1027 results about "Compute tomography" patented technology

The invention provides a correlation analysis method for a lung CT image, and the method comprises the steps: obtaining a lung multi-modal image which comprises a computed tomography image and a positron emission tomography image, and carrying out the denoising, enhancement and standardization preprocessing of an original image according to the imaging principles and image characteristics of different modals; extracting a lung lesion region of interest from the registered multi-modal image, sketching and segmenting the lesion, and quantitatively analyzing the volume and shape geometric morphological characteristics of the lesion; based on a curative effect result, correlation between radiomics features and patient prognosis is analyzed, recurrence and metastasis risks and lifetime under different feature expressions are estimated, a curative effect prediction model is constructed in combination with image features and clinical indexes, and long-term individualized evaluation is performed on patient prognosis.

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.

Systems and methods for performing a medical imaging analysis task from PCCT (photon counting computed tomography) imaging data are provided. PCCT imaging data acquired from a PCCT imaging device is received. A plurality of PCCT virtual images is generated from the PCCT imaging data. A plurality of medical imaging analysis sub-tasks is performed based on the plurality of PCCT virtual images using a plurality of machine learning based networks. Results of the medical imaging analysis sub-tasks are combined to generate results of a medical imaging analysis task. The results of the medical imaging analysis task are output.

The invention discloses a three-dimensional finite element model generation and numerical simulation method and system for a wading engineering composite material XCT digital image, and belongs to the technical field of finite element analysis. Comprising the steps of collecting a composite material XCT image in an initial state and performing preprocessing; performing statistical analysis on the two-dimensional microstructure parameters; a virtual XCT image is generated based on the Pix2PixHD style migration network; the method comprises the following steps of: accurately segmenting each component region of a real XCT (X-ray computed tomography) image based on a Swinin-Transformer network; completing contour feature extraction and smoothing of the segmented image based on a mapping relation between pixels and nodes; constructing a space coordinate system and carrying out discrete derivation on all yarn center points of each segmented image; grid division is carried out based on a multi-material moving cube algorithm in a CGAL library, and a global cohesion unit is inserted; completing material attribute and local material direction distribution; and customizing proper boundary conditions, and carrying out numerical simulation based on a finite element method.

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.

There is provided a computed tomography imaging system including a gantry including a rotating member on a rotating side, a stationary member on a stationary side, and a data communication system. The rotating member on the rotating side includes an X-ray source configured to emit X-rays, an X-ray detector configured to generate detector data, a data storage unit configured to store the detector data, and processing circuitry configured to process at least part of the stored detector data to generate a processed data set. The stationary member on the stationary side is communicatively coupled to the rotating member on the rotating side, and the data communication system is configured to transfer the processed data set from the rotating member on the rotating side to the stationary member on the stationary side.

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.

Methods for designing and fabricating palatal expanders by modeling expansion of the palatal region based on material properties for one or more regions of a maxillary jaw determined from a cone-beam computed tomography (CBCT) scan of the maxillary jaw and / or internal topological information for the maxillary jaw determined from the CBCT scan. Also described herein are series of palatal expanders having a sequence and configured to be sequentially worn by a patient to expand the patient's palate in which the left maxillary portion and the right maxillary portion are progressively separated relative to each other, and the thickness of the palatal region changes.

The invention relates to an X-ray multi-contrast imaging image quality enhancement method based on feature prior learning, and belongs to the technical field of deep learning and X-ray multi-contrast imaging. The method comprises the following steps: acquiring low-image-quality absorption, phase and dark field contrast computed tomography images and corresponding normal-image-quality three-contrast computed tomography images by using an X-ray grating multi-contrast imaging system, and constructing a training data set; constructing a conditional generative adversarial network image quality enhancement model based on feature prior learning; using the training data set to train a conditional generative adversarial network image quality enhancement model; and using the trained conditional generative adversarial network image quality enhancement model to enhance the obtained low-image-quality absorption, phase and dark field contrast computed tomography image. According to the invention, the low-quality multi-contrast images obtained under different conditions can be recovered and enhanced, and the application of the X-ray multi-contrast imaging technology in biomedical clinic and frontier material science is promoted.

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 discloses an intelligent medical remote diagnosis and treatment system based on artificial intelligence, which adopts a convolutional neural network to extract ultrasonic, computed tomography and magnetic resonance imaging characteristics, adopts a long-short term memory network to extract electrocardiogram, blood pressure and blood sugar characteristics, and subsequently fuses the characteristics with an electronic medical record. And finally, the multi-modal local large model is utilized to diagnose the structured data. The specific implementation steps of the system are as follows: S1: data acquisition; s2, data preprocessing; s3, feature extraction; s4, data transmission; s5, performing diagnosis; s6, remote interaction; s7, data analysis; s8, manual verification is carried out; s9, updating the model; the defects that traditional medical image and physiological signal detection is long in consumed time and large in error are overcome, the problem that medical resources are not evenly distributed is effectively solved, and improvement of multi-mechanism cooperation and the overall diagnosis capacity is promoted.

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 the technical field of vehicle safety evaluation, in particular to a finite element model modeling method capable of reproducing chest torsion performance. Comprising the steps that the geometric contour of a thoracic cavity is extracted through a lying posture tomography image, the thoracic cavity structure comprises a sternum structure, a thoracic vertebra structure and a rib structure, the lobe posterior angle of the thoracic vertebra is corrected based on a standing posture X-ray computed tomography image, and a three-dimensional geometric model of the thoracic cavity is reconstructed; establishing a thoracic cavity finite element model through grid division and material attribute definition; establishing a spherical hinge between the thoracic vertebra transverse process and the rib head; assembling the thoracic cavity finite element model into a whole person element model; and carrying out parameterized model optimization. According to the technical scheme, the real anatomical structure and the chest response data of the multi-direction complex collision working condition can be combined to establish the high-biological fidelity finite element model capable of reproducing the chest torsion performance, so that the chest injury condition in the VRU traffic accident can be predicted more accurately.

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.

Systems and methods are provided for increasing a quality of images generated by a computed tomography (CT) system. In one example, an initial assessment of contrast timing and flow through different anatomical regions of a patient is performed, and based on the initial assessment, different visualization schemes are applied to the different anatomical regions of a reconstructed image, where each visualization is optimized for assessing a different anatomical region. In particular, color maps (e.g., heat maps and / or probability maps) and / or color overlays based on material decomposition information may be superimposed on contrast-optimized images, where the color maps accentuate a contrast between diseased tissues and healthy tissues. An automated report may be generated including a first visualization based on a first scan, and a second visualization based on a second, earlier scan, to show a progression of a disease of the patient.

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.

Systems and methods are provided for increasing a quality of images generated by a computed tomography (CT) system. In one example, an initial assessment of contrast timing and flow through different anatomical regions of a patient is performed, and based on the initial assessment, different visualization schemes are applied to the different anatomical regions of a reconstructed image, where each visualization is optimized for assessing a different anatomical region. The visualizations may increase a contrast between diseased tissues and non-diseased tissues of the different anatomical regions, and may include color maps (e.g., heat maps and / or probability maps) and / or color overlays based on spectral decomposition information. The different visualizations may be combined into a single 2D image, where each anatomical region (e.g., organ, bone, etc.) is displayed in high contrast, and where aspects of various anatomical regions may be highlighted or colorized to visualize specific information.

Various methods and systems are provided for reducing beam hardening artifacts and metal artifacts present in images generated with a dual-energy computed tomography (CT) scan protocol. In one embodiment, a method may include obtaining projection data of a region of interest (ROI) of a subject, the projection data acquired at two different x-ray tube energy levels, generating a first monoenergetic image at a first energy level from the projection data, generating a second monoenergetic image at a second energy level from the projection data, subtracting the first monoenergetic image from the second monoenergetic image to obtain a subtracted image, and outputting the subtracted image for display and / or storage.

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 discloses a computed tomography reconstruction method and a related device, and relates to the technical field of image reconstruction, and the method comprises the steps: obtaining original projection domain data; de-noising processing is carried out on the original projection domain data to obtain projection domain data after de-noising processing; inputting the denoised projection domain data into a computed tomography reconstruction model to obtain computed tomography of an image domain output by the computed tomography reconstruction model; and enhancing the computed tomography of the image domain output by the computed tomography reconstruction model to obtain the final computed tomography. According to the method and the device, the improvement of the efficiency and the effect of computed tomography reconstruction is realized based on projection domain data denoising, computed tomography reconstruction model and enhancement of a model result.

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.

According to the multi-domain perceptual contrast enhanced computed tomography image synthesis method and system and the electronic equipment, efficient synthesis from non-contrast enhanced images to high-quality contrast enhanced images is successfully achieved by adopting the 3D auto-encoder and the de-noising diffusion probability model, the calculation process is optimized, and the image quality is improved. Due to the introduction of multi-domain perceptual regularization, fine-grained enhancement is carried out on the image at different frequency bands, and the detail expressive force and texture consistency of the synthesized image are remarkably improved. Besides, automation of an image synthesis process is realized through a deep learning framework, manual operation is reduced, processing time is shortened, and a rapid, accurate and reliable technical solution is provided for the field of medical image processing. The technical innovation provides a new path for the generation of the CECT image, and promotes the development of the medical imaging technology.

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.