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

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 Y = 1.04X + 1.74  (R 2 = 0.96). The automated algorithm estimates the number, volume, and category of WMH.

[1]  Ming Dong,et al.  Dependence of vessel area accuracy and precision as a function of MR imaging parameters and boundary detection algorithm , 2007, Journal of magnetic resonance imaging : JMRI.

[2]  J R Reichenbach,et al.  Small vessels in the human brain: MR venography with deoxyhemoglobin as an intrinsic contrast agent. , 1997, Radiology.

[3]  Yasuo Yamashita,et al.  Automated segmentation method of white matter and gray matter regions with multiple sclerosis lesions in MR images , 2011, Radiological physics and technology.

[4]  L. Axel,et al.  Intensity correction in surface-coil MR imaging. , 1987, AJR. American journal of roentgenology.

[5]  Shouliang Qi,et al.  Automatic skull stripping in brain MRI based on local moment of inertia structure tensor , 2012, 2012 IEEE International Conference on Information and Automation.

[6]  Farzad Towhidkhah,et al.  Fully automatic segmentation of multiple sclerosis lesions in brain MR FLAIR images using adaptive mixtures method and markov random field model , 2008, Comput. Biol. Medicine.

[7]  Rita Simões,et al.  Automatic segmentation of cerebral white matter hyperintensities using only 3D FLAIR images. , 2013, Magnetic resonance imaging.

[8]  Bernhard Hemmer,et al.  An automated tool for detection of FLAIR-hyperintense white-matter lesions in Multiple Sclerosis , 2012, NeuroImage.

[9]  Yu-Chung N. Cheng,et al.  Susceptibility weighted imaging (SWI) , 2004, Zeitschrift fur medizinische Physik.

[10]  W G Bradley,et al.  Suspected multiple sclerosis: MR imaging with a thin-section fast FLAIR pulse sequence. , 1995, Radiology.

[11]  N. Otsu.  A threshold selection method from gray level histograms , 1979 .

[12]  A J Thompson,et al.  Brain atrophy in clinically early relapsing-remitting multiple sclerosis. , 2002, Brain : a journal of neurology.

[13]  Patrick A Turski,et al.  Three-Dimensional Fluid Attenuated Inversion Recovery Imaging With Isotropic Resolution and Nonselective Adiabatic Inversion Provides Improved Three-Dimensional Visualization and Cerebrospinal Fluid Suppression Compared to Two-Dimensional Flair at 3 Tesla , 2008, Investigative radiology.

[14]  Angel Alberich-Bayarri,et al.  Brain atrophy and lesion load are related to CSF lipid-specific IgM oligoclonal bands in clinically isolated syndromes , 2011, Neuroradiology.

[15]  A. Kouzani,et al.  Segmentation of multiple sclerosis lesions in MR images: a review , 2011, Neuroradiology.

[16]  F. Barkhof,et al.  Isotropic 3D fast FLAIR imaging of the brain in multiple sclerosis patients: initial experience , 2002, European Radiology.

[17]  D. Louis Collins,et al.  Review of automatic segmentation methods of multiple sclerosis white matter lesions on conventional magnetic resonance imaging , 2013, Medical Image Anal..