2709 results about "Medical imaging" patented technology

The invention discloses a medical image report generation method and system based on a large language model, and relates to the field of image report generation, and the method comprises the steps: firstly obtaining original image data and a clinical background text of a patient, and respectively extracting an image embedding vector and a background embedding vector; then, case retrieval based on priori knowledge is carried out by utilizing the embedded vectors, and K highly related historical case reports are screened out from massive historical data; and inputting the image embedding vector and the historical case report into an observation large language model, and outputting the image embedding vector and the historical case report in a structured JSON (JavaScript Object Notation) format. And finally, a large language model is written to integrate the visual evidence JSON, the clinical background text and the historical case report, and a final medical image report is generated. According to the mode, through a staged and multi-modal fusion mode, the problems of incoherent report logic, inaccurate information and the like are effectively solved, and the report quality and the generation efficiency are remarkably improved.

The invention provides an acute cerebral apoplexy early-stage intelligent screening and early warning method and system based on deep learning, and relates to the technical field of medical imaging and artificial intelligence, and the method comprises the steps: obtaining a multi-temporal angiography image of the brain of a patient through a pulse-type dynamic imaging scheme, extracting hemodynamic features through a double-flow feature extraction network, and obtaining a multi-temporal angiography image of the brain of the patient; and constructing a vasculopathy risk distribution diagram by combining a vascular structure analysis model, and marking a potential embolism risk area. Furthermore, in combination with tissue perfusion parameters obtained by multispectral imaging and brain tissue function parameters obtained by electroencephalogram signals, an ischemic penumbra region is identified by using a self-adaptive threshold segmentation model. And finally, based on the ischemic penumbra area, the tissue perfusion parameters and the brain tissue function parameters, calculating a risk level by using a hierarchical decision model with a space-time attention mechanism, and generating a corresponding clinical intervention scheme. According to the invention, the early accurate screening and risk early warning of acute cerebral apoplexy can be realized, and the clinical intervention efficiency is improved.

An apparatus, system, and method are disclosed for remediating a condition present in a patient's foot. In some implementations, a resection guide includes a bone engagement feature configured to engage at least a portion of at least one foot bone to position the resection guide. The bone engagement feature may be least partially determined based on a model of the patient's foot, which is defined based on medical imaging of the patient's foot. The resection guide further includes a resection feature that extends through the resection guide from a superior side of the resection guide to an inferior side. The resection feature is configured to guide a cutting tool to form a first osteotomy in a first bone along a first trajectory. Additionally, the resection guide includes a first bone attachment feature configured to engage the first bone and a second bone attachment feature configured to engage a second bone.

The invention relates to the technical field of medical diagnosis, and discloses a DCM early noninvasive analysis method based on multi-radiomics and serum markers. Collecting image data through a multi-modal medical imaging device, and collecting serum marker data through a blood detection device; respectively generating a radiomics feature set and a serum marker time sequence feature set by using a multi-scale feature extraction algorithm and a time sequence analysis model; fusing the features by adopting a dynamic weighted fusion strategy to generate a joint feature matrix; inputting the model into a pre-trained multi-task deep learning model to predict a DCM risk probability; and finally, based on a genetic algorithm, optimizing the diagnosis decision tree and outputting an early DCM diagnosis result. The method is noninvasive and accurate, and can effectively improve the early diagnosis accuracy of DCM.

The invention relates to a multi-source data fusion management method and system for medical record information, and the method comprises the following steps: carrying out the data collection of an electronic medical record system, a medical image workstation and an inspection information system of a medical institution, and obtaining an original medical record data set; performing semantic mapping and concept association on the original medical record data set based on a preset medical ontology knowledge graph to obtain a semantic association medical record information network; performing dynamic time sequence feature analysis on the semantic association medical record information network through a time sequence feature extractor to obtain a time sequence medical record feature sequence; and carrying out heterogeneous data fusion on the time sequence medical record feature sequence to obtain a unified medical record data view, so that the technical problem that the capability of deeply understanding and mining medical record information is limited due to the fact that an existing medical record management method usually ignores semantic relevance between data is solved.

Technology is described for augmenting medical imaging for use in a medical procedure. The method can include the operation of receiving an image of patient anatomy captured by a visual image camera during the medical procedure. An acquired medical image associated with the patient anatomy can then be retrieved. Another operation can be associating the acquired medical image to the patient anatomy. An augmentation tag associated with a location in one layer of the acquired medical image can be retrieved. A further operation can be projecting the acquired medical image and the augmentation tag using an augmented reality headset to form a single graphical view as an overlay to the patient anatomy in either 2D, 3D or holographic form.

The invention provides a CT guided liver puncture training method and system based on virtual reality, and relates to the technical field of virtual reality. A deformable liver model and a virtual CT reconstruction engine under respiration driving are constructed, needle body posture mapping and image fusion display are achieved by fusing an inertia-electromagnetic dual-mode sensor, path interaction control, tissue dynamic response and score feedback are supported, the scene difficulty is automatically adjusted based on a training result, and the accuracy and the reliability of the system are improved. Progressive puncture skill training of static breath-holding, shallow breath and free breath scenes is achieved, the sense of reality of training, operation feedback and teaching efficiency are improved, and the system is suitable for development and clinical teaching application of an interventional therapy training system under the guidance of medical images.

The invention belongs to the technical field of medical image assistance, and particularly relates to an auxiliary stereo imaging system and method for a complex chest trauma operation. By integrating multi-modal image data, real-time three-dimensional space capture, dynamic correction and risk score driven view adjustment, accurate assistance of the thymotomy operation process is achieved, the accuracy and safety of the operation are improved, the operation risk and operation difficulty are remarkably reduced, and the operation efficiency is improved. By acquiring and processing multi-modal image data in real time, a high-precision three-dimensional anatomical structure model containing thymus tissues, a blood vessel network and nerve distribution can be constructed, a visual surgical navigation view is provided for a doctor, meanwhile, a dynamic correction technology is adopted, thymus displacement parameters caused by thoracic cavity respiratory movement of a patient are monitored in real time, and the thoracic cavity of the patient can be accurately and accurately positioned. And on the basis, the real-time three-dimensional space data is dynamically adjusted, so that the spatial synchronization of the image data and the anatomical structure is ensured, and the navigation error caused by respiratory movement is effectively avoided.

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

An end effector state estimation system for use in minimally invasive surgery (MIS) applications performs sensing and prediction algorithms to determine and visualize the state of an implantable therapeutic device during deployment. The end effector state estimation system includes a flexible elongate member, a therapeutic device coupled to a distal portion of the flexible elongate member, a sensor that measures at least one parameter related to the deployment state of the therapeutic device, and a processor. The processor receives image data from an imaging system representative of the therapeutic device positioned within the body cavity of the patient and the at least one parameter from the sensor, determines the deployment state of the therapeutic device based on the image data and the at least one parameter, outputs a graphical representation of the deployment state of the therapeutic device.

A method for identifying a log file for resolution of a fault of a medical imaging device, is provided. The method includes: obtaining log file browsing data describing one or more log files of the medical imaging device already viewed by a user to resolve the fault of the medical imaging device; obtaining problem data describing the fault of the medical imaging device; inputting the problem data and the log file browsing data to a machine learning algorithm, the machine learning algorithm being trained to predict, for each of a plurality of log files of the medical imaging device, and based on the browsing data, a resolution probability indicating a likelihood that the log file will assist in resolution of the fault of the medical imaging device; obtaining a prediction result from the machine learning algorithm in response to the inputting, the prediction result comprising a resolution probability for one or more of the plurality of log files of the medical imaging device; and identifying a log file for resolution of the fault of the medical imaging device based on the obtained prediction result.

The invention relates to an automatic image segmentation technology based on a convolutional neural network, and is suitable for the field of medical image and industrial detection. In order to solve the problems of rigid feature fusion, insufficient context capture, low efficiency of boundary optimization and poor small target segmentation precision in the existing method, an adaptive multi-scale feature fusion network is constructed: an encoder adopts a progressive expansion strategy and gated attention to intensify cross-scale features; the decoder optimizes hierarchical feature contribution through a dynamic weighted fusion module; the end-to-end boundary optimization is realized by integrating the lightweight differentiable CRF; and designing a composite loss function balance category weight. The segmentation recall rate of the fine structure is obviously improved by more than 18%, the boundary sawtooth rate is reduced by 41%, and the calculation efficiency is improved by 76%.

The invention relates to the technical field of super-resolution reconstruction, and provides an infrared image super-resolution reconstruction method based on noise decoupling, which comprises the following steps: S1, data input and preprocessing: down-sampling a high-resolution image to 64 * 64 or 32 * 32 low-resolution input through bicubic interpolation by adopting an FLIR infrared data set, and normalizing a pixel value to [0, 0]; the method comprises the following steps: 1) taking a low-resolution infrared image ILR adaptive to a neural network as subsequent input, and carrying out noise decoupling on the ILR; the method has remarkable advantages in the infrared image super-resolution reconstruction task, the details of the infrared image can be effectively recovered, and the resolution and quality of the reconstructed image are improved; the method is suitable for infrared image super-resolution reconstruction tasks of different imaging devices and noise levels, and especially has important application value in the fields of medical imaging, environment detection and the like with high image resolution requirements.

The invention belongs to the technical field of medical imaging and digital twinning, and discloses a thoracic cavity whole organ system level lung four-dimensional EIT detection digital motif and a library building method, and the method comprises the following steps: S1) segmenting multiple organs such as double lungs, heart, sternum, rib and thoracic vertebra central axis skeleton, main bronchus and esophagus based on an individual CT image, and building a three-dimensional geometric model integrated with an EIT electrode array; s2) simulating breathing dynamic deformation by using a solid mechanical model; s3) establishing a coupling relationship between deformation and conductivity, and generating dynamic three-dimensional conductivity distribution; s4) solving an electromagnetic field positive problem, and generating a virtual EIT voltage database synchronous with respiration; the invention also covers pathological model integration and data enhancement strategies. According to the method, a high-fidelity, large-scale and diversified EIT virtual data set can be generated, core data support is provided for training and verifying an AI intelligent reconstruction algorithm, and a model foundation is laid for developing a lung function dynamic digital twinning system.

The invention relates to the technical field of medical image processing, in particular to a medical image diagnosis method and system based on a cloud platform, and the method comprises the following steps: based on an input medical image, analyzing the texture density of a plurality of positions in the image, calculating the gray gradient change direction and rate of pixel points, and extracting boundary points of a focus region in the image; segmenting the lesion image to obtain lesion boundary data; according to the method, the accurate segmentation of the lesion area is realized by calculating the texture density of a plurality of positions in the image, and the spatial distribution of the lesion is predicted by using the tissue density value of the lesion area and the density change rate between adjacent pixels, so that the dynamic change of the lesion area is quantified, and the dynamic image sequence is analyzed; the lesion activity level grading is realized, the lesion development trend is more intuitive, the resolution of the image loading area is dynamically adjusted in combination with real-time user operation and the network state, the remote image access experience is optimized, and the image calling is more efficient and smoother.

An apparatus for synchronizing a three-dimensional model of a patient's coronary arteries with an orientation of a medical imaging device is disclosed. In an example, an apparatus is configured to receive an indication that a three-dimensional model has been rotated. The indication includes a number of degrees of rotation of the three-dimensional model along a roll and / or pitch axis. The processor uses a correlation or transfer function to determine a rotational angulation position of the medical imaging device based on the number of degrees of rotation of the three-dimensional model along the roll and / or pitch axis. The processor transmits a command or instruction to the medical imaging device that is indicative of the desired rotational angulation position, thereby causing the medical imaging device to rotate to the desired position.

The embodiment of the invention discloses a medical image tumor heterogeneity detection method and device. A specific embodiment of the method comprises the following steps: acquiring a brain glioma multi-modal medical image set from medical imaging equipment; performing image preprocessing on the brain glioma multi-modal medical images in the brain glioma multi-modal medical image set to obtain a processed medical image set; performing brain glioma region segmentation on the processed medical image set to obtain a brain glioma segmentation region set; performing high-order feature extraction and subregion division on the brain glioma segmentation region set to generate a high-order statistic feature map group and a tumor subregion image group; boundary optimization and topological repair are carried out on tumor sub-region images in the tumor sub-region image group, and a processed tumor sub-region image group is generated; and generating a tumor heterogeneity assessment report by using the processed tumor subregion image group. According to the embodiment, the automation level and precision of medical image tumor heterogeneity evaluation can be improved.

The invention relates to the technical field of osteoporosis diagnosis, and discloses an osteoporosis diagnosis method based on image recognition. The method comprises the following steps: acquiring bone density image data of a target object and bone structure image data of a target area based on medical imaging equipment, and determining a bone trabecula distribution condition of the target area according to the bone structure image data; constructing a three-dimensional bone mineral density model of the target object according to the bone mineral density image data, and determining an image analysis strategy by combining the three-dimensional bone mineral density model and the bone trabecula distribution condition; and an image analysis module acquires bone structure feature data of the target area according to the strategy, and identifies the bone density grade and the osteoporosis degree of the target area according to the feature data to obtain diagnosis data. According to the method, related data of the bone mineral density and the bone structure are integrated, comprehensive analysis is carried out in combination with the image recognition technology, an efficient and reliable new mode is provided for clinical diagnosis, the accuracy and objectivity of osteoporosis diagnosis are improved, and misdiagnosis and missed diagnosis are reduced.

The invention discloses a three-dimensional positron emission tomography data image classification method based on multi-stage progressive feature extraction, and is applied to the technical field of Alzheimer's disease auxiliary diagnosis. The auxiliary diagnosis method comprises the following steps: acquiring and preprocessing PET image data of an Alzheimer's disease patient; improving the reliability of the data set by using data enhancement; performing long-range dynamic modeling on the three-dimensional voxel sequence through a stacked Lmamba block; global context semantic adaptive fusion is realized through a layer-by-layer cross-scale channel attention fusion module (CSACF), and a channel spatial perception module (CSPM) is constructed to optimize spatial feature fusion; an inverted bottleneck module is mixed with long-distance space and position information to enhance the capturing capability of the model on detail features; and finally, predicting the disease category probability through global average pooling, full connection and softmax functions. According to the method, the precision of AD early diagnosis and MCI conversion risk prediction can be greatly improved, the defects of a medical image diagnosis method of a convolutional neural network (CNN) and Transform in long-range dependence on modeling and calculation complexity are overcome, and the method has good application prospects and is suitable for AD early detection and MCI conversion risk assessment.

The invention discloses an interactive image segmentation algorithm based on an algorithm computer, and belongs to the technical field of computer image segmentation. The interactive image segmentation algorithm comprises the following steps: S1, image uploading and preprocessing, S2, construction of a bidirectional interactive propagation mechanism, S3, adaptive image segmentation, and S4, image detection after segmentation. The interactive image segmentation algorithm is combined with lightweight semantic propagation, adaptive segmentation and a user feedback closed loop, the user interaction frequency is remarkably reduced while the precision is ensured, and compared with a pure deep learning scheme, the interactive image segmentation algorithm is more suitable for small sample scenes and is suitable for scenes with double requirements on precision and efficiency, such as medical images and industrial quality inspection; the algorithm can quickly adapt to a new field, a new category or an unknown object, reduces dependence on a large amount of training data in a specific field, minimizes user interaction times and complexity on the premise of ensuring high segmentation precision, can improve user experience, and has the characteristics of extremely simple interaction, ultrahigh precision, superstrong robustness and real-time response.

The invention discloses a medical developing marker and a manufacturing method thereof, and relates to the field of medical instruments, the medical developing marker comprises a closed tube, the outer wall of the closed tube is provided with a bionic barb structure, the bionic barb structure is distributed in a flexible zigzag convex manner, and the bionic barb structure and the closed tube are integrally formed through an injection molding process; a closed cavity is arranged in the closed tube, four developing strips are symmetrically arranged in the tube wall of the closed cavity, the tube wall of the region where the developing strips are located is etched to form a weak band, and a developing material layer and a sustained-release medicine layer are sequentially deposited at the weak band. The developing material layer and the sustained-release drug and / or marked nano-particle layer are alternately deposited to form a functional composite layer, and the functional composite layer can directionally rupture to release the therapeutic drug under the action of implantation pressure. The marker integrates the functions of precise development, tissue anchoring and drug sustained release, is adaptive to multi-mode imaging such as ultrasonic imaging, X-ray imaging, CT imaging, MRI imaging and PET-CT imaging, and has remarkable clinical value in precise positioning treatment of solid tumors such as breast cancer and liver cancer.

The invention relates to the field of medical image processing, in particular to a medical image intelligent analysis method. The method comprises the steps that original medical image data are collected and preprocessed, and an image matrix is obtained; respectively calculating a gradient value and a density value based on the image matrix, calculating a partition regulation and control value, and adaptively adjusting partitions; and on the basis of the adjusted partitions, calculating a feature matrix of the partitions, performing similarity clustering, calculating a clustering center of each cluster, performing abnormal partition detection, and generating an analysis report. The problems that traditional image analysis methods mostly depend on a fixed partition strategy, the size of a partition cannot be adjusted in a self-adaptive mode according to changes of local features in an image, and diagnosis accuracy is affected are solved; generally, a non-linear processing mechanism is lacked, the feature extraction process is easily interfered, and subsequent analysis and judgment are influenced. The problems that missing detection or false detection is easily caused and reliable diagnosis support cannot be provided due to the fact that an abnormal area is judged by simple threshold setting are solved.

Systems or techniques that facilitate electronic acquisition of troubleshooting knowledge for medical imaging scanners are provided. In various embodiments, a system can access a plain text service manual and a service complaint that are associated with a medical imaging scanner. In various aspects, the system can identify, via named-entity recognition or natural language processing, a semantic hierarchy of the plain text service manual. In various instances, the system can record, via graphical user interface tracking, how a service technician that is troubleshooting the service complaint sequentially navigates through the semantic hierarchy, thereby yielding a troubleshooting trace that corresponds to the service complaint.

The invention relates to the technical field of medical image analysis, in particular to a left ventricular compliance assessment method and system based on medical image.The method comprises the following steps that a planar three-dimensional coordinate from a left ventricular apex to a mitral annulus is obtained through a CT image, a thin-plate spline interpolation algorithm is called to establish a left ventricular long-axis model, the ultrasonic end-stage internal diameter is obtained synchronously, and a left ventricular compliance assessment result is obtained. And inputting the left greenhouse long axis model and the ultrasonic diastole end-stage inner diameter into an Euclidean distance calculation function to generate a left greenhouse length-diameter ratio. According to the method, a left greenhouse long axis model is constructed by integrating CT three-dimensional coordinates and ultrasonic inner diameter, the length-diameter ratio is calculated by combining Euclidean distance, point cloud registration is carried out, a myocardial wall area is segmented, local deformation is quantized by utilizing Gaussian curvature gradient, a correction factor is generated by inputting a radial basis function network, and a parameter coupling mechanism is established by fusing an aorta root angle. And optimizing the weight of the pressure-volume equation, establishing a dynamic model, and realizing a quantitative evaluation system of spatial heterogeneity and time continuity.

There is provided a method of performing a medical scan of a subject using a medical imaging system, the method comprising: a) initiating a medical scan session; b) performing, using the medical imaging system, a first image acquisition sequence during the medical scan session to obtain first image scan data; c) performing, using a computer system, image analysis on the first image scan data acquired during said first image acquisition sequence to identify one or more quantitative indicators of pathology and / or image quality; d) based on said identification of one or more quantitative indicators at step c), determining, using the computer system, whether any additional image acquisition sequences are required during said medical scan session; and, if so required: e) determining, using the computer system, a second image acquisition sequence based on the one or more quantitative indicators; and f) performing, using the medical imaging system, a second image acquisition sequence during the medical scan session to obtain second image scan data.

The invention provides an automatic focus identification and grading method for a medical image, and the method comprises the steps: carrying out the standardization of an obtained multi-modal original image based on anatomical constraint, and obtaining a standardized image; generating semantic enhancement features through a cross-modal feature compensation network based on the standardized image and associated radiological text description; performing dynamic feature adaptation processing on the semantic enhancement feature to generate a modal adaptive feature; performing context reasoning through a multi-scale feature interaction algorithm based on the modal adaptive features to generate context reasoning features; and lesion identification decoding processing is carried out on the context inference feature map, a lesion segmentation mask is generated, and the lesion segmentation mask is used for extracting lesion area feature parameters to carry out lesion classification. By adopting the method, the adaptability to the missing mode can be enhanced, and the focus identification and grading precision can be improved.

The present disclosure provides systems and methods for automated scan preparation and real-time monitoring / adjustment in medical imaging. The automated scan preparation may include positioning a target subject to be scanned by a medical imaging device, determining a rotation scheme of the medical imaging device, targeting a scan region of the target subject with an imaging isocenter of the medical imaging device, determining target position(s) of component(s) of the medical imaging device, performing a virtual scan, generating a reference subject model representing an internal structure of the target subject, or the like. The real-time monitoring / adjustment operations may include achieving automatic brightness stabilization, monitoring a posture of the target subject, adjusting the position of component(s) of the medical imaging device, estimating a dose distribution, monitoring a treatment of the target subject, performing a motion correction, or the like.

The invention discloses a spinal cord injury analysis method and device based on deep learning. The method comprises the following steps: acquiring processed medical image information of a spinal cord part and spinal cord injury physiological information; performing space-time alignment on the medical image of the spinal cord part and the spinal cord injury physiological information so as to obtain an image sequence and an index time sequence after space-time alignment; establishing space-time diagram features according to the image sequence and the index time sequence; splicing the space-time diagram features, the medical image information of the spinal cord part and the physiological information of the spinal cord injury to generate joint features; generating a cross-modal fusion feature; acquiring a trained spinal cord injury analysis model; and inputting the cross-modal fusion features into the trained spinal cord injury analysis model to obtain an injury result. MRI images and physiological indexes are synchronously collected, the limitation of traditional single-mode analysis is broken through, and dual-dimension information of an anatomical structure and functional metabolism is provided for spinal cord injury assessment.

Systems or techniques that facilitate knowledge graph construction via generative artificial intelligence are provided. In various embodiments, a system can access a plurality of electronic documents associated with design or fabrication of a medical imaging scanner. In various aspects, the system can construct a knowledge graph representing the plurality of electronic documents, by iteratively executing a generative text-to-text neural network on a design discovery tree associated with the medical imaging scanner. In various instances, the system can access a natural language query regarding the medical imaging scanner and can convert, via execution of another neural network, the natural language query to a structured query. In various cases, the system can execute the structured query over the knowledge graph, thereby yielding an electronic answer to the natural language query.

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.