47 results about "Metal Artifact" patented technology

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 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 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 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 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 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 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.

The invention relates to the technical field of medical image processing, provides a target focus identification method and system based on a medical image, and solves the problem that focus identification precision is limited and unstable. The method comprises the following steps: acquiring a cone beam CT image and reference grayscale data of a mandible before an oral implantation operation; enhancement processing is carried out through a self-adaptive histogram equalization algorithm with metal artifact reduction and contrast limitation, and an enhanced image is obtained; extracting a three-dimensional gray histogram of the implanted target area to calculate gray skewness and gray kurtosis, and extracting a texture feature set; performing principal component analysis on the texture feature set to obtain principal component features, and splicing the principal component features with gray skewness and gray kurtosis into a fusion feature vector; and analyzing the vector by adopting a density peak value clustering algorithm, mapping a result to an anatomical partition, and performing Z-score inspection in combination with reference gray data to identify a bone focus area. According to the invention, accurate and automatic positioning of the focus can be realized.

The application relates to the technical field of medical image processing, and provides a target lesion identification method and system based on medical images, which solves the problems of limited and unstable lesion identification precision. The method comprises the following steps: acquiring a cone beam CT image of a mandible before oral implantation and reference gray data; performing metal artifact reduction and adaptive histogram equalization algorithm enhancement processing with limited contrast to obtain an enhanced image; extracting a three-dimensional gray histogram of an implant target area to calculate gray skewness and gray kurtosis, and extracting a texture feature set; performing principal component analysis on the texture feature set to obtain principal component features, and splicing the principal component features with the gray skewness and the gray kurtosis into a fusion feature vector; analyzing the vector by using a density peak value clustering algorithm, and mapping the result to an anatomical partition, and performing Z-score testing in combination with the reference gray data to identify a bone lesion area. The application can realize accurate and automatic positioning of a lesion.

The invention discloses a low-energy spectrum CT image metal artifact improvement method based on regional model learning, and belongs to the technical field of CT imaging, and the method comprises the following steps: S1, inputting n virtual single-energy images before metal artifact correction, synthesizing a base material image pair, recognizing an optimal virtual single-energy image, carrying out the data preprocessing of the obtained images, and obtaining a data preprocessing result; identifying artifact area masks and non-artifact area masks; s2, applying a mask to the optimal monoenergetic image and the base material image pair to obtain respective artifact region images and non-artifact region images; s3, inputting the non-artifact region image obtained in the S2 into a deep learning network, and constructing a mapping relation between the base material and the non-artifact region of the optimal monoenergy diagram; and S4, outputting a base material graph with improved artifacts. According to the method, the metal artifacts in the low-energy virtual monoenergy diagram of any manufacturer can be effectively suppressed and improved, the suppression effects under low energy and high energy can be consistent, and new artifacts and pseudo structures are not introduced.

This application discloses a method and system for detecting leaks in water supply pipelines based on radar images and deep learning, belonging to the field of pipeline inspection technology. The method includes: collecting radar data from known water supply pipelines to construct a radar data sample set; training a deep learning model to obtain a leak detection model; the deep learning model includes a metal artifact filtering layer, a 3D encoder, a pipeline position attention module, a direction attention layer, and a 3D decoder; inputting the radar data to be tested into the leak detection model to obtain the corresponding detection results. This application improves upon existing methods for detecting leaks in pipelines, enhancing the accuracy and precision of leak location detection results.

The application relates to the technical field of medical image processing, and particularly discloses a three-dimensional C-arm three-dimensional image metal segmentation method and system. The method is characterized in that a step-by-step processing strategy is adopted: first, a first set of high-low threshold combinations is used to segment three-dimensional image data to obtain a general metal region, and the general metal region is removed from the image after connected domain analysis and verification; then, the image data after removal is segmented by using a second set of high-low threshold combinations to obtain a titanium alloy region and perform analysis and verification; and finally, the regions that pass the two-step verification are combined to form an accurate metal segmentation result. The system correspondingly comprises functional modules for realizing the above steps. Through double-threshold step-by-step processing and a multiple verification mechanism, the segmentation accuracy and anti-interference ability in a multi-material metal coexistence scene are significantly improved, false segmentation caused by artifacts is effectively inhibited, a reliable foundation is provided for subsequent metal artifact removal processing, and therefore the imaging quality of a three-dimensional C-arm is finally improved.

The application discloses a medical image metal artifact verification method and device, an electronic device and a storage medium, and the method comprises the following steps: identifying a visit image sequence with a metal artifact label, wherein the visit image sequence comprises a plurality of image sequences; obtaining a plain scan image sequence with a metal artifact label from the visit image sequence; and verifying a metal artifact image of a corresponding target image sequence in the visit image sequence based on the serial number range of the metal artifact image in the plain scan image sequence, wherein the layer thickness of the target image sequence is greater than the plain scan image sequence, and / or the target image sequence is an enhanced image sequence. The method can realize metal artifact verification on each image sequence in the visit image sequence, and provides the possibility for intelligent image auditing.

This invention discloses a system and method for predicting the risk of periprosthetic infection after knee arthroplasty, belonging to the field of medical data analysis and processing technology. The system includes: an image acquisition interference detection module, an initial infection area determination module, an infection risk area correction module, and a risk prediction result output module. This invention quantitatively determines the interference of metal artifacts in image acquisition by combining relevant image parameters, ensuring the authenticity and reliability of the input images. Based on this, the initial infection area determination module initially identifies the infection risk area around the prosthesis, and the infection risk area correction module further corrects the initially determined infection area, outputting a more accurate corrected infection risk area. Finally, the risk prediction result output module outputs the final infection risk prediction result based on the corrected dataset. Through the synergistic effect of the above modules, the accuracy of periprosthetic infection risk prediction after knee arthroplasty is significantly improved.

A metal artifact correction network model and a metal artifact removal method. The metal artifact correction network model is provided with: a learnable artifact coder, configured to perform extraction and feature coding on artifact information in an uncorrected metal artifact-containing CT image to obtain a corresponding multi-scale artifact coded feature map; a learnable artifact remover, configured to remove the artifact information in the uncorrected metal artifact-containing CT image; and a learnable artifact generator, configured to generate artifact information on an artifact-free CT image paired with the uncorrected metal artifact-containing CT image, wherein the network structure of the learnable artifact remover is the same as that of the learnable artifact generator. The present invention can obtain a metal artifact-reduced image with a good artifact correction effect.

The invention discloses a system for evaluating a coronary stent inner lumen through photon CT single energy and metal artifact removal, and belongs to the technical field of medical instruments. The invention discloses a system for evaluating a coronary stent inner lumen through photon CT single energy and metal artifact removal. The system comprises a photon CT scanning module, a single energy imaging optimization module, a metal artifact removal module, an intelligent evaluation analysis module and a data storage module. The problem that in the prior art, image blurring is likely to occur, and judgment of the lumen stenosis degree is affected is solved. The method is high in artifact removal accuracy, can truly recover the lumen form, realizes automatic measurement of key parameters, introduces a standard phantom calibration model to calibrate the measurement result, further improves the measurement precision, automatically recognizes common complications based on the measurement parameters and image features, and clarifies the types, positions and degrees of abnormities. The change trend of the condition of the lumen in the stent is visually presented, potential complication risks can be found early, and the occurrence rate of adverse events is reduced.

The invention provides a method, device and equipment for removing metal artifacts in a CBCT image and a medium, and the method comprises the steps: scanning a target region through dual-energy CBCT equipment, and obtaining high-energy and low-energy projections of the target region; performing base material decomposition on the high-energy projection and the low-energy projection to obtain a metal base material projection and a non-metal base material projection; carrying out contour segmentation on the metal-based material projection, extracting a two-dimensional boundary of a metal region, and generating a binary metal projection; back-projecting the binary metal projection back to a three-dimensional space, and determining a three-dimensional boundary of the metal area; cutting the three-dimensional CBCT image based on the three-dimensional boundary to obtain a metal area three-dimensional image; based on the high-energy projection data and the binary metal projection, determining a non-metal projection, and performing three-dimensional reconstruction on the non-metal projection to obtain a non-metal region three-dimensional image; and splicing the three-dimensional image of the metal region and the three-dimensional image of the non-metal region according to spatial positions to obtain a three-dimensional CBCT image without metal artifacts.

The application discloses an image needle path extraction algorithm, comprising the following steps: scanning an image containing a needle path, and performing threshold segmentation on the image; performing connected domain analysis to obtain a plurality of connected domains, and screening a target region to be extracted; performing straight line fitting on a voxel point in the target region to obtain a straight line, and extracting an end point of the straight line to complete needle path extraction. The application performs robust regression analysis on a needle path coordinate point set, eliminates errors caused by metal artifacts in the image, and has high precision and high reliability in needle path extraction, and can be applied to most scenes.

The application discloses a kind of based on real metal projection graph to generate the artifact migration method of matching data, including the following steps: from the image affected by artifact with threshold segmentation way extraction metal region, then obtain metal track image by forward projection;Using LI algorithm in the projection graph affected by artifact, in accordance with metal track, linear interpolation is obtained in corresponding area to remove metal track preprocessed sinogram;By subtracting LI sinogram from the sinogram affected by artifact to obtain residual sinogram containing only metal projection;Residual sinogram is added with artifact-free sinogram, and new sinogram affected by artifact is synthesized;Using step S4 synthesized sinogram containing artifact to reconstruct CT image containing metal artifact.The application provides data generation method, overcomes the negative influence brought by network training in traditional metal artifact removal model based on deep learning, unknown X-ray spectrum, unknown metal material and different detector sensitivity and the like.

The invention discloses a water supply pipeline leakage detection method and system based on radar images and deep learning, and relates to the technical field of pipeline detection, and the method comprises the steps: collecting radar data of a known water supply pipeline, and constructing a radar data sample set; training the deep learning model to obtain a leakage detection model; the deep learning model comprises a metal artifact filtering layer, a three-dimensional encoder, a pipeline position attention module, a direction attention layer and a three-dimensional decoder; and inputting to-be-detected radar data into the leakage detection model to obtain a corresponding detection result. The pipeline leakage scene is improved, and the precision and accuracy of the leakage position detection result are improved.

The invention relates to an image processing method and device and computer equipment. The method comprises the following steps: acquiring each image processing result and an excitation model; the image processing result is obtained by correcting the metal artifact of the metal implant in the scanning image based on a correction model; segmenting each image processing result through an excitation model to obtain a target tissue segmentation image and a metal segmentation image; calculating tissue feature loss between the target tissue segmentation image and the original tissue image based on an excitation model, and calculating metal feature loss between the metal segmentation image and the original metal image; performing excitation optimization on an application process or a training process of the correction model according to each metal feature loss and each tissue feature loss; the correction model after excitation optimization is used for image processing. By adopting the method, the accuracy of image processing can be improved.

This invention relates to a semi-supervised method for removing metal artifacts in multi-energy spectral CT images. Based on a semi-supervised average student-teacher model, the U-net network structure incorporates a slice feature attention module, enhancing the perception of deep feature information between channels and improving the feature connections between different monoenergy maps of multi-energy spectral CT slices. Simultaneously, in unsupervised learning, the student model, combined with voxel-level contrastive learning, makes the model more focused on artifact regions, enabling artifact information interaction between low-energy and high-energy maps. This allows for better removal of artifacts in low-energy maps while preserving more tissue detail information. Consequently, it provides high-quality CT images for subsequent medical diagnostic tasks such as image classification and segmentation.