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Fast method for brain image segmentation: Application to proton magnetic resonance spectroscopic imaging
Author(s) -
Bonekamp David,
Horská Alena,
Jacobs Michael A.,
Arslanoglu Atilla,
Barker Peter B.
Publication year - 2005
Publication title -
magnetic resonance in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.696
H-Index - 225
eISSN - 1522-2594
pISSN - 0740-3194
DOI - 10.1002/mrm.20657
Subject(s) - segmentation , voxel , magnetic resonance spectroscopic imaging , magnetic resonance imaging , spatial normalization , nuclear magnetic resonance , nuclear medicine , normalization (sociology) , white matter , image resolution , echo time , pattern recognition (psychology) , artificial intelligence , computer science , physics , medicine , radiology , sociology , anthropology
The interpretation of brain metabolite concentrations measured by quantitative proton magnetic resonance spectroscopic imaging (MRSI) is assisted by knowledge of the percentage of gray matter (GM), white matter (WM), and cerebrospinal fluid (CSF) within each MRSI voxel. Usually, this information is determined from T 1 ‐weighted magnetic resonance images (MRI) that have a much higher spatial resolution than the MRSI data. While this approach works well, it is time‐consuming. In this article, a rapid data acquisition and analysis procedure for image segmentation is described, which is based on collection of several, thick slice, fast spin echo images (FSE) of different contrast. Tissue segmentation is performed with linear “Eigenimage” filtering and normalization. The method was compared to standard segmentation techniques using high‐resolution 3D T 1 ‐weighted MRI in five subjects. Excellent correlation between the two techniques was obtained, with voxel‐wise regression analysis giving GM: R 2 = 0.893 ± 0.098, WM: R 2 = 0.892 ± 0.089, ln(CSF): R 2 = 0.831 ± 0.082). Test–retest analysis in one individual yielded an excellent agreement of measurements with R 2 higher than 0.926 in all three tissue classes. Application of FSE/EI segmentation to a sample proton MRSI dataset yielded results similar to prior publications. It is concluded that FSE imaging in conjunction with Eigenimage analysis is a rapid and reliable way of segmenting brain tissue for application to proton MRSI. Magn Reson Med, 2005. © 2005 Wiley‐Liss, Inc.