Support vector machine classification of complex fMRI data.
This work examines support vector machine (SVM) classification of complex fMRI data, both in the image domain and in the acquired k-space data. We achieve high classification accuracy using the magnitude data in both domains. Additionally, we maintain high classification accuracy even when using only partial k-space data. Thus we demonstrate the feasibility of using kspace data for classification, enabling rapid realtime acquisition and classification.
Peltier
Scott J
SJ
Functional MRI Laboratory, University of Michigan, Ann Arbor, MI 48109, USA. spelt@umich.edu
Lisinski
Jonathan M
JM
Noll
Douglas C
DC
LaConte
Stephen M
SM
D000465:Algorithms; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D013223:Statistics as Topic / Q000145:classification*