28 results about "Cbct imaging" patented technology

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.

This invention provides an artificial intelligence-based method and system for identifying jaw cysts. The method includes: identifying jaw cyst lesion regions in oral CBCT images; extracting image features from the region with the largest connected component as the region of interest for the cyst lesion; identifying the location regions of each tooth in the image; selecting the target tooth closest to the jaw cyst lesion region; constructing a first spatial feature of the jaw cyst lesion region to characterize whether the coordinates of the center point of the jaw cyst lesion region overlap with the coordinates of the location region of the target tooth; identifying the root apical functional region of interest of the target tooth and extracting its image features; identifying the midline region of the image; constructing a second spatial feature of the jaw cyst lesion region to characterize whether the jaw cyst lesion region overlaps with the midline region of the image; concatenating the features to generate a joint feature vector; and inputting the joint feature vector into a machine learning model to identify the lesion nature of the jaw cyst lesion.

The application discloses a CBCT-based child obstructive sleep apnea syndrome identification system and relates to the technical field of medical networks, and comprises the following steps: acquiring a CBCT image and physiological characteristic parameters of a child, performing airway segmentation and reconstructing an airway surface model; establishing an airway respiratory flow field based on the model, setting boundary conditions for simulation, extracting dynamic characteristics to construct first discriminant characteristics; inputting the first discriminant characteristics into a classification model to determine whether the first discriminant characteristics are in a fuzzy discriminant interval; if the first discriminant characteristics are not in the interval, directly outputting an obstructive sleep apnea syndrome type; if the first discriminant characteristics are in the interval, extracting airway cross-section structure information, fusing the first discriminant characteristics to obtain second discriminant characteristics; and matching the characteristics with preset rules to determine whether to adopt enhanced classification or structure registration for secondary discrimination, finally outputting a child apnea syndrome type, and improving the reliability and accuracy of the final diagnosis result.

An automatic detection method for CBCT cephalometric landmarks based on multi-geometric guidance and specific perceptual coding includes the following steps: Step S1, downsampling the 3D CBCT image and obtaining a preliminary coordinate set through a coarse localization network; Step S2, cropping image blocks centered on the coordinates and extracting local features through a visual encoder containing a shared basic encoder and a low-rank adapter; Step S3, calculating the relative position matrix of the landmarks, encoding spatial relationships using radial basis functions, and constructing a multi-anatomical heterogeneous map; Step S4, inputting visual and edge features into a multi-geometric guidance Transformer, fusing global constraints and updating features using an attention mechanism; Step S5, extracting directional geometric relationships using spherical harmonic functions to construct higher-order update terms, and dynamically updating the heterogeneous map using a gated residual mechanism; Step S6, predicting coordinate offsets through a multi-layer network and performing iterative optimization to output high-precision 3D coordinates. This method significantly improves detection accuracy and robustness.

This invention discloses a method for registering IOS and CBCT images based on particle swarm optimization and single-tooth optimization, belonging to the field of image data processing. The method includes the following steps: Preprocessing and segmentation steps are used to obtain the semantic segmentation results of teeth from IOS and CBCT images, as well as point cloud data; a particle swarm optimization algorithm is introduced to iteratively search and optimize the six-degree-of-freedom rigid transformation parameters between the IOS and CBCT image data, thereby completing the initial global registration at the dental arch scale; using the globally registered result as a basis, the registration object is divided into multiple local units based on a single tooth, and local iterative alignment is performed on each unit to eliminate minor misalignments on individual teeth and optimize the overall registration effect. This invention, based on handling cross-modal differences and achieving global registration, further ensures registration accuracy at the single-tooth level through a local optimization mechanism, providing reliable automated registration support for clinical scenarios such as orthodontics and implantology.

The invention provides a mandibular image segmentation method and device based on deep learning, equipment and a medium, and the method comprises the steps: inputting target information into a target mandibular image segmentation model, enabling the target mandibular image segmentation model to carry out the preprocessing of target CBCT image information, and obtaining target preprocessing CBCT image information; the target lower jaw image segmentation model corrects the adjacent position of the lower jaw third molar and the lower jaw second molar in the target preprocessed CBCT image information based on the target intraoral scanning information, and target corrected CBCT image information is obtained; marking mandibular neural tube key points in the target corrected CBCT image information by the target mandibular image segmentation model based on the target key point detection network to obtain target marked CBCT image information; and enabling the target lower jaw image segmentation model to carry out local differential precision segmentation on the target labeled CBCT image information to obtain a target preprocessing segmentation result. According to the scheme, multi-structure differential accurate segmentation of the lower jaw image is realized.

This invention discloses a CBCT image restoration method based on a contrastive learning-guided diffusion model. The steps are as follows: Obtaining raw CBCT projection data and respiratory state signals for phase binning reconstruction to obtain phase binning reconstructed images and non-phase complete reconstruction prior images; extracting structured contrastive learning features from the phase binning reconstructed images, and combining phase text information and phase embedding information to obtain phase conditional semantic embeddings; performing semantically guided intermediate reconstruction based on the phase binning reconstructed images and phase conditional semantic embeddings to obtain intermediate reconstruction results; applying diffusion forward noise to the intermediate reconstruction results to obtain a noisy image; performing conditional diffusion reconstruction by combining the noisy image, phase conditional semantic embeddings, and non-phase complete reconstruction prior images; and iteratively optimizing based on projection domain data consistency constraints to obtain the CBCT restoration result. This invention eliminates the need for explicit motion field estimation, effectively suppresses fringe artifacts, and improves image structural details and phase consistency.

The application discloses a kind of for bicuspid valve clamp surgery navigation multi-modal image automatic registration method, comprising the following steps: acquisition patient preoperative heart CTA image, intraoperative CBCT image and intraoperative TEE image / corresponding perspective image, and the CTA, CBCT and TEE image of this acquisition are pretreated;Complete CTA to CBCT three-dimensional automatic coarse registration, establish unified patient coordinate system, and combine with heart and breathing motion compensation, dynamic fine registration is carried out to coarse registration result;Estimate TEE probe pose and imaging plane, identify the common visual target in TEE and CBCT / perspective, complete TEE and CBCT / perspective dynamic automatic registration;Under unified coordinate system, fuse each modality image, generate the multi-modal navigation interface and navigation information of surgery.The method of the application can realize automatic, fast, accurate multi-modal space registration, improve the standardization degree and safety of bicuspid valve clamp surgery navigation.

The application provides a CBCT imaging calibration method, belongs to the technical field of CBCT imaging mechanical calibration, and is applied to an imaging device, wherein the imaging device comprises a dental column and a jaw mounting plate arranged on the dental column, and the CBCT imaging calibration method comprises the following steps: controlling the dental column to move vertically and / or horizontally along a support, and locking the dental column to the support after the movement is completed; and / or controlling the jaw mounting plate to move horizontally along the top surface of the dental column and / or rotate vertically along the top of the dental column in a direction perpendicular to the horizontal movement direction of the aforementioned dental column, and locking the jaw mounting plate to the dental column after the movement is completed; in the application, the mechanism for adjusting the jaw and the lateral head clamp assembly is mostly adjusted through fasteners, so that the cost is low, the structure is simple, the on-site debugging of after-sales personnel can be facilitated, the rigid connection form is adopted, and the advantages of reliability and strong stability are achieved; compared with the traditional algorithm processing for image calibration, distortion of the image can be avoided.

This invention discloses a CBCT multi-scan image fusion method and system. The method includes: controlling the scanning object to move along a preset scanning path and stopping at a reference scan position and multiple target scan positions respectively, synchronously acquiring the original CBCT image information and corresponding displacement information of the reference scan position and each of the target scan positions; reconstructing the original CBCT image information of the reference scan position and each of the target scan positions respectively to obtain initial three-dimensional CBCT images of the reference scan position and each of the target scan positions; aligning the initial three-dimensional CBCT images of each target scan position with the initial three-dimensional CBCT image of the reference scan position respectively to obtain aligned images; and using a multi-scale fusion algorithm to fuse the aligned images to obtain a three-dimensional CBCT stitched image. This invention solves the technical problems of low automation, low accuracy, and low efficiency in existing CBCT multi-scan image stitching.

This application provides a method, system, device, and medium for assessing the extraction risk of the mandibular third molar. The method includes inputting target risk assessment information and target medical record information into a target extraction risk assessment model. This allows the model to comprehensively quantify the proximity of the target patient's mandibular third molar to the mandibular second molar and the inferior alveolar nerve canal based on the target risk assessment information. The model also cross-validates the target patient's mandibular second molar lesion status and extraction risk-related medical history based on the target risk assessment information and target medical record information. Furthermore, the target extraction risk assessment model determines the target extraction risk level based on a fusion of medical rules and AI scoring algorithms and outputs target extraction risk warning information. This solution integrates CBCT image analysis, intraoral scan information processing, and structured medical record information to automatically identify the spatial relationship and pathological condition of the mandibular third molar and output extraction risk warning information, providing relevant surgical references.

An automatic segmentation method for sphenoid bone subregions using a cross-scale topologically constrained dual-path segmentation network is proposed. First, cranial CT or CBCT images are resampled, intensity normalized, and coarsely localized to obtain the target volume region of the sphenoid bone. Then, the target volume region is input into a dual-path encoding network. The global anatomical path is used to extract low-resolution, high-semantic features such as the overall morphology, spatial layout, and relative positions of subregions of the sphenoid bone, while the local boundary path is used to extract high-resolution detail features such as thin bone plates, fracturing boundaries, and protrusion junctions. Finally, a cross-scale coupling fusion module interactively gates and fuses the global anatomical features and local boundary features at multiple scales to obtain fused features that combine overall structural information and edge detail information. This invention significantly improves the automatic segmentation performance of complex subregions of the sphenoid bone.

A method for radiotherapy treatment plan verification during a fraction of an online adaptive radiotherapy treatment The method comprises obtaining a pre-treatment cone beam computed tomography, CBCT, image of a patient. The method further comprises adapting a treatment plan, based on the CBCT image. The method further comprises delivering a first portion of the adapted treatment plan to the patient. The method further comprises obtaining dose data from the delivery of the first portion. The method further comprises determining a dosimetric value associated with quality assurance of the adapted treatment plan, based on the dose data. If the dosimetric value is within an acceptable range, the method further comprises delivering a second portion of the adapted treatment plan. Or, if the dosimetric value is outside the acceptable range, the method further comprises stopping the delivery of the adapted treatment plan.

This application provides a method, apparatus, device, and medium for correcting truncation artifacts in CBCT images. The method includes: scanning a target region using a dual-energy CBCT device to acquire high-energy projection data and low-energy projection data of the target region; performing matrix material decomposition processing on the high-energy projection data and low-energy projection data to obtain the soft tissue projection and bone projection of the target region; locating the truncated region of the original CBCT image to be corrected, and performing edge fitting on the truncated region based on the edge features of the soft tissue projection to generate an edge fitting curve for the truncated region; using the edge fitting curve as the boundary and combining the grayscale information of the soft tissue projection, completing the truncated region and fusing it with the non-truncated region of the original CBCT image to obtain the corrected CBCT image. This application can effectively eliminate truncation artifacts, improve the integrity and accuracy of CBCT images, and meet the clinical diagnostic needs for observing edge details.

This invention proposes a method for fabricating an immediate digital implant guide, comprising: S1: acquiring a patient's dental impression and taking CBCT images; S2: identifying the positioning anchor point information on the CBCT image, registering it with the positioning anchor point coordinates of the dental model tray, placing the simulated implant according to the missing tooth location, and obtaining its spatial coordinate position information; S3: pouring quick-setting plaster to fabricate a plaster positive mold; S4: fixing the plaster positive mold onto a guide ring placement device, calculating the movement parameters of each axis of the guide ring placement device, and operating the guide ring placement device to move the guide pin and plaster positive mold to the required positions and angles respectively; S5: curing the guide ring onto the surface of the plaster positive mold using light curing, removing the guide pin, fabricating a film on the surface of the plaster positive mold, shaping and trimming, polishing, and sterilizing before use. This method improves the implantation accuracy and is simple, fast, economical, and practical. This invention also proposes a corresponding coordinate transformation mold and guide ring placement device.

The application discloses a kind of for left auricle plugging operation navigation multi-modal image automatic registration method, comprising the following steps: collecting structural heart disease intervention surgery patient preoperative CTA image and intraoperative CBCT image;Using U-Net deep learning framework, the heart region in CTA image and CBCT image is segmented and extracted, and the extracted image region is carried out data enhancement and size normalization etc.Pretreatment operation;With the network of depth convolution and visual Transformer fusion, the heart region image of CTA and CBCT is calculated Affine transformation matrix, so that the CTA image and CBCT image realize 3D affine registration;The image after registration is filled to the cutting region of the CBCT image, and the intraoperative CBCT image containing heart anatomical structure information is generated.The method of the application can realize the automatic, fast and accurate spatial registration of preoperative CTA image and intraoperative CBCT image, and the fusion image view generated by registration provides comprehensive reference for the heart three-dimensional anatomical structure and real-time instrument position, effectively assists instrument positioning, path planning and size selection, and provides a new intelligent tool for left auricle plugging operation navigation.

This invention provides a four-dimensional cone-beam CT reconstruction method based on differential Transformer motion compensation, comprising: dividing acquired continuous projection data into multiple time-phase groups according to respiratory motion signals; selecting at least one frame in each group as a base reference frame, dividing each group of images into multiple local image blocks and performing feature encoding to obtain the original feature vector of each block; calculating the difference feature vector between the original feature vector of the non-base reference frame and the original feature vector of the corresponding base reference frame; jointly encoding the original feature vector, the difference feature vector, and the angle embedding vector to form an enhanced feature vector, which is input into a Transformer network for feature modeling and motion compensation; introducing motion-aware regularization constraints to distinguish between moving and stationary regions; decoding each group of features, reconstructing the corresponding three-dimensional images of each group, and combining them to form a complete four-dimensional cone-beam CT image sequence. The method of this invention achieves high signal-to-noise ratio, clear, and morphologically accurate 4D-CBCT image reconstruction.

This invention provides a tooth segmentation method for CBCT images based on Mamba and multimodal fusion, relating to the fields of medical image processing and computer vision. This invention constructs a multi-channel input tensor that fuses and enhances jawbone images and spatial coordinate maps, explicitly providing the network with voxel absolute position priors. It also strengthens the structural boundaries of low-contrast regions such as the junction of tooth roots and alveolar bone using a spatially adaptive contrast enhancement strategy, mitigating tissue confusion caused by similar gray levels. By dynamically injecting diagnostic semantic priors from case texts into image bottleneck features based on spatial importance, it effectively suppresses category confusion between morphologically similar symmetrical teeth with the same name. Simultaneously, it utilizes the Mamba state space model to perform multi-directional sequence dependency modeling and omnidirectional aggregation of multimodal fusion features, efficiently capturing the ultra-long-range three-dimensional spatial dependencies of teeth along the arch curvature with near-linear computational complexity, achieving a balance between global context modeling capability and computational efficiency.

This invention discloses a method for constructing a three-dimensional digital model of teeth, comprising the following steps: acquiring CBCT image data of the patient's current oral cavity and a three-dimensional digital model of teeth representing the patient's current dentition; segmenting the CBCT image data using a pre-trained segmentation model of abnormal parts of teeth and oral cavity to obtain a CBCT virtual model of the patient's current dentition and obtain abnormal part identification results; registering the CBCT virtual model with the identified abnormal parts with the three-dimensional digital model of teeth, mapping the abnormal part identification results in the CBCT virtual model to the three-dimensional digital model of teeth, and obtaining a three-dimensional digital model of teeth with abnormal part information. This invention utilizes a deep neural network to obtain a three-dimensional digital model of teeth with abnormal part information. Correspondingly, this invention also provides a method for generating orthodontic treatment plans and a method for preparing shell-shaped orthodontic appliances.

The application discloses an artificial intelligence driven jaw bone defect bionic reconstruction system and method. A three-dimensional segmentation model is used to segment the facial CBCT image of a healthy person to construct a jaw bone dentition database; the segmentation model is used to segment the healthy side and the diseased side of the jaw bone of a patient, and intelligent matching is performed in the database based on the segmented healthy side jaw bone data as a reference to obtain the most similar target jaw bone; then the opposite jaw bone of the target jaw bone is taken as a to-be-applied template, three-dimensional integration is performed with the diseased side defect of the patient to generate a basic virtual jaw bone support; then a planting scheme is generated according to the matched healthy dentition data to obtain a segmented virtual support; trabecular bone structure filling is performed in the segmented virtual support to output a bionic reconstruction customized body; finally, the overhanging structure of the trabecular bone is detected according to the complex topological structure of the trabecular bone, and corresponding printing support is generated. The above can improve the bionic adaptation degree of the reconstructed defect jaw bone, and support entity production such as 3D printing.

This invention provides a method and system for predicting the risk of preoperative condylar bone resorption, relating to the field of oral medicine technology. The method includes the following steps: acquiring and preprocessing small-field CBCT image data of the temporomandibular joint region of the patient to obtain the corresponding region of interest (ROI) of the condylar cancellous bone region; segmenting and extracting features from the ROI of the condylar cancellous bone region to obtain corresponding radiomics features; constructing a condylar bone resorption risk prediction model and inputting the radiomics features into the model for prediction to obtain the corresponding risk prediction results; generating patient risk grading information based on the risk prediction results to assist clinicians in making patient management decisions. This invention, by analyzing and modeling the radiomics features of the patient's preoperative condylar cancellous bone region, achieves the prediction of the risk of postoperative condylar bone resorption without using any postoperative images as prediction input.

The application provides a method, device, equipment and medium for removing metal artifacts in a CBCT image. The method comprises: scanning a target region by a dual-energy CBCT device to obtain high-energy and low-energy projections of the target region; performing base material decomposition on the high-energy and low-energy projections to obtain metal base material projections and non-metal base material projections; performing contour segmentation on the metal base material projections to extract the two-dimensional boundary of a metal region and generate a binary metal projection; back-projecting the binary metal projection to a three-dimensional space to determine the three-dimensional boundary of the metal region; cutting a three-dimensional CBCT image based on the three-dimensional boundary to obtain a three-dimensional metal region image; determining a non-metal projection based on the high-energy projection data and the binary metal projection, and performing three-dimensional reconstruction on the non-metal projection to obtain a three-dimensional non-metal region image; and splicing the three-dimensional metal region image and the three-dimensional non-metal region image according to spatial positions to obtain a three-dimensional CBCT image with metal artifacts removed.

The application relates to the technical field of oral medical treatment, in particular to a mixed-reality-based oral visualization navigation diagnosis and treatment method and system for children. The method comprises the following steps: acquiring CBCT image data of an oral cavity of a child patient and performing pretreatment, segmenting oral cavity tissues based on the CBCT data, generating an individualized anatomical model through three-dimensional reconstruction, determining a diagnosis and treatment area and generating a diagnosis and treatment path through preoperative planning, realizing alignment and registration of the anatomical model and the real world through shape comparison and coordinate transformation, superimposing the registered model and the path in a holographic manner in the visual field of the oral cavity of the patient, and visually displaying the deviation by real-time tracking of the pose of a diagnosis and treatment instrument. The application improves the controllability of operation through deviation visualization and threshold early warning, supports stable navigation of a child patient under slight movement, alleviates body movement interference caused by anxiety, and provides a repeatable, traceable and three-dimensional space cognition training environment for teaching.

The application discloses an intelligent pulp regeneration regulation method based on targeted exovesicles, comprising the following steps: generating a root canal center line prior graph according to a CBCT image, combining a dual-channel fusion image to form network input data, inputting the network input data into a 3D U-Net++ network for segmentation, and outputting a pulp cavity binary mask and a pulp damage probability graph; constructing a functional partition graph according to the output result and inputting the functional partition graph into a type perception graph neural network for information aggregation and feature learning, obtaining the final feature representation of a node, and predicting a pulp regeneration factor demand vector by using a partition multi-task regression head; generating a targeted exovesicle formula parameter and a targeted ligand amino acid sequence by a double-head hybrid action near-end strategy optimization agent based on the regeneration factor demand vector; treating a patient based on a targeted exovesicle delivery scheme, calculating a dynamic regeneration score based on postoperative monitoring data, and regenerating the targeted exovesicle delivery scheme when the score is lower than a preset threshold. The application can improve the accuracy of pulp regeneration regulation.

The present application relates to the technical field of medical image navigation, in particular to a lung nodule four-dimensional optical flow field real-time navigation system and method thereof, the system comprising a data acquisition module, a micro-region optical flow calculation module, a high-dimensional tensor analysis module, a nonlinear dynamics prediction module, a navigation fusion module and a surgical navigation module, the data acquisition module acquiring a CBCT image sequence and respiratory monitoring point motion information; the micro-region optical flow calculation module establishing a dynamic mapping relationship based on differential topology principles, generating a lung tissue motion displacement field; the high-dimensional tensor analysis module constructing a five-dimensional tensor structure and performing singular value optimization, extracting essential motion patterns; the nonlinear dynamics prediction module establishing a chaotic system model of respiratory motion, predicting future motion trajectories of lung nodules; the navigation fusion module integrating multi-modal data to generate a four-dimensional optical flow field; the surgical navigation module providing accurate real-time navigation information, the present application improving navigation accuracy to 1-3mm and being able to predict lung nodule motion trajectories within 2 seconds in the future.

This application discloses a weighted cone-beam computed tomography (CBCT) enhancement method applied to an image processing device, comprising: acquiring a weighted CBCT source image to be processed; performing edge detection on the source image to obtain a contour feature map; performing forward diffusion processing on the source image to obtain a noisy image; concatenating the contour feature map and the noisy image by channel and inputting them into a control encoder to obtain pose features; injecting the pose features into the backbone decoder of a pre-trained diffusion model via a zero convolutional layer through skip connections to obtain fusion features; and performing reverse diffusion sampling based on the fusion features to obtain an enhanced weighted CBCT image.