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Explorative signal processing in functional MR imaging
Author(s) -
Moser Ewald,
Baumgartner Richard,
Barth Markus,
Windischberger Christian
Publication year - 1999
Publication title -
international journal of imaging systems and technology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.359
H-Index - 47
eISSN - 1098-1098
pISSN - 0899-9457
DOI - 10.1002/(sici)1098-1098(1999)10:2<166::aid-ima7>3.0.co;2-f
Subject(s) - computer science , signal processing , signal (programming language) , computer vision , artificial intelligence , digital signal processing , computer hardware , programming language
Identification and separation of artifacts as well as quantification of expected, i.e., stimulus‐correlated, and novel information on brain activity are important for both new insights in neuroscience and future developments in functional magnetic resonance imaging (MRI) of the human brain. Here, we present several examples in which gross head motion or physiologic motion (e.g., pulsation, respiration, large veins) could be identified and separated by using fuzzy cluster analysis of fMRI time series. Furthermore, our experience with single‐ and multislice fMRI (FLASH and EPI; 1.5 and 3 T) data analysis is summarized and several examples, including long echo time and high‐resolution fMRI of the motor cortex, are discussed. Explorative signal processing in fMRI, based on fuzzy clustering, represents a robust and powerful tool for screening large fMRI data sets, extracting expected and novel functional activity of the human brain, and obtaining improved reproducibility of fMRI results. Finally, it may help to improve or develop functional brain models which can then be tested by applying statistical models. © 1999 John Wiley & Sons, Inc. Int J Imaging Syst Technol, 10, 166–176, 1999