95 results about "Imaging brain" patented technology

Apparatus and methods in which very small volumes of material may be extracted, delivered, interrogated or stimulated via optical, electromagnetic or mechanical means, in vivo or in vitro, for site-specific detection, characterization, stimulation, diagnostics or therapy, comprising optical, fluidic, chemical, electromagnetic and biological techniques applied via a microprobe in a single intra-parenchymal tissue perforation procedure in the brain. The primary use of the device is in neuroscience research, clinical diagnostics and therapeutics applications in the brain, however, the device may also be beneficially applied to other organs and biological systems. Human clinical applications may include neurosurgical intra-operative monitoring, extra-operative chronic monitoring of devices introduced in an operation, and diagnostic monitoring combined with simultaneous neuroimaging.

An encephalic electrode individualization locating method based on multimode medical image data fusion relates to the fields of medical instruments and medical image processing and neuroimaging. By means of multimode medical image data including magnetic resonance images, X-ray images and intranperative optical photos, the space position relation of an encephalic electrode and a brain tissue structure is comprehensively measured, a relation between the position of the encephalic electrode and a brain anatomical structure is established, and accurate and fast individualization locating of the encephalic electrode is achieved. The encephalic electrode individualization locating method based on multimode medical image data fusion can be used in the fields of cognitive neuroscience basic research, clinic neurosciences and the like, provide accurate brain space position information for an encephalic electroencephalogram signal, and enhance application value in the aspect of electrophysiological basis research.

This invention relates to the use of a quantitative functional MRI (fMRI)—arterial spin-labeling perfusion MRI or absolute T2 mapping MRI or a combination thereof in the non-invasive neuroimaging of a subject's brain in response to stress-inducing psychological stimuli, which can be utilized to predict individual stress reactivity as well as to be used as a human model for testing or optimizing psychopharmacological agents.

The invention describes a new Imaging modality based on Linear Velocity Imaging Tomography; its applications include differentiating between malignant and benign tissues, the ability to correlate an ECG trace with actual disorders of the heart and Imaging Brain communications.

Second harmonic nanoprobes for imaging biological samples and a method of using such probes to monitor the dynamics of biological process using a field resonance enhanced second harmonic (FRESH) technique are provided. The second harmonic generating (SHG) nanoprobes are comprised of various kinds of nanocrystals that do not possess an inversion symmetry and therefore are capable of generating second harmonic signals that can then be detected by conventional two-photon microscopy for in vivo imaging of biological processes and structures such as cell signaling, neuroimaging, protein conformation probing, DNA conformation probing, gene transcription, virus infection and replication in cells, protein dynamics, tumor imaging and cancer therapy evaluation and diagnosis as well as quantification in optical imaging.

An ultra high-resolution near infrared brain imager system includes a modular cap housing closely spaced multiple vertical-cavity surface-emitting laser—single-photon avalanche photodiode array (VCSEL-SPAD) modules, each one of the VCSEL-SPAD modules including a linear VCSEL array and a SPAD detector.

The invention discloses a multi-modal MR image brain tumor segmentation method based on deep learning and multi-guidance, belongs to the field of image processing, and solves three problems in a multi-modal MRI brain glioma segmentation process: (1) the problem of inaccurate segmentation caused by unclear brain glioma boundary; (2) some discrete wrong segmentation points appear in a segmentation result due to uneven brightness distribution of the multi-mode MRI; and (3) the problem of carrying out feature fusion on various kinds of guide information in a brain glioma MRI segmentation network.Feature fusion is carried out on an overall brain glioma segmentation result and a brain glioma edge prediction result through a proposed fusion mechanism, so that multi-mode MRI brain glioma segmentation under multi-feature map guidance and fusion is realized. According to the deep segmentation network, high-accuracy segmentation is realized with less parameter quantity, so that the method is convenient to embed into edge equipment to assist doctors in diagnosing and analyzing brain glioma.

The invention discloses a group neural feedback training method and a group neural feedback training system. The group neural feedback training system comprises at least two brain imaging devices, at least three central processing units and a plurality of display devices. Each of the central processing units comprises a server-side processing unit and at least two client-side processing units. An output end of each brain imaging device is connected with an input end of a corresponding client-side processing unit. An output end of each client-side processing unit is connected with an input end of each corresponding display device. A plurality of client-side processing units are respectively connected with the server-side processing units. In the group neural feedback training system, the client-side processing units are used for obtaining a brain neural activity indicator of a local trainee and the server-side processing units are used for obtaining a brain neural activity interactivity indicator of the local trainee. Through feeding the brain neural activity interactivity indicator back to the trainee, the trainee can regulate a training strategy autonomously, so that the aim that group cognitive behaviors are changed can be achieved.

The invention discloses a wear-type microscopy device for imaging cortex of a small active animal under the waking activity state, comprising an imaging module, an illumination module and a system support base. The imaging module consists of an imaging cavity made of metal as well as an image carrying optical fibre and a gradient refractive index lens arranged inside the imaging cavity; the illumination module is an optical fibre derived compound optical source system and consists of a light source, a light source adjusting circuit, a control driving circuit and an output optical fibre; the light source consists of a semiconductor laser device (LD) and a high-power light emitting diode (LED); two drivers are controlled by a microcontroller so as to output light rays and laser with different wavelengths in a separated or mixed way; the system support base is a special metal structural body fixed on the head of the small animal to be tested and right above a target observation region, is equipped with the imaging module inside and introduces an illumination optical fibre to support the whole imaging system. The invention realizes imaging of various forms by introducing different illumination light rays.

The invention relates to a magnetic resonance image brain structure automatic dividing method based on statistics multi-map registration optimization. A manual image dividing method is utilized for obtaining a plurality of maps; the maps are registered to images to be divided one by one; the images to be divided are subjected to initial dividing; the initial dividing images are subjected to iteration optimization processing; a registration field between each map and the images to be divided is calculated by using multi-channel image registration, and the registration field is used for registering the images of the maps and the dividing results to image spaces to be divided; and the final dividing results are calculated. The iteration method is utilized for simultaneously optimizing the registration precision of the maps and the dividing images and the dividing results of the images to be divided, so that the final dividing results are obviously superior to those of the traditional multi-map dividing method.

The invention discloses a 4D transcranial focused ultrasonic neuroimaging method based on an acoustoelectric effect. The method includes the steps that a focused ultrasonic signal generator and an energy converter are adopted to achieve generation and emission of focused ultrasonic signals, and focused ultrasonic characteristics under different parameters are determined; an electroneurographic signal collecting device composed of an electroencephalogram collecting system provided with an electroencephalogram electrode, an electroencephalogram amplifier and an electroencephalogram filter and anelectroneurographic-signal measuring system based on ultrasonic modulation is installed and connected; ultrasonic waves are spread through the cranium and focused on a certain position, and based onthe acoustoelectric effect principle, scalp electroencephalogram signals after ultrasonic modulation, namely, acoustoelectric signals are collected; through the amplitude values, the frequency and thephase position relevance of the acoustoelectric signals and activation source signals, the electroencephalogram signals are subjected to spatial encoding and demodulation, 4D neuroimaging of high space-time resolution is achieved, and multiple requirements in practical application are met.

According to an exemplary embodiment of the present invention, a versatile delivery module based on a myristoylated polyarginine backbone can be provided to cross a blood-brain barrier (“BBB”). An incorporation of a fatty acid group can be achieved using a Schotten-Bauman reaction with quantitative yield, and a peptide can be further synthesized by conventional solid phase peptide synthesis (“SPPS”). An in vivo distribution of the delivery module into a brain over time using near-infrared (“NIR”) fluorescence imaging can be obtained. A fluorescent cargo can be detected in vivo after an intravenous injection and can be further characterized in perfused brains. A staining of an excised brain can show that the delivery module can primarily accumulate in neurons with occasional localization in astrocytes and endothelial cells. This exemplary approach can be used for the delivery of imaging probes and targeted therapeutics across the BBB.

The invention provides a method for measuring the distribution, metabolism and clearance of traceable small molecule compounds in the brain, the method comprising: placing the brain of the subject under test in an imaging device; using the imaging device to form the basis of the brain of the subject under test Imaging; introduce the compound into the interstitial fluid of the brain of the subject; use the imaging device to form a dynamic image of the brain of the subject; perform temporal and spatial preprocessing on the dynamic image and the basic image to obtain the net concentration image of the compound in the brain; Model estimation and analysis are performed on the net concentration images to obtain information on the distribution, metabolism and clearance process of compounds in the brain after they diffuse in the interstitial fluid of the brain. The present invention introduces a traceable small molecule compound of a corresponding size, dynamically displays and quantitatively measures the process of distribution, metabolism and clearance of the molecule and small molecules with similar molecular weight and properties in the brain through the brain tissue fluid in the living body.

The invention discloses a carp brain electrode implantation method by means of magnetic resonance positioning and navigation. Taking the first fish scale (the junction of the head and the trunk) as the origin, reference scratches are made on the surface of the skull based on the characteristics of the carp skull to establish Space coordinate system; with the help of the T2WI fast spin echo scanning sequence of the magnetic resonance equipment, the brain is scanned and imaged, the two-dimensional coordinates of the electrode stimulation site are converted into three-dimensional space coordinates, and the spatial coordinates of the brain stimulation electrode implantation site are determined The coordinate value of the system; according to the coordinate value, the brain stimulation electrode is implanted for positioning and navigation. The invention can not only locate and navigate for the implantation of the carp aquatic animal robot brain stimulation electrodes, but also provide new positioning and navigation means for the implantation of various animal robot brain stimulation electrodes.

The invention relates to the field of brain imaging, in particular to a brain imaging detection device. The brain imaging detection device comprises a head sleeve, a plurality of detectors and a plurality of near-infrared light sources, wherein a plurality of contact parts are raised on the surface of the inner side of the head sleeve in the thickness direction of the head sleeve, and the detectors and the near-infrared light sources are arranged at the tops of the contact parts. The device has the advantages as follows: the contact parts can push the hair of the human head aside to make the detectors and the near-infrared light sources to be in contact with the scalp, so that the detectors can accurately detect information in the brain; and the device has few parts and is simple in structure.

The invention belongs to the technical field of information, and specifically relates to a brain imaging intelligent test analysis method for network information recognition and a system thereof. Themethod of the invention comprises four steps of network information cognition test preprocessing, network information cognition task testing, brain imaging data cognition calculation, and network information cognition result output. The test analysis system of the invention comprises four software modules corresponding to four steps, and four hardware modules of a network information multimedia presentation module, a cognitive response signal acquisition module, a functional nuclear magnetic resonance brain imaging module and an intelligent analysis module. The method has the advantages of designing a network information test signal by using a virtual reality technology, modeling a brain function network based on a neural network model, and performing fusion analysis of a brain rest state,DTI and f-MRI test data; and realizing intelligent calculation through an artificial intelligence technology. More accurate test analysis results can be obtained by the method.

The invention relates to a three-dimensional brain magnetic resonance image brain cortex surface maximum principal direction field diffusion method, which has the technical scheme that the method comprises the following steps: carrying out pretreatment and brain cortex surface reconstruction on three-dimensional brain magnetic resonance images; calculating the principal curvature, the principal direction and the differential coefficient of the principal curvature of a peak on the brain cortex surface; using an alpha-expanon graph cut method for minimizing an energy function to carry out diffusion on the maximum principal direction field; and projecting the diffused maximum principal direction field into a tangent plane. The invention has the advantages that: 1. the method sets the weights of smooth items and data items in different regions on the brain cortex surface into different values, so the inherent geometrical structure and discontinuity in the maximum principal direction field can be perfectly maintained; and 2. the principal direction field diffusion is regarded as an energy minimization problem which is converted into a diffusion marking problem, and the alpha-expanon graph cut method can be used for effectively working out the strong local optimum solution of the energy function.

The invention discloses a construction method of a brain corpus callosum segmentation prediction image for corpus callosum state evaluation, which comprises the following steps: drawing a corpus callosum initial contour line from an acquired fetal brain ultrasonic image, and calculating a key point offset of the corpus callosum initial contour line; and cutting a brain corpus callosum segmentation prediction image from the fetal brain ultrasound image according to the initial contour line of the corpus callosum and the key point offset. According to the deep neural network model for fetal ultrasound image state analysis, the blank of brain ultrasound image brain corpus callosum state analysis is filled, and a method for evaluating the brain corpus callosum state through a brain ultrasound image is created for the first time.

The invention discloses an Alzheimer's disease classifier based on brain imaging big data deep learning. The Alzheimer's disease classifier comprises a data preprocessing module, a deep learning model, a gender classification module, an initialization module, an AD training module and a prediction module. The grey matter density and grey matter volume images registered by the data preprocessing module are input by the input module, and image feature values are extracted after convolution, reduction and pooling processing; carrying out gender deep model training on the brain imaging big database sample through a deep learning model; when the gender classification accuracy reaches the highest value; and then parameter initialization is performed on a drop out module and a Softmax function inthe deep learning model through an initialization module, finally AD training is performed on a big database sample through an AD training module by using the deep learning model, and AD detection and classification are performed after training. According to the method, the classification accuracy of AD patients and normal people is remarkably improved, the AD classification accuracy reaches 88.4%, and the AD classification accuracy on independent samples reaches 86.1%.

A system and method for identifying a patient-specific neurosurgery target location is provided. The system receives brain imaging data for a patient that includes tracts and networks in the patient brain, accesses a quantitative connectome atlas comprising population-based, disease-specific structural and functional connectivity maps comprising a pattern of tracts and networks associated with an optimal target area (OTA) identified from a population of patients, and defines the patient-specific neurosurgery target location based on a comparison between a pattern of the tracts and networks from the brain imaging data for the patient and the pattern of tracts and networks associated with the OTA identified from the population of patients in the quantitative connectome atlas. The quantitative connectome atlas comprises a disease-specific, population-based quantitative connectome atlas that identifies an optimal target location for treatment associated with a maximal clinical improvement for each disease in the population of patients.