171 results about "Cbct imaging" patented technology

The present invention relates to the technical field of stomatology. Provided is an auxiliary dental implant generation method based on a diffusion model. The method in the present invention comprises: acquiring oral CBCT image data of historical patients, preprocessing the oral CBCT image data of the historical patients to obtain a CBCT image dataset, using the CBCT image dataset to train a multi-task segmentation network, and using the segmentation network to obtain an intraoral tissue segmentation result; using the intraoral tissue segmentation result to train detection networks from the three dimensions of a cross-sectional plane, a coronal plane and a sagittal plane, respectively; using the detection networks to obtain detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane; fusing the detection results in the three directions of the cross-sectional plane, the coronal plane and the sagittal plane, and using a majority voting algorithm to construct a three-dimensional bounding box, so as to acquire an edentulous area; and using the intraoral segmentation result and the edentulous area as prompt information to guide, by means of an iterative process, a network to generate a post-implantation effect. The implantation effect obtained by the present invention is highly accurate, thereby providing a more precise auxiliary tool for stomatology.

The invention discloses a CBCT tooth segmentation method and system based on an anatomical perception cascade network, and the method comprises the steps: a first stage, carrying out the simplified dichotomy segmentation based on an original CBCT image and a coarse segmentation network taking 3D U-Net as a trunk, outputting maxillary and mandibular tooth probability graphs, and taking the maxillary and mandibular tooth probability graphs as prior information to guide the generation of an SDM; in the second stage, the original CBCT image and the calibrated maxillary tooth probability graph and the calibrated mandibular tooth probability graph are spliced together, a formed multi-channel input tensor is input into a fine segmentation network, the fine segmentation network takes Residual U-Net as a trunk, and an improved AGBR module and an improved SDMAA module are integrated in an encoder-decoder architecture of the fine segmentation network; and the decoder fuses all refined and re-calibrated feature maps, and upsamples and reconstructs 42 types of instance segmentation results with correct topology and clear boundaries. According to the method, the problem that in the prior art, when the inherent and local boundary fuzzy defect in CBCT is overcome, an effective pertinence mechanism is lacked is solved, and precise and robust 42-class instance segmentation can be achieved.

The invention relates to an oral and maxillofacial implant repair path generation system based on a CBCT image, in particular to the field of computer aided design of surgical operations, and the scheme comprises the following steps: constructing a high-quality training data set through multi-source data aggregation and intelligent labeling enhancement; training by using the mixed model to generate a high-precision jaw segmentation model; the segmentation parameters are dynamically adjusted in combination with real-time feedback in preoperative planning, and a personalized optimization result is output; the model is continuously updated and the data strategy is optimized by means of a closed-loop feedback mechanism, so that the planning accuracy, the self-adaptability and the clinical efficiency of oral maxillofacial implant restoration are comprehensively improved, and the whole process from data to decision making is intelligentized.

The invention discloses a variable convolution UNet tooth image segmentation method and system fusing an attention mechanism, and belongs to the technical field of medical image processing and computer-aided diagnosis. A tooth image data set is obtained and preprocessed; using the preprocessed data set to train a DSC-UNet segmentation model; the DSC-UNet segmentation model introduces a multi-modal feature fusion module and a deformable convolution module into a UNet framework; and segmenting an input tooth image by using the trained DSC-UNet segmentation model, and outputting a pixel-level segmentation result of the tooth structure. By constructing a multi-level feature interaction mechanism and a deformable convolution module, the capturing capability of the network on tooth edge details and the segmentation robustness of special-shaped teeth are remarkably improved, more reliable technical support is provided for an intelligent dental diagnosis and treatment system, and the method is particularly suitable for high-precision segmentation of complex tooth structures in oral panoramic X-ray films and CBCT images.

The invention discloses a tooth CBCT image super-resolution reconstruction method based on a potential diffusion model, and relates to an image processing and generating technology in the field of computer vision. The in-vivo medical image is trained based on hidden space coding and probability diffusion, so that the network has a better super-resolution effect on the real in-vivo tooth CBCT image.

The invention discloses an oral cavity tooth CBCT image segmentation method based on semi-supervised deep learning, and belongs to the technical field of oral diagnosis, and the method comprises the steps: collecting oral cavity CBCT image data, carrying out the preprocessing, and building an oral cavity CBCT image data set containing labels and no labels; a semi-supervised segmentation model is constructed based on a progressive average teacher framework, the semi-supervised segmentation model comprises double groups of teacher-student network structures, and each group comprises two student networks with different structures and a double-teacher network; iterative training is carried out on the semi-supervised segmentation model, the training process comprises a supervised learning stage and a semi-supervised learning stage, and a trained semi-supervised segmentation model is obtained; and taking any oral cavity CBCT image data as input, and performing tooth segmentation based on the trained semi-supervised segmentation model. According to the method, the accuracy of tooth segmentation can be improved, the segmentation effect at the tooth boundary position is enhanced, the tooth segmentation steps are simplified, and the diagnosis efficiency of doctors is improved.

The invention relates to the technical field of medical image diagnosis, and discloses a tooth and fracture line recognition treatment method based on the combination of an AI technology and a CBCT image, and the method comprises the steps: obtaining and preprocessing the CBCT image, and carrying out the multi-scale analysis and recognition of a tooth structure, a microcrack and a fracture line through a first AI model. And the second AI model combines the identification result and the patient characteristics, and generates a personalized treatment scheme through multi-objective optimization. Clinical feedback is used for continuously iteratively optimizing double models, and the diagnosis and treatment precision and effect are improved. The system comprises an image data acquisition unit, an image data preprocessing unit, a tooth and fracture line identification unit, a personalized treatment scheme generation unit and a feedback and optimization unit. Through AI and CBCT image fusion, accurate identification of teeth and fracture lines is realized, a personalized treatment scheme is recommended in combination with individual features of a patient and a multi-objective optimization algorithm, rapid response is realized, a closed-loop feedback mechanism continuous optimization model is established, and diagnosis and treatment precision, efficiency and individualization level are remarkably improved.

The invention relates to the technical field of digital medical treatment, and discloses a dental three-dimensional identification and model generation method based on CBCT and deep learning, and the method comprises the steps: carrying out the preprocessing of obtained CBCT image data; the preprocessed CBCT image data is segmented, and the segmentation step comprises the substeps that a tooth three-dimensional model, an alveolar bone three-dimensional model and a mandibular neural tube three-dimensional model are obtained by adopting a two-stage cascade deep learning network; based on the tooth three-dimensional model and the alveolar bone three-dimensional model, obtaining a periodontal membrane three-dimensional model through geometric and Boolean operation; a tooth root area in the tooth three-dimensional model is positioned, and a self-adaptive threshold method is adopted for CBCT image data corresponding to the tooth root area, and a root canal three-dimensional model is recognized and generated; based on at least one of the obtained three-dimensional models, a personalized clinical application model is generated, high-precision automatic segmentation of the dental complex anatomical structure is achieved, and a reliable digital model basis is provided for personalized precision medical treatment.

The invention provides an auxiliary tooth separation method and system for minimally invasive tooth extraction, and relates to the technical field of digital surgery in stomatology. According to the method, the oral anatomy three-dimensional electronic model is obtained by performing deep learning segmentation and three-dimensional reconstruction on the preoperative CBCT image, and the real-time spatial position and posture of the jaw bone are updated in combination with laser oral scanning of the preoperative anterior tooth area. On the basis, a three-dimensional simulation model is constructed, tooth division cutting path planning, interference judgment and dislocation space analysis are completed, precise tooth division cutting is implemented by a three-way mechanical arm along the planned path, and the safe and controllable automatic tooth extraction and tooth division process is completed in cooperation with water cooling and real-time track correction.

The invention discloses a tooth instance segmentation method and device based on anatomic form priori, and belongs to the field of computer vision and artificial intelligence. According to the method, model training is carried out on a CBCT image, a real segmentation mask is processed into a three-classification segmentation mask with enamel as a boundary, the three-classification segmentation mask serves as a supervision signal, a network can directly output a prediction segmentation mask containing tooth boundary information in a reasoning stage, and a detection frame is generated accordingly. And after the detection frame is scaled according to the same proportionality coefficient and projected back to the original image, cutting each tooth to obtain a CBCT image of the single tooth. A loss function based on anatomical morphological prior is introduced into segmentation training of a single tooth CBCT image, and a network is constrained through a self-adaptively extracted morphological skeleton, so that a prediction segmentation mask is kept continuous at a root tip part, and the problem of tooth root segmentation fracture is effectively relieved. According to the method, accurate positioning and high-quality segmentation of the tooth instances are realized, so that the segmentation of the tooth instances in the full-mouth CBCT image is quicker and more accurate.

The invention discloses a CBCT system bed board artifact correction method, device and equipment and a storage medium, and relates to the technical field of computed tomography. The method comprises the following steps: scanning first object projection data of an object by a bed board; performing three-dimensional reconstruction on the first object projection data to obtain a first CBCT image; constructing a three-dimensional digital image of the bed board according to the first CBCT image; forward projecting the three-dimensional digital image of the bed board to obtain an independent bed board projection; according to the independent bed board projection, separating the contribution of the bed board from the first object projection data to obtain second object projection data; performing three-dimensional reconstruction on the second object projection data to obtain a second CBCT image; and carrying out image evaluation index judgment until the evaluation index is greater than or equal to a preset threshold value, and obtaining an image without the bed board artifact. According to the method, the three-dimensional digital image of the bed board is constructed through one-time scanning to achieve artifact removal, related problems of secondary scanning are avoided, low-complexity forward projection is adopted to separate bed board contribution, and higher robustness and convenience are achieved.

The invention discloses a dual-stage CBCT and oral scanning tooth registration method and system, and relates to the field of tooth three-dimensional digital model registration, and the method comprises the following steps: extracting a tooth voxel structure in a CBCT image and a dental crown grid structure in an oral scanning image through a segmentation network; a coarse-to-fine depth registration network is introduced, geometric feature coding and feature matching are carried out on super-points obtained through multi-resolution down-sampling, local matching is carried out on a dense point set in a super-point neighborhood, a rigid transformation matrix is calculated in parallel, and an optimal registration result is screened out from the rigid transformation matrix; and dividing a registration object into a plurality of three-tooth groups containing three adjacent teeth, and performing local iteration alignment based on an initial registration result. The method still has higher robustness and accuracy under the complex conditions of large cross-modal difference, high input point cloud density and the like, is shorter in time consumption, and provides reliable data basis and automatic support for clinical scenes such as orthodontics and implantation.

The invention relates to the technical field of medical image processing and artificial intelligence diagnosis, in particular to an oral and maxillofacial surgery image recognition and diagnosis method and system based on deep learning, and the method comprises the following steps: multi-modal image collection and cooperative preprocessing: collecting an oral and maxillofacial surgery CBCT image, a cone beam CT curved surface tomography image, an oral endoscope image and an ultrasonic image, a standardized multi-modal image data set is obtained through inter-modal registration and an adaptive enhancement algorithm; according to the method, a traditional diagnosis framework of'single-mode image + manual film reading 'is broken through, and a three-order diagnosis logic of'multi-mode image cooperative enhancement-cross-scale feature dynamic fusion-focus typing and risk hierarchical linkage' is innovatively provided; and accurate identification, typing and malignant transformation risk prediction of common oral and maxillofacial surgery diseases (such as jaw cyst, wisdom tooth impediment, temporomandibular joint disorder and maxillofacial tumor) are realized.

The invention provides a non-paired graph unsupervised conversion method and system based on an anatomical fidelity Schrodinger bridge. The method comprises the following steps: acquiring a non-paired CBCT image set and a CT image set; based on a preset interpolation mechanism IPM, selecting an image sample from the CBCT image set for iterative processing, and generating an intermediate state sample; predicting the intermediate state sample through a generator network to obtain a synthesized CT image; based on the synthesized CT image and a real CT image randomly extracted from the CT image set, constructing a loss function to drive a training generator network; and converting the input CBCT image into a synthetic CT image by using the trained generator network. According to the method, the distribution difference between the CBCT image and the CT image is minimized by using the optimal transmission framework of entropy regularization, anatomical details are captured and fused under multiple scales, it is ensured that key anatomical features of the generated CT image are accurately reserved, and the accuracy of the CBCT image is improved. The method can effectively solve the problem that the modal conversion precision and the anatomical structure are lost due to lack of pairing data in an existing method.

The present application relates to the technical field of dental diagnosis, and particularly relates to a periodontal disease precise quantitative evaluation method based on IOS images and CBCT images, comprising the following steps: performing image segmentation on an intraoral scanning IOS image to obtain an IOS segmentation result of each tooth; performing image segmentation on a CBCT image to obtain a CBCT segmentation result of each tooth; performing preprocessing and registration on the IOS segmentation result and the CBCT segmentation result to obtain a data fusion model; obtaining a gum contour and a tooth long axis according to the data fusion model, obtaining a gum contour point according to the gum contour, obtaining a alveolar bone contour on the alveolar bone along the direction of the tooth long axis and obtaining a GBD distance according to the gum contour point; and evaluating periodontal disease according to the GBD distance. According to the present application, the data fusion model is obtained according to the segmented IOS result and the CBCT result, the gum contour and the tooth long axis are obtained through the data fusion model, the GBD distance of the gum contour and the alveolar bone contour is measured along the direction of the tooth long axis, and the periodontal disease health condition is evaluated.

The invention discloses an IOS image and CBCT image registration method based on particle swarm and single-tooth optimization, and belongs to the field of image data processing, and the method comprises the following steps: respectively obtaining tooth semantic segmentation results and point cloud data of an IOS image and a CBCT image through preprocessing and segmentation steps; introducing a particle swarm optimization algorithm, and performing iterative search and optimization on six-degree-of-freedom rigid transformation parameters between the IOS image data and the CBCT image data so as to complete global initial registration of the dental arch scale; and on the basis of a result after global registration, a registration object is divided into a plurality of local units based on a single tooth, and local iteration alignment is performed on each unit, so that tiny dislocation on the single tooth is eliminated, and the overall registration effect is optimized. According to the method, on the basis of processing cross-modal difference and realizing global registration, the registration precision of a single tooth level is further ensured through a local optimization mechanism, and reliable automatic registration support is provided for clinical scenes such as orthodontics and implantation.

An alveolar bone structure three-dimensional key point positioning method based on deep learning and rule constraint comprises the following steps: acquiring a maxillofacial region CBCT image data set, cutting CBCT data, manually marking the cut CBCT data, and dividing the CBCT data into a training set, a test set and a verification set; the data set is preprocessed, and data enhancement is carried out in a random intensity shifting and scaling mode; constructing an alveolar bone coordinate network model, wherein the network architecture mainly comprises an appearance branch and a spatial configuration branch; adopting the training set to train the alveolar bone coordinate network model, obtaining each parameter of the network model, and obtaining a trained alveolar bone coordinate network model; and testing the trained alveolar bone coordinate network model by adopting a test data set, and evaluating a test result. The method aims at solving the problems of anatomy prior deficiency, insufficient long-range dependence modeling and weak small sample generalization ability in the prior art, and high-precision alveolar bone key point positioning is realized.

The invention discloses an ultra-sparse CBCT (cone beam computed tomography) reconstruction method, system and equipment based on an ordinary differential equation, belongs to CBCT reconstruction in the field of artificial intelligence, and aims to solve the technical problem of low quality of CBCT reconstructed images. The method comprises the following steps: acquiring sample data, preprocessing the data, constructing and training a CBCT-CT nonlinear relation reconstruction model, and performing real-time reconstruction; during preprocessing, converting the three-dimensional image volume data into simulated X-ray projection data, and reconstructing the simulated X-ray projection data by adopting an FDK reconstruction algorithm to obtain an FDK-CBCT image; the CBCT-CT nonlinear relation reconstruction model comprises an encoder, a NODE module and a decoder; in the training process, the CBCT-CT nonlinear relation reconstruction model is trained through the obtained CT sample image and the FDK-CBCT image. In the reconstruction model, through continuous evolution of NODE modeling image features, the model can model a continuous evolution mapping process from a sparse low-quality image to a high-quality CT image during training, so that stripe artifacts and structural distortion do not easily exist in the reconstructed image, and the reconstruction quality is high.

The application discloses a kind of based on CBCT image data evaluation implant stability classification model and the construction method and device thereof.First, through the Mobilenetv2-DeepLabV3+ network after training in cross-sectional image implant is segmented, then in combination with the knowledge of oral implantology and segmentation result corresponding implant around bone image is extracted on cross-sectional image, finally using the deep residual network Resnet-50 after training is completed classification, obtains implant stability evaluation result.Test evaluation, the model of the application has higher diagnostic performance, the time consumption of evaluation is short, accuracy is high (>90%), can be effectively used for implant stability evaluation.The classification model for evaluating implant stability of the application has important significance for better guiding further implant restoration treatment, and provides important reference for artificial intelligence used for image diagnosis.

A method, system, and device for ultra-low-dose imaging of oral cone-beam computed tomography (CBCT) are provided. The method encompasses high-dose oral CBCT image data acquisition, data segmentation processing, three-dimensional (3D) reconstruction processing, model training, image data input, image enhancement, and image data output. Specifically, the process involves performing 3D reconstruction on 2D projection data of oral CBCT acquired in ultra-low-dose mode to obtain ultra-low-dose oral CBCT 3D reconstructed image data. This ultra-low-dose oral CBCT 3D reconstructed image data is then input into the oral CBCT ultra-low-dose imaging enhancement network model, from which high-quality oral CBCT image data is output for subsequent clinical diagnosis and treatment processes. This method significantly reduces the radiation dose of imaging while ensuring imaging quality, accelerates imaging speed, and enhances the safety of oral CBCT imaging, holding broad clinical application prospects.

The invention discloses a CBCT metal artifact suppression method and system based on preoperative prior guidance, and the method comprises the steps: taking second CBCT image data, obtained in real time in an operation, of a target tissue as a reference, and carrying out the rigid registration of first CBCT image data, obtained before the operation, of the target tissue; deforming the first CBCT image data to obtain third image data; the relative positions of the tissue structures in the third image data and the second CBCT image data are consistent; based on the third image data, performing coarse segmentation on the metal implant in the second CBCT image data by adopting a digital subtraction technology; segmenting to obtain first mask data and background prior data of the metal implant; performing true and false positive judgment on the first mask data and performing false positive suppression processing to obtain second mask data; carrying out data restoration on the background prior data by adopting a self-adaptive interpolation grid to obtain projection domain data; and reconstructing by using an FDK reconstruction algorithm to obtain a CBCT image of the target tissue without metal artifacts.

The application provides an X-ray beam light baffle and a low-dose CBCT imaging method. First, part of data is taken out from a training set, so that the number of remaining CBCT images and registered CT images is the same, and imbalance of training data is avoided. Secondly, the taken-out data is pre-trained in a self-supervised manner, so that the difficulty in network training caused by the random initialization of the Transformer added to the CycleGAN is avoided. Finally, the pre-trained Transformer is added to the CycleGAN to obtain a hybrid network, and the remaining data is used for unsupervised training, so that the advantages of the Transformer and the convolution are combined, and the ability of the network to enhance the CBCT image quality is improved.

The present application relates to the technical field of medical image processing, and particularly relates to a CBCT data enhancement method and system based on anatomical region constraint and physical consistency; a human body region mask is acquired and a data enhancement model only exerting enhancement disturbance on the human body anatomical tissue region is constructed, a plurality of enhancement disturbances conforming to the CBCT imaging physical mechanism are combined, the CBCT data after enhancement is strictly limited to the anatomical range, non-physical disturbance introduced by the air region is avoided, and the CBCT data after enhancement is close to the real imaging degradation process; at least one enhancement disturbance including gray scale shift, Gamma nonlinear transformation, compound noise, low-frequency scattering field and stripe artifact is executed on the CBCT image after preprocessing, real imaging degradation factors such as device calibration error, detector nonlinear response, electronic noise, photon statistical noise, scattering effect and reconstruction artifact can be simulated, and the adaptability and robustness of the model to different devices, scanning parameters and patient individuals are significantly improved.

Embodiments of the present application disclose a method and device for extracting root canal feature morphology based on CBCT data, equipment and medium, belong to the field of oral medicine and medical image technology, wherein the method comprises: filtering and denoising CBCT images and performing pulp segmentation processing, and performing surface rendering three-dimensional reconstruction on tooth root canals to generate a three-dimensional model; based on the regular point cloud data in the three-dimensional model, a point-surface method is used to realize surface reconstruction to generate a root canal entity model; the root canal entity model is subjected to coordinate alignment processing in a three-dimensional space, and the approximate center line of the fitted root canal is used to measure the root canal length, root canal opening direction, root canal curvature and pulp depth. The present application quickly reconstructs the corresponding three-dimensional model based on the CBCT tomographic images obtained by scanning the teeth of a patient, and obtains relevant data such as pulp depth and root canal opening size from the three-dimensional model of the teeth to assist the performance of oral surgery.

The invention provides an artificial intelligence-based preoperative evaluation system for maxillary posterior tooth region implantation, which comprises the following steps of: constructing an oral cavity CBCT (cone beam computed tomography) image library, and further constructing a maxillary sinus basic model, an oral cavity maxillary posterior tooth region implantation decision design model and an oral cavity maxillary posterior tooth region windowing bone grafting decision design model; constructing a maxillary sinus downstream task model according to the maxillary sinus basic model; inputting the to-be-detected oral cavity CBCT image data into the maxillary sinus downstream task model, and calling the oral cavity maxillary posterior tooth region implantation decision design model to generate a first decision or calling the oral cavity maxillary posterior tooth region windowing bone grafting decision design model to generate a second decision according to the output qualitative and quantitative characteristics, so as to carry out preoperative evaluation. According to the method, the different characteristics in the oral cavity CBCT image are intelligently identified in a whole process, and the optimal oral cavity maxillary posterior tooth area implantation decision is generated to carry out oral cavity maxillary posterior tooth area implantation preoperative evaluation, so that the reliability of the oral cavity maxillary posterior tooth area implantation preoperative evaluation is improved.

The invention discloses an offset scanning CBCT image reconstruction method. The method comprises the following steps: virtual detector conversion of projection data; carrying out geometric weighting on the data; carrying out data continuation and filtering; parker weighting is carried out on the data; and reconstructing bias back projection. According to the invention, aiming at the scanning mode of radiation source and detector bias, the FDK algorithm cannot be directly used for reconstruction, the virtual detector is introduced to convert the scanning mode into the scanning mode of detector bias, the data is subjected to continuation processing to solve the problem of data truncation, and the filtered data is subjected to Parker weighting aiming at the repeated scanning area to solve the problem of bias artifacts. And finally, carrying out three-dimensional reconstruction by adopting an FDK reconstruction method. The reconstruction method is high in speed, and the obtained cross-sectional image is high in definition.

The invention discloses a CT image-oriented implant space attitude estimation method, and belongs to the technical field of biomedical engineering and computer science. The method comprises the following steps: extracting a target three-dimensional model based on a CBCT image; the CBCT image is obtained by performing cone beam scanning on a target part where an implant is placed during or after an operation; obtaining an implant theoretical model pre-constructed for the implant; the mass center of the target three-dimensional model and the mass center of the implant theoretical model are calculated respectively, and the mass center of the target three-dimensional model and the mass center of the implant theoretical model are aligned; registering the target three-dimensional model and the implant theoretical model of which the mass centers are aligned by utilizing a main shaft rotating mode; and evaluating the registration precision based on multi-plane projection and similarity measurement, and outputting spatial attitude parameters of the implant. According to the method, the registration speed can be increased, the calculation complexity in the registration process is reduced, and the space registration precision can be further improved through a two-stage optimization strategy of centroid alignment and main shaft rotation.

This application belongs to the field of image processing and relates to a skin extraction method and system. The method includes: performing nonlinear regression fitting on the prior anatomical features of skin tissue in historical oral images to obtain a skin cutting threshold; acquiring a CBCT image of the oral cavity to be processed; extracting boundary features from the cross-sectional image layers of the CBCT image to be processed based on the skin cutting threshold to obtain a dynamic candidate masking region feature map; applying inter-layer geometric consistency constraints to the dynamic candidate masking region feature map to generate a target skin masking region; extracting the original pixel values ​​within the target skin masking region and suppressing their background values; performing isosurface evolution processing on the suppressed original pixel values ​​to generate an isosurface grid; and performing patch validity screening on the isosurface grid to generate the skin extraction result. This application can reduce the interference of irrelevant data on the extraction result during the skin extraction process and ensure the quality of skin tissue extraction as much as possible.

The application discloses a CBCT metal artifact suppression method based on an unsupervised preoperative prior repair network and belongs to the technical field of computer image processing. The method comprises the following steps: inputting a preoperative prior CBCT image and a CBCT image to be repaired into a generator after registration, respectively extracting anatomical structure features and non-metal region detail features by using two encoders of a double-flow feature extraction network, fusing the features, and then performing repair reconstruction on the CBCT image by using a decoder; inputting the result into a discriminator to perform authenticity discrimination and metal region discrimination and generate a fused prediction image; taking the prediction image information as a supervision signal to optimize the adversarial loss between the generator and the discriminator and the image similarity loss between the repaired image and the image to be repaired, and performing joint adversarial training on the discriminator and the generator; and processing an intraoperative CBCT image to be repaired by using the trained generator to realize more efficient metal artifact suppression.