Functional magnetic resonance imaging brain activation directly from k-space. Magn Reson Imaging 2009 Dec;27(10):1370-81
Date
07/18/2009Pubmed ID
19608365Pubmed Central ID
PMC2783194DOI
10.1016/j.mri.2009.05.048Scopus ID
2-s2.0-70449530580 (requires institutional sign-in at Scopus site) 2 CitationsAbstract
In functional magnetic resonance imaging (fMRI), the process of determining statistically significant brain activation is commonly performed in terms of voxel time series measurements after image reconstruction and magnitude-only time series formation. The image reconstruction and statistical activation processes are treated separately. In this manuscript, a framework is developed so that statistical analysis is performed in terms of the original, prereconstruction, complex-valued k-space measurements. First, the relationship between complex-valued (Fourier) encoded k-space measurements and complex-valued image measurements from (Fourier) reconstructed images is reviewed. Second, the voxel time series measurements are written in terms of the original spatiotemporal k-space measurements utilizing this k-space and image relationship. Finally, voxelwise fMRI activation can be determined in image space in terms of the original k-space measurements. Additionally, the spatiotemporal covariance between reconstructed complex-valued voxel time series can be written in terms of the spatiotemporal covariance between complex-valued k-space measurements. This allows one to utilize the originally measured data in its more natural, acquired state rather than in a transformed state. The effects of modeling preprocessing in k-space on voxel activation and correlation can then be examined.
Author List
Rowe DB, Hahn AD, Nencka ASAuthor
Andrew S. Nencka PhD Director, Associate Professor in the Radiology department at Medical College of WisconsinMESH terms used to index this publication - Major topics in bold
AlgorithmsBrain
Brain Mapping
Computer Simulation
Fourier Analysis
Humans
Image Processing, Computer-Assisted
Magnetic Resonance Imaging
Models, Neurological
Models, Statistical
Models, Theoretical
Time Factors