58 results about "Brain mri" patented technology

The invention discloses a brain MRI super-resolution reconstruction method based on a learnable weight linear accumulation strategy, and relates to the technical field of magnetic resonance imaging image super-resolution reconstruction. According to the method, the Swin-WKV model is built, the model is based on a Swin Transform and RWKV architecture Transform-like model, high-quality reconstruction of the brain MRI image can be achieved with quite low memory consumption, a mixed loss function is provided, image domain difference and frequency domain difference are combined, and the model is guided to restore frequency spectrum and pixel-level features of a reference image as much as possible.

The invention relates to the technical field of psychiatric disease diagnosis, and discloses a defective schizophrenia prediction and analysis system based on multi-modal brain network characteristics, which comprises an image data acquisition module, an image data processing module, a deep learning model module and a prediction module which are connected in sequence, the image data acquisition module acquires a brain MRI image; the image data processing module carries out cortical and subcortical reconstruction on the brain MRI image to obtain a model input index; the deep learning model module performs training by using a training data set composed of a plurality of brain MRI images and defective schizophrenia diagnosis results thereof to obtain a target prediction model; and the prediction module inputs the real-time brain MRI image into the target prediction model to obtain a prediction result. According to the method, the multi-dimensional brain structure image features are integrated, diagnosis information contained in brain region changes is fully mined, prediction results of defective schizophrenia and non-defective schizophrenia are improved, and a more comprehensive biological basis is provided for clinical diagnosis.

The invention discloses a whole brain positioning and health state evaluation method and system based on an adaptive attention mechanism, electronic equipment and a computer readable storage medium. The whole-brain positioning and health state evaluation method based on the adaptive attention mechanism comprises the following steps: collecting whole-brain MRI data, and marking the structure of each part of the region of the whole brain; performing data preprocessing on the whole brain MRI data; on the basis of the whole-brain MRI data after data preprocessing, detecting each partial region of the whole brain by using a whole-brain positioning network based on an adaptive weight and residual error reconstruction mechanism; and according to the regional form of each part of the whole brain in the detection result, performing comprehensive health form evaluation. The method can solve the problems that the edge detection precision of each region of the whole brain is low and each partial region of the whole brain is difficult to accurately distinguish at present; the generalization ability is improved; the health state is accurately evaluated; manual intervention is reduced, one-key rapid whole-brain positioning and accurate health state evaluation are achieved, and simplicity and high efficiency are achieved.

The invention discloses a three-dimensional surgical risk model reconstruction method based on brain anatomy and a functional atlas, and the method comprises the steps: S1, carrying out the image processing and fine segmentation of a brain MRI image or a DICOM sequence image of a patient, and recognizing a plurality of anatomical structures; s2, establishing a multi-dimensional risk assessment model, and performing quantitative scoring on each brain region from four dimensions of functional importance, structural vulnerability, recovery potential and clinical significance; s3, constructing a continuous three-dimensional risk field based on the risk score, and realizing the propagation of the risk value in the brain tissue based on a Gaussian diffusion model; and S4, based on an improved path planning algorithm, searching an optimal operation path in the three-dimensional risk field to realize risk minimization. The invention further discloses a corresponding system, electronic equipment and a computer readable storage medium.

The application provides a child neuropsychological development monitoring and management system. The system uses brain physiological parameters, brain MRI images, development scale characteristics and corresponding child neuropsychological development suffering grades to construct a child neuropsychological development evaluation model. The constructed child neuropsychological development evaluation model generates a current child neuropsychological development suffering grade of a user according to real-time brain physiological parameters collected by a system terminal, real-time brain MRI images monitored by a hospital and corresponding real-time development scale characteristics, provides a basis for whether the corresponding child needs further monitoring according to the current child neuropsychological development suffering grade, and improves the probability of finding potential abnormal development children. The application uses three kinds of fusion parameters related to child brain nerves, such as brain physiological parameters, brain image characteristics and subjective measurement table characteristics, to construct a high-precision real-time processing model, realizes intelligent monitoring of the morbidity of child neuropsychological development disorders by doctors, and reduces the burden on families.

The application provides a medical image classification and feature visualization method based on a deep learning model and a feature visualization tool, and comprises the following steps: A, removing non-key information frames from a brain MRI image; B, performing slice enhancement on the data to obtain a slice data set; C, transversely splicing the slices in sequence to obtain a spliced picture data set; D, putting the spliced picture data set into SE-ResNet50 for training and testing to obtain a classification result; E, putting the trained spliced picture network and a sample spliced picture into Grad-CAM to generate a heat map; F, selecting key frames from the heat map, putting the key frame slice set into SE-ResNet50 to extract features and classify; G, generating a key frame heat map through Grad-CAM; and H, using the obtained classification result and heat map to assist in diagnosis. The application discloses a method for classifying and visualizing features of brain images based on a deep learning method. An optimized SE-ResNet50 network is used to extract features and classify patient brain images. Abnormal regions are marked and visualized through Grad-CAM, so that the efficiency and diagnosis rate are improved.

The application provides a transfer learning driven intracranial tumor image data full-automatic segmentation method, relates to the technical field of medical image segmentation, and comprises the following steps: pre-processing a brain MRI image of a target user, obtaining a standard brain MRI image, and analyzing to obtain brain image features; generating an image feature vector, setting an adaptive transfer learning scheme, calling a sample intracranial tumor image segmentation model library, and integrally constructing a first intracranial tumor image segmenter; training a convolutional neural network by using a sample ependymoma image dataset to construct a second intracranial tumor image segmenter, and setting an adaptive fusion weight; after the standard brain MRI image is segmented by using the first intracranial tumor image segmenter and the second intracranial tumor image segmenter, the intracranial tumor image segmentation result is fitted according to the adaptive fusion weight. The technical problem of weak generalization ability and easy segmentation deviation of an intracranial tumor image segmentation model in the prior art is solved.

The application discloses an auxiliary identification method for brain inflammatory pseudotumor MRI images based on machine learning, and belongs to the technical field of medical image processing, and comprises the following steps: acquiring preoperative magnetic resonance imaging (MRI) data of a brain lesion of a to-be-measured individual, and performing standardization preprocessing on the MRI data to obtain a standardized brain MRI image; a lesion target region containing a tumor core region and a peritumoral edema region is segmented on the standardized brain MRI image, and a training set, a verification set and a test set are generated based on the lesion target region; a lesion type auxiliary identification model is constructed based on feature screening and feature topology graph attention network of SVM weights, and the training set and the verification set are used for training; the test set is input into the trained lesion type auxiliary identification model to obtain a lesion type identification result, and the problems of time-consuming and labor-consuming, low efficiency, omission of hidden dangers, low accuracy and the like in existing artificial visual measurement and traditional image processing measurement methods are solved.

The application discloses a nasopharyngeal carcinoma radioactive brain damage image auxiliary judgment method, equipment and medium, the method is: collecting MRI image and preprocessing, segmenting the temporal lobe region, using the temporal lobe region and corresponding lesion annotation training level prediction model of MRI image, the probability that a single temporal lobe image region exists radioactive brain damage lesion is predicted;With the probability that each subject all layers contain lesions output by the level prediction model as input, whether the subject exists radioactive brain damage as output, training patient level prediction model;Finally, the trained level prediction model and patient level prediction model are used to obtain the incidence probability of radioactive brain damage of a single nasopharyngeal carcinoma subject, when multiple modes coexist, the average of multiple mode prediction result outputs is taken as the final prediction result. The application can judge whether the corresponding nasopharyngeal carcinoma brain MRI image exists radioactive brain damage according to the input, provide reference for clinical doctors to diagnose, and reduce the missed diagnosis of nasopharyngeal carcinoma radioactive brain damage.

A medical image focus detection method based on edge guidance comprises the following steps that S1, brain MRI image data are obtained, and the brain MRI image data comprise a Br35H data set and an MBTBB data set; s2, carrying out image preprocessing and data enhancement, including image normalization, size adjustment and Mosaic enhancement, random erasure and CLAHE enhancement; s3, designing an edge integration multi-scale selection module and a hierarchical re-parameterization interaction fusion module; s4, training the model, and loading the preprocessed image into the model to start training; and S5, verifying the performance of the model, and outputting the category and position information of the focus. According to the method, an edge enhancement mechanism and multi-scale feature adaptive fusion are introduced into the model, so that the perception ability of the model to the focus boundary is improved, and the detection precision is improved.

The application relates to the technical field of image processing, in particular to an MRI image feature extraction method for dynamic evaluation of child brain development, which comprises the following steps: determining a cerebrospinal fluid coefficient of a child brain; dividing an MRI image into a cerebrospinal fluid region and a residual brain region based on the cerebrospinal fluid coefficient of the child brain of each pixel point; determining a white matter coefficient of each pixel point in the residual brain region according to the gray value of each pixel point in the decayed weighted image of each pixel point in different gradient directions, and the gray value of each pixel point in the first weighted image and the second weighted image in the residual brain region; segmenting the white matter region and the gray matter region of the residual brain region based on the white matter coefficient, and extracting image features in the white matter region and the gray matter region. In this way, the accuracy and reliability of the segmentation result of different tissue regions in the child brain MRI image are improved.

The invention relates to a multi-modal MRI brain tumor segmentation method based on convolution attention, and the method comprises the steps: carrying out the parallel coding of multi-modal medical image data, and extracting the specific low-layer space features of each modal; performing feature fusion and high-level coding on the low-level spatial features to obtain shared features; carrying out convolution attention processing on the shared features to obtain enhanced features; and carrying out decoding processing on the enhanced features to obtain a segmentation result. According to the method, more accurate tumor boundary segmentation and tissue differentiation can be realized in medical image analysis such as brain MRI (Magnetic Resonance Imaging), meanwhile, the parameter efficiency, the calculation speed and the generalization ability under limited data of the model are remarkably improved, and the clinical practicability is enhanced.

The invention provides a transfer learning-driven intracranial tumor image data full-automatic segmentation method, and relates to the technical field of medical image segmentation, and the method comprises the steps: carrying out the preprocessing of a brain MRI image of a target user, obtaining a standard brain MRI image, and carrying out the analysis to obtain brain image features; generating an image feature vector, setting an adaptive transfer learning scheme, calling a sample intracranial tumor image segmentation model library, and integrating and constructing a first intracranial tumor image segmentation device; training a convolutional neural network by adopting a sample room tumefacial tumor image data set to construct a second intracranial tumor image divider, and evaluating and setting an adaptive fusion weight; and after segmenting the standard brain MRI image by using the first intracranial tumor image segmentation device and the second intracranial tumor image segmentation device, fitting according to the adaptive fusion weight to obtain an intracranial tumor image segmentation result. The technical problems that in the prior art, an intracranial tumor image segmentation model is poor in generalization ability, and segmentation deviation is prone to occurring are solved.

This invention discloses a method and system for identifying brain tumors in brain MRI images. The method includes the following steps: S1, acquiring the original brain MRI image data of the object to be identified and performing preprocessing operations to obtain standardized multimodal MRI image data; S2, performing preliminary tumor region screening on the preprocessed image data using a tumor tissue probability decoupling identification method based on multi-echo relaxation differences. This invention relates to the field of medical image processing technology. This method and system for identifying brain tumors in brain MRI images employs a tumor tissue probability decoupling model based on multi-echo relaxation differences and cross-scale brain network topology perturbation analysis, which can accurately screen potential tumor regions. Furthermore, it improves identification accuracy through dual constraints of metabolism and morphology. The introduction of cross-scale topology analysis, combined with abnormal propagation detection of brain network structures, effectively identifies suspected tumor regions and improves identification accuracy.

The invention provides an MRI (Magnetic Resonance Imaging) image brain partitioning method and device. The method comprises the following steps: constructing a training model, inputting training data into the training model for histogram statistical analysis, and estimating initial mixed model parameters by utilizing histogram analysis and a clustering algorithm; inputting brain MRI data into the training model, and performing coarse segmentation according to expectation maximization parameter estimation and histogram fitting; constructing an MRF model according to the initial hybrid model parameters and an MRF potential energy function, iterating a coarse segmentation result, and updating the initial hybrid model parameters; stopping iteration until the parameters of the initial hybrid model reach a convergence condition, otherwise, resetting the parameters of the initial hybrid model, and carrying out iteration again; in the iteration process, the model parameters can be adaptively adjusted according to the current segmentation result; the adaptive strategy enables the algorithm to better deal with noise and other complex conditions in the image, and at the same time maintains a high identification rate for different brain tissue types.

The application discloses an AD characteristic information analysis method based on brain medical images, which comprises the following steps: data preprocessing is performed on brain MRI images to be analyzed to obtain standard images of brain regions; spatial characteristic analysis of the whole brain and key brain regions; whole brain image characteristic analysis; cognitive characteristic analysis of the whole brain and key brain regions; and aggregation analysis results are obtained to form corresponding explanation data. The application further discloses an AD characteristic information analysis system based on brain medical images. The method for analyzing cognitive index characteristics such as brain age, logical memory score, visual memory score and long-time delay memory score from brain MRI images by using deep learning technology can provide analysis results of brain MRI images and other important medical indexes for doctors, and can provide effective arguments and explanations for subsequent scientific research analysis and interpretation.

The invention discloses an interpretable Alzheimer's disease auxiliary judgment method guided by grey matter attention, and relates to the technical field of image processing and mode recognition. The grey matter attention-guided interpretable Alzheimer's disease auxiliary judgment method mainly comprises the following steps: carrying out preprocessing and grey matter segmentation on brain MRI image data to obtain preprocessed brain MRI image data and a grey matter structure information graph; constructing an Alzheimer's disease diagnosis framework based on a grey matter attention guidance anti-fact, and performing iterative optimization by using a diagnosis-interpretation interaction enhancement mechanism to obtain an optimized Alzheimer's disease diagnosis framework; and inputting an image to be diagnosed into the optimized Alzheimer's disease diagnosis framework to obtain an Alzheimer's disease diagnosis result and an interpretable anti-fact graph. The interpretable Alzheimer's disease auxiliary judgment method guided by grey matter attention provided by the invention can efficiently assist doctors in AD judgment and interpretation.

The invention provides a brain disease progress modeling method based on a unified control network, and the method comprises the steps: enabling a trained preprocessing module to receive and carry out the preprocessing of a baseline brain MRI image of a patient, a corresponding age, a target age, a demographic feature, and the volume measurement data of a key brain region; based on the preprocessed baseline brain MRI image of the patient and the condition information, the trained unified control network starts reasoning and denoising from preset noise to obtain a predicted brain MRI image under the target age; a unified control mechanism is adopted, a double-adapter framework is designed to process local anatomical control and global disease progress mode guidance in a unified mode, and the problems of complexity, parameter explosion and control conflicts caused by the fact that an independent adapter needs to be arranged for each condition in an existing method are fundamentally solved.

The application discloses a kind of MRI and Leksell frame mark image automatic registration and target point identification method based on deep learning, the method includes: obtaining the brain MRI image of patient, after pre-processing, input into the marker detection model based on 3D U-Net, output the position coordinates of Leksell frame marker in MRI image coordinate system;MRI image space is mapped to Leksell frame coordinate system;Obtain candidate target point coordinates and multiple channel probability graph, and verify the position accuracy of the target point coordinates and the anatomical target area to which it belongs in multiple channel probability graph;The visualized annotation view of target point position is generated.The application is combined with deep learning method and traditional image processing method, forms "double path" redundancy check and abnormal automatic correction mechanism, significantly improves the automatic registration accuracy of MRI image and Leksell stereotactic frame mark and target point identification accuracy, through probability graph guided coordinate confidence calculation and inverse space mapping, realizes the accurate visualization of target point in MRI original space.

The application discloses a neurodegenerative disease screening method based on medical image segmentation. First, the original brain MRI image is preprocessed, unified into a standard template space, and the maximum transverse cross section is found for training the image segmentation model. Then, under the condition of a small sample case, the image segmentation model is used to realize accurate segmentation of the brain MRI image, and then the neurodegenerative disease screening of different indexes is completed.

The invention provides an Alzheimer's disease image classification system and method, and relates to the technical field of medical image classification, and the system comprises a preprocessing module which is used for carrying out the preprocessing of a 3D brain MRI image, and obtaining a standard image; the hybrid backbone network is used for gradually extracting local features of the standard image through n levels of convolution blocks and performing dimension reduction compression, and splicing output features of the (n-1) th level of convolution block and the nth level of convolution block to obtain high-dimensional hybrid features; the multi-scale local feature extraction module is used for carrying out re-calibration and noise reduction on the high-dimensional mixed features through a channel attention mechanism and a space attention mechanism to obtain extracted features; the global modeling module is used for processing the extracted features through a multidirectional selective scanning mechanism of a three-dimensional state space model, and capturing three-dimensional space continuity dependence of the whole brain with linear calculation complexity; and the classification module is used for outputting an image classification result based on the global context sensing features. According to the Alzheimer's disease image classification system, accurate classification of Alzheimer's disease images can be realized.

The invention relates to the field of medical image processing, in particular to a brain age prediction model construction method combined with auxiliary classification and a program product. The method comprises the following steps: acquiring sample data, wherein the sample data comprises brain MRI data, actual age, gender and neurodegenerative disease tags of each subject; using a feature extraction network to extract shared characterization of each subject from the brain MRI data and gender, and inputting the shared characterization of each subject into an age regression head and a disease classification head; total loss is used for joint training, regression loss is used as a monitoring index, and a brain age prediction model is obtained. Compared with a regression-only single task network, the method assists the classification gradient and the Top-k expert selection of the sparse hybrid expert model to jointly shape and share characterization, so that the brain age MAE is remarkably reduced, and the age-rank consistency and clinical interpretability are improved.

The application discloses a controlled Lengyel-Epstein brain image classification method, and belongs to the technical field of brain tumor auxiliary diagnosis and medical image processing. The method comprises the following steps: firstly, acquiring a brain MRI image to be identified, and extracting a nonlinear index of a pretreated image to map to a high-dimensional calculation region; then, constructing a bivariate feature evolution field, acquiring a nonlinear evolution instruction set for an image classification task of a brain MRI data set, and applying the nonlinear evolution instruction set to the calculation region; monitoring a time evolution residual error of a feature vector potential energy component in the bivariate feature evolution field in real time, extracting a final stable polarity distribution of the feature vector potential energy component as a processing result, and realizing brain MRI image classification. The application solves the technical problem that a traditional reaction diffusion model cannot converge to a stable decision surface within a limited step, improves the brain MRI image classification accuracy, and has high robustness and interpretability.

This invention discloses a method for generating missing modalities in brain MRI based on hypergraphs and attention mechanisms. This method addresses the issue of missing modalities in multimodal brain MRI sequences such as T1, T1ce, T2, and FLAIR in clinical scenarios by constructing a unified multi-input multi-output translation framework. The method introduces hypergraph convolution and region-level self-attention into the generative network, aggregating group-level features from different tumors and healthy tissues and modeling structural relationships between sub-regions, preserving the spatial tissue structure of the tumor. Combined with bidirectional Mamba sequence modeling, it enables the 2D network to efficiently capture long-range dependencies between layers of 3D volumetric data, ensuring voxel-level spatial continuity. Through a teacher-student knowledge distillation mechanism, the student network learns structurally perceptual features without a tumor mask, achieving high-fidelity generation. This method can improve the overall quality of generated images and the detail fidelity of tumor lesions, thereby enhancing the performance of downstream tumor segmentation tasks and showing promising application prospects.

Aging in an individual refers to the temporal change, mostly decline, in the body's ability to meet physiological demands. Biological age (BA) is a biomarker of chronological aging, and can be used to stratify populations to predict certain age related chronic diseases. BA can be predicted from biomedical features such as brain MRI, retina or facial images, but the inherent heterogeneity in the aging process limits the usefulness of BA predicted from individual body systems. The methods disclosed herein teach a multi-modal Transformer-based architecture with cross-attention which was able to combine facial, tongue and retina images to estimate BA. The model was trained using facial, tongue and retina images from 11, 223 healthy subjects, and demonstrated that using a fusion of the three image modalities achieved the most accurate BA predictions. The approach was validated on a test population of 2,840 individuals with six chronic diseases, and obtained significant difference between chronological age (CA) and BA (AgeDiff) than that of healthy subjects. AgeDiff has the potential to be utilized as a standalonfe biomarker, or conjunctively alongside other known factors for risk stratification and progression prediction of chronic diseases. The results therefore highlight the feasibility of using multi-modal images to estimate and interrogate the aging process.

This application provides a training method, generation method, and device for a scalp-to-brain magnetic resonance imaging (MRI) generative model, relating to the field of image processing technology. The training method uses scalp and real brain MRI samples to train a Wasserstein generative adversarial network (GAN) with a frequency domain attention mechanism and gradient penalty. This network includes an encoder, a generator, a feature discriminator, and an image discriminator. The encoder and / or generator integrate a neural network model based on the frequency domain attention mechanism. After training, the encoder and generator together constitute the target generative model. The model parameters are jointly optimized using a composite loss function that includes a frequency domain loss term. This application solves the problems of existing brain image generation technologies ignoring scalp structural information and difficulty in modeling global dependencies and high-frequency details. It achieves high-quality generation of brain MRI data from scalp MRI data, improves the structural integrity and topological fidelity of the generated data, and enhances the reliability of assisted diagnostic applications.

This invention discloses a graph convolutional neural network (GCN) disease prediction system based on multimodal brain MRI images, belonging to the field of medical image analysis and disease prediction technology. The system includes: an image preprocessing module that acquires and standardizes raw functional and structural magnetic resonance imaging (fMRI) data to generate a corresponding dataset; a dynamic supernet construction module that extracts spatiotemporal fusion variability features of brain regions; a multimodal fusion module that fuses features and constructs an adjacency matrix; a GCN prediction module that calculates prediction results such as disease progression grading; and a results visualization module that locates abnormal brain regions and generates a clinical reference report. This invention integrates multimodal brain MRI data from functional and structural magnetic resonance imaging to build a full-process disease prediction system. Through various algorithms, it mines the spatiotemporal dynamic characteristics of brain regions and integrates multimodal features to achieve dual-task prediction of Alzheimer's disease progression grading and brain age. Furthermore, through standardization processing and feature screening, it generates clinical reports to assist in disease diagnosis and treatment.

The application discloses a kind of multimodal fusion light-weight segmentation network and segmentation method for brain MRI image, mainly including three parts: coding part, containing four independent encoders, to carry out feature extraction to four modal original drawings respectively, while different modalities adopt different attention strategies, wherein each encoder contains three convolution modules, to carry out down-sampling by convolution and pooling layer;Feature fusion part carries out feature fusion to four modalities at feature level, when fusion, with different combinations at different feature layers, join light-weight modal attention, spatial attention and channel attention, to improve the segmentation accuracy of model;And decoding part, using convolution and up-sampling, to restore the original resolution of feature map, the up-sampling is realized using transpose convolution.Compared with prior art, the network architecture designed in the application has higher segmentation accuracy while keeping the model lightweight.

The invention discloses an unsupervised magnetic resonance image registration method and device, a medium and a product, and relates to the field of image processing, and the method comprises the steps: obtaining a target MRI image and a to-be-registered MRI image; according to the target MRI image and the MRI image to be registered, using an image registration model to determine a registered MRI image; wherein the image registration model is obtained by training a PyraMLP-Net model by using a training data set; the PyraMLP-Net model comprises a double-flow feature extraction module, a pyramid feature decoding module and a space conversion network module which are connected in sequence. According to the invention, high-precision and high-efficiency registration of the brain MRI image is realized.

The invention discloses a multi-modal image fusion senile cognitive function intelligent evaluation system, and relates to the technical field of medical image processing and cognitive function evaluation.According to the system, brain MRI, PET and functional near infrared spectrum fNIRS images are integrated, and an aligned multi-modal image data set is generated through self-adaptive multi-scale registration; extracting space structure features and time sequence evolution features, and generating space-time coding feature vectors based on a space-time attention mechanism; constructing a brain region function connection map through a graph neural network, quantifying the coupling strength, and predicting the cognitive function decline rate; according to the method, an individual intervention strategy is determined according to an evaluation report, intervention effect data is collected to feed back optimized feature extraction weights and prediction model parameters, and a closed loop mechanism is formed, the early detection rate of mild cognitive impairment is improved by 60%, and a high-precision and self-adaptive intelligent evaluation tool is provided for early screening and intervention of senile cognitive impairment.