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129 results about "White matter" patented technology

White matter refers to areas of the central nervous system (CNS) that are mainly made up of myelinated axons, also called tracts. Long thought to be passive tissue, white matter affects learning and brain functions, modulating the distribution of action potentials, acting as a relay and coordinating communication between different brain regions.

Magnetic resonance diffusion tensor imaging fiber bundle tracking device

InactiveCN103049901AAccurate Tracking AlgorithmAccurate white matter fiber tractsImage analysisDiagnostic recording/measuringTensor fieldWhite matter
The invention relates to a magnetic resonance diffusion tensor imaging fiber bundle tracking device. The process is respectively completed by each component through the following steps of (1) collecting magnetic resonance diffusion tensor images; (2) carrying out brain issue dividing on a sequence without a diffusion gradient magnetic field and any sequence with the diffusion gradient magnetic field, and using the sequence without the diffusion gradient magnetic field as an image reference template of the brain tissues; (3) carrying out three-dimensional affine conversion on the two image sequences of the brain tissues after being extracted, to obtain a space conversion relationship; (4) carrying out space conversion on the remained diffusion tensor images by an optimum conversion relationship; (5) calculating tensor fields and feature vectors; (6) setting the interested areas and tracking conditions; and (7) carrying out the bidirectional tracking and displaying based on a fiber bundle with variable step size, so as to quickly and effectively carry out fiber bundle tracking and displaying on the white matters of human brain. In the diffusion tensor imaging process, the image deviation caused by space positions can be corrected, and the elastic step size is adopted in the tracking process, so as to ensure the reliable fiber bundle tracking.
Owner:UNIV OF SHANGHAI FOR SCI & TECH

Method for three-dimensional registration and brain tissue extraction of individual human brain multimodality medical images

The invention relates to a method for three-dimensional registration and brain tissue extraction of individual human brain multimodality medical images. The method comprises the following steps: reading DICOM medical image data, and converting DICOM format data into NIfTI format data; layering images adopting a nuclear magnetic resonance structure; establishing a mixture gaussian model through an east Asia brain structure template and an east Asia brain tissue probability graph of ICBM, and dividing nuclear magnetic data into a grey matter part, a white matter part and a cerebrospinal fluid part; enabling the registration methods of other modality data and nuclear magnetic structure images to be the same; according to a layering and registration result, removing a skull and other parts outside the skull of each modality, and reserving the structure of parts inside the skull; adding weighting of three tissue probability graphs of the grey matter, the white matter and the cerebrospinal fluid obtained through layering of structure images so as to obtain a brain tissue probability graph, and performing Gaussian kernel smoothness; setting a threshold, applying the threshold to each modality image data after registration and resampling, and removing the cranium and parts outside the cranium; outputting a save result in an NIfTI format. According to the method disclosed by the invention, the registration of various structure images and multiplanar reconstruction images of a tested person can be completed at the same time.
Owner:王雪原 +1

Making method of color dough for dough modeling

The invention discloses a fabrication method for colourful dough used for dough molding, comprising 5 shares of rice flour, 2-3 shares of glutinous rice flour, 0.5-1 shares of glycerine, 0.2-0.5 shares of wheat starch and 0.6 shares of preservative and suitable quantity of water. The colourful dough is prepared with the components by the method as follows: the rice flour, the glutinous rice flour, the wheat starch and the preservative are arranged in a container, suitable quantity of water is also added in the container; the components and the water added in the container are uniformly mixed for 20-30 minutes. A disc is coated with one half of the glycerine and the mixed dough is arranged on the disc and steamed in boiled water for 40 minutes to obtain flour mud; when the flour mud is cooled and does not scald, the other half of the glycerine is mixed in the flour mud and coloring matter is then added in the flour. The wheat flour in traditional raw material is replaced by the rice flour, thus leading the prepared flour mud to be crystal-like and lucent and be easy to be colorized; meanwhile, the forming colourful dough has good elasticity and is easy to be formed during the nipping process; glue is not added when the colourful dough is being fabricated, and the health of the user is ensured; normal salt raw material is saved, then the preservation time of the finished product is long, no white matter separates out, the beauty of the finished product is not affected; and the honey is not used so as to lead the finished product to be mothproof; the processing is carried out by the steaming method, thus ensuring the deserved efficacies of all raw materials.
Owner:董晓红

KNN (K-Nearest Neighbor) sorting algorithm based method for correcting and segmenting grayscale nonuniformity of MR (Magnetic Resonance) image

The invention relates to a KNN (K-Nearest Neighbor) sorting algorithm based method for correcting and segmenting the grayscale nonuniformity of an MR (Magnetic Resonance) image, belonging to the field of image processing. The method comprises the following steps of: firstly constructing a grayscale nonuniform field model by utilizing surface fitting knowledge and using a group of orthonormalization basis functions, and establishing energy functions; and then solving model parameters according to an energy function minimization principle to realize grayscale nonuniformity correction and image segmentation, wherein subordinate functions are solved by adopting an iterative algorithm and the KNN algorithm in the model parameter solving process, therefore a partial volume effect is greatly reduced while a grayscale nonuniform field is eliminated, and the influence of noises on the correction and the segmentation of the grayscale nonuniformity of the MR image is reduced. The subordinate functions are solved with KNN through the following steps of: firstly acquiring an accurate smooth normalization histogram by using a kernel estimation algorithm; then respectively solving a threshold value TCG between cerebrospinal fluids and gray matters and a threshold value TGW between the gray matters and white matters by using a maximum between-cluster variance method; carrying out rough sorting on the KNN sorting algorithm by utilizing the two threshold values; and finally accurately sorting points to be fixed by adopting the traditional KNN sorting algorithm.
Owner:UNIV OF ELECTRONICS SCI & TECH OF CHINA

Ultra-short time of echo magnetic resonance fingerprint relaxation time measuring method

The invention discloses an ultra-short time of echo magnetic resonance fingerprint relaxation time measuring method. According to the method, semi-pulse excitation and semi-projection readout are adopted to shorten TE to achieve acquisition of an ultra-short T2 time signal; and image acquisition and reconstruction are based on a magnetic resonance fingerprint imaging technology. A TE change mode of sinusoidal fluctuation is introduced, so that the distinguishing capability of a magnetic resonance fingerprint signal to short T2 and ultra-short T2 tissues is improved, and simultaneous multi-parameter quantitative imaging of the short T2 and ultra-short T2 tissues and long T2 tissues is realized. The non-uniformity of a magnetic field is modulated into phase information of the fingerprint signal through the TE of the sinusoidal fluctuation; a B0 graph is directly reconstructed according to an amplitude modulation signal demodulation principle; and the phase change caused by a B0 field iscompensated in the fingerprint signal, so that the accuracy of signal recognition is improved. In magnetic resonance skeletal muscle system imaging, simultaneous quantitative measurement of T1, T2, PDand B0 of the bone (ultra-short T2) and muscle (long T2) tissues can be realized; and the method can also be used for imaging the brain and measuring the grey/white matter relaxation time and the skull structure.
Owner:ZHEJIANG UNIV

Brain perfusion image segmentation method and device, server and storage medium

ActiveCN109410221ASolve the problem of poor edge segmentationAchieve precise segmentationImage enhancementImage analysisPerfusionWhite matter
The embodiment of the invention discloses a brain perfusion image segmentation method and device, a server and a storage medium. The method comprises the following steps: performing brain image segmentation on a pre-processed time sequence image to obtain a brain mask; determining a feature image according to the brain mask and the pre-processing time sequence image; Using the maximum gray scale projection image and the gray scale average image in the feature image to obtain a blood vessel mask; performing image standardization on the gray average image to obtain a standardized image; and segmenting the pre-treatment time sequence image superposed with the brain mask and the blood vessel mask according to the standardized image, the gray average image of the brain, the maximum gray projection image and the baseline mean value image before flowing into the contrast agent to obtain one or more of cerebrospinal fluid, gray matter and white matter. According to the embodiment of the invention, the problem of poor edge segmentation effect of different brain tissues segmented by the brain perfusion image in the prior art is solved, and the accurate segmentation of different brain tissuesin the brain perfusion image and the automation of image processing are realized.
Owner:SHANGHAI UNITED IMAGING HEALTHCARE
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