5636results about "Computerised tomographs" patented technology

This application is directed to systems, methods, and devices for image based analysis of plaque. In some embodiments, the approaches herein can be used for developing treatment plans, which can include local treatment, systemic treatment, or both. In some embodiments, the approaches herein can be used for stent selection. In some embodiments, the approaches herein can be used for surgical planning, which can include robotic surgical planning. In some embodiments, the approaches herein can be used for image normalization. In some embodiments, the approaches herein can be used for identifying plaque calcification thresholds. In some embodiments, the approaches herein can be used for identifying thin cap fibroatheroma. In some embodiments, the approaches herein can be used for coronary artery tree reconstruction. Some embodiments are directed to coronary artery disease risk stratification.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to the technical field of assessment and early warning systems, and particularly discloses a rectal cancer operation risk assessment and early warning system based on multi-modal data fusion, which comprises a multi-modal data acquisition module, a data preprocessing module, a cross-modal fusion module, a dynamic risk assessment module, an intelligent early warning module and a system self-checking module. By integrating multi-modal data and dynamically allocating weights, real-time risk updating in the whole operation period is achieved, the comprehensiveness, timeliness and precision of risk assessment are comprehensively improved by combining the grading early warning function, the autonomous detection calibration function and the risk tracing and countermeasure matching function, long-term reliable operation of the system is guaranteed, powerful support is provided for clinical decision making, and the risk assessment method is suitable for clinical decision making. The surgical risk and the complication occurrence rate are effectively reduced.

Various embodiments described herein relate to systems, devices, and methods for non-invasive image-based plaque analysis and risk determination. In particular, in some embodiments, the systems, devices, and methods described herein are related to analysis of one or more regions of plaque, such as for example coronary plaque, using non-invasively obtained images that can be analyzed using computer vision or machine learning to identify, diagnose, characterize, treat and / or track coronary artery disease.

A computer-implemented method for processing medical images may comprise: receiving image data for a patient; based on the received image data, determining: a patient-specific epicardial adipose tissue (EAT) metric or a patient-specific pericoronary adipose tissue (PCAT) metric, and at least one other patient-specific metric, and using the EAT metric or the PCAT metric, and the at least one other patient-specific metric, to determine a risk score for the patient or to classify a disease state of the patient.

Systems and methods are disclosed for evaluating a patient with vascular disease. One method includes receiving patient-specific data regarding a geometry of the patient's vasculature; creating an anatomic model representing at least a portion of a location of disease in the patient's vasculature based on the received patient-specific data; identifying one or more changes in geometry of the anatomic model based on a modeled progression or regression of disease at the location; calculating one or more values of a blood flow characteristic within the patient's vasculature using a computational model based on the identified one or more changes in geometry of the anatomic model; and generating an electronic graphical display of a relationship between the one or more values of the calculated blood flow characteristic and the identified one or more changes in geometry of the anatomic model.

The invention relates to the technical field of medical instrument control, in particular to a lung biopsy sampling needle stabilizing method and system based on dynamic respiration compensation. The stabilizing method comprises the following steps: acquiring a respiratory movement signal of a patient in real time through a multi-source biosensor, wherein the multi-source biosensor comprises an optical fiber strain sensor array, a diaphragm myoelectricity sensor and a respiratory airflow sensor which are deployed at the chest and abdomen. According to the method, millimeter-microsecond-level accurate and stable control over the lung biopsy sampling process is achieved by establishing a cooperation mechanism of a breathing phase-displacement nonlinear mapping model and a macro-micro composite driving framework. The biological rationality of displacement prediction is improved through deep coupling of multi-mode breathing signals, and the chest wall non-uniform deformation gradient is analyzed in space based on cross topology deployment of an optical fiber strain sensor in an intercostal space area in combination with cross-scale feature fusion of myoelectricity and airflow sensing.

The invention provides an hemorrhagic brain injury intracranial pressure prediction and early warning system based on a CT image, and the system comprises a data collection module which is used for collecting the original data of a target data source of a target patient; the hybrid model comprises a 3DCNN model used for extracting hematoma spatial features of the head CT image sequence; the LSTM model is used for extracting hematoma time sequence characteristics of the physiological signals; the multi-modal fusion module is used for fusing the hematoma space features and the hematoma time sequence features and outputting hematoma fusion features; the prediction module is used for predicting an ICP sequence of the target patient in a future preset time period based on the hematoma fusion features; the self-adaptive early warning module is used for outputting a visual result based on the predicted ICP sequence in the future preset time period; and through a dynamic threshold adjustment mechanism, adjusting an early warning threshold of the target patient in combination with the case information, and triggering graded early warning in real time. Through personalized data processing and dynamic early warning, accurate, efficient and real-time patient risk management is realized.

A method of identifying coronary artery disease comprising: receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient; analysing the contrast cardiac CT data using a vessel seed detector and machine learning to identify a plurality of centreline seed points in the contrast cardiac CT data predicted to correspond to locations on centrelines of the cardiac arteries of the patient; determining whether the analysis to identify centreline seed points of patient coronary arteries is incorrect or incomplete; if the analysis to identify centreline seed points of patient coronary arteries is incorrect or incomplete, reconfiguring the vessel seed detector; producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the centreline seed points identified using the reconfigured vessel seed detector;and identifying presence of coronary artery disease using the transverse image slices.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

An image processing apparatus is disclosed. The image processing apparatus of the present invention comprises: an image receiving unit for receiving a first image and a second image of the same object taken at different times; a processor for obtaining transformation information by registering the first image on the basis of the second image, obtaining a first segment image corresponding to an area of the object from the first image, and generating a second segment image corresponding to an area of the object of the second image by transforming the obtained first segment image according to the transformation information; and an output unit for outputting the second segment image.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

A method of automatically determining a plaque score for coronary arteries of a patient is disclosed. The method involves receiving cardiac CT data indicative of a cardiac CT scan carried out on the patient, and analysing the cardiac CT data to identify plaque volumes to be included in the plaque score, the plaque volumes located on the coronary arteries. The method also includes determining locations of the identified plaque volumes on the coronary arteries, applying machine learning to the cardiac CT data to identify a plurality of primary key points on the coronary arteries, determining a distance between each identified plaque volume and an associated primary key point, and determining a plaque score based on a heart dimension value and, for each identified plaque volume, the determined distance from the identified plaque volume to the associated primary key point.

A computer-implemented method for medical measurement reconstruction may comprise obtaining a first reconstruction of at least one representation of at least one set of medical measurements; presenting the first reconstruction or information about the first reconstruction to a reviewer; receiving an input from the reviewer relating to the first reconstruction or the information about the first reconstruction; processing the received input; and generating a second, modified reconstruction based on the received input.

The invention discloses a scoliosis orthosis registration method based on deep learning and biomechanical constraint, and relates to the technical field of scoliosis orthosis design, and the method comprises the steps: respectively obtaining first feature information of a three-dimensional spine model and second feature information of a three-dimensional body surface model, the second feature information comprises a geometric feature, a pressure feature and a motion feature; the first feature information and the second feature information are simultaneously input to a pre-constructed hybrid configuration network for rigid registration and non-rigid registration, the rigid registration outputs an initial alignment model, and the non-rigid registration outputs high-precision local adaptation data with reasonable dissection; and performing joint optimization on the initial alignment model and the high-precision local adaptation data with reasonable dissection to obtain a final registration result. Geometric and motion features of a spine and a body surface are extracted through double-flow features, so that a registration network of coarse rigidity and then fine non-rigidity is carried out, and transition from geometric registration to function optimization is realized.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, perform computational fluid dynamics analysis, facilitate assessment of risk of heart disease and coronary artery disease, enhance drug development, determine a CAD risk factor goal, provide atherosclerosis and vascular morphology characterization, and determine indication of myocardial risk, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention relates to a coronary artery fractional flow reserve non-invasive evaluation method fused with hemodynamics, and belongs to the technical field of medical detection, and the method comprises the following steps: obtaining coronary artery CT image data, carrying out preprocessing, and extracting a coronary artery three-dimensional geometric model; constructing a neural network fused with hemodynamics, and adding a residual term of a Navier-Stokes equation and a residual term of a Murray law into a loss function; collecting a real training sample, and generating numerical simulation data as a training data set; s4, training and optimizing the neural network fused with hemodynamics to minimize an error between an output result of the network and an FFR training value, and satisfying constraint conditions of a Navier-Stokes equation and a Murray law at the same time; and S5, processing the coronary artery CT image data by using the trained network model fused with hemodynamics to obtain a predicted FFR value.

Presented herein are systems and methods that provide for improved computer aided display and analysis of nuclear medicine images. In particular, in certain embodiments, the systems and methods described herein provide improvements to several image processing steps used for automated analysis of bone scan images for assessing cancer status of a patient. For example, improved approaches for image segmentation, hotspot detection, automated classification of hotspots as representing metastases, and computation of risk indices such as bone scan index (BSI) values are provided.

A surgical planning system for use in surgical procedures to repair an anatomy of interest includes a preplanning system to generate a virtual surgical plan and a mixed reality system that includes a visualization device wearable by a user to view the virtual surgical plan projected in a real environment. The virtual surgical plan includes a 3D virtual model of the anatomy of interest. When wearing the visualization device, the user can align the 3D virtual model with the real anatomy of interest, thereby achieving a registration between details of the virtual surgical plan and the real anatomy of interest. The registration enables a surgeon to implement the virtual surgical plan on the real anatomy of interest without the use of tracking markers.

This disclosure relates to systems, methods, and devices for plaque analysis, vessel and fluid flow analysis, and / or risk determination or prediction thereof. Some embodiments relate to determining fractional flow reserve (FFR) values. Some embodiments relate to determining FFR values using 3D-printed models. Some embodiments relate to identifying thin cap fibroatheroma based on analysis of computed tomography (CT) imaging. Some embodiments relate adjusting calcified plaque thresholds for CT images to address effects of calcium blooming.

The invention relates to the technical field of tumor interventional operations, and discloses a visual navigation method based on a tumor interventional operation robot. The method comprises the following steps: acquiring real-time medical image data of a tumor area containing multi-modal imaging information so as to comprehensively present anatomical details; and performing three-dimensional reconstruction on the image data to generate a tumor area three-dimensional anatomical structure model capable of visually displaying a space structure. Key anatomical feature points are extracted based on the model, space coordinates are calculated, a surgical robot intervention path is planned according to the coordinates, and an initial navigation track is generated; and continuously collecting real-time pose data of the robot in an operation, dynamically matching the real-time pose data with the initial navigation trajectory, adjusting motion parameters according to a matching result, and generating a corrected navigation instruction. The method can reflect the intraoperative anatomy condition in real time, dynamically optimize the path, solve the problems that traditional navigation depends on preoperative static images and lacks real-time adjustment, reduce operative complications and improve the treatment effect of patients.

Methods are described for conducting minimally invasive medical interventions utilizing instruments and assemblies thereof to stabilize and / or fixate a dysfunctional sacroiliac (SI) joint. In one embodiment, a defect creation assembly is advanced from a posterior approach into the SI joint and configured to create pilot SI joint opening; portions of which being disposed in the sacrum and ilium bone structures. After the pilot SI joint opening is created, a prosthesis is press-fit into the pilot SI joint opening, wherein the pilot SI joint opening transitions to a larger post-prosthesis insertion SI joint opening and the prosthesis is securely engaged to the sacrum and ilium bone structures.

The invention relates to the technical field of motion capture and data processing, in particular to a method for realizing real-time dynamic capture of a three-dimensional space of a human spine by applying an IMU (Inertial Measurement Unit). According to the method, a personalized three-dimensional dynamic spine model is constructed based on CT image data of a user, and a mathematical model of the spine is established by fusing a physiological anatomical structure of the spine and a robot mechanism motion theory so as to accurately describe motion characteristics of the spine. An IMU (inertial measurement unit) sensor is adhered to a key part of the back of a user, an individualized spine model is driven by acquiring and processing IMU data in real time, real-time reconstruction of dynamic movement of the spine is realized, and space angles among vertebral sections of the spine are accurately calculated. The dynamic change of the spine in the movement process can be accurately captured, the movement posture of the spine can be deeply analyzed, and the movement effect of the spine can be accurately and visually tracked and quantified in real time.

The invention discloses an auxiliary renal puncture device for the nephrology department, and belongs to the technical field of renal puncture, the auxiliary renal puncture device comprises a sickbed, a plurality of supporting legs are fixedly arranged at the bottom of the sickbed, fixing plates are fixedly arranged on the two sides of the sickbed, and second sliding blocks are slidably connected to one sides of the two fixing plates; the breathing frequency and amplitude of a patient are captured in real time through an infrared breathing monitoring module, and displacement data generated by breathing of the kidney are accurately obtained in combination with motion sensing modules such as a lead screw encoder and a driving gear angle encoder. The intelligent control system drives the first stepping motor and the second stepping motor to adjust the position of the portal frame based on six-dimensional force sensor data of the force sensing module, meanwhile, the angle of the puncture needle is adjusted in real time through feedback of a mechanical arm joint encoder, and dynamic compensation of kidney respiratory movement is achieved. According to the mechanism, the puncture deviation caused by breathing can be controlled within the range of + / -0.5 mm, the puncture positioning precision is remarkably improved, and poor sample quality or operation risks caused by breathing movement are avoided.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

A positron emission tomography (PET) scanner includes a plurality of gamma radiation detector modules arranged to form a detector ring. Each detector module includes an array of scintillator detectors. Each scintillator detector comprises a monolithic scintillation crystal and a plurality of photodetector arrays, such as silicon photomultipliers (SiPMs). A photodetector array is positioned on at least two nonparallel faces of each scintillation crystal. In some examples, a photodetector array is positioned on each of three orthogonal faces of each scintillation crystal.

The invention relates to a glioma radiotherapy effect prediction method based on habitat analysis imageomics, and belongs to the technical field of medical image artificial intelligence and tumor radiotherapy, and the method comprises the steps: collecting a multi-mode MRI image, a radiotherapy plan computed tomography CT image, a dose image and a local recurrence label before postoperative radiotherapy, and converting the images into a standard format; sequentially performing craniocerebral boning processing, N4 bias field correction, isotropic resampling, gray level normalization processing and image registration on the acquired image to obtain a preprocessed image; based on the planned target volume PTV, dividing the interior of the PTV into three types of physiological structure habitat areas and three types of physiological habitat areas; image omics features of the multi-mode MRI images are extracted in the PTV and all habitat areas, and an optimal feature subset is screened out; taking the screened optimal feature subset as an input, taking the local recurrence label as an output label, and selecting an optimal model; and generating an output result by using the optimal model.

The invention discloses a multi-section type linear array X-ray source static real-time CT imaging system and an imaging method, and the multi-section type linear array X-ray source static real-time CT imaging system is provided with a scanning device and a ray source control unit; the scanning device is provided with a multi-section X-ray source array and a detector, the transmitting end of the multi-section X-ray source array faces the detector, and the multi-section X-ray source array is connected with the ray source control unit. The multi-section X-ray source array has the advantages that the multi-section X-ray source array can freely adjust the length and the included angle of the whole section, and the multi-section X-ray source array is suitable for a large-size measured object; 2, the multi-section X-ray source array can use fewer X-ray sources to realize the same wide view field, the design is more free, and the expansion is easier; 3, the distribution of ray sources can be relatively dispersed, the crossing of ray paths is less, and the interference of scattered signals can be obviously reduced; and 4, the exposure strategy of the scanning device can be controlled through the radiation source control unit, and the radiation dose utilization rate is optimized.

The invention relates to the technical field of medical image analysis, and discloses a cardiovascular disease detection method based on image analysis. The method comprises the steps of obtaining a medical image sequence of a target patient, and extracting a blood vessel region contour to generate an initial blood vessel topological graph; the initial topological graph is registered with a standard cardiovascular model, the curvature deviation degree and the pipe diameter variation coefficient of each blood vessel branch are calculated, and a blood vessel elastic characteristic matrix is generated in combination with abnormal displacement nodes; and performing multi-scale fusion on the curvature deviation degree, the pipe diameter variation coefficient and the elastic characteristic matrix, outputting a vascular structure anomaly index, and generating a hemodynamic parameter set. Dividing risk areas according to gradient distribution of the parameter set on the three-dimensional model, performing texture co-occurrence matrix analysis on pixel clusters in the high-risk areas, and extracting texture fingerprints of calcified plaques and lipid deposition. And determining the type and severity level of the cardiovascular disease according to the peak value distribution. According to the invention, accurate and objective detection and risk assessment of cardiovascular diseases are realized.