86 results about "Imaging brain" patented technology

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

The invention discloses a low-resource and low-energy-consumption remote sensing image brain-like computing system based on an FPGA (Field Programmable Gate Array), which belongs to the cross technical field of brain-like computing and remote sensing information processing, and comprises a data interaction module which is used for high-speed data transmission between a PC (Personal Computer) end and the FPGA and comprises a PCIe (Peripheral Component Interconnect Express) interface sub-module and a data transmission control sub-module; the storage control module is used for data interaction control of the off-chip DDR and the on-chip BRAM and providing efficient data supply for the processing engine; the processing engine module is used for simulating a brain-like pulse neural network to complete convolution calculation of the remote sensing image; comprising a convolution processing sub-module, a data storage sub-module, an input buffer sub-module and an output buffer sub-module. According to the low-resource and low-energy-consumption remote sensing image brain-like computing system based on the FPGA, the brain-like pulse neural network is adopted, the BRAM is allocated to store the intermediate computing result, and the computing unit is designed in combination with SNN characteristics, so that energy consumption and resource waste are effectively reduced, and computing efficiency is improved.

The invention provides a multi-mode nuclear magnetic resonance image glioma segmentation method and application thereof, and belongs to the field of medical image processing. The invention provides an M2ES-UNet network aiming at the problems of insufficient utilization of spatial features, single multi-modal fusion mechanism and cross-level semantic loss of an existing segmentation method. According to the method, multi-view anatomical information is extracted through a multi-plane feature collaboration module; utilizing an orthogonal dimension fusion convolution and modal introspection-collaboration module to respectively realize differential fusion of shallow and deep features; and the progressive jump transmission of the features is realized through a coding information smooth transmission module. According to the method, multi-plane and multi-mode complementary information can be effectively mined, the boundary precision and robustness of brain glioma segmentation are remarkably improved, and clinical diagnosis is assisted.

The present invention describes an artificial intelligence (AI) enabled electroencephalography (EEG) system that integrates Pathway Hierarchical Adaptive Referencing (PHAR) for localized signal detection, large language models (LLMs) for automated EEG reporting, and Internet of Medical Things (IoMT) connectivity for adaptive neuromodulation control. The system can also deliver transcranial electrical stimulation (tES) pulses and function as an electrical impedance tomography (EIT) system. PHAR employs a multi-layered multiplexer hierarchy and adaptive referencing topologies to optimize EEG signal acquisition and spatial resolution. LLM integration enables automated generation of human-readable EEG reports. IoMT connectivity allows closed-loop neuromodulation, where real-time EEG analysis guides the adjustment of stimulation parameters. The system can deliver tES pulses and perform EIT expands its functionality, allowing for targeted neuromodulation and impedance-based brain imaging. This integrated system revolutionizes EEG-based diagnostics, treatment, and research in neurology and neuroscience, offering a comprehensive and versatile tool for understanding and modulating brain function.

The invention discloses an obsessive-compulsive disorder diagnosis system based on brain multi-modal indexes, and belongs to the field of mental diseases. The problem of lack of a cross-modal feature fusion mechanism is solved. The system comprises an electroencephalogram signal acquisition unit used for acquiring an EEG signal of a testee under a preset stimulation normal form and executing preprocessing operation; the brain imaging data acquisition unit is used for synchronously acquiring brain structure imaging data and brain function imaging data of the testee; the multi-modal data fusion unit is used for extracting frequency band power spectrum density characteristics and event-related potential amplitude or incubation period characteristics from the EEG signals; performing standardization processing on the EEG features, the sMRI structural features and the fMRI functional features; integrating modal features by adopting a weighted average fusion algorithm; screening fused feature subsets through a recursive feature elimination method; and the diagnosis model unit is used for inputting the fusion feature vector into a trained SVM classification model and outputting an obsessive-compulsive disorder diagnosis result. Used in the medical field.

The invention provides a fetal magnetic resonance image brain region segmentation method and system, computer equipment and a medium, and belongs to the technical field of automatic segmentation of fetal brain magnetic resonance imaging. The method comprises the steps that firstly, 2D low-resolution fetal magnetic resonance images collected in multiple directions are processed, and 3D high-resolution fetal brain images are generated; secondly, performing selective interlayer labeling on the 3D image, and generating a complete segmentation label through a contour interpolation algorithm; and finally, performing mutual supervision learning by using a double-independent initialized segmentation network, including the steps of data enhancement consistency constraint, cross pseudo label supervision, feature comparison learning and the like. According to the fetal magnetic resonance image brain region segmentation method and system, the computer equipment and the medium, a high-performance divider can be trained only through a very few labels, the labeling cost is remarkably reduced, the segmentation efficiency is improved, the method and system are suitable for actual clinical fetal brain magnetic resonance research, and efficient technical support is provided for fetal brain development evaluation.

The invention discloses a light field reconstruction brain image brain region boundary extraction method based on convex hull fitting. The method comprises the steps that the p percentile of a brain image is calculated, and image cutting, contrast stretching and Gaussian filtering smooth denoising are carried out; carrying out Canny edge detection on the filtered image to obtain a binary edge image; boundary enhancement and connection are carried out through expansion and closed operation, all connected regions are extracted, and the contour with the maximum area is selected as the contour of the brain region boundary; a datum point is determined based on the contour of the brain region boundary, the polar angle and distance of each point except the datum point are calculated and sorted, and convex hull fitting is carried out through Graham Scan scanning based on the datum point and the sorted point set; converting the convex hull into a closed boundary region, and obtaining a mask image of the brain image after light field reconstruction through a mask function; according to the method, the special boundary form of the light field reconstruction image can be accurately adapted, deep learning and data training are not needed, and the generated mask has high geometric consistency.

The invention relates to the technical field of intelligent medical adjuvant therapy, in particular to a depression targeted nerve regulation method and system based on fNI RS dynamic brain entropy, and aims to overcome the defects that an existing nerve regulation method is fixed in stimulation parameter, lacks real-time feedback, depends on subjective evaluation and the like. According to the method, a multichannel near infrared spectrum optical brain imaging device is adopted to collect a time sequence signal of prefrontal lobe resting hemoglobin; calculating the time sequence signal of the prefrontal lobe static hemoglobin to obtain a static entropy feature and a dynamic entropy feature; calculating a brain entropy ratio according to the static entropy feature and the dynamic entropy feature; and matching the brain entropy ratio with a preset brain entropy threshold value, and adjusting nerve regulation and control stimulation parameters according to the corresponding brain entropy threshold value. Through the brain entropy real-time feedback closed-loop TMS regulation and control technology, the quantitative relation between the brain entropy and the stimulation parameters is established, and a stimulation parameter dynamic adjustment method is provided for the nerve regulation and control technology.

The invention discloses a multi-mode nuclear magnetic image brain tumor segmentation system based on optical flow method pixel correlation, relates to the field of image processing, and solves the problems that complex physiological correlation among multiple modes cannot be captured and specific characteristics of a tumor area are difficult to reproduce in an existing missing mode completion method. The segmentation system comprises a data preprocessing module, a cross-modal optical flow estimation module, a pixel association weight calculation module, a missing modal complementation module and a fusion segmentation module. The system is simple in structure and reasonable in design, breaks through the limitation that a traditional optical flow model depends on a gray level consistency hypothesis, and adapts to the characteristic that the gray level difference of the multi-mode MRI is remarkable. By capturing the consistency of the gray gradient direction of the same anatomical structure in different modals, cross-modal pixel correlation mapping is accurately established, optical flow estimation deviation caused by gray mismatching is avoided, a reliable correlation basis is provided for subsequent deletion completion, and the accuracy of cross-modal information transmission is guaranteed.

A method for assessing a patient's brain disease state from brain images includes acquiring the brain images, partitioning them into predefined regions based on anatomical landmarks, extracting disease-indicative features, using a pretrained model to generate a disease-associated biomarker from the features, and determining the patient's brain disease state based on the biomarker.

The present invention discloses a medical imaging brain tumor detection method and system based on an improved Faster R-CNN. The method comprises: obtaining a magnetic resonance image dataset from a database; preprocessing the images of the dataset using an optical flow method and a generative adversarial network to perform data enhancement; constructing an improved Faster R-CNN deep network model; using the obtained dataset to train the constructed improved Faster R-CNN deep network model; inputting the magnetic resonance image to be predicted into the trained network model, and outputting the brain tumor target detection result. The present invention adopts a data enhancement method based on an optical flow method and a generative adversarial network, and designs an improved Faster R-CNN target detection network model, adds an image feature pyramid structure and a convolutional gated recurrent unit (ConvGRU) module, modifies the data reading method during network training, enhances the continuity of tumor detection results, and improves the retrieval accuracy and recall rate of brain tumors.

The invention relates to a miniature brain imaging device for two-color fluorescence and an imaging method thereof, and relates to the technical field of biomedical imaging device.The miniature brain imaging device comprises a shell and a lens end cover which are connected, and an imaging light path and an illumination light path are sequentially arranged in the shell and the lens end cover along an imaging optical axis; the imaging light path comprises a lens group, a first dichroscope, a first optical filter and a fifth lens which are arranged in sequence, the lens group is composed of a first lens, a second lens, a third lens and a fourth lens which are arranged in sequence, and the illumination light path comprises a first light source, a second light source, a second dichroscope and a sixth lens. The first light source and the second light source are combined through the second dichroscope to form a collimated light beam, and the collimated light beam is shaped through the sixth lens and then projected to the object plane through the first dichroscope. The application has the effects that different types of neural signals or cell populations can be monitored at the same time, different states or functional modules of neurons can be distinguished, and the resolution and accuracy of data can be improved.

The present invention provides a PET attenuation correction method and system for a five-modality integrated brain imaging device, relating to the field of brain imaging technology. The method comprises: generating a first attenuation coefficient map for a fixed-position hardware component; generating a second attenuation coefficient map for the optode and optical fiber of a non-fixed-position functional near-infrared spectrometer; superimposing the first attenuation coefficient map with the second attenuation coefficient map to generate a third attenuation coefficient map of the PET image under scanning conditions; and performing registration and attenuation correction on the third attenuation coefficient map to obtain an attenuation-corrected PET image. The present invention can more accurately obtain an attenuation template under each actual acquisition environment, and maximizes the correction of the effects of hardware components in the imaging field of view on the attenuation and scattering of gamma photons in PET imaging.

The invention discloses a modeling and intervention simulation method and system for children with difficulty in reading and writing Chinese based on a digital twin brain technology. A data acquisition module acquires behavior data and brain imaging data of children with difficulty in reading and writing Chinese and normal contrast children; constructing a Chinese read-write digital twin base model by using a model construction module, and simulating function shunting and consciousness formation dynamics in a brain read-write process; the individualized modeling module adopts a double constraint optimization method to carry out individualized fine tuning on the Chinese read-write digital twinborn base model and establish an individualized digital twinborn model; the intervention simulation module carries out verification and mechanism analysis on the individualized digital twinborn model, simulates different intervention strategies, predicts the improvement effect of the intervention strategies on individual read-write behaviors and brain characterization, and recommends and outputs an individualized intervention scheme; aiming at individual digital twinning modeling with difficulty in reading and writing in Chinese, an exclusive individual digital twinning model is constructed for each tester, and individual heterogeneity with difficulty in reading and writing is analyzed.

The invention relates to the technical field of neural regulation and control, in particular to a dynamic cognitive regulation and control system which is suitable for dynamic cognitive regulation and control of different types of task paradigms, and for the dynamic cognitive regulation and control of each type of task paradigm, the system comprises the following modules: a cognitive function task module, a cognitive function task module and a cognitive function task module, the mapping module is used for establishing a mapping relation between a task normal form and a cognitive function and a mapping relation between the cognitive function and a brain functional area; the data analysis module is used for analyzing the cognitive function behavior data and the cognitive function brain imaging data to obtain behavior indexes and brain imaging characteristics; the regulation and control module is used for executing cognitive regulation and control on the user according to regulation and control parameters set by the judgment result of the cognitive aging risk probability of the user; and the evaluation module is used for comparing the current training result of the user with the historical training result to obtain an evaluation result, and dynamically adjusting the regulation and control parameters of the next round of cognitive regulation and control according to the evaluation result to realize dynamic monitoring and regulation and control of cognition.

The present invention discloses a method, system, device and medium for extracting brain features from MRI images, and relates to the technical field of intelligent biomedical signal processing. When extracting imaging brain features related to the function of the cerebral lymphatic system and the brain structure and morphology in T1WI sequence images, T2WI sequence images and DTI sequence images, the present invention fully exploits the deep features of multiple MRI sequence images. The extracted imaging features related to the function of the cerebral lymphatic system and the brain structure and morphology include the diffusion degree values ​​of the bilateral brain tissue in the patient's brain when converted into fiber bundles, and the fiber bundle reference values ​​that cause the fiber bundles to deviate when the patient's brain discharges. The fiber bundle reference values ​​can reflect which parameters are diffusion parameters when the bilateral brain tissue is abnormal. The diffusion degree values ​​and diffusion parameters reflected by these imaging features are closely related to the patient's epileptic language disorder function and can be used as standards for predicting language disorders in epileptic patients.

The application claims a kind of neonatal brain imaging system and method based on photoacoustic technology, belong to the field of medical devices. Including control module, excitation source module, neonatal brain photoacoustic data acquisition module, data output module and quantitative calculation module. The control module includes two computers and laser control instrument;The excitation source module includes high-frequency laser and its water cooling system;Neonatal brain photoacoustic data acquisition module contains focusing lens and optical fiber, wearable flexible ultrasonic transducer, multichannel acquisition card;The data output module contains computer matched with real-time imaging MarsonicsDAQ software and the imaging algorithm embedded in the software;Quantitative calculation module contains multispectral linear demixing algorithm and imaging software. The application provides high-resolution, high-contrast real-time imaging of neonatal brain tissue structure and cerebral hemodynamic parameters, which can be used as a complementary method to existing brain imaging techniques for auxiliary diagnosis.

The invention relates to the field of fluorescent dyes, in particular to a near-infrared fluorescent dye for brain imaging as well as a preparation method and application of the near-infrared fluorescent dye. The near-infrared fluorescent dye for brain imaging has the advantages of novel structure, good biocompatibility, large Stokes shift, high light stability, high fluorescence signal-to-noise ratio and the like; the near-infrared fluorescent dye prepared by the invention is simple in preparation process, mild in reaction condition, short in synthesis path and beneficial to industrial production, and has a wide application prospect.

Disclosed herein are systems and methods for evaluating the underlying molecular (e.g., brain reward system molecules) cause for a depressive disorder and developing a treatment plan based on the identified molecular cause. In one or more examples, the systems and methods described herein can utilize any combination of brain imaging techniques, subjective emotion data taken from a patient, and / or one or more algorithms for translating emotional states to brain reward system molecules (e.g., neurotransmitters) to determine an underlying molecular cause for a depressive disorder of a patient. Once the underlying molecular cause of the depressive disorder of the patient is determined, a treatment plan that includes both behavioral and pharmacological aspects can be selected based on the identity of the reward system molecule that is determined to be deficient and the level of deficiency.

This application discloses a method, apparatus, storage medium, and electronic device for evaluating the signal quality of fNIRS (functional optical brain imaging). The method includes: acquiring incident light intensity data and emitted light intensity data emitted by a light source; determining the coefficient of variation based on the incident light intensity data, and determining the signal-to-noise ratio based on the incident light intensity data and the emitted light intensity data; processing the incident light intensity data to obtain the number of peaks and heartbeat signal characteristics within a preset frequency range; and determining the quality evaluation result of the fNIRS signal based on the coefficient of variation, signal-to-noise ratio, number of peaks, and heartbeat signal characteristics. The evaluation method of this application can comprehensively measure the quality of near-infrared signals and can adapt to the physiological characteristics of different individuals to guide real-time signal quality detection and iterative hardware design.

The present application relates to the technical field of risk assessment, and particularly relates to a neurodegenerative disease risk assessment method based on brain imaging technology, comprising obtaining brain imaging data of a subject in a resting state, a controlled neural disturbance state and a recovery state after the disturbance is removed, and performing same-region registration and time sequence alignment processing on the brain imaging data to generate a baseline brain network atlas, a disturbance response offset sequence and a recovery rebound sequence. The present application constructs a multi-level evaluation mechanism of fragile nodes-unstable propagation path-whole brain vulnerability cumulative value, jointly calculates the node vulnerability weight and the brain network propagation intensity, quantifies the overall stability of the brain network, and outputs the risk level and the priority degenerative brain area, realizes systematic evaluation from local brain area abnormalities to whole brain network risk, and improves the network-level analysis capability of neurodegenerative disease risk prediction.

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 invention relates to a new biomarker and combination of biomarkers and to their use in screening of traumatic brain injury in paediatric patients and selecting those patients in need of a brain image technique and / or a subsequent treatment intervention. The method allows to rule out the performance of a brain imaging technique if it is determined that the patient does not have any intracranial lesion which would be detectable by means of said brain imaging technique. Particular kits and means for carrying out the methods are also disclosed.

This invention provides a method, system, computer equipment, and medium for fetal magnetic resonance imaging (MRI) brain region segmentation, belonging to the field of automated segmentation technology for fetal brain MRI. The method includes: first, processing multi-planar acquired 2D low-resolution fetal MRI images to generate 3D high-resolution fetal brain images; second, performing selective inter-layer annotation on the 3D images and generating complete segmentation labels using a contour interpolation algorithm; and finally, using a dual-independent initialization segmentation network for mutual supervision learning, including steps such as data augmentation consistency constraints, cross-pseudo-label supervision, and feature comparison learning. This invention, employing the aforementioned fetal MRI brain region segmentation method, system, computer equipment, and medium, requires only a very small number of labels to train a high-performance segmenter, significantly reducing annotation costs and improving segmentation efficiency. It is suitable for practical clinical fetal brain MRI research, providing efficient technical support for fetal brain development assessment.

The present disclosure relates generally to medical imaging and, more particularly, it relates to methods and systems for performing processing of magnetic resonance (MR) imaging of the brain which may be useful in the diagnosis of cognitive disorders. More specifically, the invention includes methods for processing cortical diffusion data from a region of a subject's brain, comprising determining values for the Axial Columnar Refraction (ACR) using values for AngleR and Axial Diffusivity.

The utility model discloses an fNIRS brain imaging device and system, the device specifically comprises a soft sleeve, the inner side of the soft sleeve is provided with two board card slot rows, each board card slot row comprises a plurality of rubber card slots, and the two board card slot rows are spaced by a first preset distance; the number of the rubber clamping grooves of the two board card slot rows is the same, and the adjacent rubber clamping grooves in each board card slot row are spaced by a first preset distance. A control module; a plurality of detection board cards, one detection board card is arranged in one rubber card slot, and each detection board card comprises a transmitting module and a receiving module which are oppositely arranged; the transmitting module is used for transmitting an initial waveform to a brain area of a user, and the transmitting module can transmit the initial waveform to any direction; the receiving module is used for receiving a detection waveform obtained after the initial waveform passes through a brain area; in each detection board card, the transmitting module and the receiving module are spaced by a first preset distance. According to the utility model, the brain area of the user can be comprehensively detected.

The invention relates to a brain-imitating model body for an ultrasonic system to calibrate craniocerebral imaging. The brain-imitating model body comprises a shell with an opening in the top, brain-imitating tissue filled in the shell, a skull-imitating sheet which is detachably arranged above the brain-imitating tissue and abuts against the brain-imitating tissue, and a circulation-imitating assembly which is composed of a blood-imitating vessel partially located in the brain-imitating tissue and a driving part used for driving fluid in the blood-imitating vessel to flow. The calibration assembly and the lesion imitation assembly are both arranged on the brain imitation tissue and used for reflecting ultrasonic signals sent by the ultrasonic system to calibrate the imaging performance of the ultrasonic system, the calibration assembly comprises a plurality of calibration lines parallel to the skull imitation sheet, and the lesion imitation assembly is parallel to the calibration lines. According to the invention, the obvious acoustic impedance difference between the skull and the brain tissue and the sound wave refraction effect of the skull are simulated, and the imaging performance of an ultrasonic system on craniocerebral imaging can be calibrated, so that image distortion caused by the skull is avoided.

The invention belongs to the technical field of medical images, and particularly relates to a medical image brain region segmentation optimization algorithm based on deep learning, and the algorithm comprises the following specific steps: S1, data preprocessing: firstly collecting multi-modal medical image data from different hospitals, and carrying out the data marking, so as to mark real labels of different tissue regions of the brain; performing normalization processing on the image data, and mapping a gray value of the data to an interval of [0, 1] to eliminate gray difference caused by different devices and scanning parameters; and then cutting and zooming the data, and uniformly adjusting the image data into the same size. Through data preprocessing, the problem of inconsistent spatial resolution is solved, standardized data input is provided for subsequent multi-modal fusion network construction based on an attention mechanism, favorable conditions are created for multi-modal data fusion from the source, and the fusion effect is remarkably improved.

Accurate characterization of brain tissue microstructure using diffusion MRI (dMRI) data relies on optimal scanning protocols. A framework to optimize dMRI protocols for sophisticated multicompartment tissue models is based on the Cramér-Rao lower bound (CRLB). The framework is capable of capturing features like axonal diameter index and multiple fiber orientations in a voxel. The framework can handle any number of model parameters, including water diffusivities, enhancing estimation fidelity. Leveraging automatic differentiation and parallel computing, the framework is capable of systematically exploring the entire parameter space for comprehensive scanning rotocol optimization. By optimizing the data acquisition, this framework can significantly enhance biophysical modeling accuracy in dMRI, thereby deepening understanding of brain tissue properties in health and disease.

Systems and methods for training a model for identifying an imaging modality. The systems and methods can be performed by a computer system having one or more processors and memory. A plurality of image vectors can be generated from first image data using a convolutional neural network. A loss function can be applied to each of the plurality of image vectors to produce an intermediate dataset. The intermediate dataset can be projected in a space having lower dimensional space that the intermediate dataset. A plurality of clusters can be identified from the intermediate dataset in the space using a clustering technique. Each of the plurality of clusters can be classified into one of a plurality of imaging modalities.