1673 results about "Medical imaging" patented technology

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 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 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 invention discloses an efficient medical image segmentation method considering global modeling and local enhancement, and relates to the technical field of image segmentation. According to the method, adaptive space shift operation is executed in different directions through the AS-MLP module, the long-range dependence modeling capability is effectively enhanced, and the recognition performance of a complex structure focus is improved; the channel and space double attention mechanism and multi-scale convolution of the LMCAM module are combined, so that fine-grained feature extraction is realized, and the segmentation precision of the lesion boundary and the small-scale structure is remarkably improved; a lightweight network design is adopted, the calculation complexity is low, the reasoning speed is high, and the method is suitable for resource-limited clinical terminals and real-time diagnosis application; besides, the method has good cross-modal adaptability, can keep stable and efficient segmentation performance in various medical imaging modalities such as CT, MRI, ultrasound and dermatoscope, and has wide application value.

Systems and methods for performing one or more medical imaging analysis tasks on PCCT (photon-counting computed tomography) images are provided. Image acquisition parameters of a PCCT image acquisition device are determined for acquiring PCCT images. One or more PCCT images of an anatomical object of a patient acquired using the PCCT image acquisition device configured with the image acquisition parameters are received. One or more medical imaging analysis tasks analyzing the anatomical object are performed based on the one or more PCCT images using one or more machine learning based models. Results of the one or more medical imaging analysis tasks are output.

The invention discloses an X-ray machine inspection parameter automatic configuration system for pet inspection, and relates to the technical field of medical imaging equipment, a displacement prediction model is used for pre-judging a pet convulsion state before exposure, a dynamic exposure trigger is only activated in a stable interval, and motion blur is avoided from the source; compared with the scheme of relying on post-exposure image feedback adjustment in the prior art, invalid radiation and repeated exposure operation can be avoided; the partition parameter mapping module is combined with a species feature database to map the gray level of the preview image into equivalent thickness and independently generate region parameters; aiming at extreme body type difference, such as an abdominal fat layer of an obese dog and a rib region of an emaciated cat, the system automatically distributes differentiated kV / mA parameters, and the problem of overexposure or underexposure caused by a traditional fixed penetration rate standard is eliminated; and the radiation fusing unit monitors the accumulated dose in real time, dynamically adjusts a safety threshold according to the weight of the pet, and stops exposure before the dose exceeds the limit.

The invention discloses a medical informatization data intelligent analysis system based on a large model, and belongs to the technical field of medical informatization and artificial intelligence, and the system comprises a data collection and standardization module, a medical knowledge graph construction module, a knowledge enhancement inference analysis module, a data quality evaluation module and a structured output module. The system collects multi-source heterogeneous medical data from an electronic medical record system, a laboratory information system, a medical image information system and a hospital information system, performs standardization processing, automatically constructs a medical knowledge graph, and performs intelligent analysis and reasoning on the medical data by using a knowledge retrieval enhanced medical field large language model. Meanwhile, the data quality is evaluated in four dimensions of integrity, accuracy, timeliness and relevance, and finally a structured analysis report is generated. The multi-source medical data can be effectively integrated, the accuracy and interpretability of medical data analysis are improved, and intelligent support is provided for clinical decision making.

A medical imaging system including an optical identifier camera for imaging a medical device, a medical device packaging, a patient, or a technician. The system further includes an imaging probe for imaging of a target location, and a console with optical identifier logic to extract identifier markers or characteristics from the image of the medical device, packaging, patient or technician. A parameter logic determines the medical imaging parameters based on the identifiers or characteristics, and a medical imaging logic captures medical images of the medical device. The system enables efficient and accurate imaging of medical devices on the specific patient, by a specific technician, enhancing diagnostic capabilities and treatment planning in medical parameters.

A solution for training a machine learning model (305) for use in medical imaging applications is presented. A corresponding method (400) includes providing (403-410) an incomplete sample set for each imaging process, each incomplete sample set including one (or more) sample target images and one (or more) sample baseline images for a sample target dose of contrast agent. One (411-445; 450-456) sample source images are simulated (411-445; 450-456) from each of the incomplete sample sets (or a portion thereof) to mimic contrast agent at a sample source dose that is lower than the sample target dose. One or more complete sample sets are generated (446; 457) for each imaging process by combining the incomplete sample sets with sample source images that have been simulated from other incomplete sample sets. The machine learning model (305) is then trained (458-473) using the complete set of samples. Further, a method of using a machine learning model in medical imaging applications is presented. A computer program (300) and a computer program product for implementing a method (400) are presented. Furthermore, a computing system (130) for executing the method (400) is presented. A corresponding medical method is also presented.

The invention discloses a lumbar disc herniation postoperative recurrence prediction system based on a multi-modal medical image, and belongs to the field of medical images. The lumbar disc herniation postoperative recurrence prediction model based on the multi-modal image is trained by using the lumbar vertebra image set, the feature true value corresponding to each image and the recurrence prediction label corresponding to each patient, and in the training process, model parameters are updated by using a stochastic gradient descent algorithm; and a trained lumbar disc herniation postoperative recurrence prediction model based on the multi-modal image is obtained. According to the method and the system, due to rich image features provided by the multi-modal image, the prediction network trained based on the multi-modal data shows higher accuracy in postoperative recurrence prediction of the lumbar disc herniation.

A method for obtaining a motion surrogate signal in medical imaging, the method comprising the steps of: receiving 102 a projection stack comprising a plurality of two-dimensional projections of a patient volume; selecting a plurality 104 of sub-volumes of the projection stack; extracting 106 a candidate motion surrogate signal from projection data contained within each sub-volume of the projection stack to create a plurality of candidate motion surrogate signals; and combining 108 the plurality of candidate motion surrogate signals into a single final motion surrogate signal. The final surrogate may be an average of the candidate set or their weighted sum, the weights possibly being noise-dependent. The method may be implemented iteratively. A reference signal, possibly a sinusoid may be compared to the candidate surrogates. Differences between sub-stacks may be computed. High or low-frequency variations may be removed from the signals.

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 relates to a method for processing medical images of human tissue of an area of a patient's body and in particular of the breast by means of a medical imaging device (1) comprising a microwave probe array consisting of K>1 probes spaced apart from one another, the array comprising P>1 different configurations defining transmitting probes and receiving probes for one or more position(s) around the area, in which the transmitting probes are configured to transmit microwave signals so as to illuminate an area of the body and the receiving probes are configured to receive microwave signals after scattering and reflection in the area, the probes being capable, in a complementary manner, of being configured to transmit and receive simultaneously.

The invention relates to a medical diagnosis method and system based on multi-modal retrieval enhancement and guide guidance. The method comprises the steps that text information including reports and / or electronic health records and medical image information are obtained; encoding the medical image information and the text information by using an image encoder and a text encoder respectively to obtain visual features and text features; respectively utilizing a guide branch decoder and a label branch decoder, taking diagnosis guide features and disease type labels of samples in the training stage as queries of a Transform structure, taking splicing features obtained by splicing text features and visual features as keys and values, and decoding to obtain first prediction probability distribution and second prediction probability distribution of disease types, so as to obtain first prediction probability distribution and second prediction probability distribution of the disease types; and a final disease prediction result is obtained. According to the method, disease specificity knowledge is dynamically retrieved based on a multi-source medical knowledge base, redundancy and noise are removed through a large language model, a standardized and structured diagnosis guide is generated, and explicit guidance of the knowledge is achieved.

The invention discloses an AI simulation method and system for facial cosmetic plastic surgery, and the method comprises the steps: for a specific region with high feature complexity, generating a feature subset containing fine feature distribution through high-performance node distribution processing, combination with an adaptive feature extraction algorithm, and dynamic adjustment of extraction frequency and segmentation granularity; on the basis, a three-dimensional reconstruction algorithm is adopted to dynamically adjust the splicing weight, particularly, the splicing precision is improved and a high-precision three-dimensional model is generated for an area with relatively thin tissue thickness, and finally, a vivid effect simulation diagram is obtained through rendering parameter adjustment driven by a user demand and preferentially rendering a height modification area. According to the method, through adaptive parameter adjustment and fine processing, the modeling precision and rendering efficiency of the complex biological characteristic data are remarkably improved, and the method is suitable for medical images, biological recognition and other scenes.

The invention discloses a multi-modal medical image diagnosis method and system based on an AI agent. The method comprises the steps that multi-modal medical image data input by a user and a natural language diagnosis request are received; the method comprises the following steps of: performing semantic analysis on a diagnosis request of a user by utilizing a large language model (LLM), and disassembling the diagnosis request into a plurality of sub-tasks with task category labels; mapping each sub-task to a corresponding AI agent in a preset AI agent resource pool according to the task category label, and generating a corresponding sub-task result; aggregating the plurality of subtask results, and performing quality verification to generate a final diagnosis result; and feeding back the final diagnosis result to the user, receiving feedback information of the user, and if an indication result fed back by the user does not conform to the expectation, repeating the steps according to the feedback information until the user feedback is satisfied. According to the invention, the defects of single interaction, low intelligent level and insufficient processing efficiency of the existing medical image system are overcome, and efficient, accurate and reliable intelligent auxiliary diagnosis is realized.

The invention discloses a robust federated learning method for processing heterogeneous noise and non-independent identically distributed data, and belongs to the technical field of federated learning. The method provides a robust learning framework of two-stage client quality perception. The method comprises the following steps of: 1, constructing a category-level loss vector and clustering by using a Gaussian mixture model, and accurately dividing a clean and noise client set; stage 2, performing differential training: performing standard training on the clean client; dual-network cooperative training, dynamic sample screening and exchange, and a self-distillation and entropy regularization mechanism are introduced to a noise client, so that robust learning is realized; in the global aggregation stage, a distance sensing weighting strategy is further adopted to dynamically suppress the influence of a noise client; according to the method, original data does not need to be shared, the robustness and generalization performance of the federated learning model in the coexistence environment of heterogeneous noise and non-independent identically distributed data can be effectively improved, and the method has wide application value in the fields of medical images, financial risk control and the like.

The invention discloses an absolute quantitative correction method and device for medical imaging equipment, and relates to the technical field of nuclear medical imaging equipment correction. When the method is executed, the actual activity concentration of the calibration die body injected with the first known tracer activity serves as the first activity concentration; then, the calibration die body injected with the first known tracer activity is collected in medical imaging equipment, and the activity concentration in a first image domain obtained through calculation serves as second activity concentration; and finally, calculating a correction factor based on the first activity concentration and the second activity concentration, wherein the correction factor is used for correcting absolute quantification of the medical imaging equipment. Therefore, the correction factor is calculated by comparing the activity concentration calculated by the medical imaging equipment with the actual activity concentration of the calibration die body, so that the medical imaging equipment can accurately adjust the measurement result according to the correction factor during subsequent absolute quantitative analysis; the effect of improving the absolute quantification accuracy of the medical imaging equipment is achieved.

The invention discloses a wavefront coding microscopic system depth-of-field expansion joint optimization method based on deep learning, and belongs to the field of microscopic medical imaging, and the method comprises the steps: collecting a clear focusing image for neural network model training and testing; a defocusing phase is calculated according to corresponding optical system parameters, and defocusing blurring simulation is carried out on the clear focusing image; correcting a mask matrix by utilizing learnable aberration, and updating data in a training process to correct out-of-focus aberration; calculating a joint point spread function through the out-of-focus correction mask, and generating an out-of-focus correction image; and training a generative model by using a pair of the defocus correction image and the clear focusing image and a point spread function quality and image quality evaluation loss function, and recovering the optical image with high quality. According to the method, the free surface type of the mask is optimized, an optical physical mechanism is introduced to be fused with an image similarity evaluation function to form a model gradient return loss function, the aberration correction effectiveness of wavefront coding is ensured, and the imaging quality and robustness are improved.

The invention belongs to the technical field of medical imaging, and provides a tissue stress measurement method and system based on a reverberation shear wave field, and the method comprises the steps: applying steady-state harmonic excitation, and obtaining the reverberation shear wave field of a target tissue; determining a stress direction according to the prior information of the target tissue, and constructing a stress direction vector; constructing an analytic operator, and extracting an intermediate characteristic quantity of the reverberation shear wave field; and according to the intermediate characteristic quantity, combining stress direction inversion to reconstruct spatial stress distribution. According to the method, a complex reverberation wave field is modeled into superposition of multi-direction propagation wavelets by utilizing anisotropic propagation characteristics of shear waves and combining a traveling wave expansion model, and characteristic quantities related to local stress and material parameters are directly extracted from the reverberation wave field by designing a wave field operator with physical significance; the three technical problems existing in a traditional shear wave elastic imaging technology are solved, the dependence of a traditional method on a single propagation direction and wave pattern controllability is broken through, and in-situ, in-vivo and non-invasive quantification is achieved.

The invention, which relates to the technical field of medical image intelligent analysis, discloses an artificial intelligence-based medical image intelligent analysis system comprising a data acquisition module, a data processing module, a multi-modal fusion module, a model training module, a diagnosis module and a visualization module. The data acquisition module is used for acquiring medical images from various medical imaging devices and obtaining original medical image data; the data processing module is used for preprocessing the original medical image data by adopting data cleaning, normalization and enhancement technologies to obtain a high-quality image data set; the multi-modal fusion module is used for integrating the high-quality image data set by adopting a data fusion method, and combining data from different types of imaging equipment together to generate a comprehensive feature set; and the model training module is used for training the comprehensive feature set by adopting a deep learning algorithm, and improving the model performance by adjusting model parameters and optimizing a loss function.

The invention relates to the technical field of biomedical imaging and physiological signal monitoring, and provides a multi-modal physiological signal acquisition system and method. The system comprises an imaging module used for collecting an image sequence of a target; the physiological signal acquisition module is used for acquiring a contact type physiological signal of a target; the synchronous control module is used for outputting a starting signal in response to the starting instruction so as to synchronously trigger a frame acquisition task of the imaging module and a sampling task of the physiological signal acquisition module; based on an internal clock source, a frame trigger signal is output to the imaging module to control an image acquisition time sequence, and a physiological sampling clock is sent to the physiological signal acquisition module; and distributing timestamps for each frame of image acquired by the imaging module and each sampling data point acquired by the physiological signal acquisition module. According to the multi-modal physiological signal acquisition system and method provided by the invention, frame-level time alignment between the image sequence and the physiological signal can be realized.

The present disclosure relates to a method for imaging and an imaging system. The method includes obtaining a scanning protocol for an examination subject, which includes: obtaining calibration data associated with the scanning protocol in response to a commonly used scanning protocol calibration data set of a medical imaging system including the calibration data; or performing a calibration operation in response to a commonly used scanning protocol calibration data set of a medical imaging system not including calibration data associated with the scanning protocol, to generate calibration data associated with the scanning protocol. The method further includes scanning the examination subject based on the scanning protocol, to obtain raw imaging data, and performing reconstruction using the calibration data associated with the scanning protocol and the raw imaging data, to obtain a medical image of a scanned subject.

In some embodiments provided herein is a generative foundation model trained over millions of health system-scale electronic health records along with web-scale medical text corpora to acquire knowledge of both medical practices and theories, and use of the generative model for rare disease diagnosis (including rare ophthalmic, diseases and rare systemic diseases), emergency condition identification (including ophthalmic emergencies and systemic emergencies), complex disease solving ("diagnostic puzzles"), or generating multimodal medical imaging reports (including ophthalmic images and radiology images such as X-rays and CT scans). In some embodiments, the generative model involves the use of language data, for pre-training, language data for supervised finetuning using a instruction tuning approach (e.g., QA pairs), and a human-machine hybrid evaluation strategy. In some embodiments, both the pre-training and supervised finetuning phases involve the use of a particular method of scaling to extend the context window. In some embodiments, the human-machine hybrid evaluation strategy involves language data for automated evaluations, as well as evaluations by generalists and by different specialists (e.g., ophthalmologists and radiologists) of varying levels of experience. In some embodiments, the generative foundation model, MetaGP, is used for unmet clinical needs through integration of medical and multimodal imaging data.

The invention relates to a PET / CT (positron emission tomography / computed tomography) image breathing motion artifact correction method based on a triple subgroup convolutional network, and belongs to the field of nuclear medicine imaging. The invention provides a lightweight triple-subgroup convolution kernel, and the convolution kernel focuses on analyzing the rotation and translation characteristics of coronal planes, sagittal planes and cross sections closely related to PET / CT images so as to realize efficient detail analysis under the condition of clinical small samples. And then, a Transform-subgroup convolution fusion encoder and a pyramid network are constructed in combination, a constructed triple subgroup convolution registration network can combine sub-deformation fields of multiple dimensions to obtain a smooth deformation field, and the deformation field is used for spatial distortion transformation to achieve the purpose of artifact correction. According to the method, artifact correction experiment comparison is carried out by using pixel body membrane simulation data and clinical data, the superiority of the method in the aspects of robustness and generalization ability is proved, and the quality of PET / CT images is effectively improved.

According to one embodiment, an X-ray imaging system includes X-ray irradiator irradiates X-rays to an object, X-ray detector detects the X-rays transmitted through the object, and processing circuitry. The processing circuitry is configured to acquire a camera image including an examination portion of an object. The processing circuitry is further configured to input a prompt including at least the camera image into a generative model to acquire a virtual medical image from the generative model. The processing circuitry is further configured to set a main imaging condition, which is an imaging condition used for a main imaging, based on the virtual medical image, and to output the main imaging condition to a medical imaging apparatus. According to other embodiments, the processing circuitry of an information processing apparatus controls the output of the imaging condition to a medical imaging apparatus.

An imaging system for generating an x-ray image of a subject. The imaging system including an imaging gantry having an x-ray emitter for emitting x-rays through the subject and an x-ray detector for detecting the x-rays that have passed through the subject. The imaging gantry is movable relative to the subject. The imaging system further including a control system configured to provide a user of the imaging system with a quick, easy, and efficient workflow of the imaging system to lead the user through a series of well-defined steps for setting up the imaging system.

The invention discloses a multi-contrast MRI joint reconstruction method, system and device and a medium, and relates to the technical field of medical imaging and deep learning, and the method comprises the following steps: collecting under-sampling MRI image data of different contrasts, and generating an optimization objective function based on a plurality of contrasts; decomposing the optimization objective function into a first sub-problem related to an auxiliary variable and a second sub-problem related to an objective variable; and alternately solving the first sub-problem and the second sub-problem in sequence to obtain a plurality of optimal MRI reconstructed images. According to the method, feature interaction of multi-contrast data is carried out in a spatial domain and a frequency domain, so that efficient complementation and collaborative modeling of multi-contrast features are realized, and the problem of insufficient utilization of information between contrasts in a traditional method is solved. By sensing the characteristics of each contrast, targeted prompts can be generated to guide the reconstruction process. Therefore, the quality of low-quality contrast is improved, and the overall reconstruction result is enhanced.

An imaging system (MIS), optionally a medical imaging system, with wireless communication capability and related method. The imaging system comprises a gantry (RG) rotatable around a rotation axis. The gantry includes a detector device (D) capable of recording, in plural spatial positions, measurement data in relation to a subject (such as a patient) (PAT) to be imaged. The system also includes a radio transmitter (TX) for generating a directed radio beam propagatable along a propagation axis to transmit the measurement data to a radio receiver (RX). The radio transmitter (TX) is arranged at the rotatable gantry and is operable so that the propagation direction intersects the rotation axis in a location that is situated away from the rotatable gantry.

A medical imaging system includes a workstation and software configured for accessing medical image data from a database and generating one of more of 1D, 2D, 3D and 4D representations of the medical image data on one or more virtual reality imaging devices, where the virtual display is manipulatable by a user through a user interface associated with the virtual reality imaging device.