DWI-based neural fingerprinting technology: a preliminary study on stroke analysis

Ye, Chenfei, Ting Ma, Heather, Wu, Jun, Yang, Pengfei, Chen, Xuhui, Yang, Zhengyi and Ma, Jingbo (2014) DWI-based neural fingerprinting technology: a preliminary study on stroke analysis. BioMed Research International, 2014 725052.1-725052.9. doi:10.1155/2014/725052


Author Ye, Chenfei
Ting Ma, Heather
Wu, Jun
Yang, Pengfei
Chen, Xuhui
Yang, Zhengyi
Ma, Jingbo
Title DWI-based neural fingerprinting technology: a preliminary study on stroke analysis
Journal name BioMed Research International   Check publisher's open access policy
ISSN 2314-6141
2314-6133
Publication date 2014-08-12
Sub-type Article (original research)
DOI 10.1155/2014/725052
Open Access Status DOI
Volume 2014
Start page 725052.1
End page 725052.9
Total pages 9
Place of publication New York, NY, United States
Publisher Hindawi Publishing Corporation
Collection year 2015
Language eng
Abstract Stroke is a common neural disorder in neurology clinics. Magnetic resonance imaging (MRI) has become an important tool to assess the neural physiological changes under stroke, such as diffusion weighted imaging (DWI) and diffusion tensor imaging (DTI). Quantitative analysis of MRI images would help medical doctors to localize the stroke area in the diagnosis in terms of structural information and physiological characterization. However, current quantitative approaches can only provide localization of the disorder rather than measure physiological variation of subtypes of ischemic stroke. In the current study, we hypothesize that each kind of neural disorder would have its unique physiological characteristics, which could be reflected by DWI images on different gradients. Based on this hypothesis, a DWI-based neural fingerprinting technology was proposed to classify subtypes of ischemic stroke. The neural fingerprint was constructed by the signal intensity of the region of interest (ROI) on the DWI images under different gradients. The fingerprint derived from the manually drawn ROI could classify the subtypes with accuracy 100%. However, the classification accuracy was worse when using semiautomatic and automatic method in ROI segmentation. The preliminary results showed promising potential of DWI-based neural fingerprinting technology in stroke subtype classification. Further studies will be carried out for enhancing the fingerprinting accuracy and its application in other clinical practices.
Q-Index Code C1
Q-Index Status Confirmed Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2015 Collection
School of Information Technology and Electrical Engineering Publications
 
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Created: Sat, 13 Sep 2014, 19:22:51 EST by Dr Steven Yang on behalf of School of Information Technol and Elec Engineering