781 results about "Compute tomography" patented technology

A defect prediction method based on a multi-feature parallel multi-stage neural network (MF-PMSNN), includes: obtaining a trajectory dataset, and preprocessing data of a defect of a workpiece in additive manufacturing (AM); building an MF-PMSNN, and evaluating an output classification result based on evaluation indicators; and performing real-time defect prediction, and deploying a trained MF-PMSNN model to a production environment. The present disclosure combines and effectively matches thermal imaging-based in-situ monitoring data and X-ray computed tomography (XCT)-based in-situ monitoring data to ensure temporal and spatial consistency between the thermal imaging-based in-situ monitoring data and the XCT-based in-situ monitoring data. In this way, a molten pool status and a pore of the workpiece can be captured more comprehensively. The MF-PMSNN is proposed to obtain a molten pool status and the porosity distribution in the data and perform defect prediction.

A robotic surgical system includes a robotic manipulator configured to perform surgical procedures under direct surgeon control. A surgical camera system captures real-time intraoperative video. An external imaging interface receives multimodal imaging data, including preoperative and intraoperative data from at least one of magnetic resonance imaging (MRI), computed tomography (CT), ultrasound, and fluoroscopy. An artificial intelligence (AI module has a trained neural network and a deep learning model trained on multi-institutional annotated surgical datasets, The AI module is configured to execute one or more of: fuse acquired video and imaging data into temporally and spatially coherent anatomical visualizations; generate continuously updating overlays aligned with the surgical field, with segmented anatomical features; projected tissue boundaries, proximity indicators for instruments, and predictive deformation trends; provide dynamic predictive trend visualization indicating zones of future anatomical complexity or risk; register and align preoperative imaging data with intraoperative imaging data in real time; adapt overlay presentation in response to tissue deformation without actuating the robotic manipulate or; and passively augment visual feedback without initiating any autonomous actuation of surgical instruments.

The present disclosure provides a zero-shot low-dose Computed Tomography (CT) image denoising method and apparatus based on strip diffusion model. This method includes building a strip diffusion model which uses high fidelity of the diffusion model to complete end-to-end denoising of low-dose CT images with different doses, thicknesses and devices. Particularly, in the training process, only the normal-dose CT images are required, greatly reducing the data reliance of the model, and model training across dose and thickness condition can be carried out with the data of only one scenario. In a sampling process, strip scanning strategy is used in combination with overlapped strip information and the input low-dose CT images to solve the maximum a posteriori problem, thereby sequentially generating denoising results. The present disclosure only uses simple convolution and attention architecture and carries out extensive experiments on the datasets involving different doses and thicknesses.

The present disclosure relates to a cone beam computed tomography (CT) imaging system, an image processing method, and a device therefor. An image processing method performed by a cone beam computed tomography (CT) imaging apparatus including a source and a detector that rotate around an object may include: obtaining a plurality of projection images projected on the detector for the object; obtaining a correction projection matrix; and reconstructing a three-dimensional image by back-projecting the plurality of projection images based on the correction projection matrix.

The invention relates to the technical field of medical equipment, in particular to a preset tooth cutting method and system based on image data, and the method comprises the following steps: S1, obtaining multi-modal image data by using CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and optical scanning technologies, and carrying out consistent analysis after preprocessing; and S2, performing automatic image segmentation by using a deep convolutional neural network (CNN). S3, optimizing the implant design through a generative adversarial network (GANs); s4, simulating and optimizing a cutting path through an augmented reality (AR) environment; and S5, laser micromachining is used for surface treatment of the implant. According to the method, the dentin cutting scheme matched with the specific shape and size of the bone defect area is designed through the data generated by scanning the three-dimensional shape of the complete tooth in advance, so that the image definition and the diagnosis accuracy are improved, the preoperative analysis is more accurate, and the subsequent personalized treatment design is effectively supported.

The disclosure provides methods of identifying a subject for treatment with an anti-ox-LDL antibody. The disclosure also provides methods of treating a subject with an anti-ox-LDL antibody, wherein the subject has been identified to as a candidate for anti-oxLDL antibody therapy. The methods described herein use computed tomography to identify subjects who are a candidate for anti-oxLDL antibody therapy, wherein a subject may be determined to be a candidate for anti-oxLDL antibody therapy by evaluating their fat attenuation index (FAI), perivascular adipose tissue (PVAT), pericoronary adipose tissue, FAI score, FAI percentile, or CaRi-Heart® Risk score.

The invention relates to a hypertrophic obstructive cardiomyopathy virtual resection operation curative effect evaluation method based on individualized computational fluid mechanics, which can give visual postoperative hemodynamic indexes, predict postoperative curative effects, find an optimal resection scheme and realize preoperative accurate intelligent evaluation. Comprising the following steps of: acquiring coronary artery enhanced computed tomography image data, acquiring a coronary artery enhanced computed tomography image of a patient, and outputting and storing the coronary artery enhanced computed tomography image in a DICOM (Digital Imaging and Communications in Medicine) format; preprocessing image data, and segmenting and reconstructing a left ventricle-outflow tract structure three-dimensional model; setting a virtual excision scheme at the fleshy part; constructing a surgical resection model, and importing the model into finite element analysis software for solving; and the virtual postoperative curative effect is visualized. Verification and curative effect prediction of a patient excision scheme can be realized, and the operation success rate of a hypertrophic obstructive cardiomyopathy patient is improved.

The invention relates to the technical field of medical image segmentation, in particular to a medical image segmentation method combining a convolutional neural network and a graph network, and the method comprises the following steps: forming a data set CoronarySet by using coronary artery CT angiography three-dimensional image data collected by a computed tomography (CT) device, and randomly dividing the data set CoronarySet into a training set CoronarySet 1 and a test set CoronarySet 2; the method comprises the following steps of: segmenting data of a training set CoronarySet1 into a plurality of patches; inputting the patchings into a double-branch parallel encoder to obtain a multi-layer coronary artery texture feature map and a multi-layer coronary artery topological feature map; constructing a feature fusion module, and fusing the texture feature maps and the topological feature maps of different layers based on an attention mechanism module to obtain fused blood vessel features; shallow layer features of a three-dimensional convolution module and fused blood vessel features are sequentially input into each layer of a segmentation decoder, a three-dimensional blood vessel structure can be extracted, and a blood vessel segmentation result is more continuous and more accurate.

The invention provides a low-dose computed tomography image reconstruction method and system. The method comprises the steps that a first conventional-dose CT image and a first low-dose CT image are input into an image output model for training; inputting the second conventional dose CT image into the training image output model to simulate degradation; storing the second conventional dose CT image and the second low dose CT image which are matched with each other into a training image set, and inputting the images in the training image set into the image reconstruction model for hierarchical training; the to-be-reconstructed low-dose CT image is input into the training image reconstruction model for image denoising reconstruction so as to obtain a reconstructed conventional-dose CT image, the performance of the image obtained by the method in noise level and structural definition can be matched with or even better than that of a real conventional-dose CT image, and thus the risk that a patient is exposed to radiation is reduced.

The invention relates to a glioma radiotherapy effect prediction method based on habitat analysis imageomics, and belongs to the technical field of medical image artificial intelligence and tumor radiotherapy, and the method comprises the steps: collecting a multi-mode MRI image, a radiotherapy plan computed tomography CT image, a dose image and a local recurrence label before postoperative radiotherapy, and converting the images into a standard format; sequentially performing craniocerebral boning processing, N4 bias field correction, isotropic resampling, gray level normalization processing and image registration on the acquired image to obtain a preprocessed image; based on the planned target volume PTV, dividing the interior of the PTV into three types of physiological structure habitat areas and three types of physiological habitat areas; image omics features of the multi-mode MRI images are extracted in the PTV and all habitat areas, and an optimal feature subset is screened out; taking the screened optimal feature subset as an input, taking the local recurrence label as an output label, and selecting an optimal model; and generating an output result by using the optimal model.

A system includes an attenuation corrector configured to generate Computed Tomography-(CT-) based attenuation correction data from CT image data, a Positron Emission Tomography (PET) reconstructor configured to reconstruct first PET image data based on PET projection data and the CT-based attenuation correction data, an attenuation correction artifact mitigator configured to analyze the first PET image data for a presence of attenuation correction artifact, an inference engine configured to predict attenuation correction data based on non-attenuation corrected PET image data in response to the presence of attenuation correction artifact in the first PET image data, and an attenuation correction data updater configured to generate modified attenuation correction data based on the CT-based attenuation correction data and the predicted attenuation correction data. The PET reconstructor is further configured to reconstruct second PET image data based on the PET projection data and the modified attenuation correction data.

The invention discloses a microscopic crack dynamic in-situ characterization method, and relates to the technical field of crack in-situ detection. According to the method, a plurality of hardened cement paste bodies are soaked in a sulfate solution, the porosity development trend of the hardened cement paste bodies is obtained through a nuclear magnetic resonance test and a mercury injection test in the soaking process, a test sample and a test sample are prepared after soaking is finished, and the crack propagation length of the test sample is measured through a scanning electron microscope. According to the distance gradient between the sampling angle of the test sample and the exposed surface, space coordinates of crack cracking are converted into a time process along the center axis of the test sample, a delay microscope is established on the microscale, and the test sample is scanned by using X-ray computed tomography equipment in a matched mode; a crack propagation animation image is formed based on a crack form between adjacent X-ray tomography slices, and a crack development process is obtained by continuously capturing high-resolution images, so that omission of time information is avoided, and in-situ detection of microscopic cracks in a hardened cement paste sample is realized.

The invention relates to a computer-based method for gradual subdivision of large computer-aided graphic design (CAD) and part models using different principles to analyze production defects and their criticality levels, and to perform quality and life prediction of the final product.

The invention relates to the technical field of medical data processing, and discloses a non-invasive hemodynamic assessment system for coronary artery stenosis degree, comprising an image acquisition module used for acquiring a coronary artery computed tomography angiography image of a patient; the physiological signal acquisition module is used for synchronously acquiring physiological signals of a patient, and the physiological signals comprise electrocardiosignals and non-invasive arterial blood pressure signals; and the data processing module is used for preprocessing and segmenting the coronary artery computed tomography angiography image. Electrocardiogram and non-invasive arterial blood pressure signals are collected through the sensor, risks and complications caused by invasive examination of traditional coronary artery radiography are avoided, the problem that patients unsuitable for invasive examination such as contrast agent allergy and renal insufficiency are difficult to evaluate is solved, the pain of the patients is relieved, and the acceptability of the patients is improved.

A computer tomography scanner (102) includes a radiation source (112) configured to emit x-ray radiation, a detector array (116) configured to detect x-ray radiation and generate a signal indicative thereof, and a reconstructor (118) configured to reconstruct the signal and generate sequential spares time line perfusion volumetric image data. The computer tomography scanner further includes a processor (132) configured to process the sequential spares time line perfusion volumetric image data using a trained neural network of a perfusion data enhancing module (136) to produce sequential dense time line perfusion volumetric image data.

Systems and methods for reconstructing medical images based on motion estimation are disclosed. Measurement data from positron emission tomography (PET) measurement data, and modality measurement data from an anatomy modality, such as computed tomography (CT) data, is received from an image scanning system. A trained deep learning process is applied to the PET measurement data and the modality measurement data to generate displacement vector field (DVF) data characterizing motion between the PET measurement data and the modality measurement data. A modality image is reconstructed from the modality measurement data, and the modality image is adjusted based on the DVF data. A PET image is then reconstructed from the PET measurement data and the adjusted modality image, and the PET image is adjusted based on a computed inverse of the DVF data. The adjusted PET image and the modality image spatially match, and are displayed.

The invention discloses a human body meridian point visualization method based on intelligent AI and a visualization teaching system, and relates to the technical field of human body meridian points. According to the human body meridian point visualization method based on the intelligent AI, a three-dimensional virtual human body model is constructed based on magnetic resonance imaging and computed tomography; acupoint positioning and meridian drawing are carried out on the constructed three-dimensional virtual human body model, and visualization is carried out; relative spatial position relations of human meridians, acupuncture points and human tissues are displayed based on a quantitative mixed reality technology, effective integration of advantage information of two images is realized on an image level, a foundation is laid for obtaining comprehensive and accurate human tissue and organ structure data, and a model is established on the aspect of modeling. The problem that a complex human body structure and a space relation are difficult to present in traditional modeling is solved, accurate labeling and visual display of meridian points on the virtual model are achieved in meridian point processing, and in a teaching system, interaction convenience and naturalness of a user and the model are improved.

Provided herein are methods, apparatuses, computer program products, and systems for anomaly detection using machine learning models. One method can include obtaining X-ray computed tomography (CT) scan data for a scan object of a predetermined object type; producing derived data from the X-ray CT scan data, wherein the derived data reveals at least one interior structure usable for anomaly detection in objects of the predetermined object type; inputting at least the derived data into a machine learning model, which has been trained using at least derived data produced from prior X-ray CT scan data for objects of the predetermined object type; receiving an output from the machine learning model that indicates that at least one anomaly has been detected for the scan object; and providing an output to a physical device based on the at least one anomaly having been detected for the scan object.

The invention discloses a method for accurately evaluating the erosion damage depth of hydraulic concrete, which relates to the technical field of concrete damage detection, and comprises the following steps: carrying out three-dimensional scanning on a surface damage area of an abraded hydraulic concrete test piece, and selecting random points to carry out drilling and coring operation so as to obtain a concrete core sample. According to the method, multiple detection means including three-dimensional scanning, computed tomography and microhardness testing are combined, the punching wear damage depth of the hydraulic concrete is accurately measured, and the damage form and depth information of the concrete surface is rapidly obtained through three-dimensional scanning; computed tomography deeply detects microcracks and pore distribution in concrete, microhardness test further accurately positions the damage depth, the limitation of a traditional detection method on detection precision is effectively overcome, more accurate damage depth data can be provided, and a reliable basis is provided for maintenance and repair of a hydraulic concrete structure.

The invention provides a tumor cell assisted positioning system and method based on a composite image, and relates to the technical field of medical image.The system comprises a data acquisition module used for synchronously acquiring radio frequency echo signals, ultrasonic images and computed tomography data of target biological tissue to generate an original fusion data set; the feature processing module is used for performing spatial registration and feature extraction on the original fusion data set to obtain a multi-dimensional feature set; and the identification imaging module is used for extracting a multi-band radio frequency echo signal intensity level value of the target area based on the multi-dimensional feature set, generating a tissue radio frequency characteristic diagram through logarithmic compression, and performing pixel-level fusion on the tissue radio frequency characteristic diagram and the synchronous ultrasonic image to generate a composite image. According to the method, the three-dimensional coordinates of the tumor boundary are determined through multi-modal data fusion and precise calibration in combination with anatomical structure constraints, and the accuracy of tumor cell positioning is improved.

X-ray systems, including apparatus, methods, and medium-encoded computer program products, to obtain computed tomography scan data (e.g., for limited angle computed tomography) include: a stationary X-ray source; and at least one detector configured to obtain limited angle computed tomography scan data of an object, the limited angle computed tomography scan data including projections of the X-rays after interaction with the object having been moved through an angular range with respect to at least one straight line between the stationary X-ray source and the at least one detector, and a conveyor system moves the object in a product handling line, a component of the conveyor system rotates the object around an axis that is normal or oblique to a travel direction of the conveyor system, and the scan data includes the projections of the X-rays after the interaction with the object having been rotated through the angular range around the axis.

A cross-regional and cross-view learning (C2RV) framework is provided for sparse-view reconstruction in cone-beam computed tomography (CBCT) by advantageously leveraging cross-region and cross-view feature learning to enhance representation of a point in 3D space before estimating an attenuation coefficient of the point. Specifically, multi-scale 3D volumetric representations (MS-3DV) are first introduced, where features are obtained by back-projecting multi-view features at different scales to the 3D space. Explicit MS-3DV enable cross-regional learning in the 3D space, providing richer information that helps better identify different internal anatomy structures. Hence, features of the point can be queried in a hybrid way, i.e. multi-scale voxel-aligned features from MS-3DV and multi-view pixel-aligned features from projections. Instead of considering queried features equally, scale-view cross-attention (SVC-Att) is used to adaptively learn aggregation weights by self-attention and cross-attention. Finally, multi-scale and multi-view features are aggregated to estimate the attenuation coefficient.

A computer-implemented method of providing plaque data (110) for a plaque deposit (120) in a vessel (130), is provided. The method includes: receiving (S110) computed tomography, CT, data (140) representing the vessel (130); generating (S120), from the CT data (140), a cross-sectional representation (150) of the vessel (130) at each of a plurality of positions (A-A′, B-B′) along the vessel; extracting (S130), from the cross-sectional representations, plaque data (110) comprising at least one measurement of the plaque deposit (120) at the plurality of positions along the vessel; and outputting (S140) a graphical representation of the plaque data (110).

Detection of landmarks in volumes of medical images is an important step in developing various diagnostic procedures. The invention pertains to a method detection of a landmark in a volume of medical images. Said volume of medical images can be selected from a computational tomography volume or a magnetic resonance volume. The invention includes a residual U-Net architecture to produce a heatmap that describes probability of finding the landmark which can be combined with a 3D Differentiable Spatial to Numerical Transform (DSNT) in order to obtain coordinates of the landmark. The invention was evaluated on two datasets: dataset containing 157 Coronary Computed Tomography Angiography (CCTA) scans and a dataset containing 77 Computed Tomography Angiography (CTA) scans. The proposed method provides a significantly improve accuracy of landmark detection, especially for ostium landmark detection, when compared to known methods.

The present disclosure provides a workpiece defect detection method, device and equipment and storage medium, relates to the technical field of defect detection, and can improve the accuracy of defect detection. The method comprises the following steps: acquiring a computed tomography (CT) image set corresponding to a first workpiece, the CT image set comprising a plurality of first CT images, different first CT images corresponding to different parts of the first workpiece; performing image cutting on each first CT image respectively to obtain a plurality of first cut images; in order to avoid the loss of feature information in the first CT image caused by the size change of the defect detection model on the first CT image with large size, the first CT image with large size is divided into a plurality of first cut images with small size, and then all the first cut images are input into a pre-established defect detection model, so that a defect detection result of the first workpiece can be obtained, and the defect detection model can accurately detect defects of the first workpiece based on the image features in the first CT image.

The invention relates to a low-dose computed tomography (CT) denoising method of a tight frame wavelet, and the method comprises the steps: S1, collecting a low-dose CT picture and a conventional-dose CT picture to construct a CT data set, and converting the CT data set into a Numpy matrix; s2, GTF (Geometric Tigh Framelet) transformation is carried out on the image data to obtain one low-frequency component and eight high-frequency components, the high-frequency components are grouped, and the number of the high-frequency components in each group is 2, 3 and 3; s3, creating a tight frame diffusion model (Tight Frame Diffusion Model), and carrying out the denoising of the low-frequency component; s4, a group high-frequency enhanced network (GHFEN) is created, and the high-frequency component is recovered by the group high-frequency enhanced network (GHFEN), and the high-frequency component of the high-frequency component is recovered by the group high-frequency enhanced network (GHFEN), and the high-frequency component of the high-frequency component is recovered by the group high-frequency enhanced network (GHFEN); and S5, finally, carrying out inverse GTF transformation on the enhanced and denoised components to obtain a denoised image. By using the CT image denoising method and the CT image denoising device, a CT image denoising service which is more in line with a human eye observation effect can be provided for a low-dose CT obtained by a user through a CT scanner. The multi-level feature perception low-dose CT denoising method can be widely applied to the field of CT denoising.

The invention relates to the technical field of animal experiments, and particularly discloses an experimental animal PET-CT examination auxiliary fixing device which comprises a fixing base, the upper surface of the fixing base is fixedly connected with a first supporting column, the upper surface of the first supporting column is fixedly connected with a first limiting plate, and the side surface of the first limiting plate is fixedly connected with two supporting limiting blocks. The experimental animal PET-CT examination auxiliary fixing device is provided with a supporting limiting block and a positioning sliding rail, when the experimental animal PET-CT examination auxiliary fixing device works, by sliding a second supporting column, a transmission rolling wheel at the lower end of the second supporting column is used for supporting, friction force is dropped through rotation of the transmission rolling wheel, and therefore practicability is improved; and a second supporting column is conveniently guided, so that the movement stability of the second supporting column is improved, the distance between a first limiting plate and a second limiting plate is conveniently changed, and the convenience of fixing different rabbits is improved.

The invention relates to a medical imaging system (1) comprising a computer tomography gantry (20), a carriage (F), a main rail system (L) and a safety rail system (S), - wherein the computer tomography gantry (20) is movably mounted by means of the carriage (F) and the main rail system (L) such that a translational movement of the computer tomography gantry (20) can be carried out along the main rail system (L), - wherein the carriage (F) and the main rail system (L) are designed to transmit a drive force for the translational movement of the computed tomography gantry (20) from the carriage (F) to the main rail system (L) in a force-locking manner, - wherein the safety rail system (S) comprises at least one safety rail (SL) and at least one coupling element (KO), wherein the at least one safety rail (SL) is arranged on a floor section and is aligned in the same direction as the main rail system (L), wherein the coupling element (KO) is arranged on the carriage (F) or on the computed tomography gantry (20), wherein the coupling element (KO) is connected to the safety rail (SL) by means of a rear grip and / or in a form-fitting manner to prevent the computed tomography gantry (20) from tipping over.

The processing circuitry acquires examination information related to a PCCT scan, the examination information including generation purpose information indicating a generation purpose of an image and generation process information indicating a generation process of the image according to the generation purpose. The processing circuitry generates, according to the generation process, a PCCT image according to the generation purpose based on count data collected by the PCCT scan. The processing circuitry stores the examination information including the generation purpose information and the generation process information in a storage device in association with the PCCT image.

The invention relates to a scatter correction method, a cone beam computed tomography (CBCT) system and a computer storage medium. The method comprises the following steps: performing cone beam CT imaging on a to-be-measured object to obtain a first group of projection data of the to-be-measured object, and performing reconstruction according to the first group of projection data to obtain a first volume image containing scattering artifacts; performing fan-beam CT imaging on a plurality of different heights of the to-be-measured object to obtain a plurality of groups of fan-beam projection data of the to-be-measured object, performing reconstruction according to the plurality of groups of fan-beam projection data to obtain a plurality of fan-beam slice images, and using the plurality of fan-beam slice images as reference images without scattering artifacts; extracting a plurality of cone beam slice images from the first volume image; performing image registration on the plurality of fan-beam slice images and the plurality of cone-beam slice images; and training a scattering correction model based on deep learning by using the plurality of registered image pairs to obtain a trained scattering correction model.