Dual-energy cone beam ct imaging method, apparatus, and device based on prior guidance, and medium

Abstract

This application relates to the field of image processing and discloses a priori-guided dual-energy cone-beam CT imaging method, comprising: acquiring high-energy projection data and low-energy projection data of dual-energy cone-beam CT; preprocessing the high-energy and low-energy projection data to obtain high-energy projection images and low-energy projection images; performing contrast compensation on the high-energy projection images to obtain a contrast-compensated prior image; calculating an artifact suppression prior image based on the high-energy and low-energy projection images; performing thresholding segmentation on the high-energy projection images; forward projection on the thresholded images to obtain a high-density mask projection image; weighted fusing the contrast-compensated prior image and the artifact suppression prior image based on the high-density mask projection image to obtain a hybrid projection image; and performing three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image. This method can improve the quality of the hybrid reconstructed image obtained by combining the two energies of dual-energy cone-beam CT.

Description

Technical Field

[0001] This application relates to the field of image processing, and in particular to a priori-guided dual-energy cone-beam CT imaging method, apparatus, device, and medium. Background Technology

[0002] Cone-beam computed tomography (CBCT) offers advantages such as high resolution and low radiation dose, making it widely used in oral and maxillofacial imaging. In these examinations, teeth typically have high density, often with metallic components like crowns, leading to severe beam hardening artifacts in CBCT images and interfering with image interpretation. In contrast, dual-energy cone-beam computed tomography (DCT) provides two energy sources, each yielding images with distinct characteristics, thus offering rich prior information. Currently, the main approach is to obtain a hybrid reconstructed image through a simple linear combination of the two energy sources, which fails to fully utilize their complementary information for better image quality. Therefore, effectively combining the two energy sources is crucial, necessitating a priori-guided DCT imaging method to improve the quality of reconstructed images.

[0003] Current technologies suffer from the problem of low image quality when combining the two energies in dual-energy cone-beam CT to obtain hybrid reconstructed images. Summary of the Invention

[0004] This application provides a priori-guided dual-energy cone-beam CT imaging method, apparatus, device, and medium, the main purpose of which is to solve the problem of low quality of hybrid reconstructed images obtained by combining the two energies of dual-energy cone-beam CT.

[0005] To achieve the above objectives, this application provides a priori-guided dual-energy cone-beam CT imaging method, comprising:

[0006] Acquire high-energy projection data and low-energy projection data from dual-energy cone-beam CT, preprocess the high-energy projection data and the low-energy projection data to obtain high-energy projection images and low-energy projection images;

[0007] The high-energy projection image is contrast-compensated based on the low-energy projection image to obtain a contrast-compensated prior image.

[0008] Based on the high-energy projection image and the low-energy projection image, the effective energy projection information for effectively suppressing artifacts is calculated to obtain the artifact suppression prior image;

[0009] The high-energy projection image is segmented by thresholding, and the segmented image is forward-projected to obtain a high-density mask projection image.

[0010] The contrast-compensated prior image and the artifact-suppressing prior image are weighted and fused based on the high-density mask projection image to obtain a hybrid projection image;

[0011] The hybrid projection image is reconstructed in three dimensions to obtain a hybrid reconstructed image.

[0012] In some embodiments, the preprocessing of the high-energy projection data and the low-energy projection data to obtain a high-energy projection image and a low-energy projection image includes:

[0013] Obtain the preset angle compensation parameters;

[0014] The high-energy projection data and the low-energy projection data are aligned using the angle compensation parameters to obtain high-energy aligned data and low-energy aligned data.

[0015] Background and transmitted signals are acquired from a preset detector;

[0016] The number of background signals is counted as the number of incident photons;

[0017] The number of transmitted signals is counted as the number of emitted photons;

[0018] The high-energy attenuation projection of the high-energy aligned data and the low-energy attenuation projection of the low-energy aligned data are calculated based on the number of incident photons and the number of emitted photons.

[0019] A high-energy projection image and a low-energy projection image are generated based on the high-energy attenuation projection and the low-energy attenuation projection.

[0020] In some embodiments, the step of performing contrast compensation on the high-energy projection image based on the low-energy projection image to obtain a contrast-compensated prior image includes:

[0021] The low-energy projection image is normalized to obtain the weighting coefficients;

[0022] Each pixel in the high-energy projection image is compensated according to the weighting coefficient to obtain the updated pixel value of each pixel;

[0023] The updated pixel values ​​of each pixel are combined to form a contrast-compensated prior image.

[0024] In some embodiments, calculating effective energy projection information for effective artifact suppression based on the high-energy projection image and the low-energy projection image to obtain an artifact suppression prior image includes:

[0025] Obtain the modulation coefficient between the high-energy projection image and the low-energy projection image;

[0026] Based on the modulation coefficients, the high-energy projection image and the low-energy projection image are calculated to obtain a composite energy projection image;

[0027] The synthetic energy projection image and the high-energy projection image are weighted and fused to obtain an artifact-suppressed prior image.

[0028] In some embodiments, the step of thresholding the high-energy projection image and forward projecting the thresholded image to obtain a high-density mask projection image includes:

[0029] The high-energy projection image is reconstructed in three dimensions to obtain a high-energy attenuation image;

[0030] In the high-energy attenuation image, the pixel values ​​that are greater than a preset pixel threshold are set as the first pixel values;

[0031] The pixel value in the high-energy attenuation image that is less than or equal to the pixel threshold is set as the second pixel value;

[0032] A high-density mask image is created by combining several first pixel values ​​and several second pixel values;

[0033] Obtain the ray-driven forward projection operator;

[0034] The high-density mask image is projected using the ray-driven forward projection operator to obtain a high-density mask projection image.

[0035] In some embodiments, the step of weightedly fusing the contrast-compensated prior image and the artifact-suppressing prior image based on the high-density mask projection image to obtain a hybrid projection image includes:

[0036] The pixels in the high-density mask projection image are normalized to obtain a normalized image;

[0037] The normalized image is used to calculate the pixels in the contrast-compensated prior image and the pixels in the artifact-suppressing prior image to obtain a hybrid projection image.

[0038] In some embodiments, the step of performing three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image includes:

[0039] Obtain the reconstruction volume and projection angle from the 3D reconstruction parameters;

[0040] The mixed projection image is filtered to obtain several filtered projection data.

[0041] Each of the projection data is superimposed onto the reconstructed volume according to the projection angle;

[0042] The reconstructed volume is normalized to obtain a hybrid reconstructed image.

[0043] To address the aforementioned problems, the present invention also provides a priori-guided dual-energy cone-beam CT imaging device, the device comprising:

[0044] The image acquisition module is used to acquire high-energy projection data and low-energy projection data of dual-energy cone-beam CT, and to preprocess the high-energy projection data and the low-energy projection data to obtain high-energy projection images and low-energy projection images.

[0045] A contrast compensation module is used to perform contrast compensation on the high-energy projection image based on the low-energy projection image to obtain a contrast-compensated prior image.

[0046] The artifact suppression module is used to calculate the effective energy projection information for effectively suppressing artifacts based on the high-energy projection image and the low-energy projection image, and to obtain the artifact suppression prior image.

[0047] The image segmentation module is used to perform threshold segmentation on the high-energy projection image, and to perform forward projection on the threshold-segmented image to obtain a high-density mask projection image.

[0048] The image fusion module is used to weight and fuse the contrast compensation prior image and the artifact suppression prior image according to the high-density mask projection image to obtain a hybrid projection image;

[0049] The image reconstruction module is used to perform three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image.

[0050] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0051] At least one processor; and,

[0052] A memory communicatively connected to the at least one processor; wherein,

[0053] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the prior-guided dual-energy cone-beam CT imaging method described above.

[0054] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one computer program, which is executed by a processor in an electronic device to implement the aforementioned prior-guided dual-energy cone-beam CT imaging method. Attached Figure Description

[0055] Figure 1 A flowchart illustrating a priori-guided dual-energy cone-beam CT imaging method provided in an embodiment of this application;

[0056] Figure 2 A functional block diagram of a priori-guided dual-energy cone-beam CT imaging device provided in an embodiment of this application;

[0057] Figure 3 A schematic diagram of the structure of an electronic device for implementing the prior-guided dual-energy cone-beam CT imaging method provided in an embodiment of this application.

[0058] The realization of the purpose, functional features and advantages of this application will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0059] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0060] This application provides a priori-guided dual-energy cone-beam CT imaging method. The execution entity of the priori-guided dual-energy cone-beam CT 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 priori-guided dual-energy cone-beam CT 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 providing 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.

[0061] Reference Figure 1 The diagram shown is a flowchart illustrating a priori-guided dual-energy cone-beam CT imaging method according to an embodiment of this application. In this embodiment, a priori-guided dual-energy cone-beam CT imaging method includes:

[0062] S1. Acquire high-energy projection data and low-energy projection data from dual-energy cone-beam CT, preprocess the high-energy projection data and the low-energy projection data to obtain high-energy projection images and low-energy projection images.

[0063] In this embodiment of the invention, dual-energy cone-beam CT is a technique that uses X-rays of different energies to acquire images, providing richer information about tissues and materials. The dual-energy cone-beam CT system scans the object using two X-ray sources of different energies, acquiring high-energy projection data and low-energy projection data.

[0064] Specifically, the preprocessing of the high-energy projection data and the low-energy projection data to obtain high-energy projection images and low-energy projection images includes:

[0065] Obtain the preset angle compensation parameters;

[0066] The high-energy projection data and the low-energy projection data are aligned using the angle compensation parameters to obtain high-energy aligned data and low-energy aligned data.

[0067] Background and transmitted signals are acquired from a preset detector;

[0068] The number of background signals is counted as the number of incident photons;

[0069] The number of transmitted signals is counted as the number of emitted photons;

[0070] The high-energy attenuation projection of the high-energy aligned data and the low-energy attenuation projection of the low-energy aligned data are calculated based on the number of incident photons and the number of emitted photons.

[0071] A high-energy projection image and a low-energy projection image are generated based on the high-energy attenuation projection and the low-energy attenuation projection.

[0072] Specifically, the high-energy projection data and the low-energy projection data are aligned using the angle compensation parameters to obtain high-energy aligned data and low-energy aligned data, including:

[0073] Obtain the rotation angle and translation parameter from the angle compensation parameters;

[0074] The high-energy projection data and the low-energy projection data are rotated according to the rotation angle to obtain high-energy rotated data and low-energy rotated data.

[0075] The high-energy rotation data and the low-energy rotation data are moved using the translation parameters to obtain high-energy aligned data and low-energy aligned data.

[0076] In detail, the photon number information in the high-energy projection data and the low-energy projection data is converted into projection values ​​corresponding to the attenuation coefficient. High-energy projection images and low-energy projection images are then generated based on these projection values. The calculation formula is as follows:

[0077]

[0078] Where s0 represents the number of incident photons on the scanned object, s represents the number of outgoing photons after passing through the scanned object, and p h p represents a high-energy projection image. l This represents a low-energy projection image.

[0079] This process effectively improves the alignment accuracy of the projection data, thereby ensuring the accuracy of both high-energy and low-energy projection images. By accurately counting the number of incident and emitted photons and calculating the attenuation coefficient, it ensures that the generated image more realistically reflects the characteristics of the object's internal structure. This not only improves image quality but also provides a reliable data foundation for subsequent image analysis.

[0080] S2. Perform contrast compensation on the high-energy projection image based on the low-energy projection image to obtain a contrast-compensated prior image.

[0081] In this embodiment of the invention, the step of performing contrast compensation on the high-energy projection image based on the low-energy projection image to obtain a contrast-compensated prior image includes:

[0082] The low-energy projection image is normalized to obtain the weighting coefficients;

[0083] Each pixel in the high-energy projection image is compensated according to the weighting coefficient to obtain the updated pixel value of each pixel;

[0084] The updated pixel values ​​of each pixel are combined to form a contrast-compensated prior image.

[0085] In detail, the low-energy projection image is normalized to obtain the weighting coefficients, and the calculation formula is as follows:

[0086]

[0087] Where, p l (i) represents the value of the i-th pixel in the low-energy projection image, p l_min Represents a low-energy projection image p l The minimum pixel value in (i), p l_max Represents a low-energy projection image p l The maximum pixel value in (i), w p (i) represents the weighting coefficient.

[0088] The high-energy projection image is contrast-compensated based on the weighting coefficients to obtain a contrast-compensated prior image. The calculation formula is as follows:

[0089] p prior_comtrast =w p *p h

[0090] Among them, w pp represents the weighting coefficient. h p represents a high-energy projection image. prior_comtrast This represents the contrast-compensated prior image.

[0091] By performing contrast compensation on a high-energy projection image based on a low-energy projection image, the detail and visual quality of the image can be significantly improved. Gray-level normalization of the low-energy projection image generates weighting coefficients that effectively reflect the importance of different regions. Calculating the local contrast of each pixel and combining these local contrasts with the weighting coefficients yields an updated contrast, which is then used to synthesize a contrast-compensated prior image. This not only enhances image quality but also provides a clearer foundation for subsequent analysis, improving the overall effectiveness of image processing.

[0092] S3. Calculate the effective energy projection information for effectively suppressing artifacts based on the high-energy projection image and the low-energy projection image to obtain the artifact suppression prior image.

[0093] In this embodiment of the invention, the step of calculating effective energy projection information for effective artifact suppression based on the high-energy projection image and the low-energy projection image to obtain an artifact suppression prior image includes:

[0094] Obtain the modulation coefficient between the high-energy projection image and the low-energy projection image;

[0095] Based on the modulation coefficients, the high-energy projection image and the low-energy projection image are calculated to obtain a composite energy projection image;

[0096] The synthetic energy projection image and the high-energy projection image are weighted and fused to obtain an artifact-suppressed prior image.

[0097] In detail, the high-energy projection image and the low-energy projection image are calculated based on the modulation coefficients to obtain a synthetic energy projection image that is beneficial for artifact suppression. The calculation formula is as follows:

[0098] P composition (i)=P h (i)+α*[p h (i)―p l (i)]

[0099] Among them, P h (i) represents the value of the i-th pixel in the high-energy projection image, α represents the adjustable modulation coefficient, and p l (i) represents the value of the i-th pixel in the low-energy projection image, P composition (i) represents the value of the i-th pixel in the synthetic energy projection image.

[0100] The synthesized energy projection image and the high-energy projection image are weighted and fused to obtain the artifact-suppressed prior image, and the calculation formula is as follows:

[0101] P prior_artfect (i)=w*P compostion (i)+(1―w)*P h (i)

[0102] Where w represents the adjustable weight coefficient, P compostion (i) represents the value of the i-th pixel in the synthetic energy projection image, P h (i) represents the value of the i-th pixel in the high-energy projection image, P prior_artfect (i) represents the value of the i-th pixel in the artifact suppression prior image.

[0103] By obtaining the modulation coefficients between the high-energy and low-energy projection images, the contrast relationship between these two images at different energy states can be effectively quantified. After calculating the high-energy and low-energy projection images using these modulation coefficients, the resulting synthetic energy projection image more comprehensively displays the structural features of the target. Weighted fusion of the synthetic energy projection image with the high-energy projection image further helps to significantly suppress artifacts and improve image clarity.

[0104] S4. Threshold segmentation is performed on the high-energy projection image, and forward projection is performed on the threshold segmented image to obtain a high-density mask projection image.

[0105] In this embodiment of the invention, the step of thresholding the high-energy projection image and then forward-projecting the thresholded image to obtain a high-density mask projection image includes:

[0106] The high-energy projection image is reconstructed in three dimensions to obtain a high-energy attenuation image;

[0107] In the high-energy attenuation image, the pixel values ​​that are greater than a preset pixel threshold are set as the first pixel values;

[0108] The pixel value in the high-energy attenuation image that is less than or equal to the pixel threshold is set as the second pixel value;

[0109] A high-density mask image is created by combining several first pixel values ​​and several second pixel values;

[0110] Obtain the ray-driven forward projection operator;

[0111] The high-density mask image is projected using the ray-driven forward projection operator to obtain a high-density mask projection image.

[0112] In detail, the 3D reconstruction adopts the FDK reconstruction method, which mainly uses the ramp filter operator and the weighted back projection operator to reconstruct the 2D projection data into a 3D spatial image, thus obtaining a high-energy attenuation image.

[0113] Specifically, the pixel values ​​in the high-energy attenuation image are compared with the pixel threshold, and threshold segmentation is performed on the high-energy attenuation image to obtain a high-density mask image. The high-density mask image I obtained after threshold segmentation... m (i) is a binary image, where regions with a value of 1 represent high-density regions and regions with a value of 0 represent the background. The calculation formula is shown below:

[0114]

[0115] Among them, I m (i) is the value of the i-th pixel in the high-density mask image, I h (i) is the i-th pixel in the high-energy decay image, and t is the preset pixel threshold.

[0116] In detail, forward projection converts three-dimensional image data into a two-dimensional projected image. The ray-driven forward projection operator projects the high-density mask image from different angles to generate a high-density mask projection image. The calculation formula is shown below:

[0117] p m (t,θ)=∫I m (x,y)δ(t―xcosθ―ysinθ)dydx

[0118] Where, p m (t,θ) represents the projection values ​​at angle θ and position t, δ represents the Dirac function, and I m The high-density mask image is denoted by (x,y), which represents the pixel coordinates in the high-density mask image.

[0119] By performing 3D reconstruction on the high-energy projection image, a high-energy attenuation image is obtained, effectively identifying and separating high-density regions. Classifying pixel values ​​according to preset thresholds helps to clarify the characteristics of different materials or structures. The generated high-density mask image facilitates subsequent analysis, ensuring accurate representation of key regions. Projecting the mask image using a ray-driven forward projection operator not only improves the clarity and accuracy of the projected image but also provides a reliable foundation for further image processing and analysis.

[0120] S5. The contrast compensation prior image and the artifact suppression prior image are weighted and fused according to the high-density mask projection image to obtain a hybrid projection image.

[0121] In this embodiment of the invention, the step of weightedly fusing the contrast-compensated prior image and the artifact-suppressing prior image based on the high-density mask projection image to obtain a hybrid projection image includes:

[0122] The pixels in the high-density mask projection image are normalized to obtain a normalized image;

[0123] The normalized image is used to calculate the pixels in the contrast-compensated prior image and the pixels in the artifact-suppressing prior image to obtain a hybrid projection image.

[0124] Specifically, the pixels in the high-density mask projection image are normalized to a range between 0 and 1.

[0125] In detail, the contrast-compensated prior image and the artifact-suppressing prior image are weighted and fused based on the high-density mask projection image to obtain a hybrid projection image. The calculation formula is as follows:

[0126] p hybird (i)=p m (i)*P prior_artfect (i)+[1―p m (i)]·P prior_artfect (i)

[0127] Where, p hybird (i) represents the value of the i-th pixel in the mixed projection image, p m (i) represents the value of the i-th pixel in the high-density mask projection image, P prior_artfect (i) represents the value of the i-th pixel in the artifact suppression prior image.

[0128] Normalizing high-density mask projection images can effectively improve image contrast and consistency, thereby enhancing the accuracy of subsequent processing. Using the normalized image to perform weighted fusion of contrast-compensation and artifact-suppression prior images can synthesize information from different images while preserving details and suppressing artifacts. This method improves the quality of the mixed projection image, making the final result clearer and more usable.

[0129] S6. Perform three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image.

[0130] In this embodiment of the invention, the step of performing three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image includes:

[0131] Obtain the reconstruction volume and projection angle from the 3D reconstruction parameters;

[0132] The mixed projection image is filtered to obtain several filtered projection data.

[0133] Each of the projection data is superimposed onto the reconstructed volume according to the projection angle;

[0134] The reconstructed volume is normalized to obtain a hybrid reconstructed image.

[0135] In detail, the Ram-Lak filter is suitable for CT images and can effectively enhance image edges. A Fast Fourier Transform (FFT) is used to transform the hybrid projection image to the frequency domain. The Ram-Lak filter is then applied to the frequency domain data to enhance the frequency components of interest and suppress noise and low-frequency interference. An inverse transform is performed on the filtered frequency domain data to return it to the spatial domain, resulting in the filtered projection dataset.

[0136] By acquiring 3D reconstruction parameters and projection angles, an accurate 3D reconstruction volume can be effectively constructed. Filtering the hybrid projection image can eliminate noise and artifacts, improving data quality. Superimposing the filtered projection data onto the reconstruction volume according to the projection angle can comprehensively reflect the structural details of the object, while normalization ensures the consistency and comparability of data from different angles, thus ultimately obtaining a clear and accurate hybrid reconstruction image.

[0137] like Figure 2 The diagram shown is a functional block diagram of a dual-energy cone-beam CT imaging device based on prior guidance provided in an embodiment of this application.

[0138] The prior-guided dual-energy cone-beam CT imaging device 100 of this invention can be installed in an electronic device. Depending on the functions implemented, the prior-guided dual-energy cone-beam CT imaging device 100 may include an image acquisition module 101, a contrast compensation module 102, an artifact suppression module 103, an image segmentation module 104, an image fusion module 105, and an image reconstruction module 106. The module described in this application 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 which are stored in the memory of the electronic device.

[0139] In this embodiment, the functions of each module / unit are as follows:

[0140] The image acquisition module 101 is used to acquire high-energy projection data and low-energy projection data of dual-energy cone-beam CT, and to preprocess the high-energy projection data and the low-energy projection data to obtain high-energy projection images and low-energy projection images.

[0141] Contrast compensation module 102 is used to perform contrast compensation on the high-energy projection image based on the low-energy projection image to obtain a contrast-compensated prior image.

[0142] The artifact suppression module 103 is used to calculate the effective energy projection information for effectively suppressing artifacts based on the high-energy projection image and the low-energy projection image, and obtain the artifact suppression prior image.

[0143] Image segmentation module 104 is used to perform threshold segmentation on the high-energy projection image and forward projection on the threshold-segmented image to obtain a high-density mask projection image.

[0144] Image fusion module 105 is used to perform weighted fusion of contrast compensation prior image and artifact suppression prior image based on the high-density mask projection image to obtain a hybrid projection image;

[0145] The image reconstruction module 106 is used to perform three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image.

[0146] In detail, the modules described in the priori-guided dual-energy cone-beam CT imaging device 100 in this embodiment of the invention employ the same methods as described above during use. Figure 1 The technique used is the same as that of the prior-guided dual-energy cone-beam CT imaging method described in the article, and it can produce the same technical effect, so it will not be repeated here.

[0147] like Figure 3 The diagram shown is a schematic representation of an electronic device for implementing a priori-guided dual-energy cone-beam CT imaging method according to an embodiment of the present invention.

[0148] The electronic device may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13. It may also include a computer program stored in the memory 11 and capable of running on the processor 10, such as a priori-guided dual-energy cone-beam CT imaging program.

[0149] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., executing a priori-guided dual-energy cone-beam CT imaging programs) and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0150] The memory 11 includes at least one type of readable storage medium, including flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of an electronic device, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device, such as a plug-in portable hard drive, smart media card (SMC), secure digital (SD) card, flash card, etc. Furthermore, the memory 11 can include both internal and external storage units of the electronic device. The memory 11 can be used not only to store application software and various types of data installed on the electronic device, such as the code of a priori-guided dual-energy cone-beam CT imaging program, but also to temporarily store data that has been output or will be output.

[0151] The communication bus 12 can be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0152] The communication interface 13 is used for communication between the aforementioned electronic device and other devices, including a network interface and a user interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, Bluetooth interface, etc.), typically used to establish communication connections between the electronic device and other electronic devices. The user interface may be a display, an input unit (such as a keyboard), or, optionally, a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen, etc. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device and to display a visual user interface.

[0153] The figure only shows an electronic device with components. Those skilled in the art will understand that the structure shown in the figure does not constitute a limitation on the electronic device and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0154] For example, although not shown, the electronic device may also include a power supply (such as a battery) to power the various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0155] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0156] The prior-guided dual-energy cone-beam CT imaging program stored in the memory 11 of the electronic device is a combination of multiple instructions. When run in the processor 10, it can achieve the following: acquiring high-energy and low-energy projection data of dual-energy cone-beam CT; preprocessing the high-energy and low-energy projection data to obtain high-energy and low-energy projection images; performing contrast compensation on the high-energy projection image based on the low-energy projection image to obtain a contrast-compensated prior image; calculating effective energy projection information for effectively suppressing artifacts based on the high-energy and low-energy projection images to obtain an artifact suppression prior image; performing threshold segmentation on the high-energy projection image; forward projection on the threshold-segmented image to obtain a high-density mask projection image; weighted fusing the contrast-compensated prior image and the artifact suppression prior image based on the high-density mask projection image to obtain a hybrid projection image; and performing three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image.

[0157] Specifically, the specific implementation method of the processor 10 for the above instructions can be referred to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, and will not be repeated here.

[0158] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0159] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following: acquiring high-energy projection data and low-energy projection data from dual-energy cone-beam CT; preprocessing the high-energy projection data and the low-energy projection data to obtain a high-energy projection image and a low-energy projection image; performing contrast compensation on the high-energy projection image based on the low-energy projection image to obtain a contrast-compensated prior image; calculating effective energy projection information for effectively suppressing artifacts based on the high-energy projection image and the low-energy projection image to obtain an artifact suppression prior image; performing threshold segmentation on the high-energy projection image; forward projection on the threshold-segmented image to obtain a high-density mask projection image; weightedly fusing the contrast-compensated prior image and the artifact suppression prior image based on the high-density mask projection image to obtain a hybrid projection image; and performing three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image.

[0160] 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.

[0161] 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.

[0162] 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.

[0163] 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.

[0164] 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.

[0165] The blockchain referred to in this invention is a novel application model of computer technologies such as distributed data storage, peer-to-peer transmission, consensus mechanisms, and encryption algorithms. Essentially, a blockchain is a decentralized database, a chain of data blocks linked together using cryptographic methods. Each data block contains information about a batch of network transactions, used to verify the validity of the information (anti-counterfeiting) and generate the next block. A blockchain can include an underlying blockchain platform, a platform product service layer, and an application service layer.

[0166] This application embodiment can acquire and process relevant data based on image processing. Image processing refers to the techniques for manipulating and analyzing image data to extract information or improve image quality. It involves the application of various mathematical and computer algorithms and techniques to modify, analyze, or extract useful information from images. Image processing is an important component of computer vision and artificial intelligence, and can be widely applied in various fields such as medicine, industrial inspection, security monitoring, autonomous driving, entertainment, and remote sensing.

[0167] 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 recited in the system claims may also be implemented by a single unit or device through software or hardware. The terms "first," "second," etc., are used to indicate names and do not indicate any specific order.

[0168] 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 dual-energy cone-beam CT imaging method based on prior guidance, characterized in that, The method includes: Acquire high-energy projection data and low-energy projection data from dual-energy cone-beam CT, preprocess the high-energy projection data and the low-energy projection data to obtain high-energy projection images and low-energy projection images; The low-energy projection image is grayscale normalized to obtain weight coefficients; each pixel in the high-energy projection image is compensated according to the weight coefficients to obtain the updated pixel value of each pixel; The updated pixel values ​​of each pixel are combined into a contrast-compensated prior image; Based on the high-energy projection image and the low-energy projection image, the effective energy projection information for effectively suppressing artifacts is calculated to obtain the artifact suppression prior image; The high-energy projection image is segmented by thresholding, and the segmented image is forward-projected to obtain a high-density mask projection image. The contrast-compensated prior image and the artifact-suppressing prior image are weighted and fused based on the high-density mask projection image to obtain a hybrid projection image; The hybrid projection image is reconstructed in three dimensions to obtain a hybrid reconstructed image.

2. The dual-energy cone-beam CT imaging method based on prior guidance as described in claim 1, characterized in that, The preprocessing of the high-energy projection data and the low-energy projection data to obtain high-energy projection images and low-energy projection images includes: Obtain the preset angle compensation parameters; The high-energy projection data and the low-energy projection data are aligned using the angle compensation parameters to obtain high-energy aligned data and low-energy aligned data. Background and transmitted signals are acquired from a preset detector; The number of background signals is counted as the number of incident photons; The number of transmitted signals is counted as the number of emitted photons; The high-energy attenuation projection of the high-energy aligned data and the low-energy attenuation projection of the low-energy aligned data are calculated based on the number of incident photons and the number of emitted photons. A high-energy projection image and a low-energy projection image are generated based on the high-energy attenuation projection and the low-energy attenuation projection.

3. The dual-energy cone-beam CT imaging method based on prior guidance as described in claim 1, characterized in that, The step of calculating effective energy projection information for effective artifact suppression based on the high-energy projection image and the low-energy projection image to obtain an artifact suppression prior image includes: Obtain the modulation coefficient between the high-energy projection image and the low-energy projection image; Based on the modulation coefficients, the high-energy projection image and the low-energy projection image are calculated to obtain a composite energy projection image; The synthetic energy projection image and the high-energy projection image are weighted and fused to obtain an artifact-suppressed prior image.

4. The dual-energy cone-beam CT imaging method based on prior guidance as described in claim 1, characterized in that, The step of thresholding the high-energy projection image and then forward-projecting the thresholded image to obtain a high-density mask projection image includes: The high-energy projection image is reconstructed in three dimensions to obtain a high-energy attenuation image; In the high-energy attenuation image, the pixel values ​​that are greater than a preset pixel threshold are set as the first pixel values; The pixel value in the high-energy attenuation image that is less than or equal to the pixel threshold is set as the second pixel value; A high-density mask image is created by combining several first pixel values ​​and several second pixel values; Obtain the ray-driven forward projection operator; The high-density mask image is projected using the ray-driven forward projection operator to obtain a high-density mask projection image.

5. The dual-energy cone-beam CT imaging method based on prior guidance as described in claim 1, characterized in that, The step of weightedly fusing the contrast-compensated prior image and the artifact-suppressing prior image based on the high-density mask projection image to obtain a hybrid projection image includes: The pixels in the high-density mask projection image are normalized to obtain a normalized image; The normalized image is used to calculate the pixels in the contrast-compensated prior image and the pixels in the artifact-suppressing prior image to obtain a hybrid projection image.

6. The dual-energy cone-beam CT imaging method based on prior guidance as described in claim 1, characterized in that, The step of performing three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image includes: Obtain the reconstruction volume and projection angle from the 3D reconstruction parameters; The mixed projection image is filtered to obtain several filtered projection data. Each of the projection data is superimposed onto the reconstructed volume according to the projection angle; The reconstructed volume is normalized to obtain a hybrid reconstructed image.

7. A dual-energy cone-beam CT imaging device based on prior guidance, characterized in that, The device includes: The image acquisition module is used to acquire high-energy projection data and low-energy projection data of dual-energy cone-beam CT, and to preprocess the high-energy projection data and the low-energy projection data to obtain high-energy projection images and low-energy projection images. The contrast compensation module is used to perform grayscale normalization on the low-energy projection image to obtain weight coefficients; to compensate each pixel in the high-energy projection image according to the weight coefficients to obtain the updated pixel value of each pixel; and to synthesize the updated pixel values ​​of each pixel into a contrast compensation prior image. The artifact suppression module is used to calculate the effective energy projection information for effectively suppressing artifacts based on the high-energy projection image and the low-energy projection image, and to obtain the artifact suppression prior image. The image segmentation module is used to perform threshold segmentation on the high-energy projection image, and to perform forward projection on the threshold-segmented image to obtain a high-density mask projection image. The image fusion module is used to weight and fuse the contrast compensation prior image and the artifact suppression prior image according to the high-density mask projection image to obtain a hybrid projection image; The image reconstruction module is used to perform three-dimensional reconstruction on the hybrid projection image to obtain a hybrid reconstructed image.

8. An electronic device, characterized in that, The electronic device includes: At least one processor; and, A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program that can be executed by the at least one processor to enable the at least one processor to perform the prior-guided dual-energy cone-beam CT imaging method as described in any one of claims 1 to 6.

9. A computer-readable storage medium storing a computer program, characterized in that, When the computer program is executed by the processor, it implements the prior-guided dual-energy cone-beam CT imaging method as described in any one of claims 1 to 6.

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