title: Fast method for brain image segmentation: application to proton magnetic resonance spectroscopic imaging.
abstract: 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: R2 = 0.893 +/- 0.098, WM: R2 = 0.892 +/- 0.089, ln(CSF): R2 = 0.831 +/- 0.082). Test-retest analysis in one individual yielded an excellent agreement of measurements with R2 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.
authors.affiliation: Division of Neuroradiology, Russell H. Morgan Department of Radiology and Radiological Science, Johns Hopkins University School of Medicine, Baltimore, Maryland 21287, USA.
authors.forename: David
authors.initials: D
authors.lastname: Bonekamp
authors.forename: Alena
authors.initials: A
authors.lastname: Horská
authors.forename: Michael A
authors.initials: MA
authors.lastname: Jacobs
authors.forename: Atilla
authors.initials: A
authors.lastname: Arslanoglu
authors.forename: Peter B
authors.initials: PB
authors.lastname: Barker
mesh_terms: D000328:Adult; D000465:Algorithms; D001185:Artificial Intelligence; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D011522:Protons; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D011522:Protons