107 results about "Metal Artifact" patented technology

The invention discloses a tooth segmentation method of a CBCT (Cone Beam Computed Tomography) image in the technical field of medical image segmentation. The method comprises the following steps: S1, sequentially carrying out standardization and data enhancement processing on a CBCT image data set; s2, inputting the CBCT image preprocessed in the S1 into an encoder part of a network structure, and completing step-by-step extraction of image features to obtain multi-scale features; s3, further fusing the multi-scale feature maps generated by each downsampling layer of the encoder network structure to obtain richer and more effective feature expressions; and S4, performing step-by-step spatial resolution recovery on the encoder features, and finally realizing fine segmentation of the tooth image, the tooth segmentation method of the CBCT image solves the problem that the teeth and surrounding tissue boundary details are fuzzy and the teeth are not clear due to the influence of metal artifacts in CBCT segmentation.

The invention discloses a double-domain artifact correction method based on multi-energy-level data and physical prior fusion, and the method achieves the efficient and precise removal of dispersive metal artifacts through the construction of a complete technical scheme of the combination of multi-energy-level data collection, double-domain cooperative correction and physical model constraint. The system has the beneficial effects that the system covers multiple fields of medical treatment, industry, security and protection, aerospace and the like, and has extremely high universality and adaptability. On the basis of a multi-energy-level slow switching scanning protocol, full-angle scanning of at least two energy levels is completed by dynamically adjusting radiation source parameters, and obtained complete multi-energy-level projection data is converted into a high-dimensional tensor through a channel dimension splicing image fusion method. The constructed multi-channel virtual image completely retains attenuation characteristics and structure information of a target object under each energy, provides a more comprehensive input source with discrimination for a deep learning network, builds a data basis for accurate correction in different fields fundamentally, and adapts to various imaging scenes containing metal targets.

The invention provides a method for eliminating metal artifacts in a cone beam CT (computed tomography) projection domain, which comprises the following steps of: converting a current frame projection image IC into a binarization image IL and an edge detection image IG, marking connected domains in the IL and the IG, and deleting non-metal artifact area connected domains, so that binarization and edge detection results can be mutually verified; the accuracy of metal artifact area detection is improved; obtaining an extended bounding rectangle Ri, ext of each connected domain in the IM, and effectively eliminating metal artifacts according to the extended bounding rectangles Ri, ext; by segmenting the metal artifact area in the projection domain image, the resolution of the image is protected, and the artifact elimination effect is improved.

The invention discloses a DDPM-based CT image metal artifact elimination method, and aims to solve the artifact problem caused by a metal object in an existing CT image and improve image quality and diagnosis reliability. A diffusion model of unconditional training is adopted, step-by-step back diffusion repair of an artifact area is carried out in a sinogram domain, an unrepaired area is dynamically adjusted by combining with a metal mask, accurate repair of the artifact area is achieved, and original data of the area which is not affected by artifacts are kept. In the training stage of the system, artifact-free data are gradually converted into standard Gaussian noise through forward diffusion; in the inference stage, data are gradually recovered by utilizing back diffusion, block repair is carried out on an artifact region by combining with a metal mask, and a complete sinogram is generated through region merging. And finally, reconstructing a CT image by using a filtered back projection algorithm, and optimizing boundary transition through a smoothing algorithm to ensure seamless connection between the metal object and surrounding tissues.

The invention provides a CT metal artifact correction algorithm based on combination of double-domain diffusion and wavelet attention, and relates to the technical field of image correction. According to the method, the accuracy and effectiveness of CT metal artifact correction are improved by fusing the wavelet attention mechanism and the double-domain diffusion model, and the accuracy and effectiveness of CT metal artifact correction are improved by means of targeted extraction of the wavelet attention module on the high-frequency component of the image and quantitative analysis of the gradient direction consistency index and the curvature entropy. Accurate distinguishing of real edges and metal artifact fragments is achieved, and the problem of structure loss caused by confusion of edges and artifacts in a traditional method is effectively solved. Besides, on the basis of the design of edge geometric attribute dynamic distribution diffusion parameters, a small convolution kernel and a slow step length are adopted for a high-curvature edge to reserve a fine structure, and a large convolution kernel and a fast step length are adopted for an artifact area to strengthen the suppression effect, so that the artifact removal efficiency is improved, and the damage of excessive smoothness to key edge features is avoided; and the balance between the local fine structure and the global smooth demand is realized.

The invention discloses a diode packaging defect X-ray image automatic interpretation system and method, and particularly relates to the technical field of defect identification. Initial artifact interference is effectively inhibited through multi-angle X-ray imaging and image fusion based on structural similarity; further compensating structural information by combining a region self-adaptive metal artifact recognition and repair model; a deep convolutional neural network model with a metal region attention mechanism is introduced, the feature perception capability of a complex texture region is enhanced, the recognition accuracy of the metal boundary region tiny defects can be remarkably improved, and the missing detection and misjudgment rate can be reduced.

The invention discloses an intelligent sketching method for a cervical cancer radiotherapy target area and organs at risk, and relates to the technical field of medical images, and the method comprises the following steps: obtaining a CT and MRI image data set of a cervical cancer patient; inputting the CT and MRI images containing the metal artifacts into a pre-trained metal artifact removal model to obtain CT and MRI images without the metal artifacts; inputting the CT and MRI images with the metal artifacts removed into a double-encoder U-Net network comprising a cross-modal attention fusion gating module to obtain final fusion features; processing the final fusion feature through a U-Net decoder, constructing a small sample adaptive mixed loss function to optimize network parameters, and outputting a segmentation mask; and carrying out contour processing on the segmented mask to complete the delineation of the target region and the endangered organ. Through high-precision and high-efficiency automatic sketching, a patient can obtain higher-quality and higher-efficiency treatment, and meanwhile, the working intensity of a doctor is relieved.

The invention relates to a multi-degradation medical image unified fusion method based on degradation prototype learning, and belongs to the field of medical image fusion. The method comprises the steps that low-dose PET data, CT metal artifact data and MRI data with motion artifacts are generated through the imaging principle; learning a degradation prototype by using a feature selection mechanism; the basic fusion model is decomposed into a plurality of branches through a low-rank decomposition strategy, and processing can be performed through different branches when different degradation data are fused; a prompt module based on a learnable feature prototype is designed, and fusion is promoted by injecting degradation-related invariant features into different LoRa branches; and constructing a fused image through an output layer by integrating the degradation elimination fusion features in different scales. According to the method, the medical images containing degradation can be effectively fused, and the robustness and practicability in reality are improved.

The invention belongs to the technical field of CT images, and particularly relates to a double-domain CT metal artifact removal method based on deep learning. Comprising the following steps: 1) acquiring CT images of different scanning parts, and processing the CT images to obtain simulated CT images containing metal artifacts; 2) correcting and simulating the CT image containing the metal artifact through a traditional nonlinear interpolation algorithm to obtain an image after nonlinear interpolation correction, and preprocessing the image; 3) constructing a metal artifact removal model based on a deep convolutional neural network, and training the metal artifact removal model; 4) reasoning the preprocessed image by using the trained model to obtain a metal artifact-removed image, and counting the training precision; and 5) carrying out post-processing on the metal artifact-removed image, and carrying out reverse normalization to an original CT magnitude. The method not only performs metal artifact correction in the projection domain, but also performs metal artifact correction in the image domain, so that the metal artifact removal precision is ensured, and the model convergence can be accelerated.

The invention discloses a CT image metal artifact correction method and device based on a U-Net model, electronic equipment and a storage medium, and belongs to the technical field of image processing, and the method comprises the steps: obtaining a to-be-corrected CT image; inputting a CT image to be corrected into the U-Net network model to obtain a CT image which is output by the U-Net network model and is subjected to metal artifact correction; wherein the U-Net network model comprises a multi-scale attention mechanism and an adaptive fusion module. According to the method, a multi-scale attention mechanism is introduced to a U-Net network model, multi-scale feature extraction can be performed in jump connection, metal artifact regions and organization structure features are highlighted, the correction precision of metal artifacts is improved, a correction strategy is dynamically adjusted through an adaptive fusion module, over-fitting or under-fitting is avoided, and the correction precision of the metal artifacts is improved. The artifact residual is effectively reduced; and the metal artifact correction quality is improved.

The invention provides a prior projection-based metal artifact correction method and device. The prior projection-based metal artifact correction method comprises the following steps of: segmenting a to-be-processed original reconstructed image into a metal image and a non-metal image; determining prior projection data based on the non-metal image, and obtaining metal area projection data based on projection of the metal image; repairing the metal area projection data based on the prior projection data to obtain repaired metal area projection data; a corrected image is determined based on the repaired metal region projection data. According to the method, the prior projection data is determined by using the non-metal image, and the metal area projection data is repaired based on the prior projection data, so that new metal artifacts are prevented from being introduced, meanwhile, the calculated amount is relatively small, and the accuracy and efficiency of metal artifact correction are improved.

The invention provides a multi-physics field monitoring CT metal artifact suppression method based on artificial intelligence, which comprises the following steps: carrying out an indoor rock physics hydraulic fracturing experiment, and carrying out multi-physics field monitoring; constructing a training data set, wherein the data set comprises CT data not containing the metal artifacts of the piezoelectric ceramic sensor, CT data containing the metal artifacts of the piezoelectric ceramic sensor, and a position map of the piezoelectric ceramic sensor; constructing an artifact suppression module based on two domains and three channels, wherein the artifact suppression module uses two types of domain information of an image domain and a chordal graph domain; training a detection network by using the training data set; inputting the preprocessed CT data containing the metal artifacts of the piezoelectric ceramic sensor into an artifact suppression module to obtain corresponding CT data without the metal artifacts of the piezoelectric ceramic sensor and a position map of the piezoelectric ceramic sensor; and updating the data set and the artifact suppression module. According to the multi-physics field monitoring CT metal artifact suppression method based on artificial intelligence, CT metal artifacts can be suppressed, and the CT image quality is improved.

The invention discloses a CBCT (cone beam computed tomography) metal artifact removal method and system based on projection domain metal identification, which skip the traditional image domain segmentation and orthographic projection process, directly extract key geometric features of a metal area in an original projection image, and match with a pre-established metal model library to obtain a metal artifact removal result. The high-precision identification and spatial attitude estimation of the metal object are realized, the processing flow is obviously simplified, and the identification stability and the artifact repair effect are improved. In order to solve the problems of shielding and overlapping possibly occurring in a multi-metal structure, a step-by-step stripping type recognition mechanism is provided, multi-target interference is effectively avoided through round-by-round recognition, fitting and image updating, and the accuracy and integrity of model matching are ensured.

Metal artifacts are reduced in x-ray computed tomography (“CT”) images using a suitably trained neural network, such as a convolutional neural network (“CNN”). Virtual metal DATA objects are inserted to either the raw projection data or CT image data (e.g., from pre-procedural CT scans) to generate sets of matching artifact-corrupted and artifact-uncorrupted images, and a CNN, or other neural network, is trained to separate the contribution to each image pixel due to patient anatomy, metal object, or metal object-induced artifact. The contributions from metal object-induced artifacts can then be removed to generate a final, artifact-reduced image.

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 invention discloses an image metal artifact removal method and device, a storage medium and electronic equipment. The method comprises the following steps: acquiring a first image containing a metal artifact; performing image recognition on the first image to obtain an estimated metal type to which the metal object belongs and an estimated time length; and performing simulation by using the estimated metal type and the estimated time duration to obtain the interference of an object of the estimated metal type in the estimated time duration in the imaging process of the first image, and performing spontaneous movement or propagation from a high-concentration area to a low-concentration area. A simulation virtual image generated by polluting the first video image; and performing artifact removal processing on the first image by using the simulation virtual image to obtain a second image. The method can be applied to the field of image processing. The technical problem that the metal artifact removing effect of the image is poor is solved.

The invention discloses a CBCT (Cone Beam Computed Tomography) projection metal track segmentation and artifact removal method of sparse labeling fine-tuning SAM (Sparse Assisted Modulation), which comprises the following steps: firstly, collecting actual scene data, constructing a data set with lower cost by adopting a sparse labeling mode for SAM fine tuning, and containing metal objects in all shapes as far as possible when collecting the data; then, performing fine adjustment on the SAM, taking the spliced projection frames with different contrast ratios as input during fine adjustment, and taking Mask obtained by image domain threshold segmentation as Prompt; then, directly applying the fine-tuned SAM to actual projection data to predict an accurate projection domain metal Mask angle by angle; then, morphological processing is carried out on the predicted projection domain metal Mask, and triangulation interpolation is carried out on the original projection by using the processed Mask; and finally, carrying out Padding on the interpolated projection to suppress truncation artifacts, and reconstructing by adopting a weighted FDK algorithm to suppress incomplete sampling artifacts to obtain a final metal artifact removed image for subsequent application.

The application discloses a kind of simultaneously sparse angle CT reconstruction and metal artifact high-precision correction method, belong to medical imaging field, specifically: simulate the projection data under the influence of beam hardening under sparse angle sampling, obtain sparse sampling and the sinogram of containing metal trace;Interpolation processing is carried out to sparse sinogram, and the sinogram is obtained as the initial sinogram of sinogram domain;According to the additive property of artifact, preliminarily decompose the CT image containing a large number of artifacts, obtain the initial estimated image of image domain and the initial artifact image of artifact domain;A multi-domain optimization model is established, and the constructed multi-domain optimization problem is alternately iterated minimization solution;Iterative update is carried out to sinogram domain, image domain and artifact domain respectively;The high-precision CT image of reconstruction is output.The application can simulate the sinogram containing metal trace and sparse obtained by CT imaging equipment to carry out simultaneously sparse angle CT reconstruction and metal artifact correction, and the reconstruction effect is good and correction precision is high.

The invention discloses a training method and device of an oral cavity CT metal artifact removal model, electronic equipment and a storage medium. The method comprises the following steps: determining an image containing a metal artifact feature based on an oral CT slice image with a metal artifact and a slice down-sampling image; performing feature extraction on the image containing the metal artifact features through a multi-head self-attention network to obtain the metal artifact features; image reconstruction is carried out to obtain a clean slice image after metal artifacts are removed; and completing model training based on the clean slice image after the metal artifacts are removed and the real clean slice image. According to the scheme, the prior structure of the metal artifact is obtained by determining the image containing the metal artifact feature, a traditional interpolation method does not need to be used for reconstructing a prior image and extracting a metal mask any more, the model data processing efficiency is improved, in addition, the multi-head self-attention network can capture the physical regularity and rotation invariance of the artifact, and the processing efficiency is improved. And the extraction precision of the metal artifact features is improved.

The invention discloses a physical constraint-based safe metal artifact removal reasoning method, which comprises the following steps of: firstly, acquiring a CT (Computed Tomography) image to establish a data set for a metal artifact removal task, and then preprocessing the data set; then, a PRISM model used for metal artifact removal is constructed, and the PRISM model is based on a cyclic consistency framework and a double-flow attention generator structure and is combined with an antagonism discriminator and a composite optimization objective function; carrying out unsupervised training on the PRISM model by using the training set, and carrying out joint adjustment and optimization on network parameters through a composite optimization objective function; and finally, performing performance evaluation on the trained PRISM model by using the test set. According to the method, high-fidelity learning of artifact removal and structure maintenance is completed under the unsupervised condition, and the generalization ability and stability of the model in a real metal artifact scene are remarkably improved.

The application discloses a tracer identification positioning method, an electronic device, a storage medium and an apparatus. The method comprises the following steps: segmenting an intraoperative three-dimensional image by using different image segmentation thresholds, obtaining a suspected tracer ball meeting an identification condition each time, selecting suspected tracer balls with the same number as that of actual tracer balls to form a plurality of to-be-registered point sets, performing rigid registration on the actual tracer balls, and selecting a to-be-registered point set with the minimum mean square error as a candidate point set; selecting the candidate point set with the minimum mean square error as a final positioning point set; outputting coordinates of all points of the final positioning point set and a corresponding coordinate transformation matrix, and outputting that no tracer is identified if there is no candidate point set or the mean square error of the final positioning point set is greater than the radius of the actual tracer ball. The application automatically positions the tracer in the three-dimensional image in an iterative manner, can eliminate the influence of metal artifacts to the maximum extent, improves the positioning accuracy, and reduces the operation time.

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.

The present disclosure provides a method for eliminating artifacts, including: obtaining at least one two-dimensional projection image; determining whether there is a metal edge in the at least one two-dimensional projection image based on the metal edge feature; and if there is a metal edge in the two-dimensional projection image, eliminating the metal artifacts in the two-dimensional projection image based on the metal edge. The present disclosure also provides a method for volume data reconstruction, performing volume data reconstruction based on multiple two-dimensional projection images after eliminating the metal artifacts, so as to eliminate the metal artifacts after reconstruction. The present disclosure also provides an apparatus, an electronic device, and a readable storage medium for eliminating artifacts.

The application discloses a CBCT metal artifact removal method and system based on a projection domain metal recognition, which skips a traditional image domain segmentation and orthographic projection process, directly extracts key geometric features of a metal region in original projection images, and realizes high-precision recognition and spatial posture estimation of metal objects by matching with a pre-established metal model library, so that the processing flow is significantly simplified, and the recognition stability and artifact repair effect are improved. In view of the shielding and overlapping problems that may occur in a multi-metal structure, a step-by-step stripping recognition mechanism is proposed, and through round-by-round recognition, fitting and image updating, multi-target interference is effectively avoided, and the accuracy and integrity of model matching are ensured.

A method for reducing metal artifacts in CT images, including the following steps: a material decomposition (MD) calibration step, using multiple MD calibration phantoms with known characteristics and multiple spectral CT data corresponding thereto to construct a system characteristic model of spectral CT; and a MD testing step, including: the following steps: imaging multiple testing objects with a different unknown material and thickness to obtain projection-based multiple spectral CT imaging data of different energy bins; obtaining corresponding multiple basis material images of different materials based on projection data according to the spectral CT imaging data and the system characteristic model of spectral CT; and combining the basis material images and a photon energy information to be recombined with each other to obtain multiple virtual monoenergetic images.

The application discloses a high-precision CT metal artifact correction method based on self-supervised learning, and belongs to the technical field of computer medical imaging, specifically: a data set for training a network is created, and the data set is divided into a training set and a test set; a model-driven image domain subnetwork based on a tight frame, a data-driven sine domain subnetwork based on a Transformer, and a novel coupling mechanism for connecting the two subnetworks are constructed; a loss function is designed for optimizing the coupled model-data double-driven network; the data set and the loss function are used to train and optimize the coupled double-driven network until the training is completed, and an optimal network model is obtained; and the test set image is input into the network model trained in step 3, so that a metal artifact correction image is obtained. The application has high-precision metal artifact correction performance while having a small amount of network parameters, and relieves the demand of the training network for paired CT images.

The invention provides a multi-energy-spectrum CT (Computed Tomography) metal artifact removal method for oral medicine, which is used for solving the problem that the existing method cannot realize low-dose and high-precision oral metal artifact removal. The method comprises the following steps: acquiring a plurality of groups of continuous energy channel images of the multi-energy-spectrum CT system in an energy interval; data preprocessing is carried out, and CT images of the same part of adjacent energy channels are spliced in the channel dimension; constructing a metal artifact removal model (ELM) based on edge enhancement; training the model by using the data after the multi-energy-spectrum CT data channel dimension splicing; and removing the metal artifacts of the multi-energy-spectrum CT image by using the trained model. According to the method, the energy spectrum advantages of multi-energy-spectrum CT are utilized, the high-precision metal artifact removal effect and generalization performance are achieved on clinical data and a general data set, and reliable technical support is provided for CT image evaluation after oral cavity metal implantation.

The invention discloses a tooth metal artifact suppression method and device based on optical pumping magnetometer-magnetocardiogram data, and relates to the technical field of magnetocardiogram artifact signal preprocessing, and the method comprises the steps: carrying out pre-whitening processing on a data matrix of the optical pumping magnetometer-magnetocardiogram data to generate a pre-whitening matrix; generating a delay correlation matrix of the data matrix according to the pre-whitening matrix and a pre-generated delay sequence; performing joint diagonalization iterative approximation processing on the delay correlation matrix to generate a decomposition matrix of the data matrix; and inhibiting tooth metal artifacts in the optical pumping magnetometer-magnetocardiogram data according to the decomposition matrix and a predetermined time-frequency domain threshold. According to the method, the quality of MCG data is improved, the limitation of using MCG by a patient with teeth provided with metal materials is reduced, and the measurement range of MCG clinical application is expanded.

The invention relates to the technical field of medical image processing, and particularly discloses a three-dimensional C-shaped arm three-dimensional image metal segmentation method and system. The core of the method comprises the following steps: sequentially carrying out three-threshold segmentation on three-dimensional image data so as to preliminarily identify metal and titanium alloy areas; segmenting and refining the metal region through double thresholds; after performing morphological expansion on the metal area, subtracting the metal area from the artifact area to obtain an artifact area; carrying out secondary double-threshold segmentation on the artifact region to extract the titanium alloy; the titanium alloy area obtained through secondary segmentation is expanded to recall the mistakenly subtracted part; and finally, combining all metal and titanium alloy connected domains, and outputting an accurate metal segmentation result. According to the method, through combination of multiple thresholds and morphological operation, the segmentation problem caused by metal artifact interference and coexistence of multiple materials is effectively solved, the segmentation accuracy, integrity and robustness are remarkably improved, and a reliable basis is provided for subsequent high-quality metal artifact removal processing.

The embodiment of the invention provides a correction compensation method, system and device for metal artifacts and a storage medium. The correction compensation method for the metal artifacts comprises the steps that a to-be-processed image containing the metal artifacts is acquired; performing data restoration on the metal track part in the to-be-processed image to obtain restored projection data; performing gray value compensation on the restored projection data to obtain compensated projection data; and based on the compensated projection data, a restored image is obtained through back projection reconstruction.