title: Atlas-based whole brain white matter analysis using large deformation diffeomorphic metric mapping: application to normal elderly and Alzheimer's disease participants.
abstract: The purpose of this paper is to establish single-participant white matter atlases based on diffusion tensor imaging. As one of the applications of the atlas, automated brain segmentation was performed and the accuracy was measured using Large Deformation Diffeomorphic Metric Mapping (LDDMM). High-quality diffusion tensor imaging (DTI) data from a single-participant were B0-distortion-corrected and transformed to the ICBM-152 atlas or to Talairach coordinates. The deep white matter structures, which have been previously well documented and clearly identified by DTI, were manually segmented. The superficial white matter areas beneath the cortex were defined, based on a population-averaged white matter probability map. The white matter was parcellated into 176 regions based on the anatomical labeling in the ICBM-DTI-81 atlas. The automated parcellation was achieved by warping this parcellation map to normal controls and to Alzheimer's disease patients with severe anatomical atrophy. The parcellation accuracy was measured by a kappa analysis between the automated and manual parcellation at 11 anatomical regions. The kappa values were 0.70 for both normal controls and patients while the inter-rater reproducibility was 0.81 (controls) and 0.82 (patients), suggesting "almost perfect" agreement. A power analysis suggested that the proposed method is suitable for detecting FA and size abnormalities of the white matter in clinical studies.
authors.affiliation: The Russell H. Morgan Department of Radiology and Radiological Science, The Johns Hopkins University School of Medicine, Baltimore, MD 21205, USA.
authors.forename: Kenichi
authors.initials: K
authors.lastname: Oishi
authors.forename: Andreia
authors.initials: A
authors.lastname: Faria
authors.forename: Hangyi
authors.initials: H
authors.lastname: Jiang
authors.forename: Xin
authors.initials: X
authors.lastname: Li
authors.forename: Kazi
authors.initials: K
authors.lastname: Akhter
authors.forename: Jiangyang
authors.initials: J
authors.lastname: Zhang
authors.forename: John T
authors.initials: JT
authors.lastname: Hsu
authors.forename: Michael I
authors.initials: MI
authors.lastname: Miller
authors.forename: Peter C M
authors.initials: PC
authors.lastname: van Zijl
authors.forename: Marilyn
authors.initials: M
authors.lastname: Albert
authors.forename: Constantine G
authors.initials: CG
authors.lastname: Lyketsos
authors.forename: Roger
authors.initials: R
authors.lastname: Woods
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
authors.forename: G Bruce
authors.initials: GB
authors.lastname: Pike
authors.forename: Pedro
authors.initials: P
authors.lastname: Rosa-Neto
authors.forename: Alan
authors.initials: A
authors.lastname: Evans
authors.forename: John
authors.initials: J
authors.lastname: Mazziotta
authors.forename: Susumu
authors.initials: S
authors.lastname: Mori
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology*; D001185:Artificial Intelligence; D001921:Brain / Q000473:pathology*; D038524:Diffusion Magnetic Resonance Imaging / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008297:Male; D008875:Middle Aged; D009413:Nerve Fibers, Myelinated / Q000473:pathology*; D010363:Pattern Recognition, Automated / Q000379:methods*; D012016:Reference Values; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D013382:Subtraction Technique