Image construction methods for phased array magnetic resonance imaging.
OBJECTIVE
To study image construction in phased array magnetic resonance imaging (MRI) systems from a statistical signal processing point of view.


METHODS
Three new approaches for image combination with multiple coils are proposed: 1) one based on the singular value decomposition of the measurement matrix, which is asymptotically optimal in the signal-to-noise ratio sense; 2) one based on a maximum-likelihood formulation, incorporating a priori information on the coil sensitivities in a Bayesian manner; and 3) one based on a least-squares formulation, which incorporates a smoothness constraint on the coil sensitivities.


RESULTS
Numerical examples using synthetic and real data are presented to illustrate the performance of these new approaches. Results on the synthetic data show improvement in signal-to-error ratio, while results on the real data (a 4.7 T four-coil image of a cat spinal cord) show that the proposed methods can improve the SNR in the final image by up to 3 dB in the regions of interest compared to conventional sum-of-squares processing.


CONCLUSIONS
It is demonstrated that phased array MRI reconstruction performance can be improved by the use of more elaborate statistical signal processing algorithms.
Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA. deniz@cnel.ufl.edu
Deniz
D
Erdogmus
Rui
R
Yan
Erik G
EG
Larsson
Jose C
JC
Principe
Jeffrey R
JR
Fitzsimmons
D000465:Algorithms; D001499:Bayes Theorem; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015233:Models, Statistical; D012815:Signal Processing, Computer-Assisted