1109 results about "Computed tomography" patented technology

The invention discloses a multi-modal large model assisted laparoscope soft tissue registration surgical navigation method and system. The method comprises the following steps: acquiring a preoperative CT (Computed Tomography) or MRI (Magnetic Resonance Imaging) image and an intraoperative laparoscope image, and converting to obtain a multi-modal feature sequence; inputting the obtained multi-modal feature sequence into a large model for fusion to obtain a cross-modal matching matrix; random sampling is carried out by using an RANSAC random sampling consensus algorithm, an error between corresponding point sets of two data is measured by using an Euclidean distance, an analytical solution is obtained through SVD decomposition, and a feature matching error is introduced to obtain an optimal rigid body transformation pose; performing non-rigid deformation local fitting adjustment on the optimal rigid body transformation pose residual error by adopting a parameterized deformation model in combination with learning prediction deformation, and performing loop execution to obtain a soft tissue organ real-time registration image; automatic, high-timeliness and high-precision real-time registration of soft tissues is realized under the multi-mode and multi-variable conditions, and reliable navigation and positioning support is ensured to be provided in a complex operation environment.

The invention relates to the field of image segmentation, in particular to a multi-modal nuclear medicine image segmentation method and system and a storage medium. Comprising the steps that image type classification is carried out on multi-modal medical examination images, focus area positioning is carried out, and CT (computed tomography) focus images and PET (positron emission tomography) focus images are obtained respectively; carrying out lesion geometric mutation form prediction on the CT fault lesion image, and carrying out multi-modal image fusion boundary correction on the CT fault lesion image and the PET lesion image to obtain multi-modal lesion image fusion boundary correction data; according to the multi-modal lesion image fusion boundary correction data, performing multi-modal lesion region segmentation combination optimization on the CT image and the PET image to obtain a lesion region segmentation combination optimization strategy; and carrying out image segmentation model construction on the lesion region segmentation combination optimization strategy based on a strategy gradient algorithm to obtain a lesion image segmentation model. According to the method, the image segmentation technology is optimized, so that the image segmentation technology is more perfect.

A method for characterization of coronary plaque tissue data and perivascular tissue data using image data gathered from a computed tomography (CT) scan along a blood vessel, the image information including radiodensity values of coronary plaque and perivascular tissue located adjacent to the coronary plaque, the method comprising quantifying radiodensity in regions of coronary plaque, quantifying, radiodensity in at least one region of corresponding perivascular tissue adjacent to the coronary plaque, determining gradients of the quantified radiodensity values within the coronary plaque and the quantified radiodensity values within the corresponding perivascular tissue, and determining a ratio of the quantified radiodensity values within the coronary plaque and the corresponding perivascular tissue; and characterizing the coronary plaque by analyzing a gradient of the quantified radiodensity values in the coronary plaque and the corresponding perivascular, and / or the ratio of the coronary plaque radiodensity values and the radiodensity values of the corresponding perivascular tissue.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

Agentic AI, Contextual AI, and Generative AI systems and methods are provided for quantitative or qualitative patient analytics, blood work analytics, medical image analytics (x-ray, ultrasound, CT Scan, MRI and the like), brain scan analytics, present or historical genetic analytics and the like, to be used to guide, assist or even replace some of these physician functions with improved patient outcomes or with an improved understanding of the source of ailments, the analysis of treatment efficacy or treatment side-effects for individuals or populations of individuals. Genetic information is used to create individualized or population-based analysis, diagnostics, or treatment plans with even better outcomes.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

The invention relates to the technical field of medical image processing, and discloses a human body abdominal fat analysis method based on a medical image, which comprises the following steps: acquiring multi-modal medical image data such as CT (Computed Tomography), MRI (Magnetic Resonance Imaging) and ultrasonic images, constructing a fusion database through multi-scale affine transformation alignment, de-noising by using a model based on a residual self-encoder, and enhancing a boundary in combination with Canny edge detection. A segmentation model is constructed based on an improved U-Net architecture, multi-modal features are fused, and a dynamic convolution kernel and a channel attention mechanism are used for optimization. A quantitative analysis model is established by adopting a multi-task joint learning framework, gradient conflicts are solved, and a lightweight sub-network is searched and generated. Carrying out uncertainty modeling on the segmentation model, and carrying out active learning annotation to optimize the performance. A multi-granularity feature fusion framework is designed, anatomical priori knowledge is embedded to construct an association graph, a structured analysis report is generated, abdominal fat can be accurately analyzed, and diagnosis and treatment of obesity-related diseases are assisted.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

A system for computer assisted navigation during surgery includes a computer platform that operates to register a target surgical area of a patient. In certain cases, a process includes: obtaining a pre-op CT image of a pelvic region of a patient and intra-operatively obtaining a point cloud data about the pelvic region with a navigated instrument, generating a 3D bone model which excludes non-targeted area such as a femur, and then merging the 3D bone model to the point cloud to register the target surgical area.

The invention discloses an automatic identification method, device and equipment for mandibular wisdom teeth, a medium and a product, and relates to the field of image processing.The method comprises the steps that an oral maxillofacial cone beam computed tomography image is obtained; segmenting and positioning the mandibular wisdom tooth region-of-interest in the oral and maxillofacial cone beam computed tomography image by adopting a pre-trained first deep learning model to obtain a mandibular wisdom tooth segmentation mask image, and cutting a mandibular wisdom tooth region-of-interest image from the oral and maxillofacial cone beam computed tomography image; adopting a pre-trained second deep learning model to perform three-dimensional segmentation on the mandibular wisdom tooth region-of-interest image to obtain a three-dimensional segmentation mask image; and according to the mandibular wisdom tooth region-of-interest image and the three-dimensional segmentation mask image, determining a mandibular wisdom tooth recognition result by adopting a pre-trained third deep learning model. The identification accuracy and the identification efficiency of the wisdom teeth of the lower jaw are improved.

This disclosure relates to systems, methods, and devices for plaque analysis, vessel and fluid flow analysis, and / or risk determination or prediction thereof. Some embodiments relate to determining fractional flow reserve (FFR) values. Some embodiments relate to determining FFR values using 3D-printed models. Some embodiments relate to identifying thin cap fibroatheroma based on analysis of computed tomography (CT) imaging. Some embodiments relate adjusting calcified plaque thresholds for CT images to address effects of calcium blooming.

A system for pancreatic cancer image segmentation based on a multi-view feature fusion network, comprising inputting acquired computed tomography (CT) image into network model, extracting shallow features of CT image to obtain shallow feature map; reconstructing the shallow feature map into code sequence and inputting into lightweight Transformer for extracting global dependency relationship; carrying out operations on the shallow feature map to obtain first feature map; fusing the global dependency relationship and the first feature map to obtain second feature map; discarding redundant information from feature map obtained through convolution operation of each layer of the network, and regarding top-level feature map with discarded redundant information as third feature map; and carrying out pooling operations on the third feature map to obtain predicted boundary circle of target region, adding regularization item to the predicted boundary circle to obtain reference boundary circle, then outputting result.

The invention discloses an intelligent auxiliary diagnosis system and method based on big data analysis, and relates to the field of intelligent auxiliary diagnosis. Original CT scanning data and electronic medical record data of a patient are processed in parallel, and a deep learning model is introduced to extract high-dimensional image features and text features; through constructing a knowledge prior relation matrix derived from clinical guidelines and expert rules, knowledge-guided cross-modal attention fusion is carried out on image features and text features so as to realize deep and accurate joint characterization of patient conditions, and then intelligent risk prediction and classification are carried out on the basis of the deep and accurate joint characterization. Thus, through deep coupling of data-driven feature learning and medical knowledge-driven logical reasoning, intelligent and logical integration of multi-modal medical information can be realized, and the accuracy and reliability of an auxiliary diagnosis model in a complex clinical scene are effectively improved.

The invention provides a model training method, a substance element density distribution determination method and device, electronic equipment and a storage medium, and relates to the field of physics and computers, in particular to application of physics and computer technologies in the field of radiation medical treatment. The model training method comprises the following steps: acquiring respective characteristic parameters of M scanning energy spectrums of computed tomography (CT) equipment; based on the M characteristic parameters and the element density distribution of the N sample materials, M sample CT images of the N sample materials under the M scanning energy spectrums are determined, and M * N sample CT images are obtained; constructing a sample data set based on respective M sample CT images and respective element density distribution of the N sample materials; the initial model is trained through the sample data set, a target model corresponding to the CT equipment is obtained, and the target model is used for determining element density distribution based on a CT image obtained through scanning of the CT equipment.

The invention relates to the field of intelligent irrigation, and discloses an intelligent irrigation system and method for a crop root system, and the system comprises a root zone sensing module which is used for collecting soil environment parameters through multi-mode soil sensor arrays disposed at soil layers of different depths, generating a sensing matrix, and transmitting the sensing matrix to a root zone sensing module; the sensing matrix is used for representing water potential, pH value, nutrient concentration and temperature information in each soil layer; and the root system density analysis module is connected with the root zone sensing module, and is used for constructing a root system density weight matrix based on root system space distribution information obtained by underground computed tomography, and multiplying the sensing matrix by the root system density weight matrix element by element to generate a root zone state vector. Accurate generation of regulation and control signals is achieved, the micro-fluidic execution module can adjust the irrigation flow and the fertilization proportion in real time according to the actual requirements of the root zone, and therefore it is guaranteed that the rhizosphere environment is always close to the optimal physiological interval.

A medical image processing method includes obtaining a first set of projection data by performing, with a first CT apparatus including a first detector with a first pixel size, a first CT scan of an object in a first imaging region of the first detector; obtaining a first CT image with a first resolution by reconstructing the first set of projection data; obtaining a processed CT image with a resolution higher than the first resolution by applying a machine-learning model for resolution enhancement to the first CT image; and displaying the processed CT image or outputting the processed CT image for analysis. The machine-learning model is obtained by training using a second CT image based on a second set of projection data acquired by a second CT scan of the object in a second imaging region with a second CT apparatus including a second detector with a second pixel size.

The invention relates to the technical field of computer tomography, in particular to a geometric calibration method and system for an equiangular fan-beam industrial CT (computed tomography) system, and the method comprises the following steps: building a geometric model of the equiangular fan-beam industrial CT system comprising turntable center offset, detector lateral offset and radiation source radial offset; projection data are obtained through two times of scanning before and after displacement of the rotary table; performing interpolation processing on the projection data, and calculating a projection address of a ray penetrating through the rotation center of the rotary table; and calibrating geometric parameters of the geometric model based on a projection address of a ray passing through the rotation center of the rotary table and the basic parameters of the equiangular fan-beam industrial CT system. The method is used for correcting the geometric artifacts in the CT reconstructed image and improving the quality of the reconstructed image.

Computed Tomography (CT) and Magnetic Resonance Imaging (MRI) are commonly used to assess patients with known or suspected pathologies of the lungs and liver. In particular, identification and quantification of possibly malignant regions identified in these high-resolution images is essential for accurate and timely diagnosis. However, careful quantitative assessment of lung and liver lesions is tedious and time consuming. This disclosure describes an automated end-to-end pipeline for accurate lesion detection and segmentation.

The invention provides a lightweight low-dose CT (Computed Tomography) (LDCT) image denoising method based on a Transform network, and belongs to the technical field of medical image processing. The method provided by the invention is called CT-Denomer, global context information in a CT image and local context details of complex space change can be captured, and a high-quality image is generated. A core of a framework of the CT-Denoimmer is a Transform module, and the CT-Denoimmer comprises two key components, namely a multi-head convolution transpose attention (MDTA) and a hybrid context feedforward network (MCFN). The MDTA module captures global context information in an image with linear computational complexity, while the MCFN module pays attention to multi-scale local static and dynamic context information through a series of enhanced context Transformer blocks (eCoT). Besides, an intelligent operation attention layer (OWALs) is introduced to cope with a complex and changing noise mode in the LDCT image, so that the network can optimize the denoising effect in diversified scenes. According to the method, the fine structure retention and noise suppression of the denoising result of the LDCT image reach advanced performance.

The invention discloses a method for quantitatively analyzing a left auricle occlusion effect based on a CT image. The method comprises the following steps: step 1, acquiring a heart CTA image; 2, performing three-dimensional reconstruction to obtain a heart three-dimensional image, and obtaining a left atrium three-dimensional image by using the left atrium segmentation model; 3, obtaining a left auricle occluder area by using the left auricle occluder segmentation model; 4, constructing a three-dimensional model of the left auricle occluder on the basis of the segmented left auricle occluder area so as to obtain a three-dimensional image of the left auricle occluder; 5, obtaining a three-dimensional plane of the left auricle plugging device, a front view of the plugging device and a left auricle opening plane; 6, left auricle occlusion postoperative evaluation parameters are extracted, wherein the parameters at least comprise the size, position and classification of residual shunt, the position of an occluder, the shoulder exposing position and height, the endothelialization degree of the occluder and the position of instrument-related thrombus; and 7, evaluating the left auricle occlusion effect according to the obtained evaluation parameters after the left auricle occlusion operation.

The invention belongs to the technical field of image processing, and provides an iconography priori knowledge-guided craniocerebral CT (Computed Tomography) various anomaly real-time detection method, which comprises the following steps of: acquiring a craniocerebral CT image sequence in real time; inputting the craniocerebral CT image sequence into a pre-trained anomaly detection model to obtain an anomaly detection result; the anomaly detection model comprises a backbone network used for extracting multi-scale features of a craniocerebral CT image sequence, the backbone network comprising a plurality of cascaded feature extraction modules, and at least one feature extraction module being a symmetric information attention module; a neck network and a multi-detection-head network; the symmetric information attention module comprises an opposite-side difference chart attention network or more than two cascaded opposite-side difference chart attention networks, a difference information acquisition unit and a feature enhancement unit. The invention also discloses a computer program product and an electronic equipment system. According to the method, the matching flexibility of the symmetrical anatomical structures on the two sides is improved, and the accuracy and reliability of craniocerebral CT image anomaly detection are improved.

The invention discloses a buried hill core porosity calculation method based on a high-precision repair CT image, and the method comprises the steps: S1, obtaining a micron CT three-dimensional gray image of a buried hill fracture reservoir plunger sample in a research region, and obtaining a three-dimensional digital core model of the buried hill fracture reservoir plunger sample based on the micron CT three-dimensional gray image; s2, obtaining nanometer CT three-dimensional grayscale images of the sub-samples at different positions; s3, establishing a buried hill fractured reservoir core image database under different resolutions, learning and training the image through a residual network model to obtain a high-precision repaired micron CT image, and simulating and calculating the porosity through threshold segmentation; and S4, determining the actually measured porosity of the buried hill crack sample through an indoor experiment, and comparing the actually measured porosity with the simulated calculation porosity. According to the method, the pixel boundary contact information of the micron CT image is recovered through the deep residual network model, the restoration quality of the image is improved, and the simulated porosity is closer to an actual value.

The invention provides a method for acquiring a three-dimensional model of an ankle skeleton of a human body by simulating load-bearing CT scanning. The method comprises the following steps: acquiring CT scanning data; uploading the CT scanning data to medical image processing and three-dimensional reconstruction software to obtain an initial scanning image; constructing a three-dimensional model according to the initial scanning image; and carrying out model verification on the three-dimensional model to obtain a skeleton three-dimensional model. According to the method, the high-resolution three-dimensional model of the ankle skeleton can be obtained, and reliable support is provided for medical research, clinical diagnosis and treatment of the ankle and research and development of medical instruments. Compared with a traditional method, the data obtained by simulating the load is more practical, the model quality is remarkably improved through the advanced technology and algorithm, and development of related research and application in the medical field is promoted.

An apparatus, system and method are disclosed for two-dimensional (2D) and three-dimensional (3D) imaging including ultrasonic imaging and treatment of tissue micropathology within a body. An apparatus may include two or more arrays of ultrasonic transducers with a volume of tissue or other region or object between the arrays. Groups of elements in the first array are pulsed together, with the pulses time-delayed (phased) to create ultrasonic or electric beam patterns that focus the beam energy through the tissue, region or object onto individual elements of the second array. Computed tomography (CT) techniques process the signals to reconstruct a 2D or 3D structure. Micropathology within a tissue may be treated by transmitting pulses from both arrays, by pulsing groups of elements together, and / or by phasing the pulses to focus on and ablate the region of micropathology.

Provided is a program, etc. capable of acquiring information related to an amount of body tissue from an X-ray image with high accuracy using a small number of cases. A computer acquires training data including an X-ray image of a target site and information related to an amount of body tissue obtained from a CT (Computed Tomography) image of the target site. The computer generates a learning model configured to output information related to an amount of body tissue of a target site in an X-ray image when the X-ray image is input using acquired training data.

The invention relates to the technical field of road material detection, in particular to a method for adjusting the mixing uniformity degree of a modulus agent in an asphalt mixture, and the method comprises the following steps: S1, weighing aggregate and a high modulus agent in proportion, preheating the aggregate, and stirring; s2, uniformly scattering a high-modulus agent into the preheated aggregate, mixing and stirring the high-modulus agent and the preheated aggregate to obtain an intermediate material I, and calculating the wrapping degree of the intermediate material I; s3, asphalt and aluminum powder are weighed in proportion, the asphalt is heated, then the aluminum powder is added, mixing is conducted, and a second intermediate material is obtained; s4, adding the intermediate material II into the intermediate material I, uniformly stirring, adding a proper amount of mineral powder, and continuously stirring to obtain a high-modulus asphalt mixture; s5, detecting the aluminum powder in the high-modulus asphalt mixture by using industrial CT (Computed Tomography) to obtain an image; s6, constructing a mixing uniformity prediction model, and predicting the AU; by predicting the optimal blending process parameters, the modulus adjusting agent is ensured to be uniformly blended in the asphalt mixture.

The invention discloses a composite material stress distribution analysis method and system and a storage medium. The method comprises the following steps: acquiring a defect detection image obtained by computed tomography or infrared thermal imaging; identifying a defect area in the composite material structure and extracting defect information; according to the defect information and the loading direction, calculating a defect-induced stress enhancement factor and a stress transfer function, and constructing a stress correction factor distribution diagram; establishing a reference finite element model ignoring defects, and calculating a reference stress distribution field; acting the correction factor distribution diagram on the reference stress field to generate a correction stress distribution diagram; combining defect identification errors and material statistical fluctuation to construct an uncertainty propagation model, quantifying the corrected stress diagram, and outputting a stress value interval map; and finally, jointly correcting the stress map and the interval map, and identifying a high-risk area.

A method and corresponding system of identifying coronary artery disease. The method comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The method also comprises producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and / or outer walls of the coronary arteries of the patient.