scattering estimation, and thus improve the imaging performance of the multi-energy cone-beam CT spectral image, are solved.

[0091] Next, the multi-energy CT spectral imaging device proposed according to the embodiment of the present application is described with reference to the accompanying drawings.

[0092] Figure 7 It is a structural schematic diagram of the multi-energy CT spectral imaging device of an embodiment of the present application.

[0093] like Figure 7 As shown, the multi-energy CT spectral imaging device 10 includes: an acquisition module 100 , a construction module 200 and an imaging module 300 .

[0094] The acquisition module 100 is used to acquire scanning object parameters of the target object.

[0095] The construction module 200 is used to construct a target optimization criterion and establish a high- and low-energy spectrum projection model based on the target optimization criterion and the scanned object parameters to obtain the optimal spatiotemporal filter parameters.

[0096] The imaging module 300 is used to use the optimal spatiotemporal filter parameters to produce corresponding spatiotemporal filters to collect high- and low-energy projection data, and use the low-frequency properties of scattering to perform scatter correction on the high- and low-energy projection data to obtain descattered projection data. Based on the descattered projection data, the module performs basis material decomposition in the projection domain or performs descattered image reconstruction to perform basis material decomposition in the image domain, thereby completing the multi-energy CT energy spectrum imaging task.

[0097] Optionally, in one embodiment of the present application, the construction module 200 includes: an acquisition unit and a traversal unit.

[0098] The acquisition unit is used to obtain the relationship expression between the noise floor of the base material projection obtained by material decomposition of the high and low energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter.

[0099] The traversal unit is used to traverse and search the decomposition tasks of the target object according to the relational expression, obtain the appropriate parameters of the spatiotemporal filter that can achieve the best multi-energy CT energy spectrum imaging performance, and determine the target optimization criterion.

[0100] Optionally, in one embodiment of the present application, the parameters of the spatiotemporal filter include at least one of material, thickness and spatial frequency.

[0101] Optionally, in one embodiment of the present application, the formula of the target optimization criterion can be expressed as:

[0102]

[0103] Among them, γ represents the ratio of detector pixels attenuated by the spatiotemporal filter to the total detector pixels in the region, which can be used to determine the spatial structure of the spatiotemporal filter, μ m represents the material of the spatiotemporal filter, T m Represents the thickness of the filter, μ1, μ2, L1, L2 are the base material and corresponding thickness of the spatiotemporal filter respectively.

[0104] It should be noted that the aforementioned explanation of the embodiment of the multi-energy CT spectral imaging method is also applicable to the multi-energy CT spectral imaging device of this embodiment, and will not be repeated here.

[0105] According to the multi-energy CT spectral imaging device proposed in the embodiment of the present application, it is possible to select appropriate optimal spatiotemporal filter parameters based on a specific material decomposition task and apply them to the production of the spatiotemporal filter, thereby completing a multi-energy CT spectral imaging task that can improve the multi-energy CT spectral imaging performance. This achieves the quantitative derivation of the relationship expression between the noise floor of the base material projection obtained by the material decomposition of the high and low energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter, and then traverses and searches for the specific material decomposition task according to the expression, thereby obtaining the optimal spatiotemporal filter parameters and applying them to the production of the spatiotemporal filter to achieve the best multi-energy CT spectral imaging performance. This solves the problems faced by multi-energy cone-beam CT in the related art, such as ray scattering, low energy spectrum separation, and detector hysteresis, which deeply restrict the imaging performance of multi-energy cone-beam CT, and how to improve the energy spectrum separation by selecting appropriate or specially designed filters, or improve the cone-beam CT imaging quality by performing scattering estimation, thereby improving the imaging performance of the multi-energy cone-beam CT spectral.

[0106] Figure 8This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. The electronic device may include:

[0107] A memory 801 , a processor 802 , and a computer program stored in the memory 801 and executable on the processor 802 .

[0108] When the processor 802 executes the program, the multi-energy CT spectral imaging method provided in the above embodiment is implemented.

[0109] Furthermore, the electronic device further includes:

[0110] The communication interface 803 is used for communication between the memory 801 and the processor 802 .

[0111] The memory 801 is used to store computer programs that can be run on the processor 802.

[0112] The memory 801 may include a high-speed RAM memory, and may also include a non-volatile memory (non-volatile memory), such as at least one disk memory.

[0113] If the memory 801, the processor 802, and the communication interface 803 are implemented independently, the communication interface 803, the memory 801, and the processor 802 can be connected to each other via a bus and communicate with each other. The bus can be an Industry Standard Architecture (ISA) bus, a Peripheral Component Interconnect (PCI) bus, or an Extended Industry Standard Architecture (EISA) bus. The bus can be divided into an address bus, a data bus, a control bus, etc. For ease of representation, Figure 8 Only one thick line is used in the diagram, but this does not mean that there is only one bus or one type of bus.

[0114] Optionally, in a specific implementation, if the memory 801, the processor 802 and the communication interface 803 are integrated on a chip, the memory 801, the processor 802 and the communication interface 803 can communicate with each other through an internal interface.

[0115] The processor 802 may be a central processing unit (CPU), an application specific integrated circuit (ASIC), or one or more integrated circuits configured to implement the embodiments of the present application.

[0116] An embodiment of the present application further provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-mentioned multi-energy CT spectral imaging method.

[0117] An embodiment of the present application further provides a computer program product, including a computer program, which can run computer instructions. When the computer instructions are executed by a processor, the multi-energy CT spectral imaging method provided in the embodiment of the present application is implemented.

[0118] In the description of this specification, the description with reference to the terms "one embodiment", "some embodiments", "example", "specific example", or "some examples" means that the specific features, structures, materials or characteristics described in conjunction with the embodiment or example are included in at least one embodiment or example of the present application. In this specification, the schematic 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, those skilled in the art can combine and combine different embodiments or examples described in this specification and features of different embodiments or examples without contradiction.

[0119] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be understood to indicate or imply relative importance or implicitly specify the number of technical features indicated. Thus, a feature specified as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of this application, "N" means at least two, for example, two, three, etc., unless otherwise specifically defined.

[0120] Any process or method description in a flowchart or otherwise described herein may be understood to represent a module, fragment or portion of code comprising one or N executable instructions for implementing a custom logical function or process step, and the scope of the preferred embodiments of the present application includes alternative implementations in which functions may be performed in a different order than shown or discussed, including performing functions in a substantially simultaneous manner or in a reverse order depending on the functions involved, which should be understood by those skilled in the art to which the embodiments of the present application pertain.

[0121] The logic and / or steps represented in the flowcharts or otherwise described herein, for example, can be considered as a sequenced list of executable instructions for implementing the logical functions, and can be embodied in any computer-readable medium for use by, or in conjunction with, an instruction execution system, apparatus, or device (e.g., a computer-based system, a system including a processor, or other system that can fetch and execute instructions from an instruction execution system, apparatus, or device). For 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 conjunction with, an instruction execution system, apparatus, or device. More specific examples (a non-exhaustive list) of computer-readable media include the following: an electrical connection with one or N wires (electronic devices), a portable computer disk cartridge (magnetic device), random access memory (RAM), read-only memory (ROM), erasable and programmable read-only memory (EPROM or flash memory), fiber optic devices, and a portable compact disc read-only memory (CDROM). In addition, the computer-readable medium may even be paper or other suitable medium on which the program is printed, since the program can be obtained electronically by optically scanning the paper or other medium and then editing, interpreting or processing it in other suitable ways as necessary, and then storing it in a computer memory.

[0122] It should be understood that various parts of the present application can be implemented using hardware, software, firmware, or a combination thereof. In the above embodiment, the N steps or methods can be implemented using software or firmware stored in a memory and executed by a suitable instruction execution system. If implemented using hardware, as in another embodiment, it can be implemented using any one or a combination of the following technologies known in the art: a discrete logic circuit having a logic gate circuit for implementing a logic function on a data signal, an application-specific integrated circuit having a suitable combination of logic gate circuits, a programmable gate array (PGA), a field programmable gate array (FPGA), etc.

[0123] Those skilled in the art will understand that all or part of the steps in the method of the above embodiment can be completed by instructing related 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 embodiment.

[0124] In addition, the functional units in the various embodiments of the present application may be integrated into a processing module, or each unit may exist physically separately, or two or more units may be integrated into a module. The above-mentioned integrated module may be implemented in the form of hardware or in the form of a software functional module. If the integrated module is implemented in the form of a software functional module and sold or used as an independent product, it may also be stored in a computer-readable storage medium.

[0125] The storage medium mentioned above may be a read-only memory, a magnetic disk, or an optical disk, etc. Although the embodiments of the present application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present application. Persons skilled in the art may make changes, modifications, substitutions, and variations to the above embodiments within the scope of the present application.

Claims

1. A multi-energy CT spectral imaging method, characterized in that: The following steps are involved: Obtain scanning object parameters of the target object; Constructing a target optimization criterion, and establishing a high- and low-energy spectrum projection model based on the target optimization criterion and the scanned object parameters to obtain optimal spatiotemporal filter parameters, wherein the spatiotemporal filter parameters include at least one of material, thickness, and spatial frequency; Using the optimal spatiotemporal filter parameters to create corresponding spatiotemporal filters to collect high- and low-energy projection data, and using the low-frequency properties of scattering to perform scatter correction on the high- and low-energy projection data to obtain descattered projection data, and performing basis material decomposition in the projection domain or descattered image reconstruction based on the descattered projection data to perform basis material decomposition in the image domain to complete the multi-energy CT spectral imaging task; Among them, the construction of the target optimization criterion includes: obtaining a relationship expression between the noise floor of the base material projection obtained by material decomposition of high- and low-energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter; traversing and searching the decomposition tasks of the target object according to the relationship expression to obtain suitable parameters of the spatiotemporal filter that can achieve the best multi-energy CT energy spectrum imaging performance, so as to determine the target optimization criterion.

2. The method according to claim 1, characterized in that The formula of the target optimization criterion is: ， in, It represents the ratio of the detector pixels attenuated by the spatiotemporal filter to the total detector pixels in the region, represents the material of the spatiotemporal filter, represents the thickness of the spatiotemporal filter, are the base material and corresponding thickness of the spatiotemporal filter respectively.

3. A multi-energy CT spectrum imaging device, characterized in that: include: An acquisition module, used to obtain scanning object parameters of a target object; a construction module, configured to construct a target optimization criterion, and establish a high- and low-energy spectrum projection model based on the target optimization criterion and in combination with the scanned object parameters, so as to obtain optimal spatiotemporal filter parameters, wherein the spatiotemporal filter parameters include at least one of material, thickness, and spatial frequency; An imaging module is configured to use the optimal spatiotemporal filter parameters to create corresponding spatiotemporal filters to collect high- and low-energy projection data, perform scatter correction on the high- and low-energy projection data using the low-frequency properties of scattering to obtain descattered projection data, and perform basis material decomposition in the projection domain or descattered image reconstruction based on the descattered projection data to perform basis material decomposition in the image domain, thereby completing a multi-energy CT spectral imaging task; The construction module includes: an acquisition unit for obtaining a relationship expression between the noise floor of the base material projection obtained by material decomposition of high- and low-energy projection data after spatiotemporal filtering and the parameters of the spatiotemporal filter; a traversal unit for traversing and searching the decomposition tasks of the target object according to the relationship expression, and obtaining appropriate parameters of the spatiotemporal filter that can achieve the best multi-energy CT energy spectrum imaging performance, so as to determine the target optimization criterion.

4. An electronic device, characterized in that: include: 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 multi-energy CT spectral imaging method according to any one of claims 1 to 2.

5. A computer-readable storage medium having a computer program stored thereon, characterized in that: The program is executed by a processor to implement the multi-energy CT spectral imaging method according to any one of claims 1 to 2.

6. A computer program product comprising a computer program, characterized in that When the computer program is executed, it is used to implement the multi-energy CT spectral imaging method according to any one of claims 1 to 2.

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