Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
6024199 | NeuroImage | 2016 | 17 Pages |
Abstract
Automated QA of DTI can facilitate large-scale, high-throughput quality assurance by reliably identifying both scanner and subject induced imaging artifacts. The results present a practical example of the confounding effects of artifacts on DTI analysis in a large population-based sample, and suggest that estimates of data quality should not only be reported but also accounted for in data analysis, especially in studies of development.
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Authors
David R. Roalf, Megan Quarmley, Mark A. Elliott, Theodore D. Satterthwaite, Simon N. Vandekar, Kosha Ruparel, Efstathios D. Gennatas, Monica E. Calkins, Tyler M. Moore, Ryan Hopson, Karthik Prabhakaran, Chad T. Jackson, Ragini Verma, Hakon Hakonarson,