کد مقاله کد نشریه سال انتشار مقاله انگلیسی نسخه تمام متن
5630818 1580849 2017 22 صفحه PDF دانلود رایگان
عنوان انگلیسی مقاله ISI
Harmonization of multi-site diffusion tensor imaging data
ترجمه فارسی عنوان
هماهنگ سازی داده های تصویربرداری از تانسور انتشار چند سایت
موضوعات مرتبط
علوم زیستی و بیوفناوری علم عصب شناسی علوم اعصاب شناختی
چکیده انگلیسی


- Significant site and scanner effects exist in DTI scalar maps.
- Several multi-site harmonization methods are proposed.
- ComBat performs the best at removing site effects in FA and MD.
- Voxels associated with age in FA and MD are more replicable after ComBat.
- ComBat is generalizable to other imaging modalities.

Diffusion tensor imaging (DTI) is a well-established magnetic resonance imaging (MRI) technique used for studying microstructural changes in the white matter. As with many other imaging modalities, DTI images suffer from technical between-scanner variation that hinders comparisons of images across imaging sites, scanners and over time. Using fractional anisotropy (FA) and mean diffusivity (MD) maps of 205 healthy participants acquired on two different scanners, we show that the DTI measurements are highly site-specific, highlighting the need of correcting for site effects before performing downstream statistical analyses. We first show evidence that combining DTI data from multiple sites, without harmonization, may be counter-productive and negatively impacts the inference. Then, we propose and compare several harmonization approaches for DTI data, and show that ComBat, a popular batch-effect correction tool used in genomics, performs best at modeling and removing the unwanted inter-site variability in FA and MD maps. Using age as a biological phenotype of interest, we show that ComBat both preserves biological variability and removes the unwanted variation introduced by site. Finally, we assess the different harmonization methods in the presence of different levels of confounding between site and age, in addition to test robustness to small sample size studies.

ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: NeuroImage - Volume 161, 1 November 2017, Pages 149-170
نویسندگان
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