289 results about "DICOM" patented technology

Systems and methods are disclosed for preserving patient privacy while allowing health data to be analyzed, managed, and stored in different geographical areas. One method for managing cross-border health data while preserving patient privacy includes: receiving a DICOM object from a hospital computing device for analysis; generating a unique case identifier for the DICOM object; validating the received DICOM object; if, based on the validation, the received DICOM object is valid, anonymizing the received DICOM object; updating the anonymous DICOM object to include the unique case identifier; compressing the updated DICOM object; and sending the compressed DICOM object to at least one data analysis web service(s).

The invention, which relates to the technical field of medical image processing and intelligent diagnosis, discloses an ERCP image intelligent identification system based on multi-modal fusion, comprising an image acquisition module, an image preprocessing module, a multi-modal fusion model module, a parameter measurement module, an early warning judgment module and a real-time interaction module. The image acquisition module is used for acquiring an ERCP radiography original image in a DICOM format, performing privacy information desensitization processing on the original image, and reserving pixel pitch and catheter size parameters in DICOM metadata; the system has the beneficial effects that privacy protection and parameter retention are realized through the image acquisition module, precise structure segmentation is realized in combination with a multi-modal fusion model, key indexes are automatically quantized by means of the parameter measurement module, intraoperative visualization is enhanced in cooperation with the real-time interaction module, and the accuracy of the system is improved. The problems that traditional ERCP analysis depends on experience and lacks quantitative standards and decision lags are effectively solved, and the method has the remarkable advantages that diagnosis precision is improved, operation time is shortened, and the medical resource utilization rate is optimized.

Systems and methods are disclosed for preserving patient privacy while allowing health data to be analyzed, managed, and stored in different geographical areas. One method for managing cross-border health data while preserving patient privacy includes: receiving a DICOM object from a hospital computing device for analysis; generating a unique case identifier for the DICOM object; validating the received DICOM object; if, based on the validation, the received DICOM object is valid, anonymizing the received DICOM object; updating the anonymous DICOM object to include the unique case identifier; compressing the updated DICOM object; and sending the compressed DICOM object to at least one data analysis web service(s).

The invention discloses a new-generation medical equipment integration engine, which comprises an equipment data standardization module used for automatically identifying and converting various equipment data formats; the equipment data standardization module can process various medical equipment data such as HL7, DICOM and MODBUS through a built-in multi-protocol analyzer and convert the medical equipment data into a uniform standard format, so that the consistency and interoperability of the data are ensured; the real-time data processing engine module is used for collecting, transmitting and processing the data of the medical equipment in real time; the intelligent analysis and decision support module is used for performing intelligent analysis on the data from the medical equipment through deep learning and time sequence analysis technologies; a micro-service architecture design module; and a multi-level security mechanism module. The device data can be deeply analyzed and predicted, the accuracy of diagnosis and treatment decisions can be improved, early warning can be achieved, and the safety of patients is ensured.

The invention provides an index construction method for a medical ultrasonic database, and relates to the technical field of ultrasonic data processing, and the method comprises the steps: reading a multi-frame image sequence from a DICOM ultrasonic database, and generating a diagnosis intention vector for each frame; analyzing the diagnosis report, and segmenting the report into a plurality of segments; lesion detection and segmentation are performed on each frame of image to obtain lesion information, and a cross-frame aggregation strategy is adopted to identify a unified lesion object; extracting a feature vector of each focus object to obtain a focus object feature vector; establishing an alignment mapping between the focus object and the report fragment by adopting triple constraints, and generating a focus report mapping table; and constructing a multi-layer index structure, and storing the focus report mapping table and the multi-layer index structure in parallel. According to the method, a multi-layer index structure comprising a metadata index, a text index, a focus object vector index and a cross-modal embedding index is formed, so that efficient organization and focus-level accurate retrieval of medical ultrasonic data are realized.

The invention relates to a hypertrophic obstructive cardiomyopathy virtual resection operation curative effect evaluation method based on individualized computational fluid mechanics, which can give visual postoperative hemodynamic indexes, predict postoperative curative effects, find an optimal resection scheme and realize preoperative accurate intelligent evaluation. Comprising the following steps of: acquiring coronary artery enhanced computed tomography image data, acquiring a coronary artery enhanced computed tomography image of a patient, and outputting and storing the coronary artery enhanced computed tomography image in a DICOM (Digital Imaging and Communications in Medicine) format; preprocessing image data, and segmenting and reconstructing a left ventricle-outflow tract structure three-dimensional model; setting a virtual excision scheme at the fleshy part; constructing a surgical resection model, and importing the model into finite element analysis software for solving; and the virtual postoperative curative effect is visualized. Verification and curative effect prediction of a patient excision scheme can be realized, and the operation success rate of a hypertrophic obstructive cardiomyopathy patient is improved.

The invention relates to a medical image file processing method and device and electronic equipment, and the method comprises the steps: obtaining a series of DICOM files; according to the metadata of each DICOM file, shared data and non-shared data of each DICOM file are determined, and the metadata of each DICOM file comprises patient information, equipment parameter information and spatial position information corresponding to the corresponding slice image; combining the non-shared data of each DICOM file to obtain combined non-shared data, and storing the shared data and the combined non-shared data in a first metadata field; pixel metadata corresponding to each slice image is stored in a second metadata field, and the pixel metadata comprises a gray slope, an intercept, a window width and a window level; merging the pixel data corresponding to each slice image, and storing the merged image data in an image data field; and combining the first metadata field, the second metadata field and the image data field to generate a medical image file.

The invention relates to the technical field of intelligent recommendation, in particular to an intelligent inquiry recommendation method and system, and the method comprises the following steps: extracting text organ word frequency features through TF-IDF, generating a matching degree matrix through cosine similarity, screening core features, analyzing positioning parameters through DICOM, constructing a co-occurrence matrix through collaborative filtering, screening associated feature pairs, indexing a recommendation library, and predicting a symptom path through LSTM. And generating a time sequence weight feature set and a PageRank iterative sorting recommendation table. According to the method, a multi-modal association system is constructed by fusing text features and image parameters, semantic tags and space coordinates are combined to filter and screen organ domain features in a collaborative manner, the matching precision of symptoms and resources is improved, a time sequence weight reconstruction model is adopted to capture symptom evolution features, and hidden state transition is adopted to enhance the disease course prediction capability; a three-dimensional decision model is constructed through feature node sorting, time, space and feature dimension unification is achieved, a personalized diagnosis and treatment scheme is optimized by fusing multi-dimensional features and dynamic weights, and the credibility and clinical applicability of a recommendation result are improved.

The invention provides a DICOM (Digital Imaging and Communications in Medicine) file sequence processing method, a medical image analysis method, system and device, a computer storage medium and a computer program product, the processing method comprises the following steps: loading a DICOM file sequence corresponding to a series of image sets obtained by tomography, and arranging according to a scanning sequence to obtain an initial image file queue; reading DICOM files from a specified position to form a key image file queue with a fixed length; and reading sequence-level DICOM metadata and image-level DICOM metadata for representing key features from the key image file queue, and arranging the sequence-level DICOM metadata and the image-level DICOM metadata according to a preset rule to obtain a sequence DICOM metadata queue with fixed length and fixed information items of each position. By adopting the scheme provided by the invention, the obtained metadata queue with the fixed dimension provides a data basis for subsequent deep learning network input, and improvement of the adaptability with the deep learning network and improvement of the analysis accuracy are facilitated.

The invention relates to DICOM image processing, in particular to a DICOM image preprocessing and quality control system based on Spark, and the system comprises a data collection module which collects a DICOM medical image from a data source, and automatically recognizes and extracts a key metadata field in the DICOM medical image; the index construction module is used for constructing a comprehensive and accurate index system according to key metadata fields of the DICOM medical images; the data standardization module is used for carrying out standardization processing on key metadata fields of the DICOM medical images, supporting version control and audit tracking on the key metadata fields and carrying out optimization processing on the DICOM medical images; the scoring module is used for carrying out objective quality evaluation on the DICOM medical image based on a multi-dimensional image quality evaluation model and calculating a corresponding comprehensive quality score; according to the invention, the defect that the DICOM medical images from different equipment manufacturers are difficult to efficiently preprocess can be overcome.

The invention provides a medical platform management system, and relates to the technical field of medical health. Comprising a multi-source heterogeneous data intelligent acquisition module, the multi-source heterogeneous data intelligent acquisition module is connected with an external system through a distributed data acquisition framework and collects and integrates multi-source heterogeneous data, and the multi-source heterogeneous data comprises but is not limited to medical text data, multi-modal medical image data and real-time medical sensor data; aiming at the problem, the multi-source heterogeneous data intelligent acquisition module designs an intelligent data interface adapter, supports multiple medical data transmission protocols such as RESTAPI, HL7FHIR, DICOM and the like, realizes data standardized mapping through an efficient data conversion engine, solves the problem of cross-mechanism and cross-system data intercommunication in a medical body, and improves the data transmission efficiency. And a foundation is laid for realizing real medical integration.

The invention discloses an intelligent endoscope system with a multi-mode imaging function, and relates to the technical field of medical equipment, and the intelligent endoscope system comprises an imaging module which comprises a white light imaging subunit, a fluorescence imaging subunit and an infrared thermal imaging subunit; the data preprocessing module comprises noise suppression, SIFT registration and normalization; the image fusion module is used for decomposing four layers in a Laplacian pyramid, and reconstructing after feature weighting; the intelligent analysis module is used for extracting multiple types of features, performing CNN-Transform model reasoning and performing historical case verification; the display control module is used for 4K display, AR labeling and touch operation; and the data storage module is used for AES-256 encryption, DICOM conversion and three-level permission. The diagnosis accuracy is improved through multi-modal fusion, lesions are analyzed and efficiently recognized, operation risks are reduced through operation navigation, safety is guaranteed through data double backup, the system is suitable for multi-cavity diagnosis and treatment, and clinical precise diagnosis and safe operation are assisted.

The invention relates to the technical field of medical imaging, and discloses a method for automatically discriminating and enhancing phases in CT scanning by an attention mechanism based on multi-instance learning, and the method comprises the steps: extracting a CT scanning image and DICOM information from a data set, and unifying the image size after HU conversion, truncation and linear zooming; obtaining a fixed number of slices by using direct or indirect random sampling and a cubic interpolation strategy; then, a pre-trained model is adopted to extract slice features, scanning level feature vectors are formed through attention aggregation, and finally prediction probabilities of four types of phases are output through a full connection layer and a Softmax module; the model is trained by adopting cross entropy loss and an Adam optimizer, the performance is evaluated through indexes such as precision, recall rate and F1-score, and a thermodynamic diagram is generated by combining gradient back propagation, so that visual visualization of a result is realized, and clinical judgment is assisted. According to the method, the CT scanning multi-phase can be accurately, rapidly and interpretably classified in a real clinical environment.

The invention provides a cervical cancer clinical target segmentation method guided by anatomical prior position information, and the method comprises the steps: employing the CT image data of a cervical cancer patient and a reference CTV as basic data, and calculating a group of corresponding distance information image data Dimg according to the basic data; a multi-task double-branch feature fusion network MTF-Net based on a UNet network is constructed; the reference CTV and the Dimg are used as references for training the MTF-Net; in addition, CT image data is used as input information of MTF-Net intelligent segmentation; wherein the reference CTV is the DICOM format data of the cervical cancer CTV, which is drawn and determined manually according to a professional guide and a process; the distance information image data Dimg refers to a distance information graph between a surrounding adjacent organ and the cervical cancer CTV. The method has the advantages that the anatomical structure position information of the cervical cancer CTV and the surrounding adjacent organs is fully utilized, the anatomical prior position information is obtained, and the accuracy and efficiency of cervical cancer segmentation are improved.

A method for processing data relating to a radiological examination of a patient by way of a determining device, comprises the steps of acquiring doses (Ci, ti) measured at a plurality of times ti, storing these time-stamped measurements of radiation doses, and acquiring at least one DICOM digital file containing information on the examination, wherein the method comprises the following steps: acquiring and storing at least one DICOM digital file delivered by the tomograph during or after a tomography; acquiring and storing time-stamped measurements of the doses detected via a scintillating fiber placed on the table, and time-stamped movements of the table; interpolating the measurements (Ci, ti) with data of the image (DICOM) in a common interpolated space and constructing a table (Ck, DICOMk) in the interpolated space; and determining a table of the average dose levels Tz in each slice T depending on the data (DICOMk, Ck).

The invention discloses a medical image optimization method and system based on vascular branch selective blurring. The method comprises the following steps: carrying out preprocessing and blood vessel enhancement on a three-dimensional angiography DICOM image; performing precise blood vessel segmentation by using an encoder-decoder network comprising a wide activation and residual cavity space pyramid module; identifying an interference branch which shields the target blood vessel structure at a specific working angle through blood vessel topology analysis; selective blurring repair is performed on the interference branches based on a Poisson equation and a bidirectional convolution LSTM to generate an optimized image which is visually unobstructed and keeps topological continuity. And a safety mechanism of virtual-real combined display and multi-angle plan planning is introduced, so that the reliability of surgical navigation is ensured. And finally, integrating the optimized 3D blood vessel model to a radiography system supporting real-time synchronization, and using the 3D blood vessel model as a road map. The clinical problem that the optimal working angle cannot be used due to blood vessel shielding can be effectively solved, and the precision and safety of an endovascular interventional operation are remarkably improved.

The invention relates to the technical field of medical image processing, in particular to a DICOM sequence intelligent normalization and sorting method and system, and the method comprises the steps: obtaining an original DICOM file; analyzing a key DICOM tag of the original DICOM file to obtain a direction tag and body position information, and based on the direction tag, realizing normalization based on a patient coordinate system and performing direction correction; projecting the coordinates of all the slices to a normal vector direction to reconstruct a layer sequence, and counting a slice interlayer spacing sequence to estimate a real interlayer spacing based on adjacent projection difference values; identifying and rejecting abnormal slices based on the abnormal comparison target label and the slice interlayer spacing sequence; and for a multi-sequence set of the same check, extracting a sorting feature vector corresponding to each sequence, and selecting a sorting rule in combination with scene complexity to obtain a final sorting key for representing a sorting result of the phase dimension and / or the time dimension. The method has the effect of solving the problems that existing DICOM sequences are inconsistent in direction, disordered in layer sequence, difficult in time phase recognition and the like.

The invention provides a lumbar cancellous bone CT value automatic calculation method and system based on deep learning, and relates to the technical field of medical image classification and segmentation. According to the method, firstly, lumbar vertebra CT images corresponding to a plurality of lumbar vertebra segments can be classified from a large number of CT images of a target object, and then threshold segmentation is carried out on the lumbar vertebra CT images based on image characteristics of the lumbar vertebra portions to obtain threshold segmentation images; then, the lumbar vertebra CT image serves as input, the threshold segmentation image serves as prompt information to be input into an image segmentation model, a lumbar vertebra cancellous bone area is obtained through segmentation, and finally the average pixel value of the lumbar vertebra cancellous bone area of each lumbar vertebra segment is obtained through calculation based on the segmentation result returned to the original Dicom-format lumbar vertebra CT image. And finally, obtaining a lumbar vertebra cancellous bone CT value result of each lumbar vertebra section. Therefore, the original Dicom data can be automatically processed, and the CT value of the lumbar cancellous bone can be accurately calculated through the deep learning model so as to assist doctors in diagnosis.

The invention relates to the field of electrocardiogram treatment, and discloses an individualized electrocardiogram operation process relying on iconography examination and an AI large model, which comprises the following steps: S1, chest CT image acquisition: a CT scanner is used for acquiring chest image data of a patient, the CT scanner can generate high-resolution multilayer CT images, and the data format comprises but is not limited to a DICOM format; s2, heart projection position analysis: carrying out image preprocessing, heart segmentation, heart center point calculation and heart edge positioning on the acquired CT image data; and S3, lead position optimization: generating a lead electrode position based on a heart projection position by using an AI large model. According to the method, the actual position and form of the heart of each patient in the thoracic cavity can be accurately determined through CT image acquisition and heart projection position analysis, and the optimized lead position is generated by utilizing the AI large model based on the individualized projection positions, so that the lead electrode can more accurately capture the electrical activity of the heart.

A system and method for determining a data quality of DICOM data are provided. Information related to a data ingestion of the DICOM data from a radiology device to a database may be received. Respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion may be determined using a set of data validation rules corresponding to a set of data ingestion stages of the data ingestion of the DICOM data from the radiology device to the database. A user interface that displays information related to the respective data qualities of the DICOM data at the set of data ingestion stages of the data ingestion may be generated.

The invention discloses a medical image intelligent diagnosis and three-dimensional visualization interaction method and system based on artificial intelligence and Unreal Engine. The system is composed of a data acquisition module, an artificial intelligence analysis module, a three-dimensional reconstruction module, a rendering interaction module and a report generation module. The method comprises the following steps: firstly, carrying out voxel normalization on a DICOM / NIfTI image; the improved 3D U-Net is combined with CBAM attention and WGAN-GP training, and a focus initial density field is output; an AI enhanced implicit voxel reconstruction (AEIVR) algorithm is proposed, an enhanced density field is generated by using position coding + MLP, and grid extraction is completed by using improved Marking Cubes of a dynamic threshold; high-fidelity real-time rendering is realized in combination with a Nand micro polygon and Lumen global illumination in Unreal Engine, and VR / AR interaction and virtual surgery (real-time updating of resection mask driving) are supported. According to the method, the problems of large artifacts and insufficient interactivity and real-time performance of existing two-dimensional output and three-dimensional reconstruction are solved, and the end-to-end segmentation-reconstruction-rendering-interaction-reporting process is realized. On typical CT / MRI data, the segmentation Dice is about 0.90-0.94, the reconstruction voxel resolution is 0.1-0.5 mm, the artifacts are reduced by about 20%-30%, the PSNR is improved by about 3-7 dB, and the rendering interaction delay lt is achieved; the system is suitable for scenes such as lung CT, brain MRI, orthopaedic X-ray and cardiac ultrasound, and can be expanded to preoperative planning, remote consultation and teaching.

The invention discloses an intelligent patient safety risk reminding method and system based on PACS and RIS, and the method comprises the steps: collecting the multi-source medical data of HIS, EMR, LIS and the like in real time through an HL7 and DICOM multi-protocol fusion technology, constructing a dynamic evaluation model covering the core risks of allergy, metal implants, infectious diseases and the like, and carrying out the native integration of a hierarchical reminding mechanism at PACS and RIS workstations. The system adopts a Bayesian network model to realize dynamic adjustment of risk levels, a differentiated reminding interface is embedded through a webpage style technology, and risk reminding of each level forcibly requires a doctor to confirm and process. And the closed-loop feedback module records the medical care operation and returns the medical care operation to a related system, and continuously optimizes the risk assessment model. The problems that an existing system is isolated in data, single in reminding function and incomplete in risk coverage are solved, the clinical risk management level is remarkably improved, and the medical accident rate is reduced.

The invention provides a lung CT image processing method and system based on multi-scale spatial semantic fusion, and the method comprises the steps: collecting a plurality of lung CT-DICOM images, and carrying out the quality inspection processing and format conversion, and obtaining an original sample image; performing preprocessing and lung parenchyma segmentation on the original sample image to obtain a standard sample image; determining positive and negative samples according to an image labeling result, performing random interference on the positive and negative samples to obtain an expanded sample image, and generating a training data set based on the expanded sample image and the standard sample image; the YOLOv12 network is improved based on an MSSF module and a C3K2MSSF module to obtain an improved YOLOv12-3D network, and the improved YOLOv12-3D network is trained through the training data set to obtain a target detection model; and inputting the to-be-detected CT image into the target detection model to obtain detection information. According to the invention, by improving a YOLOv12-3D network architecture, deep fusion of three-dimensional space features and multi-scale space semantic features is realized in a feature extraction stage, and the extraction rate and efficiency of small targets are significantly improved.

The invention discloses a clinical performance verification system based on an infectious lung disease diagnosis model, and relates to the technical field of medical field, and the clinical performance verification system comprises a data collection module which is used for collecting and processing data by adopting a standardization process according to data types of different dimensions, and carrying out cleaning and standardization processing on all multi-modal data, the system has the advantages that in the aspect of data collection, multi-dimensional data such as patient baseline information, laboratory detection and iconography are covered, standardization processing is carried out on all kinds of data, and the data collection efficiency is improved. For example, laboratory detection data are uniformly converted into international standard units, iconography data are stored in a DICOM format, metadata are added, data quality is ensured, the model evaluation module adopts a multi-modal fusion technology, image, text, structured data and other data are fused, and model problems can be found and optimized in time.

The invention relates to a spine bone structure and bone mineral density automatic measurement method based on a CT image. The method comprises the following steps: performing CT scanning on a phantom with known bone mineral density to obtain a real bone mineral density calculation formula; all CT images in the DICOM format are read, DICOM analysis is carried out, and three-dimensional CT value data and other main information of all the images are obtained; performing outer contour segmentation on the whole spine in the CT image by using a deep learning network to obtain an outer contour of each vertebral body in the image; the method comprises the following steps: carrying out preliminary segmentation on cortical bones and cancellous bones by adopting a dynamic threshold method aiming at each vertebral body, selecting a preliminarily segmented cancellous bone region, using a maximum connected domain algorithm in a three-dimensional direction, retaining a maximum connected region, and further measuring the bone mineral density, namely calculating overall topology parameters, the bone mineral density and the bone mineral content of the current vertebral body; calculating the rubbing parameters, the bone mineral density and the bone mineral content of the current vertebral cortical bone and cancellous bone; and measuring the content of the cross section of the cone.

The invention relates to a medical image retrieval method and system based on a DICOM protocol and a storage medium, by setting a unified proxy forwarding service and combining with an image prefetching and caching mechanism linked with a clinical business event, the configuration difference of a back-end heterogeneous PACS system is shielded, single access point connection of a client is realized, and the user experience is improved. The complexity of system deployment and maintenance is reduced; meanwhile, the image data are pre-stored in the cache, so that the retrieval waiting time of the remote image is shortened, and the data access performance and the user experience are improved; and centralized authority authentication and log auditing are realized through the agent layer, so that the controllability and the security of a cross-mechanism data sharing process are guaranteed under the condition of avoiding high cost of centralized storage of full data.

The invention aims to provide a treatment scene element positioning method based on a coordinate transformation diagram. The method comprises the following steps: acquiring DICOM data; creating a first root coordinate system according to the DICOM data and the positioning relation by adopting a first standard; positioning a DICOM patient coordinate system according to the patient orientation data; creating a second root coordinate system according to the patient coordinate system by using a second standard; and converting the first coordinate system into a second coordinate system according to affine transformation. According to the method, under the simple condition, any two coordinate systems in a coordinate transformation diagram can complete coordinate transformation through reversible affine transformation, and due to the fact that the reversible affine transformation is closed about multiplication operation, any two coordinate systems in the diagram can be subjected to multiplication operation through a shortest path solving mode; all coordinates of a product path are transformed, then coordinate transformation from a corresponding starting point to an end point is obtained, and then elements in a treatment scene are positioned in a coordinate system in any graph.

The invention relates to the technical field of image recognition, in particular to a medical examination information recognition method and system based on AI, and the method comprises the steps: collecting DICOM transmission counting, screening a density deviation path, selecting a highest value path for locking as a main use, calculating the feature cosine similarity through effective nodes of the main use path, and selecting a disease code with the highest similarity to generate a pathological classification result. According to the method, medical examination efficiency is optimized through dynamic path adjustment and multi-modal verification, DICOM transmission identification abnormal paths are monitored in real time, path distribution prevention delay is optimized in combination with parameters such as loads, hop counts and success rates, meanwhile, data consistency is verified through Hash chain type comparison, multi-dimensional features are extracted to be matched with a standard pathological library in a cosine similarity mode, and therefore the accuracy of medical examination is improved. According to the method, real-time resource scheduling and cross-level data verification are fused, feature cross verification is embedded, noise interference is reduced, and processing stability and pathological classification precision in a high-concurrency scene are improved.

The system brings together patient data both clinical records and imaging studies from disparate sources to the user workstation or mobile device in real-time and on-demand. In order to do so, the system needs to establish application layer connectivity utilizing HL7 or FHIR and DICOM for imaging. Once a secure connection is established, the system is able to search and retrieve records and present it to end user.

A display apparatus includes an input device, a processor and an output device. The input device is configured to obtain initial gray scales of a display image. The processor is couple to the input device and configured to adjust the initial gray scales of the display image by using a first display data look-up table (LUT). The output device includes a display module. The output device is coupled to the processor, and configured to output a color-adjusted display image. The color-adjusted display image meets the Digital Imaging and Communications in Medicine (DICOM) Grayscale Standard Display Function (GSDF) standard, and a value of a contrast response of the color-adjusted display image is less than or equal to 11%.