Table of Contents Author Guidelines Submit a Manuscript
BioMed Research International
Volume 2014 (2014), Article ID 725052, 9 pages
Research Article

DWI-Based Neural Fingerprinting Technology: A Preliminary Study on Stroke Analysis

1Department of Electronic and Information Engineering, Harbin Institute of Technology Shenzhen Graduate School, HIT Campus, University Town, Room 205C, C Building, Xili, Nanshan, Shenzhen 518055, China
2Department of Neurology, Peking University Shenzhen Hospital, Shenzhen 18036, China
3School of Information Technology and Electrical Engineering, The University of Queensland, St. Lucia, QLD 4072, Australia

Received 28 March 2014; Revised 4 June 2014; Accepted 6 June 2014; Published 12 August 2014

Academic Editor: Ting Zhao

Copyright © 2014 Chenfei Ye et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


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.