CBCT image osteosclerosis artifact correction method, system and device and medium

Abstract

The invention relates to an image correction technology, and discloses a CBCT (cone beam computed tomography) image osteosclerosis artifact correction method, which comprises the following steps: acquiring a high-energy projection image and a low-energy projection image of a target object, performing image reconstruction, and calculating to obtain a primary artifact-free image; extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, calculating a bone weight, taking the artifact image of the target object as training input data, taking the bone image and the soft tissue image as reference data, and training to obtain a dual-channel artifact correction model; and outputting a preprocessed bone image and a preprocessed soft tissue image according to the to-be-processed osteosclerosis artifact image by using a dual-channel artifact correction model, and carrying out weighted fusion on the preprocessed bone image and the preprocessed soft tissue image according to a bone weight to obtain an artifact-free reconstructed image. The invention further provides a CBCT image osteosclerosis artifact correction device, electronic equipment and a storage medium. According to the invention, the image artifact correction effect can be improved.

Description

Technical Field

[0001] The present invention relates to the technical field of image correction, and in particular to a method, system, device and medium for correcting bone sclerosis artifacts in CBCT images. Background Art

[0002] CBCT (Cone Beam Computed Tomography) is widely used in stomatology. The X-rays used in CBCT are polychromatic, that is, they contain photons of various energies. When these photons pass through human tissue, low-energy photons are more easily absorbed, while high-energy photons are relatively difficult to absorb. Bones have a very strong absorption capacity for X-rays due to their high density and high atomic number. When X-rays pass through bones, low-energy photons are absorbed in large quantities, while high-energy photons penetrate bones relatively more. The density of bones is much higher than that of surrounding tissues, so its attenuation coefficient for X-rays is also high. This means that X-rays will be significantly attenuated when passing through bones. When X-rays pass through bones, low-energy photons are absorbed in large quantities, resulting in the photons reaching the detector being mainly high-energy. This "hardened" X-ray beam appears on the image as radial or strip-shaped artifacts around the bone area. Due to the different absorption capacity of bones for X-rays, the energy distribution of the X-ray beam after passing through the bones is uneven. This energy difference is difficult to completely correct during image reconstruction, resulting in artifacts.

[0003] Existing correction methods for bone sclerosis artifacts usually use polynomial fitting to correct them. However, due to the large difference in bone density between different patients, the correction effect of polynomial fitting may be affected by this difference, affecting the correction effect. Summary of the invention

[0004] The present invention provides a CBCT image bone sclerosis artifact correction method, system, device and medium, the main purpose of which is to solve the problem of poor correction effect of the existing CBCT image bone sclerosis artifact correction method.

[0005] To achieve the above object, the present invention provides a method for correcting bone sclerosis artifacts in CBCT images, comprising:

[0006] Optionally, the collecting of the high-energy projection image and the low-energy projection image of the target object by using a preset scanning device includes:

[0007] Acquiring a high-energy projection image of the target object using the scanning device according to a preset first scanning parameter;

[0008] The scanning device is used to acquire a low-energy projection image of the target object according to preset second scanning parameters.

[0009] Optionally, reconstructing the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image includes:

[0010] Determine the reverse projection path of each pixel point of the high-energy projection image and the low-energy projection image according to a predetermined projection angle;

[0011] Integrating the high-energy projection image and the low-energy projection image according to the back-projection path, and accumulating the integration results to a preset reconstruction space to obtain a back-projected high-energy image and a back-projected low-energy image;

[0012] Performing Fourier transform on the back-projected high-energy image and the back-projected low-energy image to obtain a frequency domain high-energy image and a frequency domain low-energy image;

[0013] The frequency domain high energy image and the frequency domain low energy image are multiplied by a pre-selected filter function and then inverse Fourier transformed to obtain a reconstructed high energy projection image and a reconstructed low energy projection image.

[0014] Optionally, the calculating and obtaining a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image includes:

[0015] The primary artifact-free image is calculated using the following formula:

[0016] I t =I H +α(I H ―I L )

[0017] Among them, I t is the primary artifact-free image, I H For the reconstructed high energy projection image, I L For the reconstructed low-energy projection image, α is a predetermined weighting coefficient.

[0018] Optionally, extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation includes:

[0019] Calculating a grayscale histogram of the primary artifact-free image;

[0020] Extracting peak features of the grayscale histogram, and determining a segmentation threshold according to the peak features;

[0021] Extracting an image portion whose grayscale value is greater than the segmentation threshold in the primary artifact-free image to obtain a primary bone image;

[0022] Extracting an image portion whose grayscale value is less than or equal to the segmentation threshold in the primary artifact-free image to obtain a primary soft tissue image;

[0023] The primary bone image and the primary soft tissue image are subjected to image filtering processing to obtain a bone image and a soft tissue image.

[0024] Optionally, calculating the bone weight according to the bone image and the primary artifact-free image includes:

[0025] Acquiring area data of the artifact-free image to obtain the artifact-free image area;

[0026] Acquiring area data of the bone image to obtain the bone image area;

[0027] The ratio of the bone image area to the artifact-free image area is calculated to obtain a bone weight.

[0028] Optionally, the training of a dual-channel artifact correction model according to the training input data and the reference data includes:

[0029] Obtaining a predicted output result based on the training input data using a pre-established neural network model;

[0030] Calculating the difference between the predicted output result and the reference data using a predefined loss function;

[0031] Calculate the gradient of the loss value function for each parameter according to the difference value using a back propagation algorithm to obtain gradient information;

[0032] Utilizing a gradient descent algorithm to determine parameters of the neural network model according to the gradient information;

[0033] When the preset number of iterations is reached, the model training is confirmed to be completed and a dual-channel artifact correction model is obtained.

[0034] In order to solve the above problems, the present invention also provides a CBCT image bone sclerosis artifact correction device, the device comprising:

[0035] An image acquisition module, used to acquire a high-energy projection image and a low-energy projection image of a target object using a preset scanning device;

[0036] An image reconstruction module, used to reconstruct the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image;

[0037] Calculate and obtain a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image;

[0038] An image segmentation module, used for extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, and calculating a bone weight according to the bone image and the primary artifact-free image;

[0039] A model training module, used to obtain the artifact image of the target object as training input data, use the bone image and the soft tissue image as reference data for the training input data, and train a dual-channel artifact correction model according to the training input data and the reference data;

[0040] The artifact correction module is used to use the dual-channel artifact correction model to output a preprocessed bone image and a preprocessed soft tissue image according to the pre-acquired bone sclerosis artifact image to be processed, and to perform weighted fusion on the preprocessed bone image and the preprocessed soft tissue image according to the bone weight to obtain an artifact-free reconstructed image.

[0041] In order to solve the above problem, the present invention further provides an electronic device, the electronic device comprising:

[0042] at least one processor;

[0043] and, a memory communicatively coupled to the at least one processor;

[0044] The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the above-mentioned CBCT image bone sclerosis artifact correction method.

[0045] In order to solve the above problem, the present invention also provides a computer-readable storage medium, in which at least one computer program is stored. The at least one computer program is executed by a processor in an electronic device to implement the above-mentioned CBCT image bone sclerosis artifact correction method.

[0046] The embodiment of the present invention uses a preset scanning device to collect a high-energy projection image and a low-energy projection image of a target object, reconstructs the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image, calculates a primary artifact-free image based on the reconstructed high-energy projection image and the reconstructed low-energy projection image, extracts a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, calculates a bone weight based on the bone image and the primary artifact-free image, obtains an artifact image of the target object as training input data, uses the bone image and the soft tissue image as reference data for the training input data, trains a dual-channel artifact correction model based on the training input data and the reference data, uses the dual-channel artifact correction model to output a preprocessed bone image and a preprocessed soft tissue image based on a pre-acquired bone sclerosis artifact image to be processed, performs weighted fusion on the preprocessed bone image and the preprocessed soft tissue image based on the bone weight, and obtains an artifact-free reconstructed image, which can improve the effect of correcting bone sclerosis artifacts in CBCT images. Therefore, the CBCT image bone sclerosis artifact correction method, device, electronic device and computer-readable storage medium proposed in the present invention can solve the problem of poor correction effect of the existing CBCT image bone sclerosis artifact correction method. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] Figure 1 A schematic diagram of a flow chart of a method for correcting bone sclerosis artifacts in CBCT images provided in one embodiment of the present invention;

[0048] Figure 2 A schematic diagram of an image reconstruction process provided by an embodiment of the present invention;

[0049] Figure 3 A schematic diagram of an image segmentation process provided by an embodiment of the present invention;

[0050] Figure 4 A functional module diagram of a CBCT image bone sclerosis artifact correction device provided by an embodiment of the present invention;

[0051] Figure 5 A schematic diagram of the structure of an electronic device for implementing the CBCT image bone sclerosis artifact correction method provided by an embodiment of the present invention;

[0052] Figure 6 A schematic diagram of the principle of obtaining an image after removing bone artifacts is provided in one embodiment of the present invention.

[0053] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0054] It should be understood that the specific embodiments described herein are only used to explain the present invention, and are not used to limit the present invention.

[0055] The embodiment of the present application provides a method for correcting bone sclerosis artifacts in CBCT images. The execution subject of the method for correcting bone sclerosis artifacts in CBCT images includes but is not limited to at least one of the electronic devices such as a server, a terminal, etc. that can be configured to execute the method provided in the embodiment of the present application. In other words, the method for correcting bone sclerosis artifacts in CBCT images 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, etc. The server can be an independent server, or it can be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (Content Delivery Network, CDN), and big data and artificial intelligence platforms.

[0056] Reference Figure 1 FIG. 1 is a flow chart of a method for correcting bone sclerosis artifacts in CBCT images provided by an embodiment of the present invention. In this embodiment, the method for correcting bone sclerosis artifacts in CBCT images includes:

[0057] S1. Using a preset scanning device to collect a high-energy projection image and a low-energy projection image of a target object.

[0058] In an embodiment of the present invention, the scanning device may be a CBCT device. Specifically, the CBCT device uses an X-ray emitter to generate a conical X-ray beam, which is composed of multiple ray beams and can simultaneously emit multiple X-rays at different angles to obtain a projection image of the patient.

[0059] In the embodiment of the present invention, the target object may be the oral cavity of a patient.

[0060] In the embodiment of the present invention, the high-energy projection image and the low-energy projection image of the target object are collected by using a preset scanning device, and different currents and voltages are set for the scanning device to perform scanning, thereby obtaining projection images of different energy levels;

[0061] In the embodiment of the present invention, the method of acquiring the high-energy projection image and the low-energy projection image of the target object by using a preset scanning device includes:

[0062] Acquiring a high-energy projection image of the target object using the scanning device according to a preset first scanning parameter;

[0063] The scanning device is used to acquire a low-energy projection image of the target object according to preset second scanning parameters.

[0064] In detail, the preset first scanning parameter may be 120kV4mA (120 kilovolts, 4 milliamperes).

[0065] In detail, the preset second scanning parameter may be 94kV4mA (94 kilovolts, 4 milliamperes).

[0066] In the embodiment of the present invention, by using a preset scanning device to collect a high-energy projection image and a low-energy projection image of a target object, the efficiency of subsequent image reconstruction is improved.

[0067] S2. Reconstruct the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image.

[0068] In the embodiment of the present invention, the image reconstruction of the high-energy projection image and the low-energy projection image is to reconstruct the high-energy projection image and the low-energy projection image using an FDK reconstruction algorithm.

[0069] In detail, the FDK reconstruction algorithm is a classic algorithm for CT image reconstruction, which restores high-quality CT images by performing back-projection and filtering operations on X-ray data.

[0070] In the embodiment of the present invention, refer to Figure 2 As shown, a schematic diagram of an image reconstruction process is provided in an embodiment of the present invention, wherein the image reconstruction is performed on the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image, including:

[0071] S21, determining a reverse projection path of each pixel point of the high-energy projection image and the low-energy projection image according to a predetermined projection angle;

[0072] S22, integrating the high-energy projection image and the low-energy projection image according to the back-projection path, and accumulating the integration results to a preset reconstruction space to obtain a back-projected high-energy image and a back-projected low-energy image;

[0073] S23, performing Fourier transform on the back-projected high-energy image and the back-projected low-energy image to obtain a frequency domain high-energy image and a frequency domain low-energy image;

[0074] S24, multiplying the frequency domain high energy image and the frequency domain low energy image by a pre-selected filter function and then performing inverse Fourier transform to obtain a reconstructed high energy projection image and a reconstructed low energy projection image.

[0075] In detail, the predetermined projection angle refers to the angular relationship between the X-ray of the scanning device and the projection image.

[0076] In detail, the Fourier transform is a method of converting a signal from its original domain to the frequency domain, and the inverse Fourier transform is the process of converting a signal from the frequency domain back to its original time domain or space domain. It is the inverse process of the Fourier transform, which is used to reconstruct the original signal from the frequency components of the signal.

[0077] In detail, the pre-selected filter function includes a Ram-Lak filter function, a Shepp-Logan filter function, etc. These filter functions can enhance the high-frequency part of the image to improve the clarity of the image.

[0078] In detail, multiplying the frequency domain high-energy image and the frequency domain low-energy image with a pre-selected filter function and then performing inverse Fourier transform to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image is a process of filtering the image, which can remove noise in the image.

[0079] In the embodiment of the present invention, by performing image reconstruction on the high-energy projection image and the low-energy projection image, a reconstructed high-energy projection image and a reconstructed low-energy projection image are obtained, thereby improving the efficiency of subsequent calculation of the primary artifact-free image.

[0080] S3. Calculate and obtain a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image.

[0081] In the embodiment of the present invention, calculating the primary artifact-free image based on the reconstructed high-energy projection image and the reconstructed low-energy projection image means fusing the reconstructed high-energy projection image and the reconstructed low-energy projection image in a preset manner according to a predetermined weighting coefficient to obtain the primary artifact-free image.

[0082] In an embodiment of the present invention, the step of calculating and obtaining a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image includes:

[0083] The primary artifact-free image is calculated using the following formula:

[0084] I t =I H +α(I H ―I L )

[0085] Among them, I t is the primary artifact-free image, I H For the reconstructed high energy projection image, I LFor the reconstructed low-energy projection image, α is a predetermined weighting coefficient.

[0086] In detail, the predetermined weighting coefficient needs to be determined through a large number of experiments and optimizations to achieve the best image quality or meet specific diagnostic requirements. By adjusting the value of the weighting coefficient, the artifacts generated by the difference between artifact images of different energy levels can be reduced or eliminated to a certain extent, thereby obtaining a clearer and more accurate image.

[0087] In the embodiment of the present invention, by calculating and obtaining a primary artifact-free image based on the reconstructed high-energy projection image and the reconstructed low-energy projection image, the efficiency and accuracy of calculating and extracting bone images and soft tissue images are improved.

[0088] S4. Extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, and calculating a bone weight according to the bone image and the primary artifact-free image.

[0089] In an embodiment of the present invention, extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation means using the grayscale value of the image as a threshold, determining the threshold according to a grayscale histogram or local features of the primary artifact-free image, and segmenting the primary artifact-free image based on the determined threshold to obtain a bone image and a soft tissue image.

[0090] In the embodiment of the present invention, refer to Figure 3 As shown, a schematic diagram of an image segmentation process is provided in an embodiment of the present invention, wherein the bone image and the soft tissue image are extracted from the primary artifact-free image based on threshold segmentation, including:

[0091] S31, calculating the grayscale histogram of the primary artifact-free image;

[0092] S32, extracting the peak feature of the grayscale histogram, and determining the segmentation threshold according to the peak feature;

[0093] S33, extracting the image portion whose gray value is greater than the segmentation threshold in the primary artifact-free image to obtain a primary bone image;

[0094] S34, extracting an image portion whose grayscale value is less than or equal to the segmentation threshold in the primary artifact-free image to obtain a primary soft tissue image;

[0095] S35 , performing image filtering processing on the primary bone image and the primary soft tissue image to obtain a bone image and a soft tissue image.

[0096] In the embodiment of the present invention, the bone weight is calculated according to the bone image and the primary artifact-free image, which is calculated based on the area ratio of the bone image in the primary artifact-free image.

[0097] In an embodiment of the present invention, calculating the bone weight according to the bone image and the primary artifact-free image includes:

[0098] The bone weight is calculated using the following formula:

[0099]

[0100] Where ω is the bone weight, S t is the area of ​​the primary artifact-free image, S bone is the area of ​​the bone image.

[0101] In the embodiment of the present invention, the step of calculating the bone weight according to the bone image and the primary artifact-free image includes:

[0102] Acquiring area data of the artifact-free image to obtain the artifact-free image area;

[0103] Acquiring area data of the bone image to obtain the bone image area;

[0104] The ratio of the bone image area to the artifact-free image area is calculated to obtain a bone weight.

[0105] In an embodiment of the present invention, by extracting bone images and soft tissue images from the primary artifact-free image based on threshold segmentation, the efficiency of calculating bone weights is improved, and by calculating bone weights based on the bone image and the primary artifact-free image, the efficiency and accuracy of subsequent weighted fusion are improved.

[0106] S5. Acquire the artifact image of the target object as training input data, use the bone image and the soft tissue image as reference data for the training input data, and obtain a dual-channel artifact correction model by training according to the training input data and the reference data.

[0107] In the embodiment of the present invention, the acquiring of the artifact image of the target object as training input data is to acquire a CBCT image of the target object with noise and bone sclerosis artifacts.

[0108] In an embodiment of the present invention, the step of training a dual-channel artifact correction model based on the training input data and the reference data includes:

[0109] Obtaining a predicted output result based on the training input data using a pre-established neural network model;

[0110] Calculating the difference between the predicted output result and the reference data using a predefined loss function;

[0111] Calculate the gradient of the loss value function for each parameter according to the difference value using a back propagation algorithm to obtain gradient information;

[0112] Utilizing a gradient descent algorithm to determine parameters of the neural network model according to the gradient information;

[0113] When the preset number of iterations is reached, the model training is confirmed to be completed and a dual-channel artifact correction model is obtained.

[0114] In detail, the method of using a pre-established neural network model to obtain a predicted output result based on the training input data is to use a forward propagation algorithm to input the training input data into the neural network model and calculate the output of each layer layer by layer to obtain a predicted result.

[0115] In detail, the back propagation algorithm is the core algorithm for training neural networks in deep learning. It updates the network weights by calculating the gradient of the loss function with respect to the network parameters, thereby minimizing the loss function.

[0116] In detail, the use of the gradient descent algorithm according to the gradient information according to the parameters of the neural network model refers to the learning rate, weight parameters and bias terms of the neural network model.

[0117] In an embodiment of the present invention, by acquiring the artifact image of the target object as training input data, using the bone image and the soft tissue image as reference data for the training input data, a dual-channel artifact correction model is obtained by training according to the training input data and the reference data, thereby improving the efficiency of subsequent output of preprocessed bone images and preprocessed soft tissue images.

[0118] S6. Outputting a preprocessed bone image and a preprocessed soft tissue image according to the pre-acquired bone sclerosis artifact image to be processed using the dual-channel artifact correction model.

[0119] In the embodiment of the present invention, the pre-processed bone image and the pre-processed soft tissue image are output results after the dual-channel artifact correction model segments the bone sclerosis artifact image to be processed.

[0120] In detail, the dual-channel artifact correction model is used to output a preprocessed bone image and a preprocessed soft tissue image according to a pre-acquired bone sclerosis artifact image to be processed, and the convolution layer and pooling layer of the dual-channel artifact correction model are used to perform convolution and pooling operations on the bone sclerosis artifact image to be processed, and then a nonlinear factor is added by an activation layer, and finally a fully connected layer is used to summarize the output results of each neuron according to weight parameters and bias items, so as to finally obtain the preprocessed bone image and the preprocessed soft tissue image.

[0121] In the embodiment of the present invention, by using the dual-channel artifact correction model to output a preprocessed bone image and a preprocessed soft tissue image according to the pre-acquired bone sclerosis artifact image to be processed, the efficiency of subsequent fusion to obtain an artifact-free reconstructed image is improved.

[0122] S7. Perform weighted fusion on the preprocessed bone image and the preprocessed soft tissue image according to the bone weight to obtain an artifact-free reconstructed image.

[0123] In the embodiment of the present invention, the weighted fusion is a technology for merging information of multiple images into one image by assigning a weight to each image and then combining the pixel values ​​of the images according to the weights.

[0124] For details, refer to Figure 6 As shown, it is a schematic diagram of the principle of obtaining an image after removing bone artifacts according to an embodiment of the present invention.

[0125] In the embodiment of the present invention, the preprocessed bone image and the preprocessed soft tissue image are weightedly fused according to the bone weight to obtain an artifact-free reconstructed image, thereby improving the efficiency and effect of CBCT image bone sclerosis artifact correction.

[0126] like Figure 4 , which is a functional module diagram of a CBCT image bone sclerosis artifact correction device provided by an embodiment of the present invention.

[0127] The CBCT image bone sclerosis artifact correction device 100 of the present invention can be installed in an electronic device. According to the functions to be implemented, the CBCT image bone sclerosis artifact correction device 100 may include an image acquisition module 101, an image reconstruction module 102, an image segmentation module 103, a model training module 104 and an artifact correction module 105. The module of the present invention can also be referred to as a unit, which refers to a series of computer program segments that can be executed by an electronic device processor and can complete fixed functions, which are stored in the memory of the electronic device.

[0128] In this embodiment, the functions of each module / unit are as follows:

[0129] The image acquisition module 101 is used to acquire a high-energy projection image and a low-energy projection image of a target object using a preset scanning device;

[0130] The image reconstruction module 102 is used to reconstruct the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image;

[0131] Calculate and obtain a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image;

[0132] The image segmentation module 103 is used to extract a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, and calculate a bone weight according to the bone image and the primary artifact-free image;

[0133] The model training module 104 is used to obtain the artifact image of the target object as training input data, use the bone image and the soft tissue image as reference data for the training input data, and train a dual-channel artifact correction model according to the training input data and the reference data;

[0134] The artifact correction module 105 is used to use the dual-channel artifact correction model to output a preprocessed bone image and a preprocessed soft tissue image according to the pre-acquired bone sclerosis artifact image to be processed, and to perform weighted fusion on the preprocessed bone image and the preprocessed soft tissue image according to the bone weight to obtain an artifact-free reconstructed image.

[0135] In detail, each module described in the CBCT image bone sclerosis artifact correction device 100 in the embodiment of the present invention is used in the same manner as described above. Figures 1 to 3 The CBCT image bone sclerosis artifact correction method described in the present invention is the same technical means and can produce the same technical effect, which will not be repeated here.

[0136] like Figure 5 , which is a schematic diagram of the structure of an electronic device of a CBCT image bone sclerosis artifact correction method provided by an embodiment of the present invention.

[0137] The electronic device 1 may include a processor 10, a memory 11, a communication bus 12, and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as a CBCT image bone sclerosis artifact correction program.

[0138] In some embodiments, the processor 10 may be composed of an integrated circuit, for example, a single packaged integrated circuit, or a plurality of packaged integrated circuits with the same or different functions, including one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and combinations of various control chips. The processor 10 is the control core (ControlUnit) of the electronic device, and uses various interfaces and lines to connect various components of the entire electronic device, and executes or executes programs or modules (such as CBCT image bone sclerosis artifact correction programs, etc.) stored in the memory 11, and calls data stored in the memory 11 to perform various functions of the electronic device and process data.

[0139] The memory 11 includes at least one type of readable storage medium, and the readable storage medium includes a flash memory, a mobile hard disk, a multimedia card, a card-type memory (for example, an SD or DX memory, etc.), a magnetic memory, a disk, an optical disk, etc. In some embodiments, the memory 11 may be an internal storage unit of an electronic device, such as a mobile hard disk of the electronic device. In other embodiments, the memory 11 may also be an external storage device of an electronic device, such as a plug-in mobile hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on the electronic device. Further, the memory 11 may also include both an internal storage unit of the electronic device and an external storage device. The memory 11 can not only be used to store application software and various types of data installed in the electronic device, such as the code of the CBCT image bone sclerosis artifact correction program, but also be used to temporarily store data that has been output or is to be output.

[0140] The communication bus 12 may be a peripheral component interconnect (PCI) bus or an extended industry standard architecture (EISA) bus, etc. The bus may be divided into an address bus, a data bus, a control bus, etc. The bus is configured to realize connection and communication between the memory 11 and at least one processor 10, etc.

[0141] The communication interface 13 is used for communication between the above-mentioned 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, a Bluetooth interface, etc.), which is generally used to establish a communication connection between the electronic device and other electronic devices. The user interface may be a display (Display), an input unit (such as a keyboard (Keyboard)), and optionally, the user interface may also be a standard wired interface, a wireless interface. Optionally, in this embodiment, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, and an OLED (Organic Light-Emitting Diode, organic light-emitting diode) touch device, etc. Among them, the display may also be appropriately referred to as a display screen or a display unit, which is used to display information processed in the electronic device and to display a visual user interface.

[0142] 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 in the figure, or combine certain components, or arrange the components differently.

[0143] For example, although not shown, the electronic device may also include a power source (such as a battery) for supplying power to each component. Preferably, the power source may be logically connected to the at least one processor 10 through a power management device, so that the power management device can realize functions such as charging management, discharging management, and power consumption management. The power source may also include one or more DC or AC power sources, recharging devices, power failure detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device may also include a variety of sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be repeated here.

[0144] It should be understood that the embodiment is for illustration only and the scope of the patent application is not limited to this structure.

[0145] The CBCT image bone sclerosis artifact correction program stored in the memory 11 of the electronic device 1 is a combination of multiple instructions. When running in the processor 10, it can achieve:

[0146] Using a preset scanning device to collect a high-energy projection image and a low-energy projection image of the target object;

[0147] Reconstructing the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image;

[0148] Calculate and obtain a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image;

[0149] Extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, and calculating a bone weight according to the bone image and the primary artifact-free image;

[0150] Acquire the artifact image of the target object as training input data, use the bone image and the soft tissue image as reference data for the training input data, and train a dual-channel artifact correction model according to the training input data and the reference data;

[0151] Outputting a preprocessed bone image and a preprocessed soft tissue image according to the pre-acquired bone sclerosis artifact image to be processed using the dual-channel artifact correction model;

[0152] The preprocessed bone image and the preprocessed soft tissue image are weightedly fused according to the bone weight to obtain an artifact-free reconstructed image.

[0153] Specifically, the specific implementation method of the processor 10 for the above instructions can refer to the description of the relevant steps in the corresponding embodiment of the accompanying drawings, which will not be repeated here.

[0154] Furthermore, if the module / unit integrated in the electronic device 1 is implemented in the form of a software functional unit and sold or used as an independent product, it 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 can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a mobile hard disk, a magnetic disk, an optical disk, a computer memory, and a read-only memory (ROM).

[0155] The present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor of an electronic device, the computer program can implement:

[0156] Using a preset scanning device to collect a high-energy projection image and a low-energy projection image of the target object;

[0157] Reconstructing the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image;

[0158] Calculate and obtain a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image;

[0159] Extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, and calculating a bone weight according to the bone image and the primary artifact-free image;

[0160] Acquire the artifact image of the target object as training input data, use the bone image and the soft tissue image as reference data for the training input data, and train a dual-channel artifact correction model according to the training input data and the reference data;

[0161] Outputting a preprocessed bone image and a preprocessed soft tissue image according to the pre-acquired bone sclerosis artifact image to be processed using the dual-channel artifact correction model;

[0162] The preprocessed bone image and the preprocessed soft tissue image are weightedly fused according to the bone weight to obtain an artifact-free reconstructed image.

[0163] In the embodiments provided by the present invention, it should be understood that the disclosed devices, apparatuses and methods can be implemented in other ways. For example, the device embodiments described above are only illustrative, for example, the division of the modules is only a logical function division, and there may be other division methods in actual implementation.

[0164] The modules described as separate components may or may not be physically separated, and the components shown as modules may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0165] In addition, each functional module in each embodiment of the present invention may be integrated into one processing unit, each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of hardware plus software functional modules.

[0166] It is obvious to those skilled in the art that the present invention is not limited to the details of the above exemplary embodiments, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0167] Therefore, no matter from which point of view, the embodiments should be regarded as illustrative and non-restrictive, and the scope of the present invention is limited by the appended claims rather than the above description, so it is intended that all changes falling within the meaning and scope of the equivalent elements of the claims are included in the present invention. Any attached figure mark in the claims should not be regarded as limiting the claims involved.

[0168] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is the theory, method, technology and application system that uses digital computers or machines controlled by digital computers to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0169] In addition, 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 stated in the system claim can also be implemented by one unit or device through software or hardware. The words first, second, etc. are used to indicate names, and do not indicate any particular order.

[0170] Finally, it should be noted that the above embodiments are only used to illustrate the technical solution of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solution of the present invention can be modified or replaced by equivalents without departing from the spirit and scope of the technical solution of the present invention.

Claims

1. A method for correcting bone sclerosis artifacts in CBCT images, characterized in that: The method comprises: Using a preset scanning device to collect a high-energy projection image and a low-energy projection image of the target object; Reconstructing the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image; Calculate and obtain a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image; Extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, and calculating a bone weight according to the bone image and the primary artifact-free image; Acquire the artifact image of the target object as training input data, use the bone image and the soft tissue image as reference data for the training input data, and train a dual-channel artifact correction model according to the training input data and the reference data; Outputting a preprocessed bone image and a preprocessed soft tissue image according to the pre-acquired bone sclerosis artifact image to be processed using the dual-channel artifact correction model; The preprocessed bone image and the preprocessed soft tissue image are weightedly fused according to the bone weight to obtain an artifact-free reconstructed image.

2. The method for correcting bone sclerosis artifacts in CBCT images according to claim 1, characterized in that: The method of acquiring a high-energy projection image and a low-energy projection image of a target object by using a preset scanning device includes: Acquiring a high-energy projection image of the target object using the scanning device according to a preset first scanning parameter; The scanning device is used to acquire a low-energy projection image of the target object according to preset second scanning parameters.

3. The method for correcting bone sclerosis artifacts in CBCT images according to claim 2, characterized in that: The reconstructing the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image includes: Determine the reverse projection path of each pixel point of the high-energy projection image and the low-energy projection image according to a predetermined projection angle; Integrating the high-energy projection image and the low-energy projection image according to the back-projection path, and accumulating the integration results to a preset reconstruction space to obtain a back-projected high-energy image and a back-projected low-energy image; Performing Fourier transform on the back-projected high-energy image and the back-projected low-energy image to obtain a frequency domain high-energy image and a frequency domain low-energy image; The frequency domain high energy image and the frequency domain low energy image are multiplied by a pre-selected filter function and then inverse Fourier transformed to obtain a reconstructed high energy projection image and a reconstructed low energy projection image.

4. The method for correcting bone sclerosis artifacts in CBCT images according to claim 1, characterized in that: The step of calculating a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image comprises: The primary artifact-free image is calculated using the following formula: I t =I H +α(I H ―I L ) Among them, I t is the primary artifact-free image, I H For the reconstructed high energy projection image, I L For the reconstructed low-energy projection image, α is a predetermined weighting coefficient.

5. The method for correcting bone sclerosis artifacts in CBCT images according to claim 1, characterized in that: The step of extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation includes: Calculating a grayscale histogram of the primary artifact-free image; Extracting peak features of the grayscale histogram, and determining a segmentation threshold according to the peak features; Extracting an image portion whose grayscale value is greater than the segmentation threshold in the primary artifact-free image to obtain a primary bone image; Extracting an image portion whose grayscale value is less than or equal to the segmentation threshold in the primary artifact-free image to obtain a primary soft tissue image; The primary bone image and the primary soft tissue image are subjected to image filtering processing to obtain a bone image and a soft tissue image.

6. The method for correcting bone sclerosis artifacts in CBCT images according to claim 1, characterized in that: The step of calculating the bone weight according to the bone image and the primary artifact-free image comprises: Acquiring area data of the artifact-free image to obtain the artifact-free image area; Acquiring area data of the bone image to obtain the bone image area; The ratio of the bone image area to the artifact-free image area is calculated to obtain a bone weight.

7. The method for correcting bone sclerosis artifacts in CBCT images according to claim 1, characterized in that: The step of training a dual-channel artifact correction model according to the training input data and the reference data includes: Obtaining a predicted output result based on the training input data using a pre-established neural network model; Calculating the difference between the predicted output result and the reference data using a predefined loss function; Calculate the gradient of the loss value function for each parameter according to the difference value using a back propagation algorithm to obtain gradient information; Utilizing a gradient descent algorithm to determine parameters of the neural network model according to the gradient information; When the preset number of iterations is reached, the model training is confirmed to be completed and a dual-channel artifact correction model is obtained.

8. A CBCT image bone sclerosis artifact correction device, characterized in that: The device comprises: An image acquisition module, used to acquire a high-energy projection image and a low-energy projection image of a target object using a preset scanning device; An image reconstruction module, used to reconstruct the high-energy projection image and the low-energy projection image to obtain a reconstructed high-energy projection image and a reconstructed low-energy projection image; Calculate and obtain a primary artifact-free image according to the reconstructed high-energy projection image and the reconstructed low-energy projection image; An image segmentation module, used for extracting a bone image and a soft tissue image from the primary artifact-free image based on threshold segmentation, and calculating a bone weight according to the bone image and the primary artifact-free image; A model training module, used to obtain the artifact image of the target object as training input data, use the bone image and the soft tissue image as reference data for the training input data, and train a dual-channel artifact correction model according to the training input data and the reference data; The artifact correction module is used to use the dual-channel artifact correction model to output a preprocessed bone image and a preprocessed soft tissue image according to the pre-acquired bone sclerosis artifact image to be processed, and to perform weighted fusion on the preprocessed bone image and the preprocessed soft tissue image according to the bone weight to obtain an artifact-free reconstructed image.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and, a memory communicatively coupled to the at least one processor; The memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the CBCT image bone sclerosis artifact correction method as described in any one of claims 1 to 7.

10. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for correcting bone sclerosis artifacts in CBCT images according to any one of claims 1 to 7 is implemented.

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