Coil-to-coil physiological noise correlations and their impact on functional MRI time-series signal-to-noise ratio

Magn Reson Med. 2016 Dec;76(6):1708-1719. doi: 10.1002/mrm.26041. Epub 2016 Jan 12.


Purpose: Physiological nuisance fluctuations ("physiological noise") are a major contribution to the time-series signal-to-noise ratio (tSNR) of functional imaging. While thermal noise correlations between array coil elements have a well-characterized effect on the image Signal to Noise Ratio (SNR0 ), the element-to-element covariance matrix of the time-series fluctuations has not yet been analyzed. We examine this effect with a goal of ultimately improving the combination of multichannel array data.

Theory and methods: We extend the theoretical relationship between tSNR and SNR0 to include a time-series noise covariance matrix Ψt , distinct from the thermal noise covariance matrix Ψ0 , and compare its structure to Ψ0 and the signal coupling matrix SSH formed from the signal intensity vectors S.

Results: Inclusion of the measured time-series noise covariance matrix into the model relating tSNR and SNR0 improves the fit of experimental multichannel data and is shown to be distinct from Ψ0 or SSH .

Conclusion: Time-series noise covariances in array coils are found to differ from Ψ0 and more surprisingly, from the signal coupling matrix SSH . Correct characterization of the time-series noise has implications for the analysis of time-series data and for improving the coil element combination process. Magn Reson Med 76:1708-1719, 2016. © 2016 International Society for Magnetic Resonance in Medicine.

Keywords: 32Channel coil; 7T; SNR; array coils; fMRI; noise covariance; physiological noise.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Artifacts*
  • Brain / physiology*
  • Brain Mapping / methods
  • Computer Simulation
  • Data Interpretation, Statistical
  • Humans
  • Image Interpretation, Computer-Assisted / methods*
  • Magnetic Resonance Imaging / methods*
  • Models, Neurological*
  • Models, Statistical*
  • Sensitivity and Specificity
  • Signal-To-Noise Ratio*
  • Statistics as Topic