35 results about "Radiology studies" patented technology

Provided herein is technology relating to radiology and radiotherapy and particularly, but not exclusively, to apparatuses, methods, and systems for multi-axis medical imaging of patients in vertical and horizontal positions.

Presented is an invention in which visible light is substituted for damaging radiation, enabling students to learn about radiology through active learning (e.g. by manipulation of a study's subject and seeing, in real time, the resulting changes in the generated image) but without exposure to damaging radiation. The invention also allows student and senior practitioners to practice radiological techniques (e.g. patient positioning, fluoroscopy) without radiation exposure. The invention also allows demonstration of the relationship between a three-dimensional object and its two-dimensional depictions, such as between a machine part and that part's views on a technical drawing.

This invention provides a hybrid manufacturing assembly of anatomical and workflow procedure in fabricating radiologically accurate animal phantoms. A method in which by utilizing additive manufacturing and molding technologies to fabricate the soft tissue, lungs, and hard tissue phantom of animal of interest and a way to assemble the organs such a radiologically heterogeneous phantom and structure is created and with the option to add provisions for radiological dosimetry devices is provided. The specified workflow is intended to address the need for low to medium volume production of small animal phantoms.

The application provides a denture design method, comprising the following steps: obtaining two-dimensional oral panoramic film and three-dimensional intraoral scanning data of a patient; calculating the radiological bone loss proportion of each tooth, and obtaining the image reliability component of each tooth position in the panoramic film based on the segmentation result; extracting the three-dimensional key point and neck edge contour point set of each tooth; calculating the registration reliability component of each tooth position; fusing the image reliability component and the registration reliability component of each tooth position in the panoramic film to generate an image and registration reliability index; and taking the radiological bone loss proportion and the reliability index of each tooth position as a design constraint to perform automatic design of a removable partial denture. The purpose of improving the clinical scientificity, safety and interpretability of the design scheme is achieved.

This specification provides a cardiac ultrasound coronary artery stenosis diagnostic system and method based on a visual language transfer model. The system includes: a database construction module for standardizing multi-view cardiac ultrasound video sequences and radiological reports; a cardiac region of interest (ROI) standardization and labeling module for segmenting myocardial segments, obtaining ROI data, and generating a coronary artery-specific perfusion region mask as the ROI for vascular-level stenosis prediction; a visual language transfer pre-training module for semantic alignment of video data and report text using a visual language transfer model; and a downstream task prediction module for utilizing the enhanced attention mechanism of the coronary artery-specific perfusion region mask, based on the global visual features output by the visual language transfer pre-training module, to output patient-level classification results for obstructive coronary artery disease and vascular-level stenosis prediction results for LAD, LCX, and RCA.

This invention relates to the field of medical image processing technology, specifically to an automatic detection and classification method and system for intracranial hemorrhage based on three-window CT fusion and label-dependent graph neural network, including the following steps: S100, acquiring the original CT slice image of the head to be detected, performing grayscale mapping on the original slice image based on preset brain window, subdural window and bone window respectively, and stacking the three mapped images along the channel dimension to generate a three-channel fused image; This invention greatly enriches the model's global perception ability of multi-level physiological structures such as brain parenchyma, microhematomas and skull lesions through innovative three-window physical mapping and channel fusion; it introduces a hybrid adjacency matrix graph neural network that combines prior knowledge of neuroradiology with data-driven approach, explicitly establishing pathophysiological causal relationships between various hemorrhage subtypes at the model architecture level, effectively eliminating logical contradictions in predictions that violate clinical common sense.

Techniques for generating a plurality of basis images for use in angiography are presented. The techniques include: acquiring at least four radiological measurements of a target image region, where the radiological measurements include: a pre-injection measurement set including at least a first pre-injection measurement and a second pre-injection measurement and a post-injection measurement set including at least a first post-injection measurement and a second post-injection measurement; decomposing, using a joint estimate, the radiological measurements into at least four basis images, where the basis images include: a pre-injection first material basis image, a post-injection first material basis image, a second material basis image, and a contrast agent basis image, where the decomposing includes performing at least three-material decomposition using all four of the radiological measurements; and providing at least the contrast agent basis image.

The invention provides a system and method for quality control in radiology reports. It collects data from reports, medical scan images, and questionnaires within a Safety, Quality, Efficiency, and Productivity (SQEP) framework. Scores for safety, quality, productivity, and efficiency are computed for radiologists and departments using weighted parameters, peer reviews, and audit outcomes. A consolidated SQEP score evaluates overall performance. The system identifies deviations in report quality through trend analysis and correlations, providing corrective feedback and training recommendations to radiologists. By addressing errors proactively, the system enhances diagnostic accuracy, operational efficiency, and compliance with quality standards. This comprehensive approach ensures improved performance and reliability in radiology departments while maintaining high-quality patient care.

This invention provides a full-stack medical imaging foundation model for performing medical image segmentation, classification, and localization using a unified language-image correlation mechanism. This invention leverages pre-trained language-guided associations to automatically identify and localize disease targets without requiring medical imaging knowledge or manual bounding box prompts. This invention enables comprehensive clinical tasks across multiple disease categories and various medical imaging techniques, improving segmentation efficiency and reducing the burden of manual input. By utilizing the operability of plain text prompts from non-radiologists, this invention improves real-world applicability and enables the deployment of foundation model-based segmentation in diverse clinical settings.

The invention relates to an integrated multimodal artificial intelligence platform designed to function as an autonomous hospital system providing end-to-end health prevention, screening, diagnosis, treatment optimization, and longitudinal digital-twin-based monitoring. The platform introduces a closed-loop clinical architecture that continuously analyzes heterogeneous patient data including radiology, pathology, genomics, laboratory findings, longitudinal clinical records, wearable streams, and environmental exposures. A multimodal transformer (MT-X) generates a unified patient-specific representation, enabling high-precision diagnostic and prognostic inference. A novel Autonomous Screening Interval Generator (ASIG) dynamically determines individualized screening schedules based on calibrated risk models and temporal disease-evolution forecasting. A Digital Twin Engine (DTE) simulates tumor progression, metastasis probability, toxicity trajectories, and therapy response. An Adaptive Therapy Optimization Engine (ATOE), based on reinforcement learning, identifies optimal treatment strategies tailored to patient biology and system-level constraints. The invention is industrially applicable to hospitals, centers, national screening programs, tele-networks, and Al-enabled health systems. The integrated nature of the invention, the closed-loop framework, and the combination of digital-twin simulation with intelligent screening and therapy design constitute a substantial improvement beyond conventional medical Al solutions.

Compositions and methods are presented to improve the efficacy and reduce the side-effects of radiological cancer treatments. Nanoparticles of cerium oxide are dispersed within a biodegradable polymer matrix to form a biomaterial that can be implanted in a patient at a selected location and remain at that location for a period of time encompassing all or part of a radiation therapy course of treatment. Among other functions of the implanted biomaterial, it may act as a spacer such that organs at risk are moved away from tissues targeted by the radiation therapy. Another function of the implanted biomaterial is as a fiducial marker. The use of cerium oxide will assist in achieving both the fiducial marker and spacer functions, particularly in view of the catalytic activity of cerium oxide in reducing a number of reactive oxygen species to non-reactive compounds The composition may permit higher doses of radiation and improved survival rates resulting therefrom.

The disclosure provides a method, device and computer equipment for evaluating radiology image reports, and relates to the technical field of medical image processing and artificial intelligence. The disclosure rewrites the first radiology image report for reference and the second radiology image report generated by the artificial intelligence model into atomic discovery sentences, reduces the matching interference caused by the difference in report writing style, improves the stability and comparability of the evaluation, performs clinical semantic matching on the atomic discovery sentences of the two reports by contradiction restraint, evaluates the second radiology image report according to the atomic level matching result, avoids that the contradictory content gets an unreasonable high score, and improves the accuracy of the evaluation.

The application discloses a kind of gradient optimization method and system of multitask radiology report generation, it is related to medical information technology field, including, obtaining medical training data set containing image, label and report text, construct and include visual encoder, clinical constraint auxiliary task branch and text decoder Multi-task report generation model;Firstly, the failure mechanism of linear scalarization is studied from the perspective of gradient dynamics, and it is formalized as the "double dilemma" of drift term bias and diffusion term attenuation using the SDE framework, thereby revealing the geometric root cause of suboptimality in RRG multitask optimization, and proposing CAME-Grad, a gradient optimization algorithm designed specifically for multitask RRG. As an optimizer independent of the backbone network, it can be integrated into various model architectures in a plug-and-play manner. On the MIMIC-CXR and IU X-Ray datasets, CAME-Grad was extensively evaluated across eight representative RRG methods, and the results showed that its average clinical performance improved by 2.3% and 1.9%, respectively.

The present invention relates to the technical field of generating artificial contrast radiological images by machine learning methods.

The present invention refers to a system (1) for training personnel in charge of interventional-radiological activities. The system (1) includes an apparatus (2) simulating a radiological-type apparatus, preferably the apparatus (2) having at least one “C-shaped” operative portion (8), a dummy (5) adapted to simulate a patient, a simulating operating table (3) provided with an abutment plane (4), the table (3) being configured to support the dummy (5). At least one of the apparatus (2) and dummy (5) includes at least one light source (6, 7) configured to simulate different types of radiological emission, and emit light radiation according to different frequencies.

This invention discloses a method for generating radiological image reports based on explicit visual evidence. The specific implementation steps include: constructing a training set for a clinical diagnostic inference chain based on anatomical priors; fine-tuning a pre-trained multimodal large model using a ternary collaborative loss function; extracting high-resolution grid features during the inference stage, locking key lesion features through an active retrieval mechanism triggered by interleaved tokens, and embedding these features into a contextual sequence after processing by a perceptual resampling module as explicit visual evidence, driving the model to generate a radiological report supported by the image. This invention achieves a paradigm shift from static full-image injection to dynamic evidence-based inference, overcoming the problems of opacity and easy neglect of small lesions in the inference process of existing radiological image reports, while improving the diagnostic reliability and computational efficiency of the model while preserving high-resolution details.

The present application provides a radiology report generation method fusing clinical semantic modulation and hyperbolic prototype classification, which comprises: acquiring chest X-ray images and their associated clinical context knowledge, extracting visual features of the images using a visual encoder, extracting clinical semantic embedding using a medical language encoder, and retrieving relevant report features from a reference report database; constructing and utilizing a clinical semantic modulation module to generate modulated visual representation; constructing and utilizing a hyperbolic prototype classification module to generate diagnosis awareness prompts; inputting the modulated visual features and diagnosis awareness prompts into a decoder to complete the autoregressive generation of the radiology report. The present application can fully utilize the cross-modal fusion of clinical knowledge and visual features, and utilize the exponential expansion embedding capacity of hyperbolic space to improve the disease detection ability under the condition of class imbalance, and improve the clinical accuracy of radiology report generation.

The invention relates to a radiological gripping device (1) for a radiological device (50), such as an X-ray device (51), a magnetic resonance tomograph (52), a computed tomograph (53) or a radiation therapy device (54), comprising a stationary device (2) and at least one carrying arm (3) with at least one shaft element (4) and a carrier element (5) pivotably seated on the stationary device. Furthermore, a locking device (6) for locking the carrying arm (3) in at least one locking position (7) is provided. An actuating mechanism (8) is provided for actuating the locking device. A gripping device (9) is also included, which is arranged for fastening to the carrying arm (3) to provide the possibility of gripping for a patient. The shaft element (4) of the carrying arm (3) is pivotably seated on the stationary device (2). The actuating mechanism (8) comprises at least one force direction unit (200), which locks the force transmission in one direction in order to use the operating force transmitted into the flexible pull element (19) for actuating the locking device (6) or to actuate with this force.

This invention relates to a medical image report generation method based on a dual-image encoder, comprising: acquiring a current chest X-ray image and historical chest X-ray images; inputting the current chest X-ray image and historical chest X-ray images into a medical image report generation model to obtain radiology report text corresponding to the current chest X-ray image; wherein, the medical image report generation model includes: a single-modal encoding module for extracting image features of the current chest X-ray image and historical chest X-ray images, as well as text features of the radiology report text; a cross-modal encoding module for performing cross-modal fusion of the image features and text features to obtain multimodal features incorporating visual difference information; and a text decoding module for generating the radiology report text corresponding to the current chest X-ray image based on the multimodal features incorporating visual difference information. This invention enables the modeling of image difference information and the generation of corresponding radiology reports.

This invention relates to the field of intelligent medical imaging diagnosis technology, specifically to an AI detection system for occult femoral neck fractures based on two-stage artifact contrast learning. The system includes a data acquisition and construction module, an artifact enhancement contrast pre-training module, a detection network fine-tuning module, an inference and visualization module, and a physician-assisted image reading module. Training and testing data are acquired through multi-center cohort construction. An anti-interference radiographic feature encoder is obtained through artifact enhancement contrast pre-training. A single-stage detection network is then constructed by combining attention feature aggregation and dynamic upsampling modules to achieve precise detection. This invention can quickly output the probability of fracture presence, bounding boxes, and attention heatmaps. It can be embedded in clinical image reading workstations to provide real-time support, exhibits high sensitivity for occult femoral neck fracture detection, strong multi-center generalization ability, significantly improves physician diagnostic efficiency, reduces the risk of missed diagnoses, and features fast inference speed and strong interpretability, making it suitable for clinical application in emergency trauma scenarios.

The application discloses a breast cancer neoadjuvant therapy prognosis multi-modal multi-task probability prediction method and system, and steps are as follows: using training data composed of breast DCE-MRI images and segmentation images of a plurality of breast cancer patients after various neoadjuvant therapies, and clinical information, radiology image reports, pathological reports and case information to perform breast cancer NAT prognosis multi-modal multi-task probability prediction training on a deep learning model; then using deep learning to analyze breast DCE-MRI images and segmentation images matched with the breast DCE-MRI images of a patient to be measured, and clinical information, radiology image reports, pathological reports and case information, generate and output pCR, DFS and OS prediction results of various different NAT schemes, risk probability and recurrence probability evaluation results of breast cancer metastasis to different organs, and benign and malignant tumor type and BI-RADS level identification results. The application can significantly improve the accuracy and comprehensiveness of NAT prognosis evaluation.

A void occlusion device is described, comprising a scaffold that defines a plurality of voids and a reinforcing element. The scaffold is configured to allow infiltration of human tissue into the plurality of voids. The reinforcing element extends through at least a portion of the scaffold and is configured to resist compressive forces exerted on the scaffold. Modalities in which the void occlusion device is biocompatible, bioresorbable, elastic, and radiologically suitable are also described.

This application discloses a skull tissue equivalent material, its preparation method, and its application, relating to the field of biomedical materials technology. The skull equivalent material disclosed in this application comprises the following raw materials: 15-30 parts bisphenol A epoxy resin, 5-15 parts octyl glycidyl ether, 5-15 parts urea, 1-10 parts red phosphorus, 1-8 parts magnesium sulfate, 1-8 parts sodium sulfate, 15-45 parts dicalcium phosphate, and 5-15 parts trimethylhexanediamine. The skull equivalent material disclosed in this application has multiple advantages, including precise CT value matching, stable attenuation over a wide energy spectrum, adjustable density, and suitability for multiple age groups. It also solves the problems of large simulation deviations, inability to cover the entire skull region, and uncontrollable radiation characteristics of existing equivalent materials. It features high simulation fidelity, stable performance, and simple processing, meeting the high-precision requirements of medical imaging calibration, radiotherapy dose verification, and radiology teaching phantoms.

A system (100) can receive a radiology report including radiologist observations about a region of interest of a subject and use a first AI model (310) to generate structured data including a predetermined set of fields and a corresponding set of values extracted from the radiology report. The system (1000) can use the first AI model (310) to determine, using the structured data, a medical image dataset of the subject stored in a medical image database (150) corresponding to the radiology report. The system (100) can use a second AI model (320) to segment and label a set of regions of each medical image of the medical image dataset. The system (100) can use the first AI model (310) to determine, using the structured data, a medical image of the medical image dataset that depicts the region of interest of the subject and perform an action based on determining the medical image that depicts the region of interest.

This invention belongs to the field of image processing technology and discloses a dynamic interactive sacroiliac joint image-assisted analysis system and image processing method that simulates a clinical image reading path. The system includes: an image data acquisition and preprocessing module for acquiring and preprocessing X-ray and MRI image data; an X-ray initial localization module for feature extraction and zonal localization of X-ray and MRI image data based on a first visual language model; an MRI-oriented iterative auxiliary analysis module for feature extraction of MRI image data based on a second visual language model; a decision module combining a large language model and a knowledge graph to generate structured auxiliary analysis information; and an auxiliary analysis convergence and output module for integrating the auxiliary analysis information and generating and outputting a structured radiological auxiliary analysis report and visualization results. This invention can significantly reduce the amount of slice processing and improve the efficiency, accuracy, and interpretability of auxiliary analysis.