1396results about "Medical imaging" patented technology

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.

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.

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.

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.

Disclosed herein are systems and methods for training an AI language model for assessing and improving happiness and wellbeing of a human user, the method comprising receiving a pre-trained AI language model; receiving a first training data set comprising non-user-specific training data comprising brain imaging data; applying one or more supervised training protocols based on the first training data set to modify the pre-trained AI language model to generate a non-user-specific language model configured for happiness and wellbeing support of human users; receiving a second training data set comprising user-specific training data comprising brain-imaging data for a specific user; and applying one or more supervised training protocols based on the second training data set to modify the non-user-specific language model to generate a user-specific language model configured for happiness and wellbeing support of the specific user.

An apparatus includes a communication interface and at least one processor. The processor is configured to obtain a size of a lumen in each of a plurality of aortic zones and first information for each of the plurality of aortic zones based on segmentation results of an aorta and the plurality of aortic zones within the aorta; and visualize the size of the true lumen, the first information, and a ratio between the size of the true lumen and the first information in each of the plurality of aortic zones by using a visualization segment corresponding to each of the plurality of aortic zones.

The invention relates to an apparatus (1) and method for automatically determining the blood flow direction (42) in a blood vessel (14) using the fluorescence light from a fluorophore (16). Blood flow direction (42) is determined by first identifying a blood vessel structure (38) in an input frame (6) from a camera assembly (2) using a pattern recognition module (26). Blood flow direction (42) is determined from the spatial gradient (dl / dx) of the fluorescence intensity (I) along the identified blood vessel structure (38) and the temporal gradient (dl / dt). An output frame (48) is displayed on a display (36) with time-varying marker data (52) overlaid on the identified blood vessels structure (38) and representative of the blood flow direction (42).

A method and corresponding system of identifying coronary artery disease. The method 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 method also comprises 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.

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.

Systems and techniques are described for determining at least one of a translational force or a rotational force applied by a dental appliance to a tooth or teeth of a patient. A system includes model teeth configured to represent a portion of a dental arch and receive a dental appliance, a post extending from a first end coupled to a respective tooth to a second end coupled to a base, at least one strain gauge integrally formed on a surface of the post between the first end and the second end, and processor communicatively coupled to the at least one strain gauge and configured to determine at least one of a translational force or a rotational force applied by the dental appliance to the respective tooth.

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 system and method for a laryngoscopic device which incorporates the electronic and imaging elements within the device with advanced functional iterations that incorporate mathematical pattern recognition / predictive modeling as well as parallel computing and supercomputing for data analysis as well as and integrating it with the patient's record and compiling images from multiple scopes in a cloud-based system for an AI platform to use in diagnosis and disease monitoring.

The invention provides a working memory ability assessment method and device for dynamic brain network attention fusion, and belongs to the technical field of intelligent medical treatment, and the method comprises the steps: segmenting a BOLD signal time sequence in functional magnetic resonance imaging data, extracting time sequence data of a region of interest, and constructing a functional connection matrix; extracting and fusing spatial dependence and time fluctuation characteristics in the functional connection matrix based on an attention mechanism to obtain a full connection graph; clustering the full connection graph by using multi-head attention to obtain a functional magnetic resonance imaging embedded representation of each testee; training the prediction network by using the functional magnetic resonance imaging embedding representation of the testee; and using the trained prediction network to complete work memory evaluation of the target subject. According to the method, dynamic multi-graph fusion and neuroscience priori knowledge are combined, individual differences and specific noise are eliminated through comparative learning, accurate evaluation of the working memory ability is achieved, and an objective basis is provided for evaluation of the cognitive function.

A method for performing multi-voxel spectroscopy includes obtaining structural magnetic resonance imaging data of a brain of a subject acquired with a magnetic resonance imaging scanner. The method also includes performing skull stripping on the structural magnetic resonance imaging data to generate a skull stripped brain image. The method further includes utilizing a trained deep learning-based segmentation model to generate a lesion core mask from the brain image. The method also includes locating a slice with largest volume of lesion present in the lesion core mask. The method includes calculating a voxel volume that avoids aliasing from the slice based on a field of view. The method includes automatically selecting a volume of interest in the brain having both the lesion and normal brain tissue for a multi-voxel spectroscopy scan by the magnetic resonance imaging scanner based on the brain image, the lesion core mask, and the voxel volume.

The invention discloses a thoracic cavity heart sound signal non-contact extraction system and method based on structured light. The system comprises a structured light projection module, an image acquisition module, an image processing and displacement extraction module and a signal processing and trigger point identification module, structured light is projected on the surface of the chest of a human body to form a regular pattern, and a high-speed image acquisition means is utilized to record micro displacement change generated by light spots under cardiac drive. Thoracic cavity vibration information is extracted through an image processing technology, periodic feature points corresponding to a cardiac rhythm are recognized in combination with a time sequence analysis strategy, a thoracic cavity heart sound signal is extracted, rhythm analysis is conducted, and a cardiac trigger point is output to serve as a trigger signal for synchronous control of medical equipment. The technical problems of signal distortion, equipment interference, potential safety hazards, inconvenience in use and the like in a strong electromagnetic environment in a traditional contact mode are solved, and a high-precision synchronous trigger signal is provided for dynamic imaging.

The invention discloses a control device and computer equipment for a brain health therapeutic apparatus, and relates to the technical field of brain stimulation control, and the method comprises the steps: synchronously collecting an fMRI activity map, sMRI data and whole-brain voxel-level DTI data of a subject, extracting a cortex target region to be stimulated, and reconstructing and generating a triangular mesh model; non-linear registration is carried out on whole-brain voxel-level DTI data and the sMRI space, and a target region vertex direction field is generated; calculating a direction field average direction and a direction field covariance of the direction field at the vertex of the target region; if the covariance of the direction field does not exceed a preset covariance threshold value, controlling the brain health therapeutic apparatus to perform magnetic stimulation operation on the cortex target region according to the average direction of the direction field; and if the direction field covariance exceeds a covariance threshold, screening a first target region vertex sector and a second target region vertex sector, and controlling the brain health therapeutic apparatus to perform time division multiplexing vector scanning between sectors. Therefore, the accuracy of targeted stimulation and the individual suitability are greatly improved.

One or more tractograms of a global tractography of a tissue of interest are determined. At least one instance of diffusion magnetic resonance imaging data of the tissue of interest is obtained. A trained machine-learning algorithm generates the one or more tractograms based on the at least one instance of the diffusion magnetic resonance imaging data.

Embodiments of the present disclosure relate to a scanning device comprising a support base, a calibration pattern disposed on an upper surface of the support base, and a plurality of cameras distributed around an outer perimeter of the support base. In at least one embodiment, the calibration pattern is undetectable or substantially undetectable to the human eye.

A system for remote monitoring of a workout, including: a processor; a non-transient memory; an imaging sensor; and a display device, the non-transient memory having computer-readable instructions stored thereon which, when executed by the processor, perform steps including: capturing imagery of at least a face of a subject, estimating and monitoring a peripheral blood volume pulse (BVP) or heart rate from the imagery using remote photoplethysmography (rPPG), performing facial age estimation using computer vision to analyze the imagery of the face to estimate an age of the subject, estimating a maximum heart rate using at least the age of the subject, calculating a heart rate zones as a predetermined percentage of the maximum heart rate, and displaying an indication of a current heart rate zone based on a current BVP or heart rate.

Photoacoustic medical-device navigation systems and methods provide alternatives to those incorporating fluoroscopy for medical-device navigation in patient bodies. A medical-device navigation system can include an optical-fiber stylet, ultrasound transducers, and a console. The stylet can transmit light to an instant location of a distal tip of an elongate medical device in a patient's body and, thereby, irradiate endogenous chromophores to generate ultrasound-frequency photoacoustic pressure waves therefrom. The ultrasound transducers can detect ultrasound signals corresponding to the photoacoustic pressure waves. The console can instantiate medical-device navigation processes for navigating the elongate medical device via the stylet as the elongate medical device is advanced to the target location in the patient's body. The medical-device navigation processes can include acquiring ultrasound-signal data from the ultrasound transducers, reconstructing images from the ultrasound-signal data, and displaying reconstructed images on a display for navigating the elongate medical device to the target location in the patient's body.

The invention discloses a voice control-based augmented reality precise transcranial magnetic stimulation system, which comprises augmented reality equipment, a voice recognition and control module and transcranial magnetic stimulation TMS equipment, and is characterized in that a magnetic resonance structure image of a tested head is projected to a real head, precise intracranial intervention sites are visualized, precise placement of a TMS coil is guided online, and the accuracy of the TMS coil is improved. The accuracy of transcranial magnetic stimulation is effectively improved, and the TMS equipment can be controlled through voice to switch a preset stimulation target, a stimulation mode, stimulation duration and the like according to a testee; the operation platform is transferred from a far-end computer screen to a tested actual head, a transcranial magnetic stimulation practitioner only needs to place a coil according to a visual model, the operation difficulty is greatly reduced, dependence on expensive and non-portable existing neural navigation hardware is reduced, and the transcranial magnetic stimulation system has the advantages of being easy and convenient to operate, portable, movable, low in cost, easy to popularize and suitable for popularization and application. And the use efficiency and the application range of the TMS equipment are greatly improved.

The invention discloses a multi-mode brain dysfunction auxiliary diagnosis method based on a dynamic function connection network. A two-stage collaborative learning framework from an individual brain graph to a group relation graph is constructed. Firstly, an individual multi-modal fusion brain map is constructed, node features of the individual multi-modal fusion brain map are obtained through node regularization regression analysis of an rs-fMRI time sequence, an adjacent matrix is obtained through calculation of the brain interval grey matter volume difference of a T1 image, and individual enhancement characterization is obtained through map convolutional network fusion. And then constructing a group relationship enhancement graph, taking individual representation as node features, constructing a dual-channel adjacency relationship for distinguishing homologous / heterologous connection according to age and gender, obtaining final discriminative representation through dual-channel graph attention network aggregation, and performing classification diagnosis according to the final discriminative representation. According to the method, deep fusion of multi-modal information and explicit modeling of key biological variables are realized, and an effective tool is provided for accurate and explainable auxiliary diagnosis of brain diseases.

The invention discloses a blood brain barrier water extraction rate imaging method and system based on artery marking and speed selection. By marking spinning of water molecules in artery blood and selectively detecting signals of residual marked blood which does not enter brain tissues in vein blood vessels, quantitative evaluation of blood-brain barrier water permeability is realized. According to the method, the neck artery blood flow is magnetized and overturned by adopting pseudo-continuous artery spin labeling, a speed selection module based on Fourier transform is applied in an acquisition stage to eliminate interference of brain tissue signals, pure vein labeling signals are obtained, and rapid and accurate measurement of water extraction fraction and permeability-surface area volume is realized. In addition, the signal-to-noise ratio and robustness are effectively improved by adopting dual suppression pulses of background tissues and blood. The method does not need an exogenous contrast agent, is non-invasive, short in scanning time and accurate and stable in measurement result, and has important clinical application value for early diagnosis of brain diseases and assessment of pathological changes of the blood brain barrier.

A method for analyzing a feature of interest using a skin inspection system is disclosed. The method comprises generating a first output from a first machine learning model, which analyzes a current image dataset from a skin inspection device to determine a detected state of the feature on a user's skin. A second machine learning model generates a second output comprising a predicted state of the feature, based on historical data for that feature. A processor compares the first output (the detected state) with the second output (the predicted state) to determine a deviation. Finally, the determined deviation is classified into one of a plurality of predefined clinical risk categories.

Methods for non-invasive or minimally invasive assessment of epithelial tissue structure are disclosed. Digital imaging and processing are used to identify cell locations. More specifically, an automated algorithm that may be used to identify epithelial tissue structure, and / or to specify the coordinates / locations of cells in the epithelial tissue structure, through non-invasive or minimally invasive imaging, and use of this information to extract values of epithelial structure related parameters are disclosed.