Document Type
Article
Language
eng
Format of Original
19 p.
Publication Date
1-2014
Publisher
Elsevier
Source Publication
Magnetic Resonance Imaging
Source ISSN
0730-725X
Original Item ID
doi: 10.1016/j.mri.2013.09.001
Abstract
Relaxation parameter estimation and brain activation detection are two main areas of study in magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI). Relaxation parameters can be used to distinguish voxels containing different types of tissue whereas activation determines voxels that are associated with neuronal activity. In fMRI, the standard practice has been to discard the first scans to avoid magnetic saturation effects. However, these first images have important information on the MR relaxivities for the type of tissue contained in voxels, which could provide pathological tissue discrimination. It is also well-known that the voxels located in gray matter (GM) contain neurons that are to be active while the subject is performing a task. As such, GM MR relaxivities can be incorporated into a statistical model in order to better detect brain activation. Moreover, although the MR magnetization physically depends on tissue and imaging parameters in a nonlinear fashion, a linear model is what is conventionally used in fMRI activation studies. In this study, we develop a statistical fMRI model for Differential T2⁎ ConTrast Incorporating T1 and T2⁎ of GM, so-called DeTeCT-ING Model, that considers the physical magnetization equation to model MR magnetization; uses complex-valued time courses to estimate T1 and T2⁎ for each voxel; then incorporates gray matter MR relaxivities into the statistical model in order to better detect brain activation, all from a single pulse sequence by utilizing the first scans.
Recommended Citation
Karaman, M. Muge; Bruce, Iain P.; and Rowe, Daniel B., "A Statistical fMRI Model for Differential T2* Contrast Incorporating T1 and T2* of Gray Matter" (2014). Mathematics, Statistics and Computer Science Faculty Research and Publications. 204.
https://epublications.marquette.edu/mscs_fac/204
ADA Accessible Version
Comments
Accepted version. Magnetic Resonance Imaging, Vol. 32, No. 1 (January 2014): 9-27. DOI. © Elsevier. Used with permission.