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International Journal of Biomedical Imaging
Volume 2014, Article ID 239123, 7 pages
http://dx.doi.org/10.1155/2014/239123
Research Article

Automated White Matter Hyperintensity Detection in Multiple Sclerosis Using 3D T2 FLAIR

1School of Sino-Dutch Biomedical and Information Engineering, Northeastern University, Shenyang, Liaoning 110004, China
2Magnetic Resonance Innovations Inc., 440 E. Ferry Street, Detroit, MI 48202, USA
3Department of Biomedical Engineering, Wayne State University, Detroit, MI 48201, USA
4Magnetic Resonance Imaging Institute for Biomedical Research, 440 E. Ferry Street, Detroit, MI 48202, USA

Received 11 April 2014; Revised 9 July 2014; Accepted 10 July 2014; Published 22 July 2014

Academic Editor: Sos Agaian

Copyright © 2014 Yi Zhong 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.

Abstract

White matter hyperintensities (WMH) seen on T2WI are a hallmark of multiple sclerosis (MS) as it indicates inflammation associated with the disease. Automatic detection of the WMH can be valuable in diagnosing and monitoring of treatment effectiveness. T2 fluid attenuated inversion recovery (FLAIR) MR images provided good contrast between the lesions and other tissue; however the signal intensity of gray matter tissue was close to the lesions in FLAIR images that may cause more false positives in the segment result. We developed and evaluated a tool for automated WMH detection only using high resolution 3D T2 fluid attenuated inversion recovery (FLAIR) MR images. We use a high spatial frequency suppression method to reduce the gray matter area signal intensity. We evaluate our method in 26 MS patients and 26 age matched health controls. The data from the automated algorithm showed good agreement with that from the manual segmentation. The linear correlation between these two approaches in comparing WMH volumes was found to be . The automated algorithm estimates the number, volume, and category of WMH.