945 results about "Magnet resonance imaging" patented technology

The invention relates to an ADHD classification diagnosis method based on a multi-modal attention fusion network. The ADHD classification diagnosis method comprises the steps of data preprocessing, feature extraction, cross-modal attention fusion, Mama module application, feature fusion and classification and loss function and model optimization. The present invention improves the diagnostic accuracy of ADHD by integrating functional magnetic resonance imaging (fMRI), structural magnetic resonance imaging (sMRI), and phenotypic data (including age, gender, average intelligence quotient, etc.

The invention belongs to the related technical field of brain image processing, and provides an Alzheimer's disease classification method and system based on topology perception and a group hypergraph in order to solve the problem of inaccurate classification of the Alzheimer's disease in the prior art. Constructing a dynamic function connection network sequence through a sliding window strategy; a local topology perception encoder and a global topology perception encoder are respectively used for extracting local topology features and global topology features of each time window, deep interaction and fusion are carried out, and comprehensive feature representation of a tested level is generated; according to the method, each subject is used as a hypergraph node, hyperedges are constructed on the basis of comprehensive feature representation of a subject level and by combining feature similarity calculated by diffusion tensor imaging features and clinical embedded features of the subject, then a group hypergraph is constructed, a classification result is obtained by using a hypergraph neural network, and the early classification diagnosis accuracy of the Alzheimer's disease is effectively improved.

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 invention discloses a cognitive improvement method and system based on multi-modal data. The cognition improvement method comprises the following steps: acquiring first multi-modal data of a patient before treatment; carrying out amyloid protein load grouping on the patients on the basis of amyloid protein loads in the brains of the patients so as to obtain corresponding initial cognitive training schemes; performing cognitive training based on the initial cognitive training scheme during the treatment period of the patient, and collecting second multi-modal data during the treatment period; based on the multi-modal data before and after the treatment of the patient, acquiring multi-modal change data of the patient in combination with the brain network group atlas; and inputting the multi-modal change data of the patient into a preset model to output a cognition improvement scheme for improving the cognition index of the patient to the greatest extent, and pushing the cognition improvement scheme to the patient for cognition training. According to the method, by integrating A beta-PET data, functional nuclear magnetic resonance imaging data and event-related potential data, the neuroplasticity change of a patient is evaluated in real time, so that a personalized cognition improvement scheme is dynamically generated.

Disclosed are approaches to non-invasively characterize a tumor or other lesion in a region of interest (ROI) based on various analyses of magnetic resonance imaging (MRI) data. The MRI data may correspond to ultrafast dynamic contrast enhanced MRI (DCE-MRI) and high spatial resolution DCE-MRI scans, and diffusion-weighted MRI (DW-MRI) scans of the ROI. Vasculature metrics may be determined, and tumor-associated blood flow velocity and / or tumor interstitial pressure may be obtained using the vasculature metrics as inputs to a computational fluid dynamics model. A combination of morphological vascular metrics and functional vascular metrics may be used to characterize the tumor. Malignancy, aggressiveness, treatment response, and other features of tumors or other lesions, in the breast or other regions of a patient, may be characterized through disclosed analyses of MRI data.

The invention relates to the technical field of medical information extraction, in particular to an image feature selection method for a schizophrenia medical record Spark model. The method comprises the following steps: acquiring brain magnetic resonance image data and resting state functional magnetic resonance imaging data of a plurality of subjects; performing multi-scale layered segmentation on the brain image of each subject by using a preset layered brain region template for the brain magnetic resonance image data to obtain brain region positioning data including a local level, a regional level and a global level; and performing adaptive hierarchical noise reduction processing on the resting state functional magnetic resonance imaging data in a Spark distributed environment to obtain brain function time sequence data after noise reduction. According to the method, the brain magnetic resonance image data is accurately divided into a local level, a regional level and a global level by using the preset layered brain region template, so that local structure details and global network characteristics of the brain region are considered, and effective integration of multi-scale information is realized.

The invention discloses a transcranial magnetic stimulation target region recommendation method based on craniocerebral position estimation. The method comprises the following steps: constructing a face key point cloud based on binocular stereo vision; reconstructing the face key point cloud into a cranial surface model by using a pre-trained generative model; the method comprises the following steps: collecting magnetic resonance imaging data of patients with various diseases, establishing a transcranial magnetic stimulation target region standardized template with disease specificity, and determining a stimulation target region of each disease in a standard space; and based on the transcranial magnetic stimulation target region standardization template, mapping the corresponding stimulation target region from the standard space brain region to the scalp of the patient according to the disease type of the patient so as to realize recommendation. According to the method, the graph neural network is innovatively combined with the SHAP algorithm, the scientificity and interpretability of target spot selection are improved, important brain region target spots are obtained by calculating the contribution value of classification model features to classification decision of each sample, and then intervention target spots are sorted and selected to obtain an optimal treatment target spot recommendation scheme.

The present disclosure provides various devices, systems, and methods for iterative shimming in magnetic resonance imaging (MRI). In one aspect, a method of iterative shimming can include generating a field map of a magnetic field B0 from an MRI system. The MRI system can include a shim tray and an array of permanent magnets to generate the magnetic field B0. The shim tray can include slots to receive shim magnets. The method can further include shimming the MRI system by iteratively: applying the field map to a genetic algorithm to determine a set of slots to receive a set of shim magnets, installing the set of shim magnets in the set of slots, generating a next field map of the magnetic field B0, and either performing a next iteration based on the next field map or determining to not perform the next iteration based on the next field map.

The invention discloses a heart motion feature extraction method based on optical flow estimation, and relates to medical image processing. Preprocessing the input four-dimensional space-time cardiac magnetic resonance imaging data, namely, scaling pixel values; inputting two frames of images which are continuous in time into a feature encoder and a context encoder for feature extraction, wherein the two frames of images are divided into a reference frame and a moving frame; calculating the correlation between the feature maps through a correlation volume calculation module, constructing a correlation pyramid, and extracting the feature maps to provide matching information for subsequent optical flow estimation; the motion feature iteration enhancement module iteratively and continuously refines an optical flow estimation result through a deformable convolution and global motion aggregation (GMA) module; model parameters are optimized based on a weighted sum of luminosity consistency loss, smoothness loss, and gradient consistency loss. Edge features are adaptively captured through deformable convolution, global and local features are fused by using a GMA module, optical flow prediction errors are effectively reduced, motion estimation quality is improved, and key features of a heart edge region are maintained.

The invention relates to the technical field of anesthesia management, in particular to an anesthesia medicine excess brain function influence assessment system applying artificial intelligence, which comprises a data acquisition module, a data preprocessing module, a model training module, a real-time assessment module, a risk early warning module, a treatment suggestion module, a system interaction module and a remote monitoring module. Through the combination of a convolutional neural network and a long-short-term memory network, spatial-temporal characteristics of brain wave and functional magnetic resonance imaging data are extracted, a brain function thermodynamic diagram is generated in real time, and the influence of anesthetic drugs is analyzed; the system is combined with a reinforcement learning algorithm to dynamically adjust the anesthetic dosage, a risk early warning module dynamically adjusts a threshold value through physiological parameters and tolerance of a patient, an expert team can provide medical assistance in real time through a remote monitoring module, and brain function safety of the patient in the operation or anesthesia process is ensured; according to the invention, the safety, accuracy and individuation level of anesthesia management are effectively improved.

The present disclosure provides various devices, systems, and methods for iterative shimming in magnetic resonance imaging (MRI). In one aspect, a method of iterative shimming can include generating a field map of a magnetic field B0 from an MRI system. The MRI system can include a shim tray and an array of permanent magnets to generate the magnetic field B0. The shim tray can include slots to receive shim magnets. The method can further include shimming the MRI system by iteratively: applying the field map to a genetic algorithm to determine a set of slots to receive a set of shim magnets, installing the set of shim magnets in the set of slots, generating a next field map of the magnetic field B0, and either performing a next iteration based on the next field map or determining to not perform the next iteration based on the next field map.

The invention relates to a high-field animal magnetic resonance imaging superconducting magnet, a gradient coil and a decoupling method. Wherein the superconducting magnet comprises two main coils, four adjusting coils and three shielding coils, and nine first-order, second-order and third-order axial superconducting shimming coils are arranged in the superconducting magnet; the gradient coil comprises an active shielding magnetic field coding coil and further comprises 11 coils in the zero-order axial direction, the second-order axial direction, the third-order axial direction and the fourth-order axial direction. Electromagnetic coupling and mechanical coupling between the superconducting magnet and the gradient coil are optimized, the basic judgment standard is that the minimum distance between coil wires is appropriate, engineering processing is feasible, and therefore the minimum stray magnetic field of the gradient coil and the minimum electromagnetic Lorentz force are obtained. Cooperative stable operation can be guaranteed without additional connection processing between the two functional parts. The superconducting magnet shielding effect is excellent, the gradient coil shielding effect is excellent, and strong Lorentz force coupling between the superconducting magnet and the gradient coil is effectively eliminated.

Embodiments can take brain activity measurement and decode into continuous language. Embodiment can use non-invasive brain recordings, such as functional magnetic resonance imaging (fMRI) and functional near-infrared spectroscopy (fNIRS) to detect changes in blood oxygen level that are coupled to neural activity. These brain recordings can be used by a language reconstruction model that involves a neural language model to predict a next word in a sequence and an encoding model to decode the recordings into continuous language, or sequences of words.

The invention discloses a boron manganese tannate nanoparticle as well as a synthesis method and application thereof. The synthesis method of the tannic acid boron manganese nanoparticles comprises the following steps: sequentially dropwise adding a bivalent manganese salt solution, a boric acid solution, a polyethylene glycol solution and a bovine serum albumin solution into pure water under water bath ultrasonic or magnetic stirring to obtain an assembly stock solution, and adding a tannic acid solution into the assembly stock solution to initiate assembly; and continuously carrying out water-bath ultrasonic treatment or magnetic stirring for a period of time, and then carrying out ultrafiltration to remove free drug molecules, so as to obtain the boron manganese tannate nanoparticles capable of mediating boron neutron capture therapy and magnetic resonance imaging. The boron manganese tannate nanoparticles can improve the boron intake in tumors and prolong the retention time of boron atoms in the tumors in boron neutron capture therapy, drug distribution is monitored through the nuclear magnetic resonance imaging technology, then the treatment effect of the boron neutron capture therapy is improved, the preparation method is simple, the stability is good, the size is uniform, the boron loading amount is high, and the application prospect is wide. The biological safety is good, and the repeatability is high.

An apparatus includes an input unit, a processing unit, and an output unit. The input unit is configured to provide the processing unit with at least one diffusion magnetic resonance imaging dMRI image of a patient's brain. The processing unit is configured to: 1) determine an estimate of an orientation of neurons at each voxel in the dMRI image; 2) determine a plurality of fiber tracts in the at least one dMRI image; 3) select a plurality of voxels along at least one fiber tract of the plurality of fiber tracts; and 4) determine a neurological disease classification.

The invention relates to a Spin Exchange Relaxation-Free (SERF)-based magnetometer system (100) for detecting ultra- weak magnetic fields, particularly suited for bio¬ sensing applications such as MEG, MCG, and MRI. The system operates at room temperature and includes a VCSEL (101) emitting dual beams tuned to Rubidium-87 transitions, a microfabricated vapor cell (102) with LIAD-based heating (103), and an optical assembly (104) for polarization control. A detection unit (105) converts Faraday rotation into electrical signals, enhanced by lock-in amplification. Magnetic shielding (106), thermal management (107), and relaxation mitigation maintain SERF conditions. A calibration subsystem (109) and embedded machine learning module (110) optimize sensitivity in real time. The compact, cryogen-free architecture enables portable and wearable applications. The invention achieves femtotesla-level sensitivity while overcoming limitations of conventional SQUID-based systems, making it suitable for clinical and field deployments.

The invention discloses an Alzheimer's disease early warning method based on white matter lesion omics characteristics, and relates to the field of wisdom medicines.The method comprises the steps that magnetic resonance imaging data of a historical subject in the period from the mild cognitive impairment period to the period before diagnosis of Alzheimer's disease are obtained, and manual labeling of white matter and white matter lesion areas is carried out; a manual annotation data set is obtained; training a deep learning model for white matter lesion recognition based on the manual annotation data set; inputting to-be-identified magnetic resonance imaging data into the deep learning model, and extracting lesion features of the white matter; performing standardization and feature alignment on the extracted lesion features, and inputting the lesion features into a deep clustering model to form clustering results for different white matter lesion feature types; and an early risk assessment model is constructed based on the clustering result and the Alzheimer's disease transformation risk tag corresponding to the clustering result, and the Alzheimer's disease transformation risk level of the subject is output, so that the problems that multiple lesion features are difficult to quantify and details are difficult to identify are solved.

The invention relates to an infantile autism classification method based on a double-branch functional topological graph neural network. The infantile autism classification method can realize the classification of the infantile autism by using functional magnetic resonance imaging data. According to the provided autism classification network, long-distance connection and short-distance connection are divided based on the shortest path between brain intervals, then an exponential decay mask is introduced through a functional topological graph Transform branch to adjust attention weight and accurately extract long-distance dependency features, a graph isomorphic network in the other branch is subjected to multiple neighborhood aggregation operations, short-distance dependency features are captured, and the short-distance dependency features are extracted. According to the method, multi-scale dependence of the brain network is extracted in parallel through a double-branch structure, information redundancy is reduced by means of a topology perception attention mechanism, and the adaptive ability of the model to the heterogeneous brain network is improved by using the adaptive fusion module, so that multi-scale dependence of the heterogeneous brain network is balanced in a self-adaptive manner. The classification accuracy is remarkably superior to that of an existing mainstream method, objective and efficient technical support is provided for autism diagnosis, and high interpretability is achieved.

The invention discloses a depression brain function connection data analysis method based on multi-map fusion, is applied to the field of deep learning and nerve imaging, and aims to solve the problem that depression brain function connection cannot be represented accurately and effectively in the prior art. According to the method, brain region level blood oxygen concentration dependency signals are obtained from resting state functional magnetic resonance imaging data based on a plurality of brain maps divided according to different scales or functions, and then a functional connection matrix of each map is obtained through calculation based on a correlation algorithm; and then abstracting the functional connection matrix into graph structure data, carrying out feature extraction by using a multi-scale graph convolutional layer, and finally fusing and analyzing multi-map features by using two different strategies of multi-head cross attention fusion and adaptive map weight learning, so as to execute downstream tasks such as diagnosis or biomarker positioning. According to the method, multi-map information of functional magnetic resonance imaging is well integrated, the reliability of a depression brain function connection analysis result is improved, and an objective reference is favorably provided for clinic.

The invention discloses a closed-loop noninvasive nerve regulation and control system based on electroencephalogram and time domain interference electrical stimulation, and the system comprises an upper computer which is used for generating an electroencephalogram signal collection instruction and transmitting the electroencephalogram signal collection instruction to a lower computer; the lower computer is used for responding to the electroencephalogram signal acquisition instruction, acquiring a high-resolution electroencephalogram signal of the target object in a preset period and sending the high-resolution electroencephalogram signal to the upper computer; the upper computer is also used for positioning a to-be-regulated brain region of the target object according to the received high-resolution electroencephalogram signal and the magnetic resonance imaging data of the target object, acquiring stimulation parameters of time domain interference electrical stimulation of the to-be-regulated brain region, and issuing the stimulation parameters to the lower computer; and the lower computer is also used for performing transcranial time domain interference electrical stimulation on the target object according to the received stimulation parameters, and when the electrical stimulation duration reaches a preset threshold value, continuing to collect the high-resolution electroencephalogram signal and sending the high-resolution electroencephalogram signal to the upper computer so as to realize closed-loop noninvasive nerve regulation and control. By adopting the method and the device, individualized closed-loop noninvasive nerve regulation and control can be realized, and the accuracy and timeliness of noninvasive nerve regulation and control are improved.

The invention discloses a brain function magnetic resonance imaging data analysis method based on a contrast graph neural network, and the method comprises the steps: firstly carrying out the data enhancement of a brain function connection graph, and simulating the heterogeneity of brain function magnetic resonance data, so as to improve the diversity of a data set; secondly, the hidden space embedding features of the brain function connection diagram are efficiently learned by fusing a double-Hough-Laplacian diagram convolutional network and a contraction incentive mechanism; the embedded features are mapped to a group of prototype vectors, and prototype allocation codes corresponding to the embedded features are calculated by adopting a Sinkhorn-Knopp algorithm; performing exchange optimization on prototype codes between different enhanced brain connection diagrams of the same subject through a contrast learning strategy, and compelling codes of homologous subjects to be aligned at the minimum cost in combination with a cross entropy loss function; and finally, applying the pre-training model to a functional magnetic resonance imaging data set of the Alzheimer's disease, and carrying out interpretability analysis on learning features to improve the classification efficiency and pathological analysis of the Alzheimer's disease under a limited tag condition.

The invention belongs to the technical field of biological medicine and electronic information technology, and discloses an atherosclerosis model based on nano-molecule magnetic resonance imaging and a machine learning algorithm, a nano-targeting probe and application of the atherosclerosis model based on the nano-molecule magnetic resonance imaging and the machine learning algorithm. A high-resolution MRI image capable of specifically displaying foam macrophage distribution in the plaque is obtained; then, image omics features are extracted from the images, a machine learning model which is constructed based on the features and verified by pathological labels is adopted for analysis, and finally an objective and quantitative plaque vulnerability score is output. According to the model disclosed by the invention, an objective and quantitative risk score can be output by fusing nano-targeted molecular imaging and machine learning, and accurate and reliable quantification of plaque vulnerability is realized. According to the invention, through systematic integration of targeted molecular imaging and machine learning, significant technical progress is brought.

The purpose of the present invention is to provide a magnetic resonance imaging apparatus and a method for controlling a refrigerator. The magnetic resonance imaging apparatus increases the operating rate of an MRI apparatus by prolonging the replacement life of the refrigerator and reducing the replacement frequency of the refrigerator. And executing a cold head life prolonging mode. In the cold head life extension mode, regardless of the temperature of the superconducting coil, the displacer of the refrigerator is moved at a constant frequency lower than a predetermined upper limit frequency, and the drive frequency of the compressor drive unit adjusted by the compressor inverter is controlled in accordance with the temperature of the superconducting coil.

A magnetic resonance imaging (MRI) apparatus that includes a multi-resonance transmit / receive (T / R) switch, one switch design having a top side and a bottom side of a microstripline based hybrid coupler, a first pin diode, a second pin diode, four vertical transmission lines and six horizontal transmission lines arranged in a bended manner on the microstripline. The top side is associated with a first port and a second port, and electrically connected to the first pin diode and the second pin diode. The bottom side is associated with a third port and a fourth port, and electrically connected with the first pin diode and the second pin diode. The switch receives radio frequency (RF) pulses of different frequencies transmitted to and received from an MRI RF coil where the different frequencies are received concurrently and without tuning by the multi-resonance T / R switch from the MRI RF coil.

A portable magnetic resonance (MR) imaging system is disclosed, including a housing including an array of magnets, a radio frequency (RF) coil assembly, a RF power amplifier, a primary control circuit and a safety control circuit. The RF power amplifier is configurable from a default configuration to an enabled configuration. The system can further include a thermocouple positioned to monitor a temperature.

The invention provides a brain age prediction method based on a twinborn pruning attention neural network, and belongs to the technical field of medical image intelligent diagnosis. While the accuracy of brain age prediction is ensured, the calculation overhead of the model for high-dimensional rs-fMRI image data is reduced, and the generalization ability of the model in a small sample and individual difference significant scene is improved. According to the technical scheme, the method comprises the following steps that S1, resting state functional magnetic resonance imaging of a subject is collected; s2, constructing a pruning module; s3, constructing a twin neural network model; s4, designing a joint loss function to comprehensively consider structural similarity and label similarity; and S5, after model training is completed, inputting test set samples into the trained twin network structure one by one for prediction analysis. The method has the beneficial effect that the accuracy and generalization ability of brain age prediction are improved.

A superconducting magnet system includes a coil support structure having a body, a superconducting magnet having a plurality of coils disposed about the body, and a helium vessel encompassing the coil support structure and the superconducting magnet. The superconducting magnet system includes a cooling system that includes a network of tubes disposed within the helium vessel and configured to transport helium within, wherein the network of tubes is retroactively thermally coupled to the plurality of coils. The cooling system includes a liquid helium storage system located within the helium vessel and a helium recondenser located outside the helium vessel. The cooling system includes one or more tubes coupling the helium storage system to the helium recondenser, wherein where the one or more tubes pass through the helium vessel is retroactively vacuum sealed to make the helium vessel a sealed helium vessel.

The invention discloses a rapid magnetic resonance diffusion kurtosis parameter imaging method based on a model, and relates to the technical field of magnetic resonance imaging. The method comprises the following steps: performing signal acquisition on a measured target; establishing a DKI mathematical model; calculating sensitivity information S of the multi-channel coil; performing initial reconstruction on the down-sampling k space data of each b value and each diffusion coding direction to obtain an initial complex image; performing DKI tensor estimation according to the amplitude information of the initial complex image to obtain an initial DKI tensor; extracting phase information P of each b value and each diffusion coding direction; constructing a target function with regularization constraint; solving a DKI tensor enabling the objective function to be minimum; calculating a DKI quantization parameter; and calculating a DKI image according to the DKI tensor obtained by optimization solution and the DKI mathematical model. According to the method, the DKI tensor can be directly reconstructed from the downsampling k space data, two steps of image reconstruction and tensor fitting are not needed, error transmission is avoided, and the DKI parameter imaging quality is further improved.

The invention relates to an antenna and an antenna arrangement for an imaging method, a method for adjusting the length of an antenna, and a tomography system, in particular for magnetic resonance imaging (MRI) or simultaneous positron emission tomography-MRI (PET-MRI). An antenna (1) for an imaging method comprises a radiation section (2) and a feed section (3). The feed section (3) comprises a capacitor (11) and an inductor (15).

The invention relates to a magnetic resonance imaging system (100). The magnetic resonance imaging system (100) comprises a memory (134) and a processor (130). The memory (134) stores machine executable instructions (140), first pulse sequence commands (142) and second pulse sequence commands (144). Execution of the machine executable instructions (140) by the processor (130) causing the processor (130) to control the magnetic resonance imaging system (100) to acquire identifying magnetic resonance data (146) using the first pulse sequence commands (142). The identifying magnetic resonance data (146) identifies on a per voxel basis, whether the respective voxel is water or fat dominated. Imaging magnetic resonance data (148) is acquired using the second pulse sequence commands (144). A magnetic resonance image (150) is reconstructed using the imaging magnetic resonance data (148). The identifying magnetic resonance data (146) is used to determine on a per voxel basis, whether the imaging magnetic resonance data (148) used for the reconstruction is dominantly induced by water or fat.