title: Support vector machine classification of complex fMRI data.
abstract: 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.
authors: Functional MRI Laboratory, University of Michigan, Ann Arbor, MI 48109, USA. spelt@umich.edu
authors: Scott J
authors: SJ
authors: Peltier
authors: Jonathan M
authors: JM
authors: Lisinski
authors: Douglas C
authors: DC
authors: Noll
authors: Stephen M
authors: SM
authors: LaConte
mesh_terms: D000465:Algorithms; D006801:Humans; D008279:Magnetic Resonance Imaging / Q000379:methods*; D013223:Statistics as Topic / Q000145:classification*