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.