Influence of noise correlation in multiple‐coil statistical models with sum of squares reconstruction

Noise in the composite magnitude signal from multiple‐coil systems is usually assumed to follow a noncentral χ distribution when sum of squares is used to combine images sensed at different coils. However, this is true only if the variance of noise is the same for all coils, and no correlation exists between them. We show how correlations may be obviated from this model if effective values are considered. This implies a reduced effective number of coils and an increased effective variance of noise. In addition, the effective variance of noise becomes signal‐dependent. Magn Reson Med, 2012. © 2011 Wiley Periodicals, Inc.

[1]  P. Roemer,et al.  Noise correlations in data simultaneously acquired from multiple surface coil arrays , 1990, Magnetic resonance in medicine.

[2]  P. Roemer,et al.  The NMR phased array , 1990, Magnetic resonance in medicine.

[3]  M. Harpen Noise correlations exist for independent RF coils , 1992, Magnetic resonance in medicine.

[4]  E. McVeigh,et al.  Signal‐to‐noise measurements in magnitude images from NMR phased arrays , 1997 .

[5]  Marvin Simon,et al.  Probability Distributions Involving Gaussian Random Variables , 2002 .

[6]  Cheng Guan Koay,et al.  Analytically exact correction scheme for signal extraction from noisy magnitude MR signals. , 2006, Journal of magnetic resonance.

[7]  Pascal Spincemaille,et al.  On the noise correlation matrix for multiple radio frequency coils , 2007, Magnetic resonance in medicine.

[8]  S. Schoenberg,et al.  Influence of multichannel combination, parallel imaging and other reconstruction techniques on MRI noise characteristics. , 2008, Magnetic resonance imaging.

[9]  Marcos Martín-Fernández,et al.  Automatic noise estimation in images using local statistics. Additive and multiplicative cases , 2009, Image Vis. Comput..

[10]  Santiago Aja-Fernández,et al.  Noise estimation in single- and multiple-coil magnetic resonance data based on statistical models. , 2009, Magnetic resonance imaging.

[11]  S. Aja‐Fernández,et al.  About the background distribution in MR data: a local variance study. , 2010, Magnetic resonance imaging.

[12]  W. Hoge,et al.  Statistical noise analysis in GRAPPA using a parametrized noncentral Chi approximation model , 2011, Magnetic resonance in medicine.