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Image construction methods for phased array magnetic resonance imaging
Deniz Erdogmus1, Rui Yan, Erik G Larsson
1Department of Electrical and Computer Engineering, University of Florida, Gainesville, FL 32611, USA. deniz@cnel.ufl.edu
Purpose:
To study image construction in phased array magnetic resonance imaging (MRI) systems from a statistical signal processing point of view.
Materials And 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.
Conclusion:
It is demonstrated that phased array MRI reconstruction performance can be improved by the use of more elaborate statistical signal processing algorithms.