Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Comprehensive analytical method for predicting early-stage Parkinson's disease

A comprehensive analysis, Parkinson's disease technology, applied in image analysis, image data processing, instruments, etc., can solve the problems of single analysis index, large method angle error, cumbersome steps, etc.

Inactive Publication Date: 2015-09-09
ZHEJIANG UNIV OF TECH
View PDF0 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0005] In order to overcome the shortcomings of the existing methods of auxiliary prediction of Parkinson's disease, such as cumbersome steps, large method angle error, low resolution, and single analysis index, the present invention provides a high-resolution, accurate and reliable method for early Parkinson's disease prediction. comprehensive analysis method

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Comprehensive analytical method for predicting early-stage Parkinson's disease
  • Comprehensive analytical method for predicting early-stage Parkinson's disease
  • Comprehensive analytical method for predicting early-stage Parkinson's disease

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0049] The present invention will be further described below in conjunction with the accompanying drawings.

[0050] refer to figure 1 with figure 2 , a comprehensive analysis method for early Parkinson's disease prediction, comprising the following steps:

[0051] Step S1, read diffusion weighted magnetic resonance (DW-MRI) data, and perform noise reduction and smoothing preprocessing on all data, and use diffusion tensor imaging (DTI) and high angular resolution diffusion imaging (HARDI) technology for modeling Imaging and fiber tracking, obtain voxel fiber direction information, and calculate six kinds of brain fiber difference analysis index data, including anisotropy fraction (FA), mean diffusion coefficient (MD), mean square displacement (MSD), generalized anisotropy Anisotropic fraction (GFA, GFApeak) and voxel average density ( ), for subsequent analysis and prediction of Parkinson's disease differences;

[0052] Step S2, extract and mark the voxel information (L...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

A comprehensive analytical method for predicting early-stage Parkinson's disease comprises the following steps: S1, reading diffusion weighted magnetic resonance data DW-MRI, carrying out noise reduction and pre-smoothing processing on all the data, carrying out modeling and imaging and fiber tracking by applying diffusion tensor imaging and high angular resolution diffusion imaging technologies to acquire voxel fiber direction information, and calculating six types of brain fiber variation analysis index data; S2, extracting and marking special brain region voxel information, selecting a continuous region as a midbrain substantia nigra region by virtue of setting a threshold value of an anisotropic fraction, and then screening and extracting the index data in interested regions; and S3, carrying out comprehensive analysis by applying a SPSS analytical tool according to the six types of brain fiber variation analysis index data obtained by the step S2 to obtain a fiber variation result on the midbrain substantia nigra regions of a testee and a normal person so as to predict the sickness status of the testee. The method provided by the invention is high in resolution and accurate and reliable.

Description

technical field [0001] The present invention relates to the fields of image processing, medical imaging, calculation methods, mathematics, non-medical disease diagnosis, neuroanatomy, etc., especially the method for comprehensively analyzing early Parkinson's disease brain fiber differences and disease prediction. Background technique [0002] Parkinson's disease is a neurodegenerative disease, and its pathological research has always been a hot spot in the medical field. Parkinson's disease is mostly caused by the loss of dopamine-containing neurons in the substantia nigra region of the midbrain. Today's research methods for Parkinson's disease are mainly through chemical drug experiments and clinical medical observations. For example, Burns et al. found that N-methyl-4-phenyl -1,2,3,6-tetrahydropyridine (NMPTP) can reduce the release and accumulation of dopamine, brain-derived neurotrophic factor (BDNF) affects the survival of dopamine-containing neurons, HLA-DR positive r...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
Patent Type & Authority Applications(China)
IPC IPC(8): G06T7/00
CPCG06T7/0012G06T2207/10092G06T2207/20076G06T2207/30004
Inventor 詹佳雯冯远静吴烨周思琪龚一隆毛文涛周侠叶峰梁朝凯李小薪梁荣华
Owner ZHEJIANG UNIV OF TECH
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products