3252results about "Radiation diagnostics" patented technology

In a measurement processing apparatus for measuring a geometric feature of an object image: a measurement-reference-element setting unit automatically sets at least one first measurement reference element for use in measurement of the geometric feature of the object image, at at least one first position on the object image based on first image data representing the object image and position information indicating at least one second position of at least one second measurement reference element which is set on a measurement reference image corresponding to the object image; and a geometric-feature measurement unit measures the geometric feature of the object image based on the at least one first position of the at least one first measurement reference element.

The invention relates to the technical field of medical image processing, in particular to an artificial intelligence assisted intraoperative imaging method, which comprises the following steps: S1, preprocessing a multi-modal medical image, segmenting and recognizing an anatomical structure by a deep learning model according to the preprocessed image, measuring anatomical parameters based on a segmentation and recognition result, and generating an operation planning path by artificial intelligence according to the anatomical parameters; s2, collecting a C-shaped arm perspective image stream in real time, dynamically tracking space coordinates of a surgical instrument, comparing the position of the instrument with a surgical planned path, calculating offset, and when the offset is greater than an offset threshold, outputting correction guidance through an AR superposition layer; and S3, monitoring an image quality index in real time, dynamically adjusting exposure parameters through a reinforcement learning model, and when a metal implant is detected, switching a dual-energy-spectrum mode and executing an artifact suppression algorithm. According to the method, preoperative precise planning and intraoperative assistance are realized through artificial intelligence, the problems of poor image quality and high radiation risk are solved through technical optimization, and the method has important clinical application value.

There is provided a method and a system for generating a 3D parametric mesh of an anatomical structure of a patient for storing multi-domain data therein. A plurality of anatomical segments having been obtained from segmentation of a set of images of a patient are received. A 3D mesh comprising a plurality of concentric 3D mesh layers is received, where each concentric 3D mesh layer includes a same predetermined number of nodes. A set of nodes in the 3D mesh corresponding to a respective anatomical segment is determined to obtain a respective correspondence rule therebetween. The set of nodes is encoded with a set of features from the respective anatomical segment by using the correspondence rule to obtain a 3D parametric mesh, each node of the set of nodes in the 3D parametric mesh being associated with a respective plurality of feature channels comprises the set of features.

A computer implemented method for training a whole medical image foundation model, including: receiving a plurality of medical image datasets; extracting local sections of image data from the plurality of medical image datasets; obtaining one or more causal variables associated with the local sections and / or patient; training one or more self-supervised learning models based on the local sections of image data and the causal variables; combining the one or more trained self-supervised learning models with a deep learning network configured to combine a latent representation of the local sections of image data from the one or more trained self-supervised learning models into a patient-level representation; and combining, with the one or more trained self-supervised learning models and the deep learning network, at least one further network or function configured to accept the patient-level representation as input, the at least one further network or function operable to perform one or more patient-specific prediction tasks.

Provided herein are imaging systems for a patient including an imaging probe and an imaging assembly. The imaging probe includes an elongate shaft with a rotatable optical core positioned within a lumen of the elongate shaft. The imaging probe further includes an optical assembly to direct light to tissue to be imaged and to collect reflected light from the tissue to be imaged. The system further includes an imaging assembly optically coupled to the imaging probe. The system further includes a processing unit with a processor and a memory coupled to the processor, and the memory stores instructions for the processor to perform an algorithm. The system records image data based on the reflected light collected by the optical assembly, such that the image data comprises data collected from a segment of a blood vessel during a pullback procedure. The algorithm can analyze the image data.

This application discloses a renal puncture guidance method, system and computer device based on multi-modal fusion, the method comprises: acquiring a medical image sequence of an object to be examined, and performing three-dimensional reconstruction based on the medical image sequence to obtain a three-dimensional reconstruction model; obtaining a slice corresponding to an ultrasonic probe currently in the three-dimensional reconstruction model; displaying a fusion image obtained from a real-time ultrasound image acquired by the ultrasonic probe and the slice in a first region of a puncture guidance interface; acquiring a needle insertion angle of a puncture needle, and displaying a puncture guide line in the fusion image in real time; and displaying the ultrasonic probe model, the puncture needle model and the three-dimensional reconstruction model in real time in a second region of the puncture guidance interface.

Disclosed are approaches that may provide image-guidance to interventionalists by providing true 3D visualization and quantitative feedback in real-time. A guidance system may allow a physician to manipulate a medical device and see a 3D rendering with quantitative feedback floating in mixed reality, next to standard monitors. Image tracking may detect and co-register the medical device's 3D position using, for example, bi-plane C-arm X-ray fluoroscopy and provide a 3D trajectory as quantitative feedback. Patterns in a fluoroscopic image may be used to accurately determine an object's z-position from a single angle projection.

Methods and apparatus for changing a brain state of a person from an initial brain state to a target brain state are described. The method includes receiving information characterizing the initial brain state, the information including a structural composition and a functional architecture of the brain, estimating based, at least in part, on the received information, a potential for the brain to change from the initial brain state to the target brain state, determining based, at least in part, on the received information and the estimated potential for the brain to change from the initial brain state to the target brain state, a non-invasive brain stimulation protocol, and controlling at least one non-invasive brain stimulation device to stimulate the brain according to the non-invasive brain stimulation protocol. The method also includes using brain information to inform the design of computational general artificial intelligence agents.

Described herein is a platform and supported graphical user interface (GUI) decision-making tools for use by medical practitioners and / or their patients, e.g., to aide in the process of making decisions about a course of cancer treatment and / or to track treatment and / or the progress of a disease.

A system, apparatus and method for a handheld x-ray system including a stand alone detector panel. The system includes a handheld instrument with a high energy x-ray source (120 keV or greater). The detector panel includes a wireless communication system employed to transmit x-ray images that are captured by the panel to the handheld instrument where they are displayed in substantially real time. The handheld instrument also includes a camera and a set of line lasers that are employed together to align the x-ray beam emitted from the instrument with the object being scanned.

The invention relates to the technical field of medical image diagnosis, and discloses a cardiovascular disease diagnosis model construction method based on image processing. The method comprises the steps that cardiac medical image data of a target patient is collected, a standardized data set is generated through preprocessing, and a morphological and hemodynamic feature set is extracted; establishing a heart state evolution characteristic spectrum according to a characteristic dynamic evolution rule, dividing a pathological state space, and calculating the characteristic distribution density of a historically diagnosed case; acquiring real-time image data of a patient to be diagnosed, and constructing a real-time diagnosis feature vector; mapping the vector to a pathological state space, and calculating a space matching degree to generate a pathological association index; and combining the association index and the two types of feature sets to construct a heart pathology probability prediction model, outputting a pathology probability prediction value and generating a hierarchical diagnosis suggestion. According to the method, through multi-dimensional feature analysis and space matching analysis, precise and graded diagnosis of the cardiovascular diseases is realized, and an efficient and feasible technical path is provided for diagnosis of the cardiovascular diseases.

The invention provides a pulmonary nodule display method and device, electronic equipment and a storage medium, and the method comprises the steps: segmenting a three-dimensional reconstruction image of the chest of a target patient to obtain a preoperative segmentation image, the three-dimensional reconstruction image being obtained based on preoperative CT data, and the preoperative segmentation image comprising the position information of a pulmonary nodule; segmenting the video image of the intraoperative lung tissue of the target patient collected by the thoracoscope in real time to obtain an intraoperative segmented image; feature matching is conducted on the preoperative segmented image and the intra-operative segmented image, the lens pose of the thoracoscope is determined, the preoperative segmented image is mapped into a two-dimensional reference image based on the lens pose, and the two-dimensional reference image comprises the position information of the pulmonary nodule; and carrying out image registration on the two-dimensional reference image and the intra-operative segmented image, and carrying out pulmonary nodule marking on the intra-operative segmented image to obtain a video image for displaying pulmonary nodules in real time. The position of the pulmonary nodule in the operation is accurately displayed in real time in a non-invasive mode, and the operation efficiency is improved.

Example systems and techniques are disclosed that may determine at least one treatment strategy for a lesion. An example system may include memory configured to store clinical guidance and / or informatics for a PCI procedure and processing circuitry communicatively coupled to the memory. The processing circuitry may be configured to determine the plurality of treatment pathways. The processing circuitry may be configured to obtain angiogram imaging data of a coronary vasculature of a patient. The 2024 / 058837 processing circuitry may be configured to determine the clinical guidance and / or informatics based at least in part on the angiogram imaging data. The processing circuitry may be configured to output for display the angiogram imaging data and at least a portion of the clinical guidance and / or informatics, wherein the at least a portion of the clinical guidance and / or informatics is overlaid onto the angiogram imaging data.

The invention relates to the technical field of medical instruments, in particular to an electromagnetic focusing extracorporeal shock wave pancreatic lithotripsy device and method.The electromagnetic focusing extracorporeal shock wave pancreatic lithotripsy device comprises a device body, a bed body is installed on one side of the device body through a supporting frame, and a lithotripsy notch is formed in the side, close to the device body, of the bed body; a large C-shaped arm and a small C-shaped arm installed on the inner side of the large C-shaped arm are installed at the position of the notch of the device body, an X camera imager is installed at the upper end of the large C-shaped arm, and a shock wave generator and an ultrasonic probe installed on one side of the shock wave generator are installed on the small C-shaped arm. An energy adjusting module used for controlling the shock wave generator and a safety detection module used for detecting the skin temperature are arranged in the device body, and a displayer and a human-computer interaction interface are arranged on one side of the device body. The technical scheme in the prior art has limitations on shock wave generation, positioning precision, energy regulation and control and treatment strategies.

A system (DSS) and related method for synthesizing training data or machine learning, based on a set (TD) including two types of training imagery, high image quality, IQ, imagery and low IQ imagery. The system comprises a data synthesizer (DSY), configured to register the at least two types of imagery and to transfer i) image information from high IQ imagery to the registered low IQ imagery to obtain synthesized high IQ imagery, or ii) image information from low IQ imagery to the registered high IQ imagery to obtain synthesized low IQ imagery. The synthesized data may be used for improved training of machine learning models for IQ enhancement.

A real-time artificial intelligence (AI) framework is provided for the automated analysis of radiological images and detection of disease, such as extracapsular extension (ECE) in prostate cancer. The system includes a dual deep learning architecture comprising a first convolutional neural network (CNN) for identifying diagnostically relevant image slices from three-dimensional MRI data, and a second CNN for classifying disease presence based on those slices. A preprocessing pipeline standardizes and harmonizes image input, and cropping algorithms isolate the region of interest for enhanced model performance. This framework enables scalable, high-accuracy diagnosis across various imaging modalities including but not limited to MRI, CT, PET, ultrasound, and diverse disease types, improving clinical decision-making and supporting integration into real-time radiology workflows.

A medical system useful in the determination of future disease progression in a subject. More specifically the present invention applies machine learning techniques to aid prediction of disease pathology and clinical outcomes in subjects presenting with symptoms of cognitive decline and to expedite clinical development of novel therapeutics.

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.

A system includes a cylindrical phantom and a conical structure disposed in the phantom. The conical structure is shaped as a frustum and emits Cherenkov radiation when exposed to ionizing radiation. The system can be used for calibration of an MR-Linac system by exposing the system to ionizing radiation. The Cherenkov radiation can be imaged during exposure to ionizing radiation.

A histotripsy therapy system configured for the treatment of tissue is provided, which may include any number of features. Provided herein are systems and methods that provide efficacious non-invasive and minimally invasive therapeutic, diagnostic and research procedures. In particular, provided herein are optimized systems and methods that provide targeted, efficacious histotripsy in a variety of different regions and under a variety of different conditions without causing undesired tissue damage to intervening / non-target tissues or structures.

The invention provides a pixelated flexible composite scintillation thin film based on a template-assisted nanoimprint technology and a preparation method and application of the pixelated flexible composite scintillation thin film. The preparation method of the pixelated flexible composite scintillation film comprises the following steps: preparing the flexible composite scintillation film; and constructing a periodic nanometer photon structure pattern on the flexible composite scintillation film by using a template-assisted nanoimprint technology to obtain the pixelated flexible composite scintillation film. According to the preparation method of the pixelated flexible composite scintillation film based on the template-assisted nanoimprint technology, the process is controllable, the preparation process flow is simplified, the production cost is reduced, the spatial resolution of the flexible composite scintillation film can be effectively improved, the imaging performance of the flexible scintillation film is greatly improved, and the application prospect is wide. Wide application prospects are realized in the fields of high-resolution X-ray imaging, radiation detection and wearable medical devices.

The invention provides an auxiliary tooth separation method and system for minimally invasive tooth extraction, and relates to the technical field of digital surgery in stomatology. According to the method, the oral anatomy three-dimensional electronic model is obtained by performing deep learning segmentation and three-dimensional reconstruction on the preoperative CBCT image, and the real-time spatial position and posture of the jaw bone are updated in combination with laser oral scanning of the preoperative anterior tooth area. On the basis, a three-dimensional simulation model is constructed, tooth division cutting path planning, interference judgment and dislocation space analysis are completed, precise tooth division cutting is implemented by a three-way mechanical arm along the planned path, and the safe and controllable automatic tooth extraction and tooth division process is completed in cooperation with water cooling and real-time track correction.

A computer-implemented method of providing plaque data (110) for a plaque deposit (120) in a vessel (130), is provided. The method includes: receiving (S110) computed tomography, CT, data (140) representing the vessel (130); generating (S120), from the CT data (140), a cross-sectional representation (150) of the vessel (130) at each of a plurality of positions (A-A′, B-B′) along the vessel; extracting (S130), from the cross-sectional representations, plaque data (110) comprising at least one measurement of the plaque deposit (120) at the plurality of positions along the vessel; and outputting (S140) a graphical representation of the plaque data (110).

Systems / techniques that facilitate task-specific image style transfer are provided. In various embodiments, a system can access a first medical image, wherein the first medical image can exhibit anatomical content and a first visual style. In various aspects, the system can generate, via execution of an optimization algorithm, a second medical image based on the first medical image, wherein the second medical image can exhibit the anatomical content and a second visual style that is different from the first visual style. In various instances, the optimization algorithm can be based on feature maps extracted from a pre-trained deep learning neural network that has been configured to perform an inferencing task on medical images exhibiting the second visual style.

A system and method of three-dimensional image processing. comprising the steps of pre-processing at least one set of raw images each including a plurality of two-dimensional source images. wherein each of the two-dimensional source image represents a cross-sectional view of a three-dimensional object at different positions along an axis in a three-dimensional space: and constructing a three-dimensional image representing the three-dimensional object by integrating 3D mesh points extracted from the at least one set of raw images being pre-processed: wherein the three-dimensional image is readable by a first image viewer arrange to render and to facilitate manipulation of the three-dimensional image.

Systems and methods for fully automated anatomical analysis of an anatomical structure are provided to facilitate pre-operative planning. The computerized method may include obtaining a plurality of images, e.g., MSCT images, of patient-specific cardiovascular anatomy, and analyzing the MSCT images with a trained artificial intelligence module to identify one or more anatomical landmarks and to construct a virtual three-dimensional model of the anatomical structure. For example, the trained artificial intelligence module may execute segmentation, point detection, curve detection, or plane detection deep learning modules, independently or in combination, to identify the anatomical landmarks. The method further may include deriving anatomical measurements of the one or more identified anatomical landmarks, and displaying the virtual three-dimensional model alongside the anatomical measurements of the one or more identified anatomical landmarks.

A detector system for molecular imaging of a radionuclide comprises a 3D semiconductor detector comprising a plurality of sensor stacks of sensors made of a semiconductor material having an average atomic number Z below 40. A read-out circuitry connected to the pixels is configured to output, for each interaction induced by an incident gamma ray in the detector, a signal representative of a time, a position and an energy of the interaction in the detector. The interactions in the detector belonging to a same event induced by the incident gamma ray are predicted based on the output signals and used to estimate a direction of the incident gamma ray and reconstruct an image based on the estimated directions of incident gamma rays.

In some embodiments, a system for operating a fluid injection system may include at least one processor to receive patient data related to a patient undergoing a fluid injection procedure, in one embodiment, at least one parameter of an injection protocol for a fluid injection procedure is automatically generated based on patient data and independently of an input associated with a dose of a medical fluid for the fluid injection procedure received through a user interface associated with a fluid injection system performing the fluid injection procedure, when the at least one parameter is automatically generated, the at least one processor is configured to determine a dose of the medical fluid for the fluid injection procedure and provide the at least one parameter of the injection protocol to the fluid injection system. Methods and computer program products are also disclosed.

Systems and methods are disclosed for performing operations comprising: receiving a plurality of training medical images used to train a machine learning technique; applying a model to the plurality of training medical images to generate a distribution representation of the plurality of training medical images; computing a first distance between a given training medical image in the plurality of training medical images and the distribution representation; computing a second distance between a new medical image and the distribution representation; and computing a quality factor indicating a measure of similarity between a new medical image and the plurality of medical images as a function of the first distance and the second distance.

It is disclosed a method processing imaging data of a patient having cancer, for instance lymphoma, comprising:—Providing three-dimensional imaging data of the patient,—computing from said three-dimensional imaging data, at least one two-dimensional Maximum Intensity Projection image. corresponding to the projection of the maximum intensity of the three-dimensional imaging data along one direction onto one plane,—extracting a mask of the MIP image corresponding to cancerous lesions by application of a trained model. Using the extracted mask it is possible to compute one or more cancer prognosis indicators.