title: Automated brain tissue segmentation based on fractional signal mapping from inversion recovery Look-Locker acquisition.
abstract: Most current automated segmentation methods are performed on T(1)- or T(2)-weighted MR images, relying on relative image intensity that is dependent on other MR parameters and sensitive to B(1) magnetic field inhomogeneity. Here, we propose an image segmentation method based on quantitative longitudinal magnetization relaxation time (T(1)) of brain tissues. Considering the partial volume effect, fractional volume maps of brain tissues (white matter, gray matter, and cerebrospinal fluid) were obtained by fitting the observed signal in an inversion recovery procedure to a linear combination of three exponential functions, which represents the relaxations of each of the tissue types. A Look-Locker acquisition was employed to accelerate the acquisition process. The feasibility and efficacy of this proposed method were evaluated using simulations and experiments. The potential applications of this method in the study of neurological disease as well as normal brain development and aging are discussed.
authors.affiliation: Neuroimaging Research Branch, National Institute on Drug Abuse, National Institutes of Health, Baltimore, MD 21224, USA. shinwa@nida.nih.gov
authors.forename: Wanyong
authors.initials: W
authors.lastname: Shin
authors.forename: Xiujuan
authors.initials: X
authors.lastname: Geng
authors.forename: Hong
authors.initials: H
authors.lastname: Gu
authors.forename: Wang
authors.initials: W
authors.lastname: Zhan
authors.forename: Qihong
authors.initials: Q
authors.lastname: Zou
authors.forename: Yihong
authors.initials: Y
authors.lastname: Yang
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted
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