112 results about "Grey matter" patented technology

The present invention is capable of separating/removing the influence of skin blood flow contained in near infrared spectroscopy (NIRS) signals and extracting a brain- or brain cortex-origin signal. Moreover, the present invention enables versatile separation of brain-origin and skin-origin signals in view of differences among individuals. A biological photometric device, wherein light transmitters and light receivers are located in such a manner that measurement can be conducted at a plurality of source-detector (SD) distances and light received by the individual light-receivers can pass through the gray matter to thereby separate a brain-origin signal and a skin-origin signal. Individual component analysis (ICA) is conducted on data obtained at the individual measurement points. Then, it is determined whether each individual component originates in the brain or in the skin with the use of the SD distance-dependency of the weighted value of each of the separated components.

The invention provides an infant brain magnetic resonance image partitioning method based on a fully convolutional network. The main content of the method comprises the multi-stream three-dimensionalfully convolutional network (FCN) with jump connection, partial transfer learning, training and testing and evaluation. According to the process of the method, first, a probability graph of each pieceof brain tissue is learned from a multi-modal magnetic resonance image; second, initial partitions of different brain tissue are obtained from the probability graphs and used for calculating a distance graph of each piece of brain tissue; third, spatial context information is simulated according to the distance graphs; and last, final partitioning is realized by use of spatial correlation information and the multi-modal magnetic resonance image, wherein the training process mainly comprises training data increasing, training patch preparation and iterative training, and various detection values are used for evaluation after testing is performed. Through the partitioning method, a white matter area, a grey matter area and a cerebrospinal fluid area are successfully divided, the potential gradient vanishing problem of multi-level deep supervision is relieved, training efficiency is improved, and partitioning performance is greatly enhanced.

The invention provides a diffusion tensor imaging white matter fiber clustering method. The method includes the steps of pre-processing original rsf magnetic resonance imaging (MRI) data and the original diffusion tensor imaging (DTI) data, registering the pre-processed rsf MRI data into a DTI space, respectively conducting fiber tracking and brain tissue segmentation on the pre-processed DTI data, conducting fiber projection on white matter fiber which can not reach grey matter or exceed a grey matter surface in a white matter fiber obtained by DTI, then calculating functional similarities among the white matter fiber, obtaining a matrix of the similarities and clustering by adopting an affine spread clustering algorithm. Fiber bundle has functional independence, accuracy without need to relay on of a genetic linkage map and require complicated registration.

The invention discloses a progressive type mild cognitive impairment identification method based on neuroimaging, and belongs to the technical field of computer image processing. The MRI (Magnetic Resonance Imaging) graph and the PET (Positron Emission Tomography) graph of a test sample are downloaded from an ADNI (Alzheimer's Disease Neuroimaging Initiative) database and are subjected to preprocessing and sample screening to obtain N groups of sample images; the AAL (Anatomical Automatic Labeling) template of the human is selected to independently manufacture 90 cerebral region templates for the sample images, and the grey matter voxel value of a corresponding cerebral region is obtained to obtain N*180-dimensional data; and finally, a second level integration classifier is constructed, feature dimension reduction is carried out on the obtained data, a reduced dimension is subjected to optimization, and the data is applied to the second level integration classifier to carry out classification identification on progressive type MCI (Mild Cognitive Impairment) patients and non-progressive type MCI patients. The data is subjected to the dimension reduction processing by a random projection method, then, the data is applied to the second level integration classifier, classification accuracy is 74.22%, sensitivity is 66.25%, specificity is 82.19%, operation speed is improved, and the classification accuracy is improved.

In a method and system for outlining a region of interest in a Positron Emission Tomography (PET) scan study, a processor may, based on application of Masked Volume Wise Principal Component Analysis (MVW-PCA) to a plurality of scan images, generate a PC2 image showing kinetic behavior of a particular part of a subject, in particular, the grey matter of the cerebellar cortex of the subject, and may outline, in the PC2 image, a region of the PC2 image having highest pixel intensity values of the PC2 image or of a portion thereof as a region of interest, and, in particular, as a reference region. The processor may generate a PC3 image showing kinetic behavior of a different part of the subject, in particular, blood vessels of the subject, import the outline into the PC3 image to determine the correctness of the outline, and modify the outline if it is incorrect.

The invention discloses a multi-modal fusion image sorting method based on RLS-ELM. The method is characterized by comprising the steps that 1. rs-fMRI, sMRI and DTI data of a plurality of tested objects are obtained, preprocessing is carried out, and tested data which do not accord with a provision are removed; 2. ReHo values of voxels in the rs-fMRI data are computed; 3. grey matter density values of voxels in the sMRI data are computed; 4. FA values of voxels in DTI data are computed; 5. the ReHo values, the grey matter density values and the FA values of the voxels are connected to form a new characteristic matrix A; 6. the new characteristic matrix A is subjected to PCA dimension reduction processing; and 7. an RLS-ELM sorter is subjected to training, and a trained RLS-ELM sorter is obtained. According to the multi-modal fusion image sorting method based on RLS-ELM, sorting accuracy and sorting speed are obviously improved, disease early discovering and early treating are achieved, and great significance is achieved in a clinical medicine study process for revealing disease progression is achieved.

The invention relates to a construction method for a health people white matter fiber tract atlas. The method comprises the steps that through seeking the corresponding relationship between individual gray matter structure space and DTI space, the information of the tensor image average space of group people is calculated, and the DTI data of individuals are transformed into the average space; parameters jointly registered by a magnetic resonance anatomical structure image and an MNI standard form are integrated with registration parameters in the group by using related integration means and applied in an individual T1 dataset, and a group transition tensor image template is established; the individual T1 data are registered into the transition template through non-linear registration, and transformation parameters of a T1 structure image registered to the transition template are obtained; the transformation parameters are used in the individual T1 data, the correction for direction of a tensor field on each voxel point is carried out by using methods of keeping the main characteristic direction, the linear average of the voxel point one by one is carried out on the corrected tensor, and finally the diffusion tensor atlas of the specific health people group is obtained.

The invention discloses an epilepsy paradoxical discharge locus positioning method and system based on EEG-fMRI. The method includes the steps of synchronously collecting brain electric data and functional magnetic resonance imaging data of a patient in a resting state to be preprocessed, marking epilepsy discharge time points and corresponding duration in the interictal phase, aligning the brainelectric data and the functional magnetic resonance imaging data on terms of time, selecting image frames of the functional magnetic resonance imaging data to extract grey matter part signals so as toconduct layered clustering to establish a epilepsy discharge grey matter template, marking epilepsy discharge related time points, obtaining expected BOLD time signals, conducting correlation analysis through a generalized linear model to find a brain area related to epilepsy discharge activities, and comprehensively obtaining an epilepsy paradoxical discharge locus. Through brain electric-functional magnetic resonance imaging fusion analysis, the epilepsy discharge time points marked by a doctor are expanded, the positioning accuracy of the paradoxical discharge locus is improved, and the method has the advantages of being simple in principle, convenient to implement and stable in result.

A fully-automated method for segmentation of white matter, gray matter, and cerebral spinal fluid (CSF) and for calculating their respective volumes is disclosed. The technique preferably uses a T2-weighted scan and a proton-density scan. The T2-weighted scans are used to highlight those portion of the brain corresponding to CSF. Thereafter, the proton density images are separated in CSF-containing and CSF-free portions, in accordance with the T2-weighted images. The CSF-free portion is then segmented into white matter and gray matter portions. The CSF-containing portion is further segmented into pure CSF-regions, and regions containing a mixture of gray and white matter and CSF. After CSF is segmented from this mixture, the white and gray matter and again segmented, and the respective volumes of white matter, gray matter, and CSF are calculated. A technique for determining a threshold gray scale value for segmenting the white and gray matter to assist in the visual identification of such regions is also disclosed.

The invention relates to an automatic discrimination analysis method of mild cognitive impairment based on a support vector machine. According to the automatic discrimination analysis method of mild cognitive impairment based on the support vector machine, multiple detection index data including aresting state fMRI, structure MRI and neuropsychological examination data are used, and methods of Functional Connectivity (FC) analysis, Voxel-based Morphometry (VBM) analysis and Fiber Tractography are adopted to extract a resting state functional connectivity characteristic, a gray matter structure characteristic and a white matter fiber connectivity characteristic, reduction is conducted on the characteristics based on a rough set approach, and finally classifier construction is conducted on multimodal MRI data based on the SVM method, so that automatic discrimination analysis of mild cognitive impairment is achieved, diagnosis accuracy of mild cognitive impairment is improved, and the accuracy rate of the diagnosis in experimental data reaches more than 90 percent. The method can be applied to practical clinical diagnosis.

The invention provides an interlayer identification prediction method in a fluvial facies thick oil layer. The interlayer identification prediction method in the fluvial facies thick oil layer comprises the following steps: (1) interlayer identification standards of mud interlayer, ash interlayer and matter interlayer are built by using a coring well and logging information; (2) the well point interlayer identification is performed according to the interlayer identification standards; (3) the prediction of interlayer in a well is performed; and (4) an interlayer three-dimensional space distribution space is built by adopting a method of combining deterministic modeling with random modeling. The interlayer identification prediction method in the fluvial facies thick oil layer realizes the purpose of the research on the control of remaining oil distribution through the interlayer by using the closed coring well data and the numerical simulation research according to the interlayer description prediction result.

The invention provides a method and a device for establishing an age prediction model, an age prediction method and a device. The method comprises: building m references on the x-axis, y-axis, z-axisData sets (described in the description) corresponding to T1-weighted images of brain slices in each slice direction and T<n><n>(z, g), where w refers to the white matter T1-weighted image corresponding to the white matter region and g refers to the grey matter T1-weighted image corresponding to the grey matter region; n is the number of the reference person and n has a value ranging from 1 to m.According to the true age of the reference person corresponding to the T1-weighted image, age identification is added to the T1-weighted image in each data set. Convolution-based neural network pairdata sets (described in the description) and T<n><n>(z, g), corresponding data sets (described in the description)and <n><n>(z, g) are established. The age predictor model with the highest precision of age prediction is selected as the age predictor model.

The invention discloses a preparation method of independent and ordered titanium dioxide nanotube arrays among tubes, comprising the following steps of: feeding a metallic titanium sheet as an anode in water-based electrolyte prepared from ammonium fluoride, sulfuric acid and water with the temperature of 25-35DEG C by stirring; adjusting direct current voltage to be 20V improved from 0V at the speed rate of 0.8-1.2V/s and maintaining for at least 25 minutes; taking out the metallic titanium sheet and washing, and feeding the metallic titanium sheet as the anode into organic electrolyte prepared from ammonium fluoride, water and glycol with the temperature of 25-35DEG C; and adjusting direct current voltage to be 60 V improved from 0V at the speed rate of 0.8-1.2V/s and maintaining for at least one hour; feeding a metallic titanium sheet subjected to anodic oxidation twice in glycol; and ultrasonically oscillating until a grey matter falls off the surface of the metallic titanium sheet completely to obtain the independent and ordered titanium dioxide nanotube arrays among the tubes with the outer diameter of 150-200nm and the thickness of the tube wall of 10-15nm. The preparation method can be widely applied to preparation of the titanium dioxide nanotube arrays which have no barrier layers on the surfaces and have smooth and flat tube wall surfaces and can be applied to the fields of photocatalysis, clean energy, and the like.

An apparatus and method for performing automatic 3D image segmentation and reconstruction of organ structures, which is particularly well-suited for use on cortical surfaces is presented. A brain extraction process removes non-brain image elements, then classifies brain tissue as to type in preparation for a cerebrum segmentation process that determines which portions of the image information belong to specific physiological structures. Ventricle filling is performed on the image data based on information from a ventricle extraction process. A reconstruction process follows in which specific surfaces, such as white matter (WM) and grey matter (GM), are reconstructed.

The invention discloses a spinal cord fusion agent and a preparation method and use thereof. The spinal cord fusion agent disclosed by the invention is prepared from polyethylene glycol, bone mesenchymal stem cells, Schwann cells, glial cell lined derived neurotrophic factor (GDNF), salvianolic acid and nano graphene. Studies show that the use of the spinal cord fusion agent of the invention enables rapid reconnection of divided white matter fiber tracts and gray matter staple fibers to reconstitute spinal integrity and electrical conduction and is capable of recovering motion and sensory functions below the lesion level in a short period of time, thereby effectively reducing the incidence of paraplegia and complications thereof, and no relevant products exist at home and abroad. The spinal cord fusion agent disclosed by the invention can have a wide application prospect clinically.

The invention discloses a brain magnetic resonance image segmentation method combining global and local information, which comprises the following steps: segmenting the brain magnetic resonance imageby using an end-to-end convolution neural network constructed to obtain various prediction probability distributions; The supervoxels are generated by linear iterative clustering supervoxel algorithmfor brain MRI images. A magnetic resonance image of that brain in which a segmentation result is obtain by fusing the segmentation result prediction probability distribution and the generate supervoxels comprises the following step of: finding out the corresponding regions of the supervoxels in the prediction probability distribution of each category; Background, cerebrospinal fluid (CSF), gray matter (GM) and white matter (WM) were counted and the specific gravity of each category was calculated. The supervoxel class proportion method is used to re-assign the prediction probability distribution of each class. The class with the highest probability of classification and the class label of the pixel are obtained to obtain the segmentation result of the brain magnetic resonance image. The invention can improve the segmentation precision and obtain a better segmentation result of the magnetic resonance image of the brain.

The invention relates to a method for preparing a spinal cord injury repair tissue engineering stent. The problems that any complicated model cannot be made, a requirement of a spinal cord bracket model cannot be met, and the porosity is low exist in the conventional method. According to the method, an in-vivo biotic environment is used as an important basis for designing a tissue engineering spinal cord stent by adopting a low temperature forming technology and a porogenic agent leaching process; the spinal cord bracket is partitioned into a grey matter induction functional region and a white matter induction functional region by using a microporous isolation layer with a small size by simulating a spinal cord structure and a living environment, so that the tissue engineering spinal cord stent is made; and an aim of respective regeneration is fulfilled by planting different seed cells and growth factors by using two different biotic environments. According to the method, a made mellow and full and regular stent has macroscopical pores with good penetration property, contains a large quantity of irregular microporous structures, has the average porosity of 89.92 percent and better mechanical property, and can meet the requirement of the tissue engineering spinal cord stent.

In the invention, through an improved threshold segmentation algorithm, segmentation of a grey matter and a white matter of a brain image is realized, which is good for auxiliary diagnosis of brain atrophy of a patient. The method is characterized by firstly, using an adaptive filter to process an original image, reducing a noise of the original image and enhancing a contrast ratio of the image; then, for the processed image, using a region growing algorithm to carry out brain decollement on the image, rejecting a non-parenchymal portion; and then using an iterative threshold method to calculate a threshold and using a near weighted value algorithm to carry out binarization segmentation on the image so that an obtained result is a segmentation result of a brain white matter; finally, based on a result of the previous step, carrying out segmentation so that the obtained result is a segmentation result of a brain grey matter.

The invention provides a method and a device for determining tuffaceous contents by means of elementary capture energy spectrum well logging, and belongs to the technical field of oil and gas exploration well logging. The method includes reconstructing real natural gamma well logging response curves of to-be-logged wells according to elementary capture energy spectrum well logging curves; comparing the real natural gamma well logging response curves with measured natural gamma well logging response curves to obtain layer sections of tuffaceous components; creating cross plots of the measured natural gamma well logging response curves and products of thorium curves-compensate neutron well logging curves; determining the tuffaceous contents according to locations of sample points of the measured natural gamma well logging response curves in the cross plots. The method and the device have the advantages that the cross plots of the measured natural gamma well logging response curves and the products of the thorium curves-compensate neutron well logging curves are created, the tuffaceous contents are determined according to regions of the sample points of the measured natural gamma well logging response curves in the cross plots, and accordingly obtained results are high in accuracy and precision and good in applicability.

The invention discloses a magnetic resonance artery spin labeling cerebral perfusion imaging data artifact graph removal method. The method comprises steps that a structure image of a detected brain is acquired, the structure image is segmented to generate a grey matter probability graph GM and a white matter probability graph WM, the grey matter probability graph GM and the white matter probability graph WM form a reference image pmCBF, and artifact graphs of rheoencephalograms CBF1-CBFn at n time points of the detected brain are removed by utilizing the reference image pmCBF. The method is advantaged in that the artifact graphs of the rheoencephalogram sequence can be effectively removed, error reduction is realized, and image quality is improved.

The invention discloses a zoning tissue engineering spinal cord which comprises a spinal external catheter and a central catheter. A gray matter catheter which is provided with the same shape as spinal gray matter is arranged in the spinal external catheter. Corresponding catheters are respectively arranged in a white matter region according to the geometric shapes of uplink fiber bundles and downlink fiber bundles. The zoning tissue engineering spinal cord can play the role of correct guidance and lead to the result that the spinal gray matter, white matter and the uplink fiber bundles and the downlink fiber bundles in the white matter respectively grow in different regions.

A computerized method is disclosed for detecting a brain region with neurodegenerative change and also a brain region with vascular change in the brain of a patient and also an imaging arrangement suitable for this. In at least one embodiment, the method includes recording a first data record of the brain via positron emission tomography and a second data record of the brain via magnetic resonance imaging; reconstructing a PET image from the first data record and an MRI image from the second data record; identifying evidence for a brain region with vascular change in the MRI image; segmenting the MRI image into gray matter and white matter; identifying a brain region with neurodegenerative change in the PET image; and superposing the PET image and the segmented MRI image to determine whether the brain region with neurodegenerative change is present in gray matter or in white matter, the recording of the first data record and the second data record being carried out in quick succession without repositioning the patient or is even carried out simultaneously.

Systems and methods related to imaging biomarkers for determining neurological disease states of a subject are provided. In one embodiment, a method for producing an image indicative of a neurological disease using a magnetic resonance imaging (“MRI”) system is provided. The method includes directing the MRI system to perform a double inversion-recovery (“DIR”) pulse sequence to generate data where signals from gray matter and cerebral spinal fluid are substantially suppressed. The method also includes analyzing the DIR images, reconstructed from the acquired data, to identify cortical and white matter lesions. This includes identifying imaging biomarkers based on visual signatures of brain tissue, including white matter tissue. In some aspects, diffusion-weighted data may also be obtained using the MRI system. Diffusion-weighted data may be used in a tractography process to determine connectivities, or connectivity patterns between the identified lesions, including cortical lesions, to determine neurological disease states of the subject.