49 results about "Brain White Matter" patented technology

Rapid and accurate in-vivo assessment of cerebral white matter injury particularly for pre-term infants, for timely treatment and / or prediction of outcomes has been very limited. This invention exploits the discovery that reduced high-frequency EEG intensity, particularly as shown by the upper spectral edge frequency, is a good indicator of cerebral white matter neural injury and is well correlated with MRI results. With more experience of clinical cases, a set of simple rules such as “if the spectral edge value is below 8 Hz there is a high likelihood of injury” may be validated, yet the EEG technology involved is largely invisible to the user. In the invention, EEG signals are processed by software to obtain, store, and graphically display bilaterally collected EEG spectral edge and intensity values over from hours to weeks. Rejection of corrupted signals by filtering and gating means is responsive to incoming signal characteristics, to additional inputs such as motion sensors or impedance tests, and to patient data (gestational age in particular). The invention includes the software and methods of use.

The invention discloses a DTI image analytical method based on multiple variables. The DTI image analytical method based on the multiple variables is used for determining and extracting diseased areas of the brain white matter in the disease brain mechanism research, thereby providing an imaging basis for clinical treatment. The method includes the specific steps that data are preprocessed, wherein the preprocessing procedures include eddy current removal, head motion correction, cranium removal, dispersing and fitting and white matter skeleton building; characteristic extraction is conducted on the preprocessed data, the significant difference areas of a patient group and a healthy people group are obtained through replacement inspection and with the ages serving as the concomitant variables, the average value of the certain variables in the significant difference areas is worked out respectively, and the characteristic value is obtained; cross validation is conducted through a leave-one-out method, whether the stop criterion is met or not is judged, and if not, the average weight value of each characteristic is worked out, and the characteristic with the minimum average weight value is removed until the stop criterion is met; the finally obtained brain area is the brain lesion area. According to the method, the DTI imaging mode is adopted, the imaging basis is provided for finding the lesion area in the clinical treatment through the multivariable research method.

The invention relates to a method for automatic clustering and labeling of mixed brain white matter nerve fibers, which is characterized in that: firstly, a standard brain atlas is matched to a single brain space by using a high-dimensional elastic registration method, so that a single brain can be divided into 104 anatomical regions with structural information; then use different nerve fiber bundles to extract and mark 15 kinds of nerve fiber bundles through the characteristics of different brain anatomical regions; for the other 4 kinds, the above-mentioned ones cannot be used because they are strongly entangled with peripheral nerve fiber bundles The nerve fiber bundles extracted by the method are automatically clustered and analyzed by using the method based on the similarity measure combined with Kernel-PCA and Fuzzyc-mean. Finally, a feature-based identification method is used to mark the results of the cluster analysis . Through the above method, the 19 main white matter nerve fiber bundles of the brain can be automatically extracted and labeled.

The invention discloses a method for acquiring the imaging data of a neuro navigation system, which comprises the following steps of: acquiring scanned images in a functional magnetic resonance imaging mode, wherein the functional magnetic resonance imaging mode comprises BOLD scan cortex functional imaging and DTI scan alba functional imaging; transmitting the scanned images according to DICOM and classifying and converting the scanned images; and according to category, and carrying out the registration and fusion of the neuro navigation structural images and the converted scanned images, wherein the registration and fusion comprises the registration mapping of BOLD activation maps and the fusion of DTI maps. Therefore, the neuro functional images are registered and fused in neuro navigation, cerebral white matter fiber tracks is carried out and used as image data of the neuro navigation system so as to provide a powerful tool for making surgical plan before a surgery and protecting normal brain functions in the surgery and to provide a platform for brain function research.

The invention discloses a method for multimodal fusion analysis of genetic images based on brain white matter integrity and DNA methylation. In the present invention, by analyzing the FA value of the whole brain white matter fiber integrity of the subject, the patients with a certain neuropsychiatric disease are compared with normal people, and the name of the difference area is entered in the Allen brain bank to obtain the highly expressed in these brain areas. Gene names, and then extract the methylation levels of these genes corresponding to CpG sites from the genome methylation samples, and use correlation analysis to count the relationship between the methylation levels of these CpG sites and the FA value. The present invention conducts multi-modal fusion analysis on the brain structure development model of neuropsychiatric diseases and the epigenetic basis behind it. The in-depth mining of the image plus epigenetic multimodal data information has brought significant application and guiding value.

The invention discloses an obtaining method of a rat head magnetic resonance image Monte Carlo simulation model, which belongs to the technical field of tissue optics. The obtaining method based on the specific combination of multiple image segmenting methods comprises the following steps of: firstly, determining a gray threshold value through analysis, and segmenting out skull tissues by adopting a threshold value segmentation, corrosion and expansion algorithm and a binary image maximum region labeling method; then removing all the tissues at the upper part of a skull in an original image, and segmenting out scalp tissues by adopting a narrow-band level set of special parameters; afterwards, segmenting out cerebrospinal fluid, ectocinerea and alba in the image in which the scalp and theskull are removed by adopting fast fuzzy clustering; and finally respectively endowing the five segmented tissues and background parts with different gray values to obtain a multi-tissue model. The invention can overcome the limitation of a single image segmenting method, rapidly and accurately obtains the rat head magnetic resonance image Monte Carlo simulation model, and has good practical application prospect.