Automated quality assurance routines for fMRI data applied to a multicenter study

Abstract

Standard procedures to achieve quality assessment (QA) of functional magnetic resonance imaging (fMRI) data are of great importance. A standardized and fully automated procedure for QA is presented that allows for classification of data quality and the detection of artifacts by inspecting temporal variations. The application of the procedure on phantom measurements was used to check scanner and stimulation hardware performance. In vivo imaging data were checked efficiently for artifacts within the standard fMRI post-processing procedure by realignment. Standardized and routinely carried out QA is essential for extensive data amounts as collected in fMRI, especially in multicenter studies. Furthermore, for the comparison of two different groups, it is important to ensure that data quality is approximately equal to avoid possible misinterpretations. This is shown by example, and criteria to quantify differences of data quality between two groups are defined. © 2005 Wiley-Liss, Inc.