TY - JOUR
T1 - Partially adaptive STAP algorithm approaches to functional MRI
AU - Huang, Lejian
AU - Thompson, Elizabeth A.
AU - Schmithorst, Vincent
AU - Holland, Scott K.
AU - Talavage, Thomas M.
N1 - Funding Information:
Manuscript received September 12, 2007; revised June 18, 2008. First published October 3, 2008; current version published March 25, 2009. This work was supported by the National Institutes of Health under Grant 1R21MH68267-01A1 and Grant 1R01EB003990. Asterisk indicates corresponding author. ∗L. Huang is with the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 USA (e-mail: lejian@purdue.edu). E. A. Thompson is with the Department of Engineering, Purdue University, Fort Wayne, IN 46805 USA (e-mail: thompson@engr.ipfw.edu). V. Schmithorst and S. K. Holland are with the Imaging Research Center, Children’s Hospital Medical Center, Cincinnati, OH 45229 USA (e-mail: vince.schmithorst@cchmc.org; scott.holland@cchmc.org). T. M. Talavage is with the School of Electrical and Computer Engineering, Purdue University, West Lafayette, IN 47907 USA (e-mail: tmt@purdue.edu). Color versions of one or more of the figures in this paper are available online at http://ieeexplore.ieee.org. Digital Object Identifier 10.1109/TBME.2008.2006017
PY - 2009/2
Y1 - 2009/2
N2 - In this paper, the architectures of three partially adaptive space-time adaptive processing (STAP) algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in the construction of brain activation maps in functional magnetic resonance imaging (fMRI). Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing time and maximum memory requirements, and thus demonstrates potential in fMRI analysis.
AB - In this paper, the architectures of three partially adaptive space-time adaptive processing (STAP) algorithms are introduced, one of which is explored in detail, that reduce dimensionality and improve tractability over fully adaptive STAP when used in the construction of brain activation maps in functional magnetic resonance imaging (fMRI). Computer simulations incorporating actual MRI noise and human data analysis indicate that element space partially adaptive STAP can attain close to the performance of fully adaptive STAP while significantly decreasing processing time and maximum memory requirements, and thus demonstrates potential in fMRI analysis.
KW - Element space partially adaptive STAP
KW - Functional magnetic resonance imaging (fMRI)
KW - Image processing
KW - Space-time adaptive processing (STAP)
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U2 - 10.1109/TBME.2008.2006017
DO - 10.1109/TBME.2008.2006017
M3 - Article
C2 - 19272913
AN - SCOPUS:63849122933
SN - 0018-9294
VL - 56
SP - 518
EP - 521
JO - IRE transactions on medical electronics
JF - IRE transactions on medical electronics
IS - 2
M1 - 4637869
ER -