PURPOSE: To develop a strategy for structural brain imaging when using FSL software for segmentation and subsequent volumetry. MATERIALS AND METHODS: Three-dimensional (3D) structural MRI of 1-mm isotropic resolution was performed on a 3-Tesla clinical imaging system. Prescribed signal evolution of a multiple spin-echo (SE) sequence with variable refocusing flip angle for T2 weighting, and a modified driven equilibrium Fourier transform (MDEFT) sequence were used for T1 weighting. Postprocessing included rigid-body coregistration, brain extraction, and segmentation using the tools of the FSL 3.2 software package. RESULTS: T2 weighting provided reliable delineation of the subarachnoidal space, while T1 weighting provided better segmentation of gray matter (GM) and white matter (WM). The combination of T1-weighted (T1-w) and T2-w data allowed the identification of a T2-hypointense class of "nonbrain" (NB) representing larger vessels and structures of connective tissue, as well as partial volume of bone and air-filled cavities. CONCLUSION: Brain extraction on T2-w data and subsequent segmentation of the combined T1- and T2-w intensity distribution into four classes are recommended. Copyright (c) 2006 Wiley-Liss, Inc.
PURPOSE: To develop a strategy for structural brain imaging when using FSL software for segmentation and subsequent volumetry. MATERIALS AND METHODS: Three-dimensional (3D) structural MRI of 1-mm isotropic resolution was performed on a 3-Tesla clinical imaging system. Prescribed signal evolution of a multiple spin-echo (SE) sequence with variable refocusing flip angle for T2 weighting, and a modified driven equilibrium Fourier transform (MDEFT) sequence were used for T1 weighting. Postprocessing included rigid-body coregistration, brain extraction, and segmentation using the tools of the FSL 3.2 software package. RESULTS: T2 weighting provided reliable delineation of the subarachnoidal space, while T1 weighting provided better segmentation of gray matter (GM) and white matter (WM). The combination of T1-weighted (T1-w) and T2-w data allowed the identification of a T2-hypointense class of "nonbrain" (NB) representing larger vessels and structures of connective tissue, as well as partial volume of bone and air-filled cavities. CONCLUSION: Brain extraction on T2-w data and subsequent segmentation of the combined T1- and T2-w intensity distribution into four classes are recommended. Copyright (c) 2006 Wiley-Liss, Inc.
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