Asymptotic SNR-performance of some image combination techniques for phased-array MRI

  • Authors:

  • Deniz Erdogmus;Erik G. Larsson;Rui Yan;Jose C. Principe;Jeffrey R. Fitzsimmons

  • Affiliations:

  • Computational NeuroEngineering Laboratory, Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL;Department of Electrical & Computer Engineering The George Washington University, Washington, DC;Computational NeuroEngineering Laboratory, Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL;Computational NeuroEngineering Laboratory, Department of Electrical & Computer Engineering, University of Florida, Gainesville, FL;Department of Radiology, University of Florida, Gainesville, FL

  • Venue:
  • Signal Processing
  • Year:
  • 2004

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Abstract

Phased-array magnetic resonance imaging technology is currently flourishing with the promise of obtaining a profitable trade-off between image quality and image acquisition speed. The image quality is generally measured in terms of the signal-to-noise ratio (SNR), which is often calculated using samples taken from the reconstructed image. In this paper, we derive analytical expressions for the asymptotic SNR in the final image for three different phased-array image combination methods, namely: (1) sum-of-squares, (2) singular value decomposition, and (3) normalized coil averaging. The SNR expressions are expressed in terms of the statistics of the noise in the measurements, as well as the coil sensitivity coefficients. Our results can facilitate a better understanding for the phased-array image combination problem, as well as provide a tool for the optimal design of coils.