UNDERSTANDING SCANNER UPGRADE EFFECTS ON BRAIN INTEGRITY & CONNECTIVITY MEASURES

被引：0

作者：

Zhan, L. ^{[1
,2
]}

Jahanshad, N. ^{[1
,2
]}

Jin, Y. ^{[1
,2
]}

Nir, T. M. ^{[2
]}

Leonardo, C. D. ^{[2
]}

Bernstein, M. A. ^{[3
]}

Borowski, B. ^{[3
]}

Jack, Clifford R., Jr. ^{[3
]}

Thompson, P. M. ^{[1
,2
,4
]}

机构：

[1] Univ Calif Los Angeles, Sch Med, Dept Neurol, Los Angeles, CA 90024 USA

[2] Univ Southern Calif, Inst Neuroimaging & Informat, Imaging Genet Ctr, Los Angeles, CA USA

[3] Mayo Clin, Rochester, MN USA

[4] Univ Calif Los Angeles, Dept Psychiat, Semel Inst, Los Angeles, CA USA

来源：

2014 IEEE 11th International Symposium on Biomedical Imaging (ISBI) | 2014年

关键词：

Scanner; Diffusion MRI; SNR; Anisotropy; Brain Network; Connectivity; ALZHEIMERS-DISEASE;

D O I：

暂无

中图分类号：

R318 [生物医学工程];

学科分类号：

0831 ;

摘要：

Large multi-site studies, such as the Alzheimer's disease Neuroimaging Initiative (ADNI) are designed to harmonize imaging protocols as far as possible across scanning sites. ADNI-2 collects diffusion-weighted images (DWI) at 14 sites, with a consistent scanner manufacturer (General Electric), magnetic field strength (3T) and consistent acquisition parameters - including voxel size and the number of gradient directions. Here we studied how the SNR, voxel-wise and ROI-based diffusion measures, and derived connectivity matrices and network properties depended on the scanner platform (with "HD" denoting version 16.x software and lower and DV being 20.x and higher). We found scanner platform effects on voxel-based FA, in several ROIs, but not on SNR or network properties. These results indicate the importance of accounting for any differences in scanner platform in multi-site DTI studies, even when the protocols are harmonized in all other respects.

引用

页码：234 / 237

页数：4

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