2327results about "Medical imaging" patented technology

A driving state monitoring and feedback method and system based on multi-modal human factors intelligent data analysis, and an edge computing terminal device. The method comprises: receiving multi-modal human factors data of a tested driver that is collected in real time (S110); pre-processing the multi-modal human factors data, wherein the pre-processing comprises denoising processing and data normalization processing (S120); sending the pre-processed multi-modal human factors data into a pre-trained first state recognition model, so as to obtain a real-time recognized driver state, wherein driver states include a normal state and abnormal states, and the types of the abnormal states include a plurality of states such as a fatigue state, a distracted state and an angry state (S130); and when it is recognized that the driver state is an abnormal state, generating, for different categories of abnormal states, driving state feedback instructions to a driving intervention system, such that the driving intervention system performs state feedback adjustments on the driver on the basis of the received driving state feedback instructions (S140). The method and system can recognize different driving states of a driver in real time and then perform processing on the basis of different driving states, thereby avoiding the occurrence of traffic accidents.

A computer-implemented method of processing patient image data comprises: receiving a patient-specific vascular model of an individual, wherein the patient-specific vascular model is based at least in part on medical images acquired of the individual; receiving a patient-specific tissue model of a tissue of the individual; observing an actual perfusion value of the tissue; determining at least one vessel pathology that causes the actual perfusion value; and selecting a candidate updated vascular model of the plurality of candidate updated vascular models that results in a minimum difference between the hypothetical perfusion value and the actual perfusion value; updating the patient-specific vascular model to include the at least one vessel pathology of the selected candidate updated vascular model; and outputting an estimate of a location of the at least one vessel pathology or an estimate of a severity of the at least one vessel pathology.

Methods and corresponding systems of identifying coronary artery disease. The methods comprises receiving contrast cardiac CT data indicative of a contrast cardiac CT scan carried out on a patient, and analysing the contrast cardiac CT data using machine learning to identify a plurality of seed points in the contrast cardiac CT data expected to correspond to locations in cardiac arteries of the patient. The methods also comprise producing data indicative of transverse image slices of the cardiac arteries of the patient using the contrast cardiac CT data and the identified seed points, analysing the transverse image slice data using machine learning to produce inner artery wall data and outer artery wall data indicative of predicted respective inner and outer walls of the coronary arteries of the patient, and identifying presence of coronary artery disease using the predicted inner and / or outer walls of the coronary arteries of the patient.

The invention provides a zero-sample dynamic MRI reconstruction method based on global and local diffusion models, and the method comprises the following steps: generating an image data set based on original two-dimensional dynamic full-sampling heart magnetic resonance multi-coil data; designing a global and local time interlaced sampling strategy to construct two types of training data sets for generating a network model by adopting global and local cascade learning networks of a two-stage learning mechanism; performing prediction, correction, low-rank constraint and data consistency on data alternation by using an iterative solution algorithm for rapidly extracting prior information of a network model; and applying an iterative solution algorithm to the trained network model, testing, outputting a dynamic magnetic resonance reconstruction image, and evaluating the image quality. According to the method, the complete coding data is constructed through different time interlaced sampling strategies, the image is quickly and accurately reconstructed under the under-sampling condition in combination with the image prior information of the network model, and the quality and efficiency of the reconstructed image are remarkably improved.

Embodiments include methods of identifying a personalized cardiovascular device based on patient-specific geometrical information, the method comprising acquiring an anatomical model of at least part of the patient's vascular system; performing, using a processor, one or more of geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis on the anatomical model; and identifying, using the processor, a personalized cardiovascular device for the patient, based on results of one or more of the geometrical analysis, computational fluid dynamics analysis, and structural mechanics analysis of anatomical model.

An apparatus for treating or preventing a valvular disease, said apparatus comprising : - a ultrasound probe (2) located externally to a heart (H) of patient ( P), able to produce ultrasound waves focused inside said heart and suitable to generate, at a focal spot, a pressure sufficient to result in cavitation, - an imaging device (4) for mapping in real time a treatment region (C) of a cardiac valve of the patient, said treatment region comprising at least one leaflet of the cardiac valve, - a controller (5, 7, 8 ) configured for driving the ultrasound probe to emit a sequence of focused ultrasound waves, the controller being further configured for steering the focused ultrasound waves so as to scan the entire treatment region to soften the tissues of the treatment region. A method for treating or preventing valvular disease, carried out using said apparatus.

A method and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

The present disclosure relates to a multi-modal brain network computation method associated with structural function, apparatus, device, and medium. The method is applied to train a brain disease prediction model, and the brain disease prediction model includes an association perception dual-channel generation module, a disease feature regression module, a topological structure discriminator, and a time-space joint discriminator. In a model training process, by performing a multi-level interactive fusion learning on a high-order topological feature of brain functional magnetic resonance data and magnetic resonance diffusion tensor imaging data, a multi-modal time series activity signal of each brain region is obtained.

An apparatus for performing a vascular assessment is disclosed. The apparatus creates a three-dimensional model that is representative of a coronary vessel tree of a patient based on at least two angiographic images. The apparatus estimates first blood flow resistance values for points along at least some vascular segments of the coronary vessel tree using vascular geometrical dimensions of the three-dimensional model. The apparatus also estimates second blood flow resistance values for the points along the at least some vascular segments of the coronary vessel tree using a volume of a crown of the vascular segment downstream from the respective point. The apparatus determines fractional flow reserve (“FFR”) by calculating a ratio of the first blood flow resistance values and the second blood flow resistance values at each of the points along the at least some vascular segments of the coronary vessel tree.

Systems and methods are disclosed for to determining a blood supply and blood demand. One method includes receiving a patient-specific model of vessel geometry of at least a portion of a coronary artery, wherein the model is based on patient-specific image data of at least a portion of a patient's heart having myocardium; determining a coronary blood supply based on the patient-specific model; determining at least a portion of the myocardium corresponding to the coronary artery; determining a myocardial blood demand based on either a mass or a volume of the portion of the myocardium, or based on perfusion imaging of the portion of the myocardium; and determining a relationship between the coronary blood supply and the myocardial blood demand.

A surgical device includes a plurality of cameras integrated therein. The view of each of the plurality of cameras can be integrated together to provide a composite image. A surgical tool that includes an integrated camera may be used in conjunction with the surgical device. The image produced by the camera integrated with the surgical tool may be associated with the composite image generated by the plurality of cameras integrated in the surgical device. The position and orientation of the cameras and / or the surgical tool can be tracked, and the surgical tool can be rendered as transparent on the composite image. A surgical device may be powered by a hydraulic system, thereby reducing electromagnetic interference with tracking devices.

Disclosed are system, method, and computer program product embodiments for determining a digital marker. An embodiment operates by determining one or more oculometric parameters associated with an eye of a user using a video stream. The video stream comprises at least one image of a face of the user. The embodiment then determines a pupillary response function of brightness or a gaze response function by performing a deconvolution process based on at least the one or more oculometric parameters. The pupillary response function of brightness or the gaze response function is indicative of a response of the eye of the user to an external factor. The embodiment determines one or more digital markers of the user based on the pupillary response function of brightness or the gaze response function. The one or more digital markers are indicative of a neurological condition or a mental health condition of the user.

A method of automatically determining a calcium score for at least one coronary artery is disclosed. The method comprises receiving cardiac non-contrast CT data indicative of a cardiac non-contrast CT scan carried out on a patient, analysing the cardiac non-contrast CT data in a calcified components identifier to detect candidate coronary artery calcified components, and analysing cardiac non-contrast CT data associated with the candidate coronary artery calcified components using a radiomics analyser to determine radiomic characteristics of the candidate coronary artery calcified components. The method also comprises applying machine learning to the determined radiomic characteristics associated with each candidate coronary artery calcified component to identify any calcifications that are located on a coronary artery, analysing the cardiac non-contrast CT data to identify at least one body component in the cardiac non-contrast CT data not associated with a coronary artery of the patient, and using the identified at least one body component in the cardiac non-contrast CT data to remove or avoid misclassification of calcifications on a coronary artery that are located on the at least one identified body component.

A wearable device consists of a smart band and a display unit. The smart band comprises a microbial biosensor, a particulate matter sensor, an enviro sensor, a physiological sensor, a biofluid sensor, a biokinetics sensor, a lifestyle sensor, and a single board computer. The microbial biosensor detects, measures, and monitors microorganisms, and a sterilizer kills pathogens. The particulate matter sensor detects, measures, and monitors a set of suspended particles in the surrounding air. The enviro sensor monitors environmental conditions surrounding the user. The biofluid sensor detects, measures, and monitors biological fluid parameters of the user. The physiological sensor detects, measures, and monitors physiological parameters of the user. The biokinetics sensor detects, measures, and monitors physical activities of the user. The lifestyle sensor detects, measures, and monitors healthy lifestyle activities of the user. The wearable device allows for continuous monitoring of user health for accurate clinical outcomes and wellness programs.

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.

There are provided methods, systems and non-transitory storage mediums for predicting growth of an abdominal aortic aneurysm (AAA) of a patient having been diagnosed with AAA. Segmented regions of interest (ROI) comprising the aorta and adjacent structures are received by segmenting a set of images. A wall shear stress parameter and intraluminal thickness parameter is determined. A 3D parametric mesh comprising a plurality of concentric 3D mesh layers is generated, where each concentric 3D mesh layer includes a same predetermined number of nodes. The generation includes encoding the segmented ROIs, the wall shear stress parameter and the intraluminal thickness parameter as features at respective node locations in the 3D parametric mesh. A trained growth prediction machine learning model predicts, based at least on a subset of features of the 3D parametric mesh, if the given patient will show AAA growth. The training of the growth prediction model is also disclosed.

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 and apparatus for vascular assessment are disclosed. The apparatus, in some embodiments, receives, from a medical imaging device, a medical image of a coronary vessel tree of a subject and calculates a plurality of geometric measurements associated with individual portions of a vascular segment of the coronary vessel tree. The apparatus also determines a plurality of resistances associated with the plurality of geometric measurements associated with the individual portions of the vascular segment and determines a plurality of pressure drops across the individual portions of the vascular segment based on the determined resistances and a calculated or estimated blood flow. The apparatus further calculates based on the plurality of pressure drops, a functional index indicative of a presence or an absence of a stenosis within the vascular segment.

A system, method, and apparatus for displaying a fused reconstructed image with a multidimensional image are disclosed. An example imaging system receives a selection corresponding to a portion of a displayed multidimensional visualization of a surgical site. At the selected portion of the multidimensional visualization, the imaging system displays a portion of a three-dimensional image which corresponds to the selected multidimensional visualization such that the displayed portion of the at least one of the three-dimensional image or model is fused with the displayed multidimensional visualization.

Methods and system for atrial fibrillation ablations and other cardiac procedures utilizes a cardiac mapping system for navigation and guidance, and includes an additional virtual reality (VR) system and an augmented reality (AR) or mixed reality (MR) system. The Cardiac mapping system utilizes medical images and patient's electrical signals. The virtual reality (VR) system is utilized pre-procedure for visualization and pre-procedure planning. The augmented reality (AR) or mixed reality (MR) system is utilized pre-procedure or intra-procedure. Methods and system are disclosed utilizing pre-built virtual device models which interact with 3D volume rendered structures from patient's CT or MRI of the region of interest (ROI) for the different type of cardiac procedure. With virtual reality (VR) the operator utilizes hand-held sensors. With augmented reality (AR) or mixed reality (MR) a combination of hand gestures and an X-box controller is used to manipulate virtual devices, anatomical structures in the Hologram.

A sequence of images acquired consecutively in time at fixed time intervals is received, the images comprising image data of at least part of a heart having a heart wall, identifying, in each image, at least part of the heart wall and defining, in each image, at least one vector point on the identified heart wall. The method further comprises determining motion of the vector point over time, based on the sequence and receiving a first reference related to the heart. Based on the tracking of the vector point and the first reference, a motion vector is determined indicating motion of the vector point relative to the reference.

A system and method for analysis of a vessel automatically detects a pathology in a first image of the vessel and attaches a virtual mark to the pathology in the first image. The system may detect the same pathology in a second image of the vessel, based on the virtual mark, and may then provide analysis (e.g., determine an FFR value) of the pathology based on the pathology detected in the first and second images.

A system and method for analysis of a vessel automatically detects a pathology in a first image of the vessel and attaches a virtual mark to the pathology in the first image. The system may detect the same pathology in a second image of the vessel, based on the virtual mark, and may then provide analysis (e.g., determine an FFR value) of the pathology based on the pathology detected in the first and second images.

The invention discloses a brain data processing method and device, electronic equipment and a storage medium, and the method comprises the steps: collecting multi-modal image data of the brain of a target object, the multi-modal image data at least comprising resting state functional magnetic resonance imaging data and diffusion tensor imaging data at a plurality of collection moments; for each brain region of the brain, a brain region dynamic model of the brain region is constructed according to the diffusion tensor imaging data and the resting state functional magnetic resonance imaging data at the multiple acquisition moments, and the brain region dynamic model comprises disturbance parameters; by adjusting disturbance parameters of a brain region kinetic model of the brain region, simulation time sequences of the brain region under the multiple disturbance parameters are obtained, critical indexes of the brain region are determined according to the multiple simulation time sequences, and a critical toughness coefficient of the brain region is determined according to the critical indexes under the multiple disturbance parameters; constructing a critical toughness map of the brain according to the critical toughness coefficients of the plurality of brain regions, and displaying the critical toughness map; therefore, the brain health state is quantitatively evaluated.

The present disclosure relates to a line-scanning based super-resolution imaging method and apparatus. A line-scanning based super-resolution imaging method includes: providing a plurality of line-type structured excitation lights, each of the plurality of line-type structured excitation lights having a different illumination pattern; illuminating a target area in a line-by-line scanning manner along a single first direction while switching the plurality of line-type structured excitation lights, such that each row in the target area is illuminated by a corresponding one of the plurality of line-type structured excitation lights; and detecting response light generated by each row in the target area in response to being illuminated by the corresponding one of the plurality of line-type structured excitation lights.

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.

An automated measurement device and method for coronary artery disease scoring is disclosed. An example device includes a processor configured to obtain a computerized model of a plurality of vascular segments of a patient and create an unstenosed computerized model from the computerized model by virtually enlarging at least some locations of the vascular segments of the computerized model. The processor also determines vascular state scoring tool (“VSST”) scores based on characteristics of vascular locations along the vascular segments. The processor further determines a severity of stenosis for the vascular locations based on comparisons of first blood flow parameter values at the vascular locations in the computerized model to corresponding second blood flow parameter values at the same vascular locations in the unstenosed computerized model. A user interface of the device displays the severity of stenosis in conjunction with the VSST scores for the vascular locations.

The invention provides a four-dimensional cardiac function imaging method and device based on a biological magnetic signal. The method comprises the following steps: acquiring a magnetocardiogram signal of a to-be-detected object and dynamic structure information of a target trunk part in a full cardiac cycle; wherein the target trunk part at least comprises double lungs, a trunk, a heart cavity of the trunk and a pericardium structure, and the dynamic structure information comprises a contour structure and a multi-frame dynamic image; performing spatial registration on the dynamic structure information and the sensor for acquiring the magnetocardiogram signal to obtain coordinates of the sensor in a coordinate system of the dynamic structure information; according to the dynamic structure information, the coordinate of the sensor and the magnetocardiogram signal, calculating and reconstructing cardiac electrical excitation activity to obtain a calculation and reconstruction result; and converting the calculation reconstruction result into a four-dimensional visual image. According to the method, heart magnetic imaging and heart movement are combined, on the basis of obtained dynamic structure information and heart magnetic signals, the characteristics of biological magnetic signals are fully utilized, and the signal processing technology is integrated through space registration, so that accurate reconstruction of four-dimensional representation of the heart and visual display of functional activities are realized; therefore, the obtained four-dimensional visual image can completely represent the dynamic electrophysiological activities of the whole heart (including epicardium, endocardium and intrawall tissues) in a cardiac cycle, and a technical scheme with different concepts is provided for non-invasive imaging of cardiac electrical excitation activities.

Provided are a driving state monitoring and feedback method and system based on multi-modal human-factor intelligent data analysis, and an edge computing terminal device. The method includes: receiving multi-modal human-factor data collected in real time from a tested driver; preprocessing the multi-modal human-factor data; inputting the preprocessed multi-modal human-factor data to a pre-trained first state identification model to obtain a driver state identified in real-time, the driver state including a normal state and a plurality of abnormal states; and generating, in response to the driver state being identified as an abnormal state, a driving state feedback instruction for the category of the abnormal state and sending the driving state feedback instruction to a driving intervention system, to cause the driving intervention system to perform state feedback adjustment on the driver based on the received driving state feedback instruction.