Open AccessAutomated quality assurance routines for fMRI data applied to a multicenter study
Author(s) -
Stöcker Tony,
Schneider Frank,
Klein Martina,
Habel Ute,
Kellermann Thilo,
Zilles Karl,
Shah N. Jon
Publication year - 2005
Publication title -
human brain mapping
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.005
H-Index - 191
eISSN - 1097-0193
pISSN - 1065-9471
DOI - 10.1002/hbm.20096
Subject(s) - quality assurance , computer science , imaging phantom , data quality , scanner , functional magnetic resonance imaging , medical physics , artificial intelligence , quality (philosophy) , pattern recognition (psychology) , data mining , nuclear medicine , medicine , radiology , metric (unit) , philosophy , operations management , external quality assessment , epistemology , pathology , economics
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. Hum Brain Mapp 25:237–246, 2005. © 2005 Wiley‐Liss, Inc.