SNR-optimality of sum-of-squares reconstruction for phased-array magnetic resonance imaging

SNR-optimality of sum-of-squares reconstruction for phased-array magnetic resonance imaging

We consider the commonly used “Sum-of-Squares” (SoS) reconstruction method for phased-array magnetic resonance imaging with unknown coil sensitivities. We show that the signal-to-noise ratio (SNR) in the image produced by SoS is asymptotically (as the input SNR→∞) equal to that of maximum-ratio comb...

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Veröffentlicht in:Journal of magnetic resonance (1997) 2003-07, Vol.163 (1), p.121-123
Hauptverfasser: Larsson, Erik G., Erdogmus, Deniz, Yan, Rui, Principe, Jose C., Fitzsimmons, Jeffrey R.
Format: Artikel
Sprache:eng
Schlagworte:
Computer Simulation
Image Enhancement - methods
Least-Squares Analysis
Linear Models
Magnetic resonance imaging
Magnetic Resonance Imaging - instrumentation
Magnetic Resonance Imaging - methods
Models, Statistical
Quality Control
Signal Processing, Computer-Assisted
Signal-to-noise ratio
Statistical properties
Statistics as Topic
Stochastic Processes
Sum-of-squares
TECHNOLOGY
TEKNIKVETENSKAP
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Zusammenfassung:We consider the commonly used “Sum-of-Squares” (SoS) reconstruction method for phased-array magnetic resonance imaging with unknown coil sensitivities. We show that the signal-to-noise ratio (SNR) in the image produced by SoS is asymptotically (as the input SNR→∞) equal to that of maximum-ratio combining, which is the best unbiased reconstruction method when the coil sensitivities are known. Finally, we discuss the implications of this result.
ISSN:1090-7807
1096-0856
1096-0856
DOI:10.1016/S1090-7807(03)00132-0