678 results about "Magnet resonance imaging" patented technology

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.

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 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 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.

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 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 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.

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.

The invention provides a multi-modal brain network classification method based on feature decoupling and dynamic graph construction, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining functional magnetic resonance imaging data and structural magnetic resonance imaging data of a to-be-detected person, and obtaining multi-modal brain network data according to the functional magnetic resonance imaging data and the structural magnetic resonance imaging data, and processing the multi-modal brain network data based on a preset multi-modal brain network classification model to obtain the brain network state of the to-be-tested person. According to the method, by combining functional magnetic resonance imaging (fMRI) and structural magnetic resonance imaging (sMRI) data, the advantages of the two modes can be fully utilized, so that the accuracy and comprehensiveness of brain network state classification are improved, a dynamic graph attention module based on a graph attention network (GAT) can capture the dynamic change characteristics of a brain network, dynamic graph representation is constructed, and the classification accuracy of the brain network state is improved. And the sensitivity of the model to the dynamic connection relationship between brain regions is enhanced.

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 relates to the technical field of brain anomaly detection and artificial intelligence auxiliary diagnosis, in particular to a graph convolutional network brain disease diagnosis method based on sub-graph sampling and multi-feature fusion, and aims to improve the accuracy of brain disease diagnosis. The method comprises the following steps: obtaining resting state functional magnetic resonance imaging data, preprocessing the data, and constructing a brain function connection diagram; and the brain function connection graph represents a brain interval collaborative activation relationship in a graph structure. Subgraph sampling is carried out based on function module division and node degree sorting, and an initial subgraph set is generated; and performing optimization selection on the initial sub-graph set by utilizing reinforcement learning to obtain an optimal sub-graph, introducing a node attention mechanism into the optimal sub-graph, screening key nodes based on attention scores, and generating a discriminant sub-graph. And extracting and fusing position features, neighborhood features and structural features of the discriminant subgraphs, and performing brain disease diagnosis based on the fused features.

The invention discloses an autism detection method based on multi-mode collaborative embedding, and belongs to the technical field of medical image analysis and artificial intelligence. The method comprises the following steps: firstly, obtaining resting state functional magnetic resonance imaging data and non-imaging data of a subject; a Markov transition field is utilized to encode the time sequence into an image so as to retain dynamic features, and feature extraction is carried out through an efficient multi-scale attention module; then realizing effective fusion and semantic alignment of multi-modal information by adopting a three-level fusion architecture and a joint loss function; and then adaptively constructing a graph structure based on the fusion features, dynamically learning a node relationship by using a graph attention network, and completing a classification decision. According to the method, the defects of a traditional method in the aspects of dynamic feature modeling, multi-modal fusion and heterogeneous graph structure processing are effectively overcome, the autism detection accuracy and robustness are remarkably improved, and a reliable tool is provided for clinical intelligent diagnosis.

The invention discloses a coupling tensor decomposition method based on sparse low-rank constraint, which is used for characteristic decomposition of a multi-frequency dynamic function network connection tensor in resting state function magnetic resonance imaging data. According to the algorithm, on the basis of the traditional coupling canonical factorization (CCPD), an optimization model of sparse and low-rank constraint is constructed, and the sparse and low-rank constraint optimization model is constructed by the algorithm. On the spatial connectivity dimension, redundant function connection is reduced through an L1 sparse penalty term, and the spatial specificity of the key brain network is enhanced; and in time and frequency band dimensions, low-rank regularization constraint is adopted to improve discrimination of cross-subject time sequence characteristics. Generally speaking, the method can effectively extract connectivity characteristics with statistical significance and time states of different frequency bands from dynamic function network connection tensors of multiple frequency bands, thereby effectively identifying functional connection heterogeneity characteristics between schizophrenia patients and healthy control groups.

The invention discloses a federated learning-based functional magnetic resonance imaging data classification method. The method comprises the following steps of obtaining multi-site resting state functional magnetic resonance imaging data and performing preprocessing; dividing a brain region and extracting a time sequence; constructing a brain function connection network by adopting a Pearson correlation coefficient; a graph sampling aggregation neural network fusing a residual connection structure and a multi-head attention mechanism is trained at each site, and shallow brain region features are reserved; adopting a linear kernel maximum mean value difference loss function to align the brain region node feature distribution of each site, and minimizing the data distribution difference between the sites; and carrying out classification training by adopting cross validation, aggregating parameters of each station through federal weighting, and evaluating classification performance indexes to obtain a final classification prediction result. According to the method, the graph sampling aggregation neural network and the cross-network layer feature alignment method are combined, the heterogeneity problem of multi-site functional magnetic resonance imaging data can be solved, and therefore generalization and classification performance of a global model are improved.

A magnetic resonance imaging (MRI) recovery method using a score-based diffusion model and an apparatus thereof are provided. The MRI recovery method using the score-based diffusion model, which is performed by a computer, is implemented, including training a continuous time-dependent score function with denoising score matching and sampling data from a conditional distribution given the measurements, leveraging the learned score function, and recovering an image.

The disclosure relates to MRI reconstruction of multiple MRI measurement datasets acquired throughout a measurement time duration. Patient motion can occur during the measurement time duration. Warping operators, sometimes also referred to as motion field, are incorporated into an iterative optimization of the MRI reconstruction.

The invention provides a scalp-to-brain magnetic resonance imaging generative model training method, a scalp-to-brain magnetic resonance imaging generative model generating method and scalp-to-brain magnetic resonance imaging generative model generating equipment, and relates to the technical field of image processing. The network comprises an encoder, a generator, a feature discriminator and an image discriminator, the encoder and / or the generator is integrated with a neural network model based on a frequency domain attention mechanism, and the encoder and the generator jointly form a target generation model after being trained; and the model parameters are jointly optimized by using a composite loss function comprising a frequency domain loss item. According to the method, the problems that an existing brain image generation technology neglects scalp structure information and is difficult to model global dependence and high-frequency details can be solved, brain magnetic resonance imaging data can be highly generated from scalp magnetic resonance imaging data, the structural integrity and topological fidelity of the generated data can be improved, and the application reliability of auxiliary diagnosis can be improved.

The invention relates to the technical field of medical auxiliary diagnosis, and discloses an ASD auxiliary diagnosis method fusing dynamic low-order and high-order dynamic central moment networks, and the method comprises the steps: obtaining resting state functional magnetic resonance imaging data of a subject, and carrying out the preprocessing; constructing a low-order dynamic function connection network and a high-order dynamic function connection network to respectively describe direct connection and connection collaboration of the brain region; extracting multi-order central moment characteristics of the two types of networks to obtain stable statistics; generating a low-high order dynamic central moment network by adopting a puzzle fusion strategy; inputting the fusion features into a visual Transform model for classification, and outputting an auxiliary diagnosis result of the infantile autism spectrum disorder; and the key brain region is identified by analyzing the attention weight of the model so as to enhance the interpretability. According to the method, multi-level dynamic connection information is effectively fused, the influence of time asynchronism is overcome, the method has the advantages of being high in classification accuracy, high in generalization ability and good in biological interpretation, and a reliable solution is provided for ASD auxiliary diagnosis.

The invention discloses a cross-subject brain decoding system based on a functional magnetic resonance image and feature decoupling. The cross-subject brain decoding system comprises a functional magnetic resonance image feature extraction module, an image generation module and an image output module, the functional magnetic resonance image feature extraction module extracts neural activity features related to visual stimulation from functional magnetic resonance imaging fMRI data, the image generation module comprises a feature decoupling module and a diffusion transformer module, and the feature decoupling module extracts common features among subjects from the neural activity features; the diffusion transformer module generates a high-accuracy image by using the common features as conditions, and the output module processes and outputs the generated image. According to the method, the features of the brain activity signals are extracted through the mask auto-encoder, the common features are extracted through the decoupling module and combined with the diffusion transformer, and external visual images can be efficiently and accurately reconstructed from functional magnetic resonance imaging (fMRI) data of different subjects.

The invention discloses a calculation method for integrating task induction and intrinsic spontaneous brain function activity. The calculation method comprises the following steps: calculating a brain activation mode when an individual executes a corresponding cognitive task based on task state functional magnetic resonance imaging data and a general linear model; identifying individual large-scale nerve avalanche with spatial continuity based on resting state functional magnetic resonance imaging data; the method comprises the following steps: performing principal component analysis on resting state functional magnetic resonance data of an individual to construct a low-dimensional state space; a task-induced brain activation mode and intrinsic spontaneous nerve avalanche are projected to an individual low-dimensional state space; calculating the Euclidean distance between the task-induced brain activity and the intrinsic spontaneous nerve avalanche in the low-dimensional state space; and detecting the prediction effect of the geometric distance on the performance of the tested task through the regression model. The method is verified on a real data set, and experimental results show that the method not only can integrate two basic brain function activities, but also can significantly predict individual cognitive performance differences.

The invention relates to a process for the preparation of the gadolinium dimeric contrast agent [p-[1-[bis[2-(hydroxy-KO)-3-[4,7,10-tris[(carboxy-KO)methyl]-1,4,7,10-tetraazacyclododec-1-yl-K / V1,K / V4,K / V7,K / V10]propyl]amino]-1-deoxy-D-glucitolato(6-)]]di-gadolinium complex. Such process includes preparation steps carried out one-pot and without isolation of the obtained intermediates. The gadolinium dimeric contrast agent can be for use in diagnostic imaging, in particular in Magnetic resonance Imaging (MRI).