Obese patient functional image analysis method based on mutual sample entropy

A technology of mutual sample entropy and analysis method, which is applied in the field of medical image processing and analysis, can solve problems such as brain network changes in obese patients, and achieve the effect of high spatial resolution

Inactive Publication Date: 2014-12-17
XIDIAN UNIV
View PDF1 Cites 12 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] The purpose of the present invention is to provide a functional image analysis method for obese patients based on mutual sample entropy, aiming to solve the problem of brain network changes in obese patients that has not yet been understood. This method can be used for the description of the interaction relationship between nuclei. Provide imaging evidence for the study of the physiological mechanism of obesity

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
  • Obese patient functional image analysis method based on mutual sample entropy
  • Obese patient functional image analysis method based on mutual sample entropy

Examples

Experimental program
Comparison scheme
Effect test

example 1

[0098] Example 1: The cross-sample entropy Q between OFC (orbitofrontal cortex) and VTA (ventral tegmental area) in obese patients 肥胖 The size of is 0.3527, and the cross-sample entropy value Q between OFC and VTA of normal subjects 正常 The size of is 0.2948, the cross-sample entropy value Q 肥胖 Compared with cross-sample entropy value Q 正常 23.18% higher at a P-value of less than 0.05

[0099] The cross-sample entropy value of obese patients increased, indicating that the connection density between OFC and VTA regions of interest was lower in obese patients than in normal subjects.

[0100] OFC is part of the frontal lobe area of ​​the brain, which belongs to 10, 11 and 47 in Brodman's division, and is mainly responsible for driving and is part of the driving circuit. The VTA is an important part of the reward circuit and plays a vital role in regulating the feeding behavior of the human body.

[0101] Due to long-term large-scale eating, the neurophysiological mechanism o...

example 2

[0102] Example 2: The mutual sample entropy value Q between the two regions of Caudate (caudate nucleus) and Putamen (putamen) in obese patients 肥胖 The size of is 0.3279, the cross-sample entropy value Q between the two areas of Caudate and Putamen of normal subjects 正常 The size of is 0.3358, and the cross-sample entropy value Q 肥胖 Compared with cross-sample entropy value Q 正常 2.35% reduction at P-values ​​less than 0.05

[0103] The cross-sample entropy value of obese patients decreases, indicating that the connection density between the two ROIs of Caudate and Putamen is greater in obese patients than in normal people.

[0104] From a neurophysiological point of view, Caudate is part of the ventral striatum, which belongs to the reward circuit of the brain. Putamen is an important part of the brain's learning and memory circuits.

[0105] Previous studies have shown that compared with normal controls in the resting state, the activity level of the reward circuit of obe...

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

The invention discloses an obese patient functional image analysis method based on mutual sample entropy. The method is characterized by comprising the following steps of collecting resting-state functional magnetic resonance data of a brain; selecting interest areas; extracting a time sequence of voxels in each interest area, up-sampling the time sequence of each voxel, and calculating the mutual sample entropy value of any two interest areas; comparing the mutual sample entropy value of any two interested area of the obese patient with the mutual sample entropy value of corresponding two interested areas of a normal testee, and determining the reason for obesity or worsening of the obesity. The method has the beneficial effects that starting with the physiological line of the obese patient through the resting-state imaging way, the changing of physiological activity of the brain of the patient can be accurately reflected; the EEG signal brain network establishing method is applied to fMRI, and the problem that the fMRI time resolution is not high can be overcome by utilizing the up-sampling method; the mutual sample entropy overcomes the matching problem of the mutual approximate entropy.

Description

technical field [0001] The invention relates to an image analysis method, in particular to an obese patient functional image analysis method based on cross-sample entropy, and belongs to the technical field of medical image processing and analysis. Background technique [0002] At present, the study of brain network has become a hot spot in the field of brain science research. Brain network connections can be divided into structural brain networks, functional brain networks and causal brain networks. Structural brain networks are mainly studied based on imaging methods such as MRI and DTI that can reflect the physiological structure of the brain, while functional brain networks and causal brain networks are mainly studied based on methods such as EEG, MEG and fMRI that can reflect brain functional imaging. . Among them, the functional brain network is an undirected network, while the causal brain network is a special functional brain network whose functional connections ar...

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): G06T11/00A61B5/055
Inventor 张毅姚建亮刘菊张官胜王婧罗回春蔡伟伟朱强刘道民田捷刘一军
Owner XIDIAN UNIV
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap