A dual-energy-based panoramic imaging method and device

Abstract

This invention relates to the field of oral imaging technology and discloses a method for applying dual-energy technology to panoramic oral imaging, comprising: dual-energy panoramic projection data acquisition: acquiring high- and low-energy panoramic narrow-side projection images using techniques such as area array filtering, high-pressure fast cutting, or multi-layer flat panel; panoramic projection domain material decomposition: decomposing the high- and low-energy projections to obtain the target matrix material density integral map; variable-center finite-angle reconstruction: reconstructing the matrix material density integral map to generate the corresponding panoramic base map; application expansion: generating various clinically relevant images based on the panoramic base map. This invention also proposes a dual-energy-based panoramic imaging device. This invention can improve the expressiveness of panoramic images and expand more application scenarios.

Description

Technical Field

[0001] This invention relates to the field of oral imaging technology, and in particular to a dual-energy panoramic imaging method and apparatus. Background Technology

[0002] In the field of oral diagnosis, panoramic X-ray imaging is a key tool for obtaining a comprehensive view of teeth and jaws. This technology uses single-energy (kVp) X-rays to perform variable-center finite-angle exposure scans along a preset scan trajectory, generating two-dimensional images that include the upper and lower jaws, all teeth, and other oral structures. Despite its ease of operation and cost-effectiveness, it is widely used.

[0003] Existing panoramic dental imaging methods perform panoramic reconstruction by scanning along a pre-defined scanning trajectory with a finite, non-uniform angle and a variable center, based on a pre-set dental arch curve. In practical applications, different manufacturers use different scanning trajectories and reconstruction algorithms, resulting in a lack of unified or clear physical meaning in the grayscale values ​​of the tissues generated by panoramic reconstruction. This makes it impossible to represent or accurately measure the characteristics of the substances at that location, and clinical disease diagnosis can only be made based on the relative relationships between pixels. Dual-energy X-ray imaging technology, by acquiring X-ray projection data at different energy levels, can significantly improve tissue discrimination and the accuracy of substance classification and identification by utilizing the differences in the attenuation characteristics of different substances at different energies. This technology shows great potential in improving the accuracy of CT image diagnosis and expanding the scope of clinical applications. However, existing panoramic imaging methods are difficult to perform precise substance decomposition in conventional ways, and have certain limitations in terms of substance resolution, image detail representation, and clinical application scope, which may lead to poor imaging results when performing panoramic imaging. Summary of the Invention

[0004] This invention provides a dual-energy-based panoramic imaging method and apparatus, the main purpose of which is to solve the problem of poor imaging performance when performing panoramic imaging.

[0005] To achieve the above objectives, the present invention provides a dual-energy-based panoramic imaging method, comprising:

[0006] A dual-energy ray is generated using a high-pressure fast-cutting method, and projection imaging is performed using the dual-energy ray to obtain a high- and low-energy sparse projection map group.

[0007] Sparse compensation is performed on the high- and low-energy sparse projection map group to obtain the high- and low-energy projection map group.

[0008] The energy spectrum of the dual-energy ray was measured to obtain high- and low-energy energy spectrum curves;

[0009] A dual-energy decomposition mapping table is generated based on the preset virtual matrix material model and the high and low energy spectrum curves. The high and low energy projection map group is then decomposed into a projection domain based on the dual-energy decomposition mapping table to obtain the matrix material density integral decomposition result.

[0010] The density integral decomposition results of the base material are reconstructed by a variable center finite angle to obtain a panoramic base map set.

[0011] Optionally, the step of performing energy spectrum measurement on the dual-energy ray to obtain high- and low-energy energy spectrum curves includes:

[0012] The dual-energy ray is used to expose and scan a preset pure measurement phantom to obtain a set of projection data.

[0013] The projection data in the projection data group is selected one by one as the target projection data. The target projection data is then subjected to back projection reconstruction, 3D segmentation, reassignment and orthographic projection to obtain the integral path length.

[0014] Initialize the initial energy spectrum curve, and calculate the energy spectrum attenuation based on the initial energy spectrum curve and the integration path length to obtain the predicted ray attenuation value:

[0015]

[0016] Wherein, P refers to the predicted ray attenuation value. This refers to the initial energy spectrum curve for energy E, where exp is the symbol for the exponential function. is the mass decay coefficient of the pure measurement phantom for energy E, μ is the linear decay coefficient, ρ is the density symbol, l is the integration path length, d is the differential symbol, and E is the energy value;

[0017] Calculate the projection attenuation difference between the predicted ray attenuation value and the target projection data;

[0018] The initial energy spectrum curve is iteratively updated based on the projection attenuation difference to obtain the energy spectrum curve, and high and low energy spectrum curves are generated based on the energy spectrum curves of all target projection data in the projection data group.

[0019] Optionally, the step of performing back-projection reconstruction, 3D segmentation, reassignment, and orthographic projection on the target projection data to obtain the integral path length includes:

[0020] The target projection data is back-projected and reconstructed to obtain the reconstruction result;

[0021] The reconstruction result is segmented according to a preset attenuation coefficient range to obtain segmented region phantoms;

[0022] The voxel reassignment is performed on the segmented region phantom to obtain the voxel reassignment phantom;

[0023] The voxel reassignment is then projected orthogonally to obtain the integral path length.

[0024] Optionally, generating a bi-energy decomposition mapping table based on a preset virtual matrix phantom and the high and low energy spectrum curves includes:

[0025] By performing orthographic projection on the preset virtual matrix phantom, the density integrals of the first and second matrix materials are obtained.

[0026] Extract the low-energy energy spectrum curve and the high-energy energy spectrum curve from the high-energy and low-energy energy spectrum curves;

[0027] The low-energy ray attenuation value is calculated based on the density integral of the first matrix material, the density integral of the second matrix material, and the low-energy spectrum curve:

[0028]

[0029] m A =∫ρ A (l)dl

[0030] m B =∫ρ B (l)dl

[0031] Among them, P L This refers to the attenuation value of the low-energy rays, where L represents low energy and S represents low energy. L (E) refers to the low-energy spectrum curve for energy E, and exp is the symbol for the exponential function. It is the mass decay coefficient of the first matrix A preset in the virtual matrix phantom, μ A ρ is the linear decay coefficient of the first matrix A preset in the virtual matrix phantom. A The density, m, of the first base material A preset in the virtual base material phantom is... A This refers to the density integral of the first basic substance. It is the mass decay coefficient of the second matrix material B preset in the virtual matrix material motif, μ B ρ is the linear decay coefficient of the second matrix material B preset in the virtual matrix material motif. B The density, m, of the first base material B preset in the virtual base material phantom is... B This refers to the density integral of the second basic substance, where d is the differential symbol, E is the energy value, and l is the preset integration path length.

[0032] The high-energy ray attenuation value is calculated based on the density integral of the first matrix material, the density integral of the second matrix material, and the high-energy spectrum curve.

[0033]

[0034] Among them, P H This refers to the attenuation value of the high-energy rays, where H represents high energy and S represents high energy. H (E) refers to the high-energy spectrum curve for energy E, and exp is the symbol for the exponential function. It is the mass decay coefficient of the first matrix A preset in the virtual matrix phantom, μ A ρ is the linear decay coefficient of the first matrix A preset in the virtual matrix phantom. A The density, m, of the first base material A preset in the virtual base material phantom is... A This refers to the density integral of the first basic substance. It is the mass decay coefficient of the second matrix material B preset in the virtual matrix material motif, μ B ρ is the linear decay coefficient of the second matrix material B preset in the virtual matrix material motif. B The density, m, of the first base material B preset in the virtual base material phantom is... B This refers to the density integral of the second basic substance, where d is the differential symbol, E is the energy value, and l is the preset integration path length.

[0035] A primary mapping table is generated based on the density integral of the first base material, the density integral of the second base material, the low-energy ray attenuation value, and the high-energy ray attenuation value.

[0036] The primary mapping table is homogenized by equal-interval interpolation to obtain a bi-energy decomposition mapping table.

[0037] Optionally, the step of performing projection domain decomposition on the high- and low-energy projection map group according to the dual-energy decomposition mapping table to obtain the integral decomposition result of the base material density includes:

[0038] Attenuation calculations are performed on the high and low energy projection map group to obtain high and low energy attenuation value pairs;

[0039] The nearest neighbor value group is obtained by filtering the bi-energy decomposition mapping table according to the nearest search method;

[0040] Bilinear interpolation is performed on the most recent decay value set to obtain the density decomposition values;

[0041] Density reconstruction is performed based on the density decomposition values ​​to obtain the integral decomposition results of the base material density.

[0042] Optionally, the step of performing variable-center finite-angle reconstruction on the density integral decomposition result of the base material to obtain a panoramic base map set includes:

[0043] The density integral decomposition result of the matrix material is subjected to ramp filtering to obtain the filtered matrix material decomposition result.

[0044] The filtered base material decomposition results are weighted to obtain the weighted base material decomposition results;

[0045] Multi-layer dental arch curve back projection was performed on the weighted base material decomposition results to obtain a hierarchical panoramic base map group;

[0046] The hierarchical panoramic base map group is automatically focused and fused to obtain a panoramic base map group.

[0047] To address the aforementioned problems, the present invention also provides a dual-energy-based panoramic imaging device, the device comprising:

[0048] The projection acquisition module is used to generate dual-energy rays using a high-pressure fast-cutting method, and to perform projection imaging using the dual-energy rays to obtain a high- and low-energy sparse projection map group.

[0049] A sparsity compensation module is used to perform sparsity compensation on the high- and low-energy sparse projection map group to obtain the high- and low-energy projection map group.

[0050] The energy spectrum measurement module is used to perform energy spectrum measurement on the dual-energy ray to obtain high- and low-energy energy spectrum curves;

[0051] The mapping decomposition module is used to generate a dual-energy decomposition mapping table based on a preset virtual matrix material model and the high and low energy spectrum curves, and to perform projection domain decomposition on the high and low energy projection map group based on the dual-energy decomposition mapping table to obtain the matrix material density integral decomposition result.

[0052] The base map reconstruction module is used to perform variable-center finite-angle reconstruction on the density integral decomposition results of the base material to obtain a panoramic base map group.

[0053] This invention utilizes dual-energy X-rays for projection imaging, enabling the decomposition of materials by taking advantage of the unique attenuation characteristics of different materials under X-rays at different energy levels. This can significantly improve the recognition accuracy of specific oral tissues. By performing sparsity compensation, deep learning or linear interpolation methods can be used to interpolate the projection image at the intermediate angle, thereby avoiding the quality degradation caused by the angular sparsity of the projection image during subsequent image reconstruction.

[0054] By performing energy spectrum measurements on the dual-energy X-rays, the accuracy of energy spectrum measurements can be improved, the calibration process can be simplified, and reliable reference data can be provided. Through relational mapping and projection domain decomposition, noise and artifacts in images of different energies can be eliminated, image contrast can be enhanced, and different substances can be easily distinguished, enabling material identification and precise quantification, thereby improving the accuracy of subsequent panoramic imaging. By performing variable-center finite-angle reconstruction and panoramic base map imaging, reconstruction accuracy can be improved, local area optimization can be achieved, and thus a high-contrast and high-detail oral cavity structure can be comprehensively displayed, improving the accuracy of panoramic images. Therefore, the dual-energy-based panoramic imaging method and device proposed in this invention can solve the problem of poor imaging performance in oral panoramic imaging. Attached Figure Description

[0055] Figure 1 This is a flowchart illustrating a dual-energy-based panoramic imaging method according to an embodiment of the present invention.

[0056] Figure 2 This is a schematic diagram of the unit structure of the filter array unit of an area array filter scanner provided in an embodiment of the present invention;

[0057] Figure 3 This is a schematic diagram of a high-voltage fast-switching acquisition structure provided in an embodiment of the present invention;

[0058] Figure 4 This is a schematic diagram of a scanning energy spectrum curve provided in an embodiment of the present invention;

[0059] Figure 5 This is a schematic diagram of a bi-energy decomposition mapping table provided in an embodiment of the present invention;

[0060] Figure 6 This is a functional block diagram of a dual-energy-based panoramic imaging device provided in an embodiment of the present invention.

[0061] The realization of the objective, functional features and advantages of the present invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0062] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0063] This application provides a dual-energy-based panoramic imaging method. The executing entity of the dual-energy-based panoramic imaging method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the dual-energy-based panoramic imaging method can be executed by software or hardware installed on a terminal device or a server device, and the software can be a blockchain platform. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communication, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0064] Reference Figure 1 The diagram shown is a flowchart illustrating a dual-energy-based panoramic imaging method according to an embodiment of the present invention. In this embodiment, the dual-energy-based panoramic imaging method includes:

[0065] S1. A dual-energy ray is generated using a high-pressure fast-cutting method, and the dual-energy ray is used for projection imaging to obtain a high- and low-energy sparse projection map group.

[0066] Specifically, the dual-energy X-rays refer to X-rays with both high and low energies. Dual-energy X-rays can be generated using a filtered array scanner, a dual-layer flat panel detector, or a high-pressure fast-cut method. The high- and low-energy sparse projection map set includes a high-energy sparse projection map and a low-energy sparse projection map. The high-energy sparse projection map refers to the sparse projection map obtained by projecting high-energy X-rays, and the low-energy sparse projection map refers to the sparse projection map obtained by projecting low-energy X-rays.

[0067] Specifically, the filter array scanner refers to an X-ray scanner equipped with filter array units, which uniformly cover each detector channel of the X-ray scanner. The filter array units can be referenced from... Figure 2 As shown, black represents strong filtering materials, such as aluminum, copper, etc., and white represents weak filtering or even no filtering materials. During scanning, the exposure parameter is set to high kV. The pixels corresponding to the black strong filtering area of ​​the filtering module are low-energy sparse projection images, and the white area is a high-energy sparse projection image. At this time, each sparse projection image contains high and low energy projection values, but they are distributed in an alternating and staggered manner.

[0068] Specifically, the dual-layer flat panel detector is a method for simultaneously acquiring high and low energy spectrum data in a single exposure. Since the data is acquired in a single exposure, motion artifacts are avoided and temporal resolution is improved. By using two closely arranged flat panel detector layers, each of which has different sensitivities to X-rays of different energies, the upper flat panel detector is generally more sensitive to low-energy X-rays and can use a thinner absorption layer or a material with higher absorption efficiency for low-energy X-rays. The lower flat panel detector is more sensitive to high-energy X-rays and can use a thicker absorption layer or a material with higher absorption efficiency for high-energy X-rays.

[0069] Specifically, refer to Figure 3 As shown, the method of generating dual-energy rays using high-voltage fast switching refers to using a flat panel detector to alternately acquire high- and low-energy data by rapidly switching the high-voltage tube voltage. The projection data of the high- and low-energy sparse projection map group is arranged in the order of "low, high, low, high...". During acquisition, the rotation axis is not fixed, but moves along a preset rotation center trajectory. Common designs include fixing the x-axis and moving back and forth on the y-axis, with the trajectory being a "1" shape; or adding a fixed direction of movement to the x-axis, with the trajectory being a "V" shape, such as the panoramic preset dental arch trajectory in the figure.

[0070] In this embodiment of the invention, by using dual-energy X-rays for projection imaging, the specific attenuation characteristics of different substances under X-rays at different energy levels can be utilized to decompose substances, which can significantly improve the recognition accuracy of specific oral tissues.

[0071] S2. Perform sparse compensation on the high and low energy sparse projection map group to obtain the high and low energy projection map group.

[0072] In detail, there is a small acquisition time difference between adjacent high and low energy projections obtained by high-voltage fast-cut acquisition, but the ray path still changes. The projection data is not pixel-level corresponding and has a certain degree of sparsity. Therefore, sparsity compensation is required for the high and low energy sparse projection map group.

[0073] In this embodiment of the invention, sparse compensation is performed on the high- and low-energy sparse projection map group to obtain the high- and low-energy projection map group:

[0074] The high- and low-energy sparse projection map group is subjected to feature convolution and feature pooling using a pre-trained deep interpolation model to obtain a sparse projection feature group.

[0075] The sparse projected feature set is obtained by performing skip connections and full connections on the sparse projected feature set using the depth interpolation model.

[0076] The high- and low-energy projection map group is obtained by using the depth interpolation model to perform feature activation and feature upsampling on the standard projection feature group.

[0077] In another embodiment of the invention, sparsity compensation can be performed using continuous linear interpolation to obtain a set of high- and low-energy projection maps. For example, continuous linear interpolation can collect projection maps with n projection angles and positions. The low-energy sparse projection map numbers are 1, 3, 5, ..., n-1, and the high-energy sparse projection map numbers are 2, 4, 6, ..., n. Interpolation is performed using adjacent odd-numbered data to obtain intermediate even-numbered data. That is, for low-energy data 1 and 3, 2 is interpolated; for 3 and 5, 4 is interpolated. Both high- and low-energy data can be interpolated to obtain complete projection maps with numbers 1, 2, 3, ..., n, for a total of n angles.

[0078] Specifically, the deep interpolation model can be a convolutional neural network of U-Net. This deep interpolation model includes convolutional layers, pooling layers, skip connection layers, fully connected layers, activation sampling layers, and upsampling layers. The convolutional layers extract local features from the input image, providing rich image information to the network. The pooling layers reduce the spatial dimension of the features while enhancing the network's abstraction capabilities, helping the model capture higher-level visual information. The skip connection layers, in some architectures such as U-Net, directly connect feature maps from the encoder to the corresponding layers in the decoder, helping to retain more details and positional information during image reconstruction. The fully connected layers, located deep within the network, map the learned features to the output space, generating a complete data representation. The activation sampling layers include activation functions such as ReLU, which enhance the model's expressive power by introducing non-linearity, allowing the network to learn more complex data relationships. The upsampling layers, located in the decoder, progressively restore the spatial resolution of the image, generating a high-resolution output.

[0079] Specifically, before performing feature convolution and feature pooling on the high- and low-energy sparse projection map group using a pre-trained deep interpolation model to obtain the sparse projection feature group, the process further includes:

[0080] The pre-collected standard sparse projection map is split into training sparse projection maps and real sparse projection maps.

[0081] The training sparse projection map is subjected to feature convolution and feature pooling using a convolutional neural network to obtain the training projection features.

[0082] The trained projection features are subjected to skip connections and full connections to obtain connected projection features.

[0083] The connected projection features are subjected to feature activation and feature upsampling to obtain the predicted sparse projection map;

[0084] The composite loss value between the predicted sparse projection map and the true sparse projection map is calculated using the following composite loss algorithm:

[0085]

[0086] Wherein, Loss refers to the composite loss value, α and β are preset balancing weights, N refers to the total number of pixels in the predicted oral projection map, and the total number of pixels in the predicted sparse projection map is equal to the total number of pixels in the actual sparse projection map, and i is the pixel index. This refers to the value of the i-th pixel in the predicted sparse projection map. It refers to the i-th pixel value in the real sparse projection map, and SSIM() is the symbol for the structural similarity exponential function;

[0087] The convolutional neural network is iteratively updated with the composite loss value to obtain a depth interpolation model.

[0088] In detail, the standard sparse projection map is a high-energy projection map and a low-energy projection map obtained in advance by projection imaging using dual-energy rays. The dataset splitting refers to selecting the projection map used for training and the corresponding projection map used as the real dataset. The composite loss dispersion can reduce pixel-level errors and generate prediction results that are visually closer to the projected image while maintaining the image structure features.

[0089] In this embodiment of the invention, by performing sparsity compensation, deep learning or linear interpolation methods can be used to interpolate the projection image at the intermediate angle, thereby avoiding the degradation of quality in subsequent image reconstruction caused by the sparse angle of the projection image.

[0090] S3. Perform energy spectrum measurement on the dual-energy ray to obtain high and low energy spectrum curves.

[0091] For details, refer to Figure 4 As shown, the high and low energy spectrum curves refer to the distribution curves of photons of different energies (keV) in X-rays from a X-ray tube at a certain voltage. They usually need to be normalized into weighted curves with an integral of 1. When designing high voltage, the overlap between the high and low energy spectrum curves should be minimized. The energy spectrum of the high voltage at different voltages can be directly measured using an energy spectrum measuring instrument, or an actual pure phantom can be designed for scanning and approximation calculations can be performed through algorithm iteration. The high and low energy spectrum curves include high energy spectrum curves and low energy spectrum curves, where the dashed part is the low energy spectrum curve and the solid part is the high energy spectrum curve.

[0092] In this embodiment of the invention, the step of performing energy spectrum measurement on the dual-energy ray to obtain high- and low-energy energy spectrum curves includes:

[0093] The dual-energy ray is used to expose and scan a preset pure measurement phantom to obtain a set of projection data.

[0094] The projection data in the projection data group is selected one by one as the target projection data. The target projection data is then subjected to back projection reconstruction, 3D segmentation, reassignment and orthographic projection to obtain the integral path length.

[0095] Initialize the initial energy spectrum curve, and calculate the energy spectrum attenuation based on the initial energy spectrum curve and the integration path length to obtain the predicted ray attenuation value:

[0096]

[0097] Wherein, P refers to the predicted ray attenuation value. This refers to the initial energy spectrum curve for energy E, where exp is the symbol for the exponential function. is the mass decay coefficient of the pure measurement phantom for energy E, μ is the linear decay coefficient, ρ is the density symbol, l is the integration path length, d is the differential symbol, and E is the energy value;

[0098] Calculate the projection attenuation difference between the predicted ray attenuation value and the target projection data;

[0099] The initial energy spectrum curve is iteratively updated based on the projection attenuation difference to obtain the energy spectrum curve, and high and low energy spectrum curves are generated based on the energy spectrum curves of all target projection data in the projection data group.

[0100] In detail, the pure measurement phantom refers to a measurement phantom with known parameters composed of a known pure base material, which may be water or bone, and the projection data set includes projection data of high-energy rays and projection data of low-energy rays.

[0101] Specifically, the step of performing back-projection reconstruction, 3D segmentation, reassignment, and orthographic projection on the target projection data to obtain the integral path length includes:

[0102] The target projection data is back-projected and reconstructed to obtain the reconstruction result;

[0103] The reconstruction result is segmented according to a preset attenuation coefficient range to obtain segmented region phantoms;

[0104] The voxel reassignment is performed on the segmented region phantom to obtain the voxel reassignment phantom;

[0105] The voxel reassignment is then projected orthogonally to obtain the integral path length.

[0106] Specifically, the back-projection reconstruction method can be the Feldkamp-Davis-Kress projection reconstruction method, the segmented region phantom can be a phantom of a polymethyl methacrylate (PMMA) region, and the voxel reassignment refers to setting the voxel value of the projection interest region to 1 and the voxel value of the remaining regions to 0. The Radon algorithm can be used for orthographic projection.

[0107] Specifically, the primary energy spectrum curve is a discrete and normalized initial energy spectrum weight curve simulated by software such as Spectrum. The projection attenuation density algorithm can more accurately calculate the attenuation of X-rays passing through an object by integrating X-rays of different energies.

[0108] In this embodiment of the invention, by performing energy spectrum measurements on the dual-energy rays, the accuracy of energy spectrum measurements can be improved, the calibration process can be simplified, and reliable reference data can be provided.

[0109] S4. Generate a dual-energy decomposition mapping table based on the preset virtual matrix material model and the high and low energy spectrum curves, and perform projection domain decomposition on the high and low energy projection map group based on the dual-energy decomposition mapping table to obtain the matrix material density integral decomposition result.

[0110] In detail, the integral decomposition result of the matrix density includes the density of each type of substance in the oral cavity projection and the corresponding image region. The integral decomposition result of the matrix density can improve the accuracy of subsequent panoramic imaging.

[0111] In this embodiment of the invention, the projection domain decomposition of the high- and low-energy projection map group according to the bi-energy decomposition mapping table to obtain the integral decomposition result of the base material density includes:

[0112] Attenuation calculations are performed on the high and low energy projection map group to obtain high and low energy attenuation value pairs;

[0113] The nearest neighbor value group is obtained by filtering the bi-energy decomposition mapping table according to the nearest search method;

[0114] Bilinear interpolation is performed on the most recent decay value set to obtain the density decomposition values;

[0115] Density reconstruction is performed based on the density decomposition values ​​to obtain the integral decomposition results of the base material density.

[0116] Specifically, the attenuation calculation refers to splitting the enhanced oral projection image into high and low energy spectra and extracting the energy spectrum attenuation to obtain high energy attenuation values ​​and low energy attenuation values, and then combining the high energy attenuation values ​​and the low energy attenuation values ​​into high and low energy attenuation value pairs.

[0117] Specifically, the step of selecting the nearest neighbor value group from the dual-energy decomposition mapping table according to the nearest search method to obtain the nearest decay value group means that the four nearest decay values ​​are found from the dual-energy decomposition mapping table and the nearest decay value group is formed. The density decomposition substance refers to the density integral decomposition result of the two substances corresponding to the dual-energy decomposition mapping table.

[0118] In detail, the step of generating a bi-energy decomposition mapping table based on a preset virtual matrix material motif and the high and low energy spectrum curves includes:

[0119] By performing orthographic projection on the preset virtual matrix phantom, the density integrals of the first and second matrix materials are obtained.

[0120] Extract the low-energy energy spectrum curve and the high-energy energy spectrum curve from the high-energy and low-energy energy spectrum curves;

[0121] The low-energy ray attenuation value is calculated based on the density integral of the first matrix material, the density integral of the second matrix material, and the low-energy spectrum curve:

[0122]

[0123] m A =∫ρ A (l)dl

[0124] m B =∫ρ B (l)dl

[0125] Among them, P L This refers to the attenuation value of the low-energy rays, where L represents low energy and S represents low energy. L (E) refers to the low-energy spectrum curve for energy E, and exp is the symbol for the exponential function. It is the mass decay coefficient of the first matrix A preset in the virtual matrix phantom, μ A ρ is the linear decay coefficient of the first matrix A preset in the virtual matrix phantom. A The density, m, of the first base material A preset in the virtual base material phantom is... A This refers to the density integral of the first basic substance. It is the mass decay coefficient of the second matrix material B preset in the virtual matrix material motif, μ B ρ is the linear decay coefficient of the second matrix material B preset in the virtual matrix material motif. B The density, m, of the first base material B preset in the virtual base material phantom is... B This refers to the density integral of the second basic substance, where d is the differential symbol, E is the energy value, and l is the preset integration path length.

[0126] The high-energy ray attenuation value is calculated based on the density integral of the first matrix material, the density integral of the second matrix material, and the high-energy spectrum curve.

[0127]

[0128] Among them, P H This refers to the attenuation value of the high-energy rays, where H represents high energy and S represents high energy. H (E) refers to the high-energy spectrum curve for energy E, and exp is the symbol for the exponential function. It is the mass decay coefficient of the first matrix A preset in the virtual matrix phantom, μ A ρ is the linear decay coefficient of the first matrix A preset in the virtual matrix phantom. A The density, m, of the first base material A preset in the virtual base material phantom is... A This refers to the density integral of the first basic substance. It is the mass decay coefficient of the second matrix material B preset in the virtual matrix material motif, μ B ρ is the linear decay coefficient of the second matrix material B preset in the virtual matrix material motif. B The density, m, of the first base material B preset in the virtual base material phantom is... B This refers to the density integral of the second basic substance, where d is the differential symbol, E is the energy value, and l is the preset integration path length.

[0129] A primary mapping table is generated based on the density integral of the first base material, the density integral of the second base material, the low-energy ray attenuation value, and the high-energy ray attenuation value.

[0130] The primary mapping table is homogenized by equal-interval interpolation to obtain a bi-energy decomposition mapping table.

[0131] In detail, the virtual matrix material phantom is a three-dimensional phantom of the same volume with the density distribution of the selected first matrix material A and second matrix material B (such as water, calcium, etc.) for decomposition. The density values ​​of the two phantoms are required to exceed the maximum density integral value that the actual scanned object can possibly have, so as to ensure that the density integral values ​​of materials A and B are distributed as evenly as possible within the range of the maximum density integral value.

[0132] In detail, the virtual matrix phantom is a conceptual tool used in dual-energy CT and multi-energy CT imaging techniques. The virtual matrix phantom is used to decompose and analyze the material composition inside an object. It describes and calibrates the absorption and attenuation characteristics of different materials to X-rays by using models based on known materials. The core idea of ​​the virtual matrix phantom is to use the known physical properties of the matrix material to infer the composition and distribution of unknown materials.

[0133] Specifically, by integrating X-rays of different energies, the attenuation of X-rays as they pass through an object can be calculated more accurately, thus obtaining a more accurate dual-energy decomposition mapping table.

[0134] For details, refer to Figure 5 The diagram shown is a schematic of the bienergy decomposition mapping table, where P H The value refers to the high-energy projection attenuation value, P. L The value refers to the low-energy projection attenuation value. It refers to the u-th P L value, It refers to the vth P H value, It refers to P L Value And P H Value The first fundamental density integral at time, It refers to P L Value And P H Value The density integral of the second fundamental substance at that time.

[0135] In another embodiment of the present invention, the dual-energy decomposition mapping table can be established in reverse based on the high and low energy spectrum curves using a fixed grid method, including:

[0136] Obtain the density integral interval of the first and second basic substances;

[0137] A first base substance density sequence is generated based on the first base substance density integral interval, and a second base substance density sequence is generated based on the second base substance density integral interval.

[0138] The high-energy projection attenuation value sequence and the low-energy projection attenuation value sequence are calculated based on the first base material density sequence and the second base material density sequence.

[0139] A primary mapping table is generated based on the first base material density sequence, the second base material density sequence, the high-energy projection attenuation value sequence, and the low-energy projection attenuation value sequence.

[0140] The primary mapping table is bilinearly interpolated to obtain a bienergy decomposition mapping table.

[0141] In this embodiment of the invention, by performing relation mapping and projection domain decomposition, noise and artifacts in images of different energies can be eliminated, image contrast can be enhanced, and different substances can be easily distinguished, enabling material identification and accurate quantification, thereby improving the accuracy of subsequent panoramic imaging.

[0142] S5. Perform variable-center finite-angle reconstruction on the density integral decomposition results of the base material to obtain a panoramic base map group.

[0143] In detail, the panoramic base map set refers to a set of panoramic base maps of oral cavity projection, which provides a panoramic view of the oral cavity region, covering all teeth and related structures from one side to the other.

[0144] In this embodiment of the invention, the step of performing variable-center finite-angle reconstruction on the density integral decomposition result of the base material to obtain a panoramic base map set includes:

[0145] The density integral decomposition result of the matrix material is subjected to ramp filtering to obtain the filtered matrix material decomposition result.

[0146] The filtered base material decomposition results are weighted to obtain the weighted base material decomposition results;

[0147] Multi-layer dental arch curve back projection was performed on the weighted base material decomposition results to obtain a hierarchical panoramic base map group;

[0148] The hierarchical panoramic base map group is automatically focused and fused to obtain a panoramic base map group.

[0149] In detail, the weighted calculation refers to the weighted summation of the integral values ​​of the matrix density of all the matrix points that have passed through the preset reconstruction points in the decomposition result of the filtered matrix material to obtain the panoramic base map of the material. The multi-layer dental arch curve back projection refers to the calculation of panoramic images of different depths corresponding to the weighted matrix material decomposition result using preset multi-layer parallel dental arch curves to obtain a hierarchical panoramic base map group.

[0150] Specifically, the automatic focus fusion refers to using a local sharpness detection algorithm to calculate the sharpness of each local part of each level panoramic base map in the hierarchical panoramic base map group, and selecting the local images with the highest sharpness to fuse and reassemble them into a panoramic base map group.

[0151] In detail, other images with tissue-specific discrimination functions can be generated based on the panoramic base map set, such as virtual conventional maps, virtual decalcification maps, virtual monoenergetic maps, and electron density maps.

[0152] In this embodiment of the invention, by performing variable center finite angle reconstruction and panoramic base map imaging, the reconstruction accuracy can be improved, local area optimization can be achieved, thereby fully displaying the high-contrast and high-detail oral cavity structure and improving the accuracy of the panoramic image.

[0153] This invention utilizes dual-energy X-rays for projection imaging, enabling the decomposition of materials by taking advantage of the unique attenuation characteristics of different materials under X-rays at different energy levels. This can significantly improve the recognition accuracy of specific oral tissues. By performing sparsity compensation, deep learning or linear interpolation methods can be used to interpolate the projection image at the intermediate angle, thereby avoiding the quality degradation caused by the angular sparsity of the projection image during subsequent image reconstruction.

[0154] By performing energy spectrum measurements on the dual-energy X-rays, the accuracy of energy spectrum measurements can be improved, the calibration process can be simplified, and reliable reference data can be provided. Through relational mapping and projection domain decomposition, noise and artifacts in images of different energies can be eliminated, image contrast can be enhanced, and different substances can be easily distinguished, enabling material identification and precise quantification. This improves the accuracy of subsequent panoramic imaging. By performing variable-center finite-angle reconstruction and panoramic base map imaging, reconstruction accuracy can be improved, local area optimization can be achieved, and thus a high-contrast and high-detail oral cavity structure can be fully displayed, improving the accuracy of the panoramic image. Therefore, the dual-energy-based panoramic imaging method proposed in this invention can solve the problem of poor imaging performance in oral panoramic imaging.

[0155] like Figure 6 The diagram shown is a functional block diagram of a dual-energy-based panoramic imaging device provided in an embodiment of the present invention.

[0156] The dual-energy-based panoramic imaging device 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the dual-energy-based panoramic imaging device 100 may include a projection acquisition module 101, a sparsity compensation module 102, an energy spectrum measurement module 103, a mapping decomposition module 104, and a base map reconstruction module 105. The module described in this invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by the processor of an electronic device and can perform a fixed function, and are stored in the memory of the electronic device.

[0157] In this embodiment, the functions of each module / unit are as follows:

[0158] The projection acquisition module 101 is used to generate dual-energy rays using a high-pressure fast-cutting method, and to perform projection imaging using the dual-energy rays to obtain a high- and low-energy sparse projection image group.

[0159] The sparse compensation module 102 is used to perform sparse compensation on the high and low energy sparse projection map group to obtain the high and low energy projection map group.

[0160] The energy spectrum measurement module 103 is used to perform energy spectrum measurement on the dual-energy ray to obtain high and low energy spectrum curves;

[0161] The mapping decomposition module 104 is used to generate a dual-energy decomposition mapping table based on a preset virtual matrix material model and the high and low energy spectrum curves, and to perform projection domain decomposition on the high and low energy projection map group based on the dual-energy decomposition mapping table to obtain the matrix material density integral decomposition result.

[0162] The base map reconstruction module 105 is used to perform variable-center finite-angle reconstruction on the density integral decomposition result of the base material to obtain a panoramic base map group.

[0163] In detail, the modules in the dual-energy-based panoramic imaging device 100 described in this embodiment of the invention employ the same methods as described above during use. Figure 1 The method uses the same techniques as the dual-energy panoramic imaging method described above and can produce the same technical effects, so it will not be repeated here.

[0164] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and other division methods may be used in actual implementation.

[0165] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0166] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0167] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0168] Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of equivalents of the claims are intended to be embraced within the invention. No appended diagram markings in the claims should be construed as limiting the scope of the claims.

[0169] The embodiments of this application can acquire and process relevant data based on artificial intelligence technology. Artificial intelligence (AI) refers to the theories, methods, technologies, and application devices that use digital computers or machines controlled by digital computers to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use that knowledge to obtain optimal results.

[0170] Furthermore, it is clear that the word "comprising" does not exclude other units or steps, and the singular does not exclude the plural. Multiple units or devices described in the apparatus embodiments may also be implemented by a single unit or device through software or hardware. Terms such as "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0171] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A panoramic imaging method based on dual energy, characterized in that, The method includes: A dual-energy ray is generated using a high-pressure fast-cutting method, and projection imaging is performed using the dual-energy ray to obtain a high- and low-energy sparse projection map group. Sparse compensation is performed on the high- and low-energy sparse projection map group to obtain the high- and low-energy projection map group. The energy spectrum of the dual-energy ray was measured to obtain high- and low-energy energy spectrum curves; A dual-energy decomposition mapping table is generated based on the preset virtual matrix material model and the high and low energy spectrum curves. The high and low energy projection map group is then decomposed into a projection domain based on the dual-energy decomposition mapping table to obtain the matrix material density integral decomposition result. The density integral decomposition results of the base material are reconstructed by a variable center finite angle to obtain a panoramic base map set.

2. The panoramic imaging method based on dual energy as described in claim 1, characterized in that, The step of performing energy spectrum measurements on the dual-energy ray to obtain high- and low-energy energy spectrum curves includes: The dual-energy ray is used to expose and scan a preset pure measurement phantom to obtain a set of projection data. The projection data in the projection data group is selected one by one as the target projection data. The target projection data is then subjected to back projection reconstruction, 3D segmentation, reassignment and orthographic projection to obtain the integral path length. Initialize the initial energy spectrum curve, and calculate the energy spectrum attenuation based on the initial energy spectrum curve and the integration path length to obtain the predicted ray attenuation value: Wherein, P refers to the predicted ray attenuation value. This refers to the initial energy spectrum curve for energy E, where exp is the symbol for the exponential function. is the mass decay coefficient of the pure measurement phantom for energy E, μ is the linear decay coefficient, ρ is the density symbol, l is the integration path length, d is the differential symbol, and E is the energy value; Calculate the projection attenuation difference between the predicted ray attenuation value and the target projection data; The initial energy spectrum curve is iteratively updated based on the projection attenuation difference to obtain the energy spectrum curve, and high and low energy spectrum curves are generated based on the energy spectrum curves of all target projection data in the projection data group.

3. The panoramic imaging method based on dual energy as described in claim 2, characterized in that, The process of performing back-projection reconstruction, 3D segmentation, reassignment, and orthographic projection on the target projection data to obtain the integral path length includes: The target projection data is back-projected and reconstructed to obtain the reconstruction result; The reconstruction result is segmented according to a preset attenuation coefficient range to obtain segmented region phantoms; The voxel reassignment is performed on the segmented region phantom to obtain the voxel reassignment phantom; The voxel reassignment is then projected orthogonally to obtain the integral path length.

4. The panoramic imaging method based on dual energy as described in claim 1, characterized in that, The step of generating a bi-energy decomposition mapping table based on a preset virtual matrix material motif and the high and low energy spectrum curves includes: By performing orthographic projection on the preset virtual matrix phantom, the density integrals of the first and second matrix materials are obtained. Extract the low-energy energy spectrum curve and the high-energy energy spectrum curve from the high-energy and low-energy energy spectrum curves; The low-energy ray attenuation value is calculated based on the density integral of the first matrix material, the density integral of the second matrix material, and the low-energy spectrum curve: m A =∫ρ A (l)dl m B =∫ρ B (l)dl Among them, P L This refers to the attenuation value of the low-energy rays, where L indicates low energy and S indicates low energy. L (E) refers to the low-energy spectrum curve for energy E, and exp is the symbol for the exponential function. It is the mass decay coefficient of the first matrix A preset in the virtual matrix phantom, μ A ρ is the linear decay coefficient of the first matrix A preset in the virtual matrix phantom. A The density, m, of the first base material A preset in the virtual base material phantom is... A This refers to the density integral of the first basic substance. It is the mass decay coefficient of the second matrix material B preset in the virtual matrix material motif, μ B ρ is the linear decay coefficient of the second matrix material B preset in the virtual matrix material motif. B The density, m, of the first base material B preset in the virtual base material phantom is... B This refers to the density integral of the second basic substance, where d is the differential symbol, E is the energy value, and l is the preset integration path length. The high-energy ray attenuation value is calculated based on the density integral of the first matrix material, the density integral of the second matrix material, and the high-energy spectrum curve. Among them, P H This refers to the attenuation value of the high-energy rays, where H represents high energy and S represents high energy. H (E) refers to the high-energy spectrum curve for energy E, and exp is the symbol for the exponential function. It is the mass decay coefficient of the first matrix A preset in the virtual matrix phantom, μ A ρ is the linear decay coefficient of the first matrix A preset in the virtual matrix phantom. A The density, m, of the first base material A preset in the virtual base material phantom is... A This refers to the density integral of the first basic substance. It is the mass decay coefficient of the second matrix material B preset in the virtual matrix material motif, μ B ρ is the linear decay coefficient of the second matrix material B preset in the virtual matrix material motif. B The density, m, of the first base material B preset in the virtual base material phantom is... B This refers to the density integral of the second basic substance, where d is the differential symbol, E is the energy value, and l is the preset integration path length. A primary mapping table is generated based on the density integral of the first base material, the density integral of the second base material, the low-energy ray attenuation value, and the high-energy ray attenuation value. The primary mapping table is homogenized by equal-interval interpolation to obtain a bi-energy decomposition mapping table.

5. The panoramic imaging method based on dual energy as described in claim 1, characterized in that, The step of performing projection domain decomposition on the high- and low-energy projection map group according to the dual-energy decomposition mapping table to obtain the integral decomposition result of the base material density includes: Attenuation calculations are performed on the high and low energy projection map group to obtain high and low energy attenuation value pairs; The nearest neighbor value group is obtained by filtering the bi-energy decomposition mapping table according to the nearest search method; Bilinear interpolation is performed on the most recent decay value set to obtain the density decomposition value; Density reconstruction is performed based on the density decomposition values ​​to obtain the integral decomposition results of the base material density.

6. The panoramic imaging method based on dual energy as described in claim 1, characterized in that, The variable-center finite-angle reconstruction of the density integral decomposition result of the base material yields a panoramic base map set, including: The density integral decomposition result of the matrix material is subjected to ramp filtering to obtain the filtered matrix material decomposition result. The filtered base material decomposition results are weighted to obtain the weighted base material decomposition results; Multi-layer dental arch curve back projection was performed on the weighted base material decomposition results to obtain a hierarchical panoramic base map group; The hierarchical panoramic base map group is automatically focused and fused to obtain a panoramic base map group.

7. A panoramic imaging device based on dual energy, characterized in that, The device includes: The projection acquisition module is used to generate dual-energy rays using a high-pressure fast-cutting method, and to use the dual-energy rays for projection imaging to obtain a high- and low-energy sparse projection map group. A sparsity compensation module is used to perform sparsity compensation on the high- and low-energy sparse projection map group to obtain the high- and low-energy projection map group. The energy spectrum measurement module is used to perform energy spectrum measurement on the dual-energy ray to obtain high- and low-energy energy spectrum curves; The mapping decomposition module is used to generate a dual-energy decomposition mapping table based on a preset virtual matrix material model and the high and low energy spectrum curves, and to perform projection domain decomposition on the high and low energy projection map group based on the dual-energy decomposition mapping table to obtain the matrix material density integral decomposition result. The base map reconstruction module is used to perform variable-center finite-angle reconstruction on the density integral decomposition results of the base material to obtain a panoramic base map group.

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