期刊
NEUROIMAGE
卷 45, 期 2, 页码 370-376出版社
ACADEMIC PRESS INC ELSEVIER SCIENCE
DOI: 10.1016/j.neuroimage.2008.12.028
关键词
Diffusion tensor imaging; Tractography; Streamline; Clustering; Region of interest; Schizophrenia
资金
- Canadian Institutes of Health Research Clinician Scientist Award (ANV)
- National Institutes of Health [U41-RR019703]
- Brain Science Foundation [NIH R01 MH074794, NIH P41 RR13218, R01 MH 50740, NIH 1P50 MH08272, U54GM072977-01 NIG/HS NIH]
- Sandra A. Rotman Research Institute (BGP)
MR diffusion tensor imaging (DTI) can measure and visualize organization of white matter fibre tracts in vivo. DTI is a relatively new imaging technique, and new tools developed for quantifying fibre tracts require evaluation. The purpose of this study was to compare the reliability of a novel clustering approach with a multiple region of interest (MROI) approach in both healthy and disease (schizophrenia) populations. DTI images were acquired in 20 participants (n = 10 patients with schizophrenia: 56 +/- 15 years; n = 10 controls: 51 +/- 20 years) (1.5 T GE system) with diffusion gradients applied in 23 non-collinear directions, repeated three times. Whole brain seeding and creation of fibre tracts were then performed. Interrater reliability of the clustering approach, and the MROI approach, were each evaluated and the methods compared. There was high spatial (voxel-based) agreement within and between the clustering and MROI methods. Fractional anisotropy, trace, and radial and axial diffusivity values showed high intraclass correlation (p<0.001 for all tracts) for each approach. Differences in scalar indices of diffusion between the clustering and MROI approach were minimal. The excellent interrater reliability of the clustering method and high agreement with the MROI method, quantitatively and spatially, indicates that the clustering method can be used with confidence. The clustering method avoids biases of ROI drawing and placement, and, not limited by a priori predictions, may be a more robust and efficient way to identify and measure white matter tracts of interest. (c) 2008 Elsevier Inc. All rights reserved.
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