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Contrast‐driven approach to intracranial segmentation using a combination of T2‐ and T1‐weighted 3D MRI data sets
Author(s) -
Helms Gunther,
Kallenberg Kai,
Dechent Peter
Publication year - 2006
Publication title -
journal of magnetic resonance imaging
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.563
H-Index - 160
eISSN - 1522-2586
pISSN - 1053-1807
DOI - 10.1002/jmri.20692
Subject(s) - weighting , segmentation , computer science , partial volume , artificial intelligence , white matter , pattern recognition (psychology) , magnetic resonance imaging , medicine , radiology
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. J. Magn. Reson. Imaging 2006. © 2006 Wiley‐Liss, Inc.