1654 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.

Methods and corresponding systems of identifying coronary artery disease. The methods 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 methods also comprise 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.

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 the invention comprises obtaining training dental CT scans, identifying individual teeth and jaw bone in each of these CT scans, and training a deep neural network with training input data obtained from these CT scans and training target data. A further method of the invention comprises obtaining a patient dental CT scan, identifying individual teeth and jaw bone in this CT scan and using the trained deep learning network to determine a desired final position from input data obtained from this CT scan. The (training) input data represents all teeth and the entire alveolar process and identifies the individual teeth and the jaw bone. The determined desired final positions are used to determine a sequence of desired intermediate positions per tooth and the intermediate and final positions and attachment types are used to create three-dimensional representations of teeth and / or aligners.

A method includes accessing a computerized tomography (CT) scan of a human subject and defining a first two-dimensional (2D) slice (200) of the scan and a second 2D slice (204) of the scan, so that the first and the second slices intersect in an intersection line (224) defining a desired trajectory. The method further includes overlaying on the intersection line an icon (250) representing an object used in a procedure on the human subject, and rendering a three-dimensional (3D) image (650) of the human subject, incorporating the overlayed icon, from the CT scan.

The invention discloses a cardiac interventional operation scene reconstruction method based on 3D ultrasonic imaging rendering. The method comprises the following steps: collecting a preoperative computed tomography or magnetic resonance imaging image as prior information; the real-time 3D ultrasonic image sequence is preprocessed; the method comprises the following steps of: performing voxelization on an image by combining prior information, converting image data into voxel representation in a three-dimensional space, and creating an initial 3D Gaussian function set; parameters of the initial Gaussian function are optimized through an iterative optimization algorithm, so that the difference between the reconstructed image and the actual image is minimized; in combination with interventional catheter shape constraint, adaptive density control of a 3D Gaussian function is alternately carried out; and rendering tiles one by one by adopting a Gaussian sputtering technology based on the tiles to realize real-time three-dimensional scene reconstruction and display. According to the method, prior information and interventional catheter shape constraints are combined, various structures in an operation scene are represented and simulated by using a 3D Gaussian function, and efficient rendering is realized through a tile-based sputtering technology.

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 a multi-source data volume fused coal reservoir fracture prediction model construction method, which comprises the following steps of: acquiring a coal reservoir coal sample of a well in a certain research area, and adopting a higher fracture strength value in core fracture strength and imaging logging information for a fracture surface strength non-overlapping part obtained by artificially describing the core fracture strength and the imaging logging information; generating a coal sample crack strength curve; establishing a fractal dimension curve; carrying out full-diameter CT (Computed Tomography) scanning on the coal sample, and carrying out quantitative calculation on the single-layer fracture rate to obtain a single-layer fracture rate curve; fusing the obtained three types of curves, and establishing a comprehensive indication curve; constructing a fault distance attribute body; extracting a seismic data body, simulating the correlation between the seismic data body and the comprehensive indication curve, and selecting a sensitive seismic body; constructing a crack comprehensive discrimination function body; and a coal reservoir fracture prediction model is constructed, and coal reservoir fracture prediction is carried out. According to the method, coring data, conventional logging, imaging logging, CT scanning and earthquake crack prediction data are integrated, and the prediction precision of the coal reservoir crack is improved.

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 provides a radiation therapy dose distribution prediction method and system based on deep learning, and relates to the technical field of medical image processing, and the method comprises the steps: collecting a data set containing a CT scanning image of a patient, radiation therapy plan parameters and actual dose distribution, taking an electron density map and a three-dimensional flux map as the input of a training deep learning model, and carrying out the prediction of the radiation therapy dose distribution. Taking the endangered organ mask image, the radiotherapy target area mask image and the three-dimensional dose distribution image corresponding to the CT scanning image of the patient as the output of the model, and training to obtain an optimal dose distribution prediction model; and predicting the three-dimensional dose distribution of the new patient by using the trained dose distribution prediction model. According to the invention, by using the data of the patient and the radiotherapy plan information in the past, the dose distribution can be predicted more quickly and accurately, the delineation dependence of the doctor on the region of interest is reduced, and the treatment efficiency and the result stability are improved.

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.

A two-stage multi-task learning based method is disclosed for automated lesion response assessment according to Response Evaluation Criteria in Solid Tumors (RECIST). Pre-processed venous phase CT scans are inputted into a convolutional neural network (CNN) and a transformer to obtain coarse lesion segmentation masks, which are then optimized by a multi-task learning framework that incorporates longitudinal CT correlation and clinical prior knowledge to generate difference maps, and adopts an ordinal regression strategy that encodes the ground truth of response outcome. Sum of the volume (SOV) -based response assessment and new lesion assessment results are combined to obtain the overall RECIST response, i. e., response, stable disease, and progressive disease.

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.

The present disclosure relates to a method for detecting a landmark of the head and neck based on artificial intelligence. The method includes an operation of obtaining an original computerized tomography (CT) image of a head and a neck of a patient, an operation of converting the obtained original CT image into a low-resolution CT image, an operation of detecting a first landmark by inputting the low-resolution CT image into a landmark detection model, an operation of detecting a peripheral area based on the detected first landmark of the head and neck, an operation of restoring an original CT image of the detected peripheral area, and an operation of detecting a second landmark by inputting the restored image of the peripheral area into the landmark detection model.

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 discloses a medical image automatic segmentation method of a U-shaped network based on channel mixing and a self-attention mechanism. According to the method, channel grouping and a disturbance strategy are introduced into a self-attention mechanism, so that the problems of channel redundancy, high operation cost and the like of a traditional self-attention method are effectively relieved; and the capability of extracting local space information and global channel information by the convolutional layer and the capability of extracting long-distance space dependence and modeling local channel information by the self-attention layer are combined more closely, so that the method has more comprehensive modeling capability for features. According to a network used in the method, firstly, an input medical nuclear magnetic resonance image or a computed tomography image passes through a down-sampling layer to preliminarily extract feature information and reduce the spatial dimension of the image, then a feature map is sent into a multi-level symmetric encoder-decoder network, and each level comprises a designed module; the module structure comprises a patch embedding module formed by depth separable convolution, a self-attention layer based on channel grouping and channel disturbance, and a bottleneck convolution layer formed by convolution layers of different dimensions. Wherein the patch embedding module is used for extracting position information, and the middle self-attention layer is used for extracting a long-distance dependency relationship and extracting local channel information through channel grouping and disturbance. The bottleneck convolution layer is used for exploring inductive bias and aggregating global channel information. Besides, in the jump connection process of the decoder, an efficient fusion module of channel and space cross attention is constructed, and the recovery capability of the feature information is further enhanced. The provided model is evaluated on a plurality of common data sets, and experimental results show that the method makes full use of channel information and spatial information between features, obtains a better segmentation result compared with an existing method, can be used for improving the performance of medical image segmentation, and has important application prospects and practical significance.

Disclosed herein is a method for training a system to segment a computerized tomography (CT) image, comprising receiving a plurality of data groups, each group comprising an image pair comprising a CT image, a magnetic resonance (MR) image and a segmentation mask for the MR image; training a first generator of a first generative adversarial network (GAN) using the image pairs, to generate a synthetic MR image from a CT image based on relationships between image features of the CT image and MR image in each image pair; and training a second generator of a second GAN to generate a synthetic mask for the synthetic MR image based on relationships between features of the synthetic MR image and segmentation mask of each data group.

A coronary artery disease (CAD) analysis system is disclosed that comprises a CAD analysis device and a user interface. The CAD analysis device is arranged to analyse received patient CT scan data and produce CAD analysis data indicative of presence and characterisation of coronary artery disease in the patient CT scan data. The CAD analysis data is indicative of at least one individual stenosis lesion on a coronary artery and a characterisation of the stenosis lesion, and the CAD analysis system is arranged to identify a start location and an end location of the individual stenosis lesion on the coronary artery. The user interface displays a model of coronary arteries of a patient based on the patient CT scan data, and the model is configured to visually indicate an individual stenosis lesion and the individual stenosis lesion characterisation to a user so that the user is able to identify presence and characterisation of each individual stenosis lesion based on the visual indication.

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.