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64 results about "Cerebral structure" patented technology
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Device and method for altering neurotransmitter level in brain
The present disclosure relates to a device and a method for non-invasively applying optical radiation to photosensitive parts of the brain. In particular the present invention is directed to a device for non-invasive / trans-cranial light therapy comprising one or more radiation units adapted to direct optical radiation at one or more neuroanatomical brain structures of a user from at least one extra-cranial position below the cerebrum of the user, said device applied for use in altering and / or controlling the production, release, re-uptake and / or metabolism of dopamine or serotonin in at least one of said one or more neuroanatomical brain structures and / or in the body of the user.
Owner:VALKEE
Novel genotype analysis method based on multimodal brain images
InactiveCN107507162AOptimize forecast resultsImage enhancementImage analysisDiagnostic Radiology ModalityNODAL
The invention discloses a novel genotype analysis method based on multimodal brain images. With utilization of information, including a structural image and a functional image, of two kinds of modes, regression on the rs429358 locus genotype linked to the Alzheimer's disease closely is realized. The method comprises: extracting voxel features from a brain structure images and extracting connecting network features from a brain function image; carrying out feature extraction on the two kinds of features by using an elastic network and keeping brain area and network connection with a high association degree; and then carrying out fusion and genotype regression on selected nodes and side attributes by using a multi-kernel support vector. The method has the following advantages: for the brain image with the complicated structure and functions and genotype data, a novel regression analysis method for a single locus genotype based on structural and functional multimodal brain images is put forward; the analysis effect is good; and complementary information of the two modes is utilized fully.
Owner:NANJING UNIV OF AERONAUTICS & ASTRONAUTICS
Multi-modal brain image depression identification method and system based on graph node embedding
ActiveCN111127441AImprove recognition resultsImprove accuracyImage enhancementImage analysisImaging dataNeural network nn
The invention provides a multi-modal brain image depression identification method and system based on graph node embedding. Deep learning is applied to the depression recognition of a multi-modal brain image, and a bridge is built between a multi-modal brain network and a convolutional neural network (CNN) through graph node embedding, so that the CNN can be used for the depression recognition ofthe multi-modal brain image, and therefore, depression recognition accuracy is improved. The method comprises the following steps of: 1) acquiring the resting state fMRI and DTI image data of a depressive patient and a normal control group; 2) preprocessing the acquired fMRI and DTI image data; 3) respectively constructing a brain function network and a brain structure network according to the preprocessed fMRI and DTI image data to obtain a brain network adjacency matrix, and 4) adopting graph node embedding to express the adjacency matrix as an image, inputting the image into a convolutionalneural network for classifying the image, and establishing a classification model for identifying depressive patients and normal subjects.
Owner:LANZHOU UNIVERSITY
Transcranial brain map generation method for group application, and transcranial brain map prediction method and device
ActiveCN109416939AAct quicklySolve inherent problems such as positioningMedical imagesInstrumentsMarkov chainScalp
A transcranial brain map generation method, a transcranial brain map prediction method for group application and a corresponding transcranial brain map prediction device are provided. The transcranialbrain map prediction method includes the steps of: creating a craniofacial coordinate system at an individual level; establishing a transcranial mapping system for connecting the position of a skullwith the position of a brain; and constructing a transcranial brain map using a two-step stochastic process in a Markov chai. The transcranial brain map projects invisible intracranial map label information onto the visible scalp, allowing researchers or physicians to use the brain structures and functional map information to greatly enhance the effect of the brain map in the use of transcranial mapping techniques.
Owner:BEIJING NORMAL UNIVERSITY
Individualized brain covariant network construction method based on three-dimensional textural features
ActiveCN110838173AEasy to describeGood personal informationImage enhancementImage analysisVoxelData set
The invention relates to an individualized brain covariant network construction method based on three-dimensional textural features, which comprises the following steps of: 1) segmenting a brain structure image into brain tissue component concentration graphs by using tissue segmentation, and registering the brain tissue component concentration graphs to a standard space template to obtain a standardized brain structure image; 2) extracting three-dimensional texture features corresponding to the standardized brain structure image at a voxel level through at least two gray feature extraction modes, and obtaining a spatial distribution diagram of each texture feature; and 3) defining a brain region map as a network node, extracting the texture feature of each brain region of the individual subject from the grayscale matrix texture feature data set, calculating the Pearson's correlation of the texture feature vectors of any two brain regions, and constructing a covariant matrix of the texture features between the brain regions. According to the method, brain image data of an individual subject can be utilized, the similarity of brain region texture feature vectors serves as measurement of a brain network edge, and then a brain covariant network of the individual subject is constructed.
Owner:TIANJIN MEDICAL UNIV
Construction method of first episode schizophrenia individualized prediction model
PendingCN111627553AImprove classification accuracyInterpretableMedical data miningMedical automated diagnosisNetwork modelThresholding
The invention belongs to the field of psychiatry, nerve images and artificial intelligence, discloses a construction method of a first episode schizophrenia individualized prediction model, and solvesthe problem of low accuracy of auxiliary diagnosis of an existing SCH brain structure network model. The method comprises the following steps: A, acquiring a diffusion tensor image of a first episodeschizophrenia patient; B, preprocessing the obtained diffusion tensor image; C, constructing a sparse brain structure network based on the preprocessed image; D, constructing each sparse multi-threshold fusion brain structure network of the subject by adopting a similar network fusion method; E, extracting multi-threshold fusion brain structure network topology attribute features, and then carrying out feature screening; F, based on the screened features, adopting a classifier for classification training, and obtaining a first episode schizophrenia individualized prediction model is obtained;and G, performing performance verification and evaluation on the first episode schizophrenia individualized prediction model obtained by training.
Owner:WEST CHINA HOSPITAL SICHUAN UNIV
Comprehensive magnetic resonance imaging device and method
InactiveCN104207776AImprove accuracyAids in diagnosisDiagnostic recording/measuringSensorsPrimary motor neuronMotor neurone
The invention discloses a comprehensive magnetic resonance imaging device. According to the invention, the cerebral function imaging, the MRI perfusion weighted imaging and the magnetic resonance diffusion imaging are united, so that the assessment for the cerebral injury caused by motor neuron disease and Parkinson disease is assisted, a beneficial judging basis is supplied for illness state judgment, and the accuracy rate of disease judgment is increased; an ALS (amyotrophic lateral sclerosis) patient is accompanied with FTD (frontal temporal dementia), so that the detection means, such as cortex thickness, also is beneficial to the disease diagnose and research; functional magnetic resonance imaging (fMRI) and diffusion tensor imaging techniques have strong complementarity; the combination of the functional magnetic resonance imaging and diffusion tensor imaging techniques has a wide application prospect in neurosciences research; unique brain structure and function information can be supplied.
Owner:NANCHANG UNIV
Abnormal brain connection prediction system, method and device and readable storage medium
PendingCN113724880AInterpretableUnusual Connection Feature ExtractionMedical simulationMedical automated diagnosisFunctional connectivityBrain section
The invention discloses an abnormal brain connection prediction system, method and device and a readable storage medium, and the method comprises the steps: automatically extracting high-order correlation features in different modes and high-order complementary features between different modes through a deep learning method; and realizing the analysis of abnormal connection of the multi-modal brain network and prediction of different cognitive diseases through an adversarial training method. The method solves the problem that an existing method cannot accurately evaluate the change rule of brain structural morphology and functional connection. According to the method, a prior knowledge guide model is used for learning interpretable characterization, the consistency of different modal characterization distribution is restrained through a paired collaborative discriminator, and then brain graph data is reconstructed for feature codes through a reverse generator and a decoder; and finally, inter-modal and intra-modal high-order correlation features are extracted through a hypergraph perception fusion module, and an adversarial loss function, a reconstruction loss function and a classification loss function are set to guide model learning so as to achieve the purpose of mining the abnormal brain connectivity of the Alzheimer's disease.
Owner:SHENZHEN INST OF ADVANCED TECH +1
Graph convolutional neural network evolution method for dynamic brain structure
ActiveCN110797123AReduce noiseReduce mistakesMedical simulationNeural architecturesBrain networkCerebral structure
The invention discloses a graph convolutional neural network evolution method for a dynamic brain structure. A graph convolutional neural network is adopted to simulate an evolution process of evolving a normal human brain structure network into depression. A direction vector is introduced in the evolution process, the vector not only contains brain structure network information of a normal person, but also contains brain structure network information of a depression patient, the characteristics of the normal person and the depression patient can be extracted at the same time through graph convolution operation, and the evolution direction and the evolution degree can be controlled. The invention provides a graph convolutional neural network model of brain structure network evolution. A deep learning method based on a tensorflow framework is utilized, the cross entropy of a first evolution result and a real brain network of a depression patient is calculated, and a gradient descent optimization method is utilized to enable the evolution of the network to always face the direction of the brain network of the depression patient. And finally, a brain structure network close to a realdepression patient is outputted, and an evolution model closer to the real network is obtained.
Owner:DALIAN MARITIME UNIVERSITY
Method and system for determining brain state
Owner:GLOBAL ADVANCED VISION
Method for MRI scanning of animals for transmissible spongiform encephalopathies
InactiveUS20060184001A1Diagnostic recording/measuringSensorsTransmissible spongiform encephalopathyMR - Magnetic resonance
A method for detecting the presence of various transmissible spongiform encephalopathy (TSE) in mammals is provided. Steps include taking a magnetic resonance imaging (MRI) image of the brain of the mammal, selecting a section within the image, determining a measurement of a first brain structure in the section, and determining a measurement of a second brain structure in the section. The ratio of the measurements is calculated and used to determine the probability that the mammal has a TSE. Areas and / or volumes of the first and second brain structures may be used. In one embodiment, the image section is sagittal or axial. An embodiment uses the lateral ventricle or frontal lobe as the first brain structure, and the cerebrum as the second brain structure. The method of determining the areas and volumes of the brain structures is provided. MRIs that can be used along with the MRI parameters are provided.
Owner:MINKOFF LAWRENCE A +2
Lactobacillus reuteri DSM 17938 for the development of cognitive function
InactiveCN104185427ANervous disorderBacteria material medical ingredientsPhysical medicine and rehabilitationBrain pathway
Owner:BIOGAIA AB
Tracking brain deformation during neurosurgery
Owner:KONINK PHILIPS ELECTRONICS NV
Deformable registration for multimodal images
ActiveUS20210042878A1Surgical operation is facilitatedEasy to operateImage enhancementReconstruction from projectionAnatomical structuresNonlinear deformation
The subject matter discussed herein relates to the automatic, real-time registration of pre-operative magnetic resonance imaging (MRI) data to intra-operative ultrasound (US) data (e.g., reconstructed images or unreconstructed data), such as to facilitate surgical guidance or other interventional procedures. In one such example, brain structures (or other suitable anatomic features or structures) are automatically segmented in pre-operative and intra-operative ultrasound data. Thereafter, anatomic structure (e.g., brain structure) guided registration is applied between pre-operative and intra-operative ultrasound data to account for non-linear deformation of the imaged anatomic structure. MR images that are pre-registered to pre-operative ultrasound images are then given the same nonlinear spatial transformation to align the MR images with intra-operative ultrasound images to provide surgical guidance.
Owner:GENERAL ELECTRIC CO
Tracking brain deformation during neurosurgery
Owner:KONINKLIJKE PHILIPS NV
Establishment of conversion model of intracranial hemorrhage of dog after autologous thrombus embolic infarction thrombolysis by using intervention method and application thereof
InactiveCN109044555AImprove accuracyImprove reliabilitySurgical veterinaryIntracranial HemorrhagesThrombus
The invention relates to an establishment of a conversion model of the intracranial hemorrhage of a dog after autologous thrombus embolic infarction thrombolysis by using an intervention method and anapplication thereof. The method comprises the following steps of: preparation of thrombus, preparation of a cerebral infarction model, thrombolysis and determination of hemorrhage result. The dog hemorrhage conversion model has the advantages that: first, the cerebral structure of a beagle is similar to that of a human being, the variation of the cerebral vascular diameter is small, compared withsmall animals, such as a mouse and the like, the related experimental result can achieve better clinical conversion. Second, in small animal experiments, a laser Doppler blood flow meter is used to monitor the changes of cerebral blood flow, thus judging the occlusion and recanalization of blood vessels. DSA is a gold standard for judging whether the blood flow is smooth or not, the injection ofthe thrombus can be guided, accurate embolism can be carried out, and direct evidence for judging whether the thrombolysis is valid or not is provided. Thirdly, an intervention procedure is used as aminimally invasive technique, other unnecessary operation damage can be reduced to the utmost extent, so that the accuracy and reliability of the experimental result can be improved.
Owner:姜润浩
Tracking brain deformation during neurosurgery
The present invention relates to the determination of brain deformation. The invention relates in particular to a device for tracking brain deformation, an imaging system for tracking brain deformation, a method for tracking brain deformation, a method of operating a device for tracking brain deformation, as well as to a computer program element and a computer readable medium. For providing enhanced information about the brain deformation, a first 3D representation (112) of a cerebrovascular vessel structure of a region of interest of an object is provided (110) and (114) a second 3D representation (116) of the cerebrovascular vessel structure. Then, at least a part of the first 3D representation is elastically three dimensionally registered (118) with at least a part of the second 3D representation. A deformation field (122) of the cerebrovascular vessel structure is determined (120) based on the elastic registration. The determined vessel deformation is applied (124) to a brain structure representation to determine a deformation (126) of the cerebral structure. For example, planning data (130) for an intervention of the cerebral structure is provided (132) and the determined deformation of the cerebral structure is applied (134) to the planning data to generate deformation adjusted planning data (136).
Owner:KONINKLIJKE PHILIPS ELECTRONICS NV
Method for automatically identifying and positioning focal cortex dysplasia epilepsy focus
PendingCN112927187ALess dependent on experienceImprove the efficiency of diagnosis and treatmentImage enhancementImage analysisData setExtratemporal epilepsy
The invention discloses a method for automatically identifying and positioning focal cortex dysplasia epilepsy focus. The method comprises the following steps: acquiring multi-modal nerve image data including MRI and PET; forming a large number of data sets with labels through manual labeling; from the angles of brain structure, brain metabolism and the like, deeply excavating a multi-modal biomarker of FCD epilepsy; and establishing and training a CNN focus identification segmentation model based on the original features of the multi-modal data for automatic positioning diagnosis of FCD. According to the invention, aiming at the common pathology FCD of the epilepsy surgery, a part of cases are negative in magnetic resonance and cannot be visually distinguished, intelligent identification is carried out aiming at the FCD epileptic focus, the experience dependence of subjective film reading is greatly reduced, the time cost and the labor cost are reduced, and finally, the diagnosis and treatment efficiency of the epileptic focus is improved, and the operation prognosis is improved.
Owner:BEIJING TIANTAN HOSPITAL AFFILIATED TO CAPITAL MEDICAL UNIV
Resting-state electroencephalogram rTMS curative effect prediction and intervention closed-loop feedback diagnosis and treatment method
PendingCN113180693AConducive to dynamic optimizationReduce blindnessMagnetotherapy using coils/electromagnetsSensorsPhysical medicine and rehabilitationResting state fMRI
The invention relates to a resting-state electroencephalogram rTMS curative effect prediction and intervention closed-loop feedback diagnosis and treatment method, multi-source heterogeneous data such as EEG, brain structure and psychological behavior detection data are fused, a curative effect prediction model is introduced, and through EEG collection which is low in price and good in portability, curative effect prediction and classification are conducted on insomnia patients; besides, the treatment effect is evaluated and fed back before treatment and during treatment, so that an operator can conveniently adjust a treatment scheme, and dynamic optimization of the prediction model is also facilitated; meanwhile, individualized scheme making and accurate regulation and control under navigation are carried out on patients insensitive to treatment, and reasonable medical suggestions are made for patients with poor effects after 20 times of accurate treatment; the invention relates to a closed-loop feedback diagnosis and treatment system covering evaluation, classification, treatment, reevaluation and retreatment, which greatly saves social resources and medical cost while reducing the blindness of rTMS treatment, improving the accuracy of rTMS treatment and realizing high efficiency of rTMS intervention, and has important application value.
Owner:SHENZHEN PEOPLES HOSPITAL
Brain structure and function coupling method based on directed graph harmonic analysis
The invention provides a brain structure and function coupling method based on directed graph harmonic analysis, and belongs to the technical field of biomedical signal processing. The method comprises the following steps: firstly, constructing an asymmetric matrix directed weight brain structure connection matrix by utilizing cerebral cortex tracer injection tracking or structural magnetic resonance imaging diffusion tensor imaging fiber bundle probability tracking; secondly, introducing a random walk operator to convert the asymmetric directed weight brain structure connection matrix into a real symmetric Laplacian matrix, and taking a feature vector of the Laplacian matrix as a brain structure connection harmonic wave; then, decomposing the brain function signals into brain structure and function coupled (namely, low-frequency characteristic mode of the graph) and separated (namely, high-frequency characteristic mode of the graph) harmonic components through graph harmonic analysis; and finally, taking a logarithm value of a ratio of two norms of the cross-time low-frequency and high-frequency filtering signals as a brain structure separation index to describe separation and coupling of a brain structure and functions. The method of the present invention has higher adaptability.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA
Brain structure three-dimensional reconstruction method and device and terminal equipment
PendingCN112598790AEfficient extractionFacilitate decision-makingNeural architecturesNeural learning methodsPoint cloudTerminal equipment
The invention provides a brain structure three-dimensional reconstruction method and device and terminal equipment. The method comprises the following steps: acquiring a 2D image of a brain, inputtingthe 2D image of the brain into a trained 3D brain point cloud reconstruction model for processing, and outputting to obtain a 3D point cloud of the brain, wherein the 3D brain point cloud reconstruction model comprises a ResNet encoder and a graph convolutional neural network, the ResNet encoder is used for extracting an encoding feature vector of a 2D image of the brain, and the graph convolutional neural network is used for constructing a 3D point cloud of the brain according to the encoding feature vector. Based on a brain structure three-dimensional reconstruction method provided by the invention, the 2D image of the brain can be converted into the 3D point cloud of the brain, more visual information is provided for doctors, and the doctors can better diagnose and treat.
Owner:SHENZHEN INST OF ADVANCED TECH CHINESE ACAD OF SCI
Alzheimer's disease auxiliary diagnosis system, data processing method thereof and terminal
PendingCN113298758AImprove forecast accuracyImage enhancementImage analysisImage resolutionDisease patient
The invention discloses an Alzheimer's disease auxiliary diagnosis system. The system comprises a preprocessing module which carries out preprocessing of an original MRI structure image, obtains a preprocessed image, converts the preprocessed image into an MNI space, obtains a preprocessed image, and carries out resampling of the image at an isotropic resolution; an ROI positioning module which performs rigid registration on the image obtained after preprocessing and a standard brain template to obtain a deformation field of non-rigid registration, and selects a plurality of ROIs on the preprocessed individual image according to the deformation field; and an analysis module which constructs, trains, verifies and tests a deep learning model, calculates the characteristic spectrum and the probability value of each ROI in the plurality of ROI individuals by using the deep learning model, and judges whether a subject is an Alzheimer's disease patient according to the probability values. According to the system, the deep learning technology is adopted to learn features of brain structure changes, whether a subject suffers from the Alzheimer's disease or not is predicted, the prediction accuracy is high, and an auxiliary function is provided for diagnosis of doctors.
Owner:深圳市铱硙医疗科技有限公司
Brain structure imaging system and method based on MEG and EEG fusion
ActiveCN111973180ASatisfy the claustrophobiaLess likely to cause discomfortMedical imagingDiagnostic signal processingTissue architectureMedicine
The invention relates to a brain structure imaging system and method based on MEG and EEG fusion. The system mainly comprises a magnetic shielding room, a magnetoencephalogram measurement module, an electroencephalogram measurement module, a data synchronization and acquisition module and a structure imaging module. According to the brain structure imaging method based on MEG and EEG fusion, MEG (magnetoencephalogram) and EEG (electroencephalogram) of a person are collected at the same time; and the MEG is not affected by the conductivity of each tissue structure of the brain, and the EEG is affected by the conductivity of each tissue structure of the brain, so that according to the difference between the MEG and the EEG, a brain structure image related to the conductivity can be obtained.The method comprises the specific steps that according to the collected MEG, the condition of an activity source in the brain is obtained; according to the obtained brain source, potentials needing to be received by EEG electrodes are calculated; and the potentials are compared with actually-collected electrode values. By continuously modifying the structure size of each part in the brain, calculation is performed for multiple times until the difference reaches a set value. At the moment, the structure size of each part in the brain is a measured brain structure.
Owner:BEIHANG UNIV
System for variably configurable, adaptable electrode arrays and effectuating software
ActiveUS10946196B2Accurate informationImprove performanceElectroencephalographyUltrasound therapyElectrical polarityNon invasive
Owner:STIMSCI INC
PET molecular imaging computer-aided diagnosis system based on brain symmetry
ActiveCN114334130APreserve anatomical detail informationReduce misalignment areasImage analysisInternal combustion piston enginesVoxelMolecular imaging
The invention discloses a PET molecular image computer-aided diagnosis system based on brain symmetry. The system comprises an image acquisition and preprocessing module, an asymmetric brain template construction module, a symmetric brain template construction module, a symmetry analysis module and an auxiliary diagnosis module. According to the system, an asymmetric brain template is constructed according to a normal brain PET molecular image, and a symmetric brain template is further constructed; then, registering individual images to a symmetric brain template space to obtain priori knowledge of a symmetric structure, and introducing individual image information by using a symmetric differential homeomorphic algorithm to obtain a voxel-level brain structure symmetric relationship; finally, metabolism or receptor information of the focus area and the symmetrical brain area of the focus area is comprehensively utilized, and the diagnosis capacity of the PET molecular image is improved in an auxiliary mode.
Owner:ZHEJIANG UNIV
Task state functional magnetic resonance scanning data denoising method, device thereof, equipment and medium
PendingCN112790756AReduce in quantityImprove reliabilityMedical imagingDiagnostic recording/measuringSparse learningVoxel
The invention provides a task state functional magnetic resonance scanning data denoising method, and a device thereof, electronic equipment and a storage medium, and the method comprises the steps: determining N non-neuron voxel scanning data in task state functional magnetic resonance scanning data corresponding to the execution of a target task by a subject according to brain structure magnetic resonance scanning data of the subject; performing sparse learning based on the N non-neuron voxels to obtain a noise dictionary; for voxels in the task state functional magnetic resonance scanning data, using each noise dictionary element as a regression parameter, performing linear regression on a BOLD signal sequence corresponding to the voxels, and obtaining a noise sparse coefficient; and generating de-noised task state functional magnetic resonance scanning data based on the noise sparse coefficient corresponding to each voxel in the task state functional magnetic resonance scanning data and each noise dictionary element. The noise in the task state functional magnetic resonance scanning data can be removed in a targeted manner.
Owner:BEIJING GALAXY CIRCUMFERENCE TECH CO LTD
Structural-functional brain network bidirectional mapping model construction method and brain network bidirectional mapping model
PendingCN114242236AImplement the buildMedical simulationMedical automated diagnosisData setFeature extraction
The invention relates to a structure-function brain network bidirectional mapping construction method and a brain network bidirectional mapping model, and the method comprises the steps: constructing a feature preprocessing module, and obtaining a brain structure network and a brain function network; constructing a structural feature extraction module and a functional feature extraction module to obtain structural features and functional features of the brain; constructing a structure classifier module and a function classifier module, and obtaining an illness state classification result based on the structure features and an illness state classification result based on the function features; constructing a structure-function bidirectional mapping network, and performing bidirectional mapping on the brain structure network and the brain function network; and training and learning the constructed structure feature extraction module, the constructed function feature extraction module, the constructed structure classifier module, the constructed function classifier module and the constructed structure-function bidirectional mapping network by using the preprocessed data sets of the brain structure network and the brain function network. And the constructed brain network model is helpful for revealing a complex relationship between a brain structure and functions.
Owner:SHENZHEN INST OF ADVANCED TECH
Neurodegenerative disease brain image generation prediction method based on depth generation model
ActiveCN113171075AImprove the efficiency of disease diagnosis and treatmentAvoid consumptionImage enhancementImage analysisPredictive methodsDisease course
The invention provides a neurodegenerative disease brain image generation prediction method based on a depth generation model. The method comprises the following steps: acquiring brain structure image data of a patient, and performing data division and arrangement according to the subtype expression and natural disease course period of clinical symptoms of the patient to obtain a disease subtype tag and a disease course tag; constructing an image prediction generation model by adopting a deep neural network; performing training test on the constructed image prediction generation model by using K-fold cross validation and brain image data with a disease subtype label and a disease course label to obtain reconstruction loss, cross entropy loss and discrete uniformity loss of a test set, and storing an optimal model; inputting the brain image data of the patient in the current disease course period into the optimal model, and completing the generation of the brain image data of the next natural disease course of the patient, namely predicting the natural disease course development of the patient. The invention provides an effective scientific basis for early diagnosis and timely intervention treatment of neurodegenerative diseases.
Owner:NORTHEASTERN UNIV
Brain structure network connection optimization method based on random partitioning model
ActiveCN106251379AImplement the buildIncrease credibilityReconstruction from projectionDiffusionFiber bundle
The invention relates to an image processing technology, and specifically relates to a brain structure network connection optimization method based on a random partitioning model. The problem that a brain structure network built using the existing brain structure network building method is of low credibility is solved. The brain structure network connection optimization method based on a random partitioning model is implemented according to the following steps: S1, preprocessing a magnetic resonance diffusion weighted image, and partitioning the preprocessed magnetic resonance diffusion weighted image; S2, calculating the number of fiber bundles of every two brain intervals; S3, binarizing a fiber bundle number matrix of the brain intervals according to a threshold; S4, building a brain structure central network model based on multiple brain structure network model samples; S5, calculating the credibility of connection in the brain structure central network model; and S6, rebuilding and optimizing the brain structure central network model. The method is suitable for brain structure network building.
Owner:TAIYUAN UNIV OF TECH
Anxiety trait quantification method based on multi-dimensional internal perception features
PendingCN111938671AAccurate and objective quantitative evaluationAchieve ultra-early recognitionSensorsPsychotechnic devicesInformatizationElectronic information
The invention relates to the technical field of electronic informatization of biological indexes, in particular to an anxiety trait quantification method based on multi-dimensional internal perceptionfeatures. The anxiety trait quantification method comprises the following steps: obtaining multi-dimensional data of tested ethology, electroencephalogram physiology and nuclear magnetic resonance image; preprocessing ethological heartbeat perception sensitivity, heartbeat consciousness potential, brain structure and task state data; and constructing a multi-dimensional deep learning network andestablishing an automatic quantization system. The multi-dimensional features of ecology, electroencephalogram physiology and nuclear magnetic resonance image under an internal perception normal formare combined, feature learning is carried out through deep network learning by means of automatic learning and nonlinear hierarchical system advantages, feature value extraction modeling is carried out, individualized anxiety trait level scoring results are obtained, a tool is provided for quantitatively and objectively evaluating the anxiety trait level, the anxiety level is accurately quantified, the anxiety disorder can be effectively recognized in the ultra-early stage, the biological objective diagnosis effect is huge, and diagnosis and treatment of the anxiety disorder can be assisted.
Owner:SHANGHAI MENTAL HEALTH CENT (SHANGHAI PSYCHOLOGICAL COUNSELLING TRAINING CENT)
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