345 results about "Compute tomography" patented technology

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-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).

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.

The invention relates to the field of medical image processing, in particular to a cross-modal medical image alignment method based on anatomical feature one-dimensional distribution, and the method comprises the steps: obtaining a computed tomography (CT) image sequence and a magnetic resonance imaging (MRI) image sequence of a hip joint region of a patient; extracting a CT one-dimensional anatomical feature sequence representing the morphological change of the first side femoral head from the CT image sequence; extracting an MRI one-dimensional anatomical feature sequence representing the morphological change of the first side femoral head from the MRI image sequence; based on the CT one-dimensional anatomical feature sequence and the MRI one-dimensional anatomical feature sequence, a similarity alignment process is executed; wherein the similarity alignment process comprises the following steps: determining an optimal similarity alignment offset between a CT image sequence and an MRI image sequence, and aligning the MRI image sequence and the CT image sequence according to the optimal similarity alignment offset. According to the method, the cross-modal image alignment problem is subjected to dimensionality reduction into one-dimensional anatomical feature sequence matching, so that the precision, efficiency and robustness of registration are remarkably improved.

The invention discloses a method for determining a hydrogen and water relative permeability curve of an underground hydrogen storage and related equipment, and relates to the field of underground space engineering.The method comprises the steps that a microscopic computed tomography image of a rock sample in the hydrogen flooding process is obtained; performing characterization unit analysis on the pore structure of the microscopic computed tomography image to construct a first pore structured grid model, the first pore structured grid model being used for characterizing the pore structure of the rock sample, and the first pore structured grid model comprising a coordinate system of the pore structure; by taking a coordinate system of the first pore structured grid model as a reference, mapping the wettability distribution data to the first pore structured grid model through the corresponding coordinate position to obtain a second pore structured grid model, the wettability distribution data is calculated along a three-phase contact line of rock, hydrogen and water extracted from the microscopic computed tomography image; and performing water-driven hydrogen two-phase flow simulation operation on the second pore structured grid model to obtain a hydrogen and water relative permeability curve of the hydrogen storage bank.

Noise-reduced images of a subject are generated from x-ray projection data acquired from the subject using a computed tomography (“CT”) system. A neural network or other machine learning algorithm is trained to receive images reconstructed from the projection data as an input and to generate an output as noise-reduced images. The neural network or other machine learning algorithm is trained using a self-training procedure, in which the training data used to train the neural network or other machine learning algorithm are generated directly from the projection data acquired from the subject using data augmentation (e.g., random rotations of the projection data and / or noise insertion).

A method, system, device and storage medium for measuring and classifying CT-FFR, dividing lesions into different types according to coronary artery computed tomography angiography (CCTA) images, determining lesion lengths at lesion positions according to different lesion types, and determining the lesion lengths at the lesion positions according to different lesion types. Then, the measurement range of computed tomography fractional flow reserve is determined according to the focus lengths of different lesion types, a statistical matrix is established to calculate the numerical values of all computed tomography fractional flow reserve CT-FFR in the measurement range, and finally, hemodynamic classification is conducted on calculation results. According to the CT-FFR evaluation method, the accuracy and the stability of CT-FFR evaluation are enhanced by carrying out pathological change specificity classification on the pathological change in the haemodynamics sense, interference is reduced, and a more reliable solution is provided for clinicians and patients.

The invention discloses a rapid prediction method for a particle thermal-mechanical coupling process, and the method comprises the steps: scanning energy carrier particles based on a microfocus computed tomography technology, and generating a three-dimensional digital model; importing the three-dimensional digital model into a multi-physics field simulation platform, and establishing a transient full-order model; based on the transient full-order model, generating a training data set and a test data set under different working conditions and corresponding temperature fields and stress fields; performing decomposition calculation on the training data set by using an intrinsic orthogonal decomposition POD reduced-order model to obtain a POD primary function and a spectral coefficient; establishing a mapping model from an input working condition to an output spectral coefficient based on a BP neural network; inputting the target working condition into the mapping model, and outputting a POD spectral coefficient; and on the basis of the calculation result and the POD spectral coefficient, reconstructing full-field physical quantity distribution at any moment under the target working condition through linear combination. The method solves the problems that traditional numerical simulation is low in calculation efficiency and large in resource consumption.

A deformation analysis device and a non-destructive analysis method for a secondary battery are capable of accurately diagnosing electrode deformation of the secondary battery using a computed tomography (CT) image. The deformation analysis method for the secondary battery includes performing computed tomography (CT) scanning on each of a first area and a second area of the secondary battery to acquire a first image corresponding to the first area and a second image corresponding to the second area, measuring a first angle from a winding front end to a winding distal end of an electrode of an electrode assembly in the first image and measuring a second angle from the winding front end to the winding distal end of the electrode of the electrode assembly in the second image, and determining that the electrode assembly is deformed if a difference value between the first angle and the second angle is greater than a reference value.

The invention relates to the technical field of cone-beam computed tomography, and discloses a scattering correction method and system for cone-beam computed tomography. The method comprises the following steps: acquiring original projection data and main scattering data acquired by a first flat panel detector; wherein the first flat panel detector is arranged in the emission direction of the radiation source; acquiring secondary scattering data acquired by a second flat panel detector; wherein the second flat panel detector is arranged in the scattering direction of the radiation source; determining target projection data based on the original projection data, the main scattering data and the auxiliary scattering data; the target projection data is used for cone beam computed tomography imaging. According to the method, scattering correction can be completed on the premise of not depending on special hardware, not additionally increasing radiation dose and not losing imaging view, and the imaging precision can be effectively improved.

The utility model belongs to the technical field of CT machine main bearing damping, and particularly relates to a high-speed CT machine main bearing damping device which comprises a limiting block. Connecting blocks are fixedly connected between the multiple limiting blocks. Inner rings of double-row angle bearings are fixedly connected to the side walls of the multiple connecting blocks. A connecting rod is slidably connected into the limiting block. The ends of the two connecting rods are fixedly connected with fixing blocks, and the other ends of the two connecting rods are fixedly connected with centrifugal blocks. A fixed convex block is fixedly connected to the side wall of the centrifugal block; clamping wheels are fixedly connected between the corresponding fixing protruding blocks, and the multiple clamping wheels are arranged corresponding to the size of the double-row angle bearing. According to the utility model, the plurality of clamping wheels are arranged to extrude the inner ring of the double-row angle bearing which rotates at a high speed, so that the inner ring and the outer ring of the double-row angle bearing are tightly attached, the vibration condition caused by a raceway structure error and a ball size error is further reduced, the negative influence of bearing vibration on CT imaging is favorably relieved, and the imaging quality of a CT machine is improved.

The invention discloses a multi-scale analysis-based gas-water seepage field simulation method, which specifically comprises the following steps of: based on a digital rock core reconstructed by a CT (Computed Tomography) scanning image of a typical rock sample of a target reservoir, adopting a microcosmic pore network flow simulation technology; the change rule of the stratum seepage capacity in different stratum pressure gradients and water drive processes, the use degree of a pore network and the water drive process in a pore are calculated, and then a correlation chart with the rock permeability and the pressure gradients is established and used for rapid measurement and calculation of the actual reserve use degree and the recovery degree of the water-invaded sandstone gas reservoir. The method starts from a rock microcosmic pore channel, the scale is amplified to a macroscopic oil reservoir during application, the method has the cross-scale characteristic, and the method is used for rapidly measuring, calculating and evaluating the reserve utilization degree and the recovery degree of the actual water-invaded sandstone gas reservoir and is beneficial to rapid development of the gas reservoir engineering scheme design in a mine field.

A method, system, and device for ultra-low-dose imaging of oral cone-beam computed tomography (CBCT) are provided. The method encompasses high-dose oral CBCT image data acquisition, data segmentation processing, three-dimensional (3D) reconstruction processing, model training, image data input, image enhancement, and image data output. Specifically, the process involves performing 3D reconstruction on 2D projection data of oral CBCT acquired in ultra-low-dose mode to obtain ultra-low-dose oral CBCT 3D reconstructed image data. This ultra-low-dose oral CBCT 3D reconstructed image data is then input into the oral CBCT ultra-low-dose imaging enhancement network model, from which high-quality oral CBCT image data is output for subsequent clinical diagnosis and treatment processes. This method significantly reduces the radiation dose of imaging while ensuring imaging quality, accelerates imaging speed, and enhances the safety of oral CBCT imaging, holding broad clinical application prospects.

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

Systems and methods include generation of a first image volume based on electrical signals output by energy-integrating photon detectors, input of the first image volume to a network trained to generate a simulated photon-counting CT image from an energy-integrating CT image, reception of a first simulated photon-counting CT image generated by the network in response to the input first image volume, and presentation of the first simulated photon-counting CT image on the display.

The utility model discloses an integrated die body for CBCT quality control calibration and performance detection, and relates to the technical field of medical imaging equipment, the integrated die body comprises a geometric calibration module, the outer wall of the geometric calibration module is provided with a plurality of steel balls, the plurality of steel balls are fixedly connected on the outer side surface of the geometric calibration module in a spiral step shape, and the steel balls are connected with the geometric calibration module. The left side of the geometric calibration module is in threaded connection with a CT value calibration module, a water mold is arranged in the CT value calibration module, a water injection hole is formed in the center of the inner side of the water mold, and the center of the left side of the water mold is in threaded connection with a water mold cover. Geometric calibration of the system is supported, accurate imaging is facilitated, quality control, calibration and quality detection functions are integrated, the quality control process of cone beam X-ray computer body layer imaging equipment is greatly simplified, operation convenience is improved, the structure is stable, and use is flexible.

A cone beam breast computer tomographic scanning system includes a vertical plane gantry and a patient support subsystem, where the patient support subsystem has a patient support saddle for patient positioning during imaging.

The invention discloses a system for evaluating coronary artery calcification points through photon CT non-electrocardiogram gated lung CT plain scanning, and relates to the technical field of coronary artery calcification point evaluation. Through the CT data acquisition module, the coronary artery calcification recognition module, the integral calculation evaluation module and the risk evaluation generation module, X-ray photons are directly counted, electronic noise is eliminated, the signal-to-noise ratio and the contrast noise ratio are improved, in conventional chest low-dose CT, electrocardio gating is not needed, and the cost is reduced. Automatic processing from low-dose lung CT flat scanning data acquisition to structured report generation is achieved, manual data transmission and intervention among all links are not needed, coronary artery calcification evaluation is fused into a conventional lung CT examination process, the extra burden caused by the fact that a patient needs to independently conduct coronary artery CT scanning is avoided, and the examination efficiency is improved; errors possibly caused by manual operation are reduced, consistency and reliability of evaluation results are ensured, and clinical examination and diagnosis processes are optimized.

A cone beam breast computed tomography scanning system includes a vertical plane gantry subsystem pivotally connected to a foundation element for ergonomic patient positioning.

One embodiment is a method of deconvolving overlapping first and second pulses in a photon-counting CT scanning system, the method comprising detecting a first pulse event having a first detected level; detecting a second pulse event having a second detected level; determining an amount of time that elapses between the detected first pulse event and the detected second pulse event; and reconstructing the first pulse and the second pulse using the first and second detected levels, the duration of time between the first and second pulse events, and a known pulse shape.

A method of analyzing computed tomography (CT) images comprises receiving an initial CT image of an object, identifying calcium-free regions and a calcified region in the initial CT image of the object, generating a calcium-free image patch, and applying the calcium-free image patch to the initial CT image patch to produce a final CT image. The initial CT image shows a calcium deposit and a target structure in the object. The calcified region in the initial CT image shows the calcium deposit in the object obscuring a portion of the target structure. The calcium-free regions show the remaining portions of the target structure. The calcium-free image patch corresponds to the calcified region in the initial CT image. The final CT image shows the calcium-free image patch and the calcium-free region from the initial CT image. The calcium-free image patch is generated and applied using a convolutional neural network.

Combining positron emission tomography (PET) scan 111 and computed tomography (CT) scan 121 of portion of body (i.e. lung) into combined image scan 132 for segmentation. Training machine learning for

Systems and methods are disclosed herein for using structure from motion (SfM) techniques to reconstruct three-dimensional (3D) surface models of tubular patient anatomies, such as the larynx and trachea, from clinical endoscopy videos. The disclosed methods may improve understanding of upper airway disease including vocal fold paralysis, laryngeal cancer, subglottic hemangiomas, subglottic stenosis, tracheal stenosis, tracheal cartilaginous sleeves, complete tracheal rings and tracheomalacia, and allow for quantitative analysis of complex laryngotracheal geometries. Quantitative measures of airway caliber and shape, which are critical for diagnostic purposes, may be obtained using the disclosed methods as a cost-effective and radiation-free alternative to relying on imaging studies. Results have demonstrated excellent resolution of reconstructions, when compared to high-resolution computed tomography (CT) scans (surface errors <0.300 mm).

Method for determining the position of windows in aortic endoprostheses, using images from preoperative CT computed tomography, wherein such endoprosthesis comprises a tubular sleeve (1) having a diameter slightly greater than the diameter of such aorta, on which one or more windows (2) must be made in correspondence with the centre of origin of a blood vessel branching from such aorta. The method comprises the following steps: a) on the axial image of such tomography, calculating the aortic arc of circle (AA) between the position (PB) of such centre of origin of the vessel with respect to a reference point (OA) taken on the circumference of the aorta, b) calculating a conversion parameter (P) dependent on the difference between the diameter of the aortic artery and that of the tubular sleeve (1), c) determining the position of the window (PF) on the prosthesis by calculating the arc of circle on the prosthesis (AM) between a reference point (OM) taken on the tubular sleeve (1), placed in juxtaposition to such reference point (OA) on the aorta (A), and such position (PF), by subtracting the value of such conversion parameter (P) from the value of the aortic arc of circle (AA).

The invention discloses an image point cloud extraction method for face registration, which comprises the following steps: S1, acquiring an electronic computed tomography image of the head of a patient to obtain a skin surface model of the patient, and converting the skin surface model into a point cloud; s2, transforming the point cloud obtained in the step S1 to a standard coordinate system; s3, roughly extracting the point clouds, clustering the point clouds obtained in the step S2 to obtain four clustering centers, and filtering out the point clouds meeting a first preset condition according to the four clustering centers; and S4, finely extracting the point clouds, clustering the point clouds obtained in the step S3 to obtain three clustering centers, and filtering out the point clouds meeting a second preset condition according to the three clustering centers. Using a clustering method to reversely determine an area which needs to be extracted and has unobvious feature points from a plurality of clustering centers of the obtained point clouds through the plurality of clustering centers; clustering and extraction are carried out step by step, the number of clustering points each time is controlled, and the influence of noise features is eliminated.

In the present invention, a cone-beam breast computed tomography (CBBCT) scanning system includes a vertical planar gantry and a patient support subsystem, wherein the patient support subsystem is equipped with a patient support chair for positioning a patient during imaging.

The utility model relates to the technical field of medical instruments, in particular to a CT (computed tomography)-guided pulmonary nodule positioning needle which comprises a puncture needle, a rotating component inserted into the top end of a needle handle and comprising a limiting block, an insertion hole formed in one end of a fixing sleeve, the needle handle inserted into the insertion hole, and a rotating shaft sleeved at the other end of the fixing sleeve. The limiting block is arranged on the upper portion of the rotating shaft, a positioning assembly is fixedly installed on one side of the limiting block and comprises a U-shaped fixing plate, the two ends of the U-shaped fixing plate are fixedly installed at the two ends of the limiting block, the supporting plate is welded to one side of the U-shaped fixing plate, and one ends of the vertical clamping plates are fixedly installed at the two ends of the supporting plate. The two ends of the transverse clamping plate and the other end of the vertical clamping plate are fixedly installed, the protractor is fixedly installed on the top of the fixing shaft, and a clamping assembly is fixedly installed on the other side of the limiting block. Through the use of the rotating assembly and the positioning assembly, the angle and the position of the puncture needle are accurately controlled, and the accuracy of pulmonary nodule positioning is improved.

The invention relates to a detection system (100) for a computed tomography system (200). The detection system (100) comprises a first detector (110) and a second detector (120), each detector being configured for detecting X-ray radiation of the computed tomography system (200) impinging on a respective detector. The detection system further comprises a repositioning mechanism (130) configured for repositioning at least one of the first detector (110) and the second detector (120) relative to the other of the first detector (110) and the second detector (120).

The invention belongs to the technical field of inorganic scintillator materials, and particularly relates to a multi-element co-doped yttrium lutetium silicate scintillator and a preparation method and application thereof, and the material has excellent comprehensive performance of high light output, short decay time, high radiation hardness, high matching degree of emission spectrum and novel photoelectric sensors and the like. The invention further relates to a preparation method of the scintillator material and application of the scintillator material in radiation detection fields such as positron emission tomography (PET), time flight PET (TOF-PET), interaction depth PET (DOI-PET), single photon emission computed tomography (SPECT), high-energy physical experiments, nuclear medicine imaging, industrial nondestructive inspection, safety detection (such as luggage and cargo container inspection) and the like.

The invention relates to a CT countercurrent prevention system and method, and relates to the technical field of countercurrent prevention, and the CT countercurrent prevention system comprises a polymorphic networking module which is used for maintaining network distribution connection and data transmission between a plurality of communication protocols and different devices according to a directional frequency wave algorithm, and obtaining original electric energy parameters; the electric energy detection module is used for cleaning original electric energy parameters of a power grid side and a load side to obtain standard electric energy parameters; the anti-countercurrent module is used for constructing a countercurrent detection model according to the historical electric energy parameters, monitoring standard electric energy parameters, outputting countercurrent risk electric energy parameters and generating countercurrent warning information; the abnormity regulation and control module is used for dividing the countercurrent risk electric energy parameters according to a preset countercurrent risk level, matching a corresponding countercurrent prevention strategy and generating a countercurrent prevention progress notice; the information sharing module is used for sharing the countercurrent warning information and the countercurrent prevention progress notification to the portable terminal and the cloud; high-precision rapid detection in a complex electromagnetic environment is realized, and the limitation of a single communication mode is broken through.