79 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 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 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 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 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 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 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 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.

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 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 invention provides a CBCT metal artifact reduction method and system based on a coupling diffusion model, and belongs to the field of medical image processing. The method comprises the following steps: acquiring clinical data, and respectively training an MA CBCT image diffusion model and a clean CBCT image diffusion model based on the clinical data; training a noise conversion module by using the synthesized pairing data; inputting an MA CBCT image, and gradually adding noise to the MA CBCT image to an intermediate state through diffusion; mapping the noise features of the MA CBCT image to the noise space of the clean CBCT image diffusion model by using the trained noise conversion module; denoising step by step based on a noise level coupling mechanism; and generating an artifact reduction image through an MA adaptive reasoning fusion module. According to the method, full learning of metal artifact features is ensured, the problem that artifact feature conversion is inaccurate in a traditional method is solved, and the artifact removal effect is optimized.

The present application relates to a multi-degradation medical image unified fusion method based on degradation prototype learning, belonging to the field of medical image fusion. The present application comprises: generating low-dose PET data, CT metal artifact data, and MRI data with motion artifacts through imaging principles; learning degradation prototypes using a feature selection mechanism; decomposing the basic fusion model into multiple branches through a low-rank decomposition strategy, which can process different branches when fusing different degraded data; designing a prompt module based on learnable feature prototypes, which promotes fusion by injecting degradation-related invariant features in different LoRa branches; and constructing a fused image through an output layer by integrating degradation-eliminating fusion features in different scales. The present application can effectively fuse medical images containing degradation, improving robustness and practicality in reality.

The application discloses a metal artifact removal method of a CT image, and the method comprises the following steps: constructing an initial adaptive iterative learning model based on wavelet transform, decomposing a CT image by using a target adaptive iterative learning model obtained by optimizing an optimization objective function, calculating the area of a metal artifact, removing the metal artifact in the CT image, and obtaining a CT image with reduced metal artifacts; the initial adaptive iterative learning model based on wavelet transform is used for artifact removal, so that the spatial distribution characteristics of the metal artifact under different domains and resolutions can be fully utilized, the artifact removal is better, and the interpretability is high; in addition, when a first optimization objective function is solved by combining a proximal gradient descent algorithm and a Taylor formula, a proximal gradient operator obtained can be replaced by a simple network module, so that the network can be more easily constructed, and the adaptability of the network is enhanced. The application can be widely applied to the technical field of CT image processing.

The invention relates to a metal artifact correction method for a cervical brachytherapy CT image, and the method comprises the following steps: carrying out the metal artifact synthesis processing of a cervical cancer EBRT CT image, and obtaining a synthesized image; constructing a metal artifact correction model; training a metal artifact correction model by adopting the composite image and the original EBRT CT image; performing metal artifact correction processing on the cervical BT CT image by adopting the trained metal artifact correction model to obtain a corrected CT image; according to the method, the artifact image containing the synthetic cavity artifact is simulated and generated from the EBRT CT image, the large-scale synthetic pairing data set is constructed and used for model training, the trained metal artifact correction model is adopted to carry out metal artifact correction processing on the cervical BT CT image, and the metal artifact of the cervical BT CT image can be effectively removed.

The invention discloses a CT image metal artifact removing method based on Fourier transformation and a convolutional neural network, and belongs to the technical field of medical image processing. The method comprises the following steps: a feature extraction step: extracting an initial spatial feature map of a metal artifact-containing CT image; a multistage feature enhancement step: gradually enhancing feature expression through space down-sampling, convolution operation and a Fourier block module fusing spatial domain and frequency domain features; a feature reconstruction and fusion step of recovering the image resolution step by step through up-sampling and jump connection; and a result output step: generating a CT image after artifact removal. According to the method, Fourier transform is deeply integrated into the neural network, frequency domain global information and spatial domain local features are fully utilized, the inhibition capability of metal artifacts and the image structure recovery quality are remarkably improved, and the method is suitable for post-processing and auxiliary diagnosis of clinical CT images.

The application discloses a kind of metal artifact correction methods of three-dimensional image, comprising: the original projection sequence image of scanning with metal implant is obtained and is carried out back projection reconstruction, and the metal region image and non-metal region image in it are obtained by segmentation to reconstructed image;The metal projection image is obtained by forward projection to the metal region image, and the corresponding metal region in original projection sequence image is linearly interpolated with metal projection image, and is carried out back projection reconstruction;The projection priori graph without metal implant is obtained by forward projection to the back projection reconstruction image of preceding interpolation, the pixel value of each point in the region where metal is located is calculated, and the original projection sequence image is interpolated accordingly, and the final image is fused after back projection reconstruction with metal region image.The application improves the definition of tissue boundary, improves the accuracy and robustness of metal artifact correction, avoids the problem of image quality reduction due to interpolation error.

The invention provides a detail enhancement method and device based on CT metal artifact removal, equipment and a medium, and the method comprises the steps: carrying out the CT metal artifact removal of an original CT image through a preset metal artifact removal algorithm, and obtaining an artifact-removed image; generating a metal artifact mask based on an image difference between the original CT image and the artifact-removed image; performing positive and negative data separation on the metal artifact mask to obtain a binary mask of a bright artifact area and a binary mask of a dark artifact area; fusing the binary mask of the bright artifact area and the binary mask of the dark artifact area in the original CT image to obtain a feature enhanced image; carrying out artifact region feature enhancement on the feature enhancement image to obtain an image domain feature image; performing wavelet transform enhancement on the original CT image to obtain a wavelet domain feature image; and fusing the image domain feature image and the wavelet domain feature image to obtain a detail enhanced image. Therefore, the metal artifact removal quality is improved.

This invention discloses a composite sealant for metal artifacts and its preparation and application method, comprising a base sealant and a top sealant. The base sealant comprises 20-40 parts by weight of a film-forming substance, 0.5-5 parts by weight of a composite corrosion inhibitor, and 55-79 parts by weight of a first solvent. The composite corrosion inhibitor comprises benzotriazole and 2-mercaptobenzothiazole, with a mass ratio of benzotriazole to 2-mercaptobenzothiazole of 1:0.2-1:5. The top sealant comprises 20-40 parts by weight of a film-forming substance, 1-10 parts by weight of nano-silica, 0.5-5 parts by weight of a hydrophobic modifier, and 45-78 parts by weight of a second solvent. This invention achieves efficient and durable protection for metal artifacts through the synergistic combination of benzotriazole and 2-mercaptobenzothiazole in the base sealant, exhibiting advantages such as good water resistance, strong salt spray corrosion resistance, high transparency, and good reversibility.

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 is that a step-by-step processing strategy is adopted: firstly, three-dimensional image data is segmented by using a first group of high-low threshold combination, a common metal region is obtained, and the common metal region is removed from an image after connected domain analysis and verification; segmenting the cleared image data by using a second group of high and low threshold combinations to obtain a titanium alloy region, and analyzing and checking the titanium alloy region; and finally, combining the areas passing the two-step verification to form an accurate metal segmentation result. The system correspondingly comprises functional modules for realizing the steps. Through double-threshold step-by-step processing and a multi-check mechanism, the segmentation accuracy and the anti-interference capability in a multi-material metal coexistence scene are remarkably improved, false artifact segmentation is effectively inhibited, and a reliable basis is provided for subsequent metal artifact removal processing, so that the imaging quality of the three-dimensional C-shaped arm is finally improved.

The embodiment of the present specification provides a metal artifact correction method, a system and an X-ray image reconstruction method. The metal artifact correction method comprises: acquiring a to-be-processed image containing a metal artifact; using a data repair method to repair data of a metal track part in the to-be-processed image to obtain repaired projection data; performing low-pass filtering on the repaired projection data to obtain filtered repaired projection data; and based on the filtered repaired projection data, obtaining a repaired image through back projection reconstruction.

The application relates to the technical field of medical image processing and computer-aided diagnosis, in particular to an orthopedic auxiliary examination system based on image processing; the system comprises a tensor field mapping, a texture flow construction, diffusion enhancement, singularity analysis and a quantization unit; the system uses a structure tensor to construct a texture flow field to suppress background noise and metal artifacts; the core is to use a diffusion equation evolution flow field to amplify texture dislocation, identify topological singular points serving as fracture endpoints by calculating Poincare indexes, and reconstruct a lesion boundary by using a geodesic algorithm; the application solves the problem of microtexture loss under low bone density or metal interference, and realizes high-signal-to-noise ratio lesion accurate positioning and healing quantization.

The invention relates to the technical field of magnetic resonance imaging, in particular to a two-dimensional magnetic resonance diffusion imaging method and device for inhibiting metal artifacts, and the method comprises the steps: rearranging three-dimensional k-space data corresponding to a magnetic resonance signal based on a frequency band separation algorithm of echo migration, carrying out the conversion to obtain a two-dimensional image arranged in a target direction, separating frequency band components, and obtaining a two-dimensional image; therefore, a frequency band graph capable of reflecting the partial resonance frequency space distribution caused by the metal is generated; and on the basis, inter-layer and intra-layer signal offset is further corrected, and finally a two-dimensional magnetic resonance diffusion imaging image after correction of the metal artifacts is obtained. According to the invention, the metal artifacts can be reduced and inhibited while the scanning time, the imaging resolution, the diffusion weighting intensity and the scanning coverage range are ensured and maintained, so that rapid and high-fidelity magnetic resonance diffusion imaging is realized in the presence of a metal implant.