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Noise correlations and SNR in phased-array MRS.
N Martini1, M F Santarelli, G Giovannetti
1Interdepartmental Research Center 'E. Piaggio', University of Pisa, Pisa, Italy. nicola.martini@ifc.cnr.it
NMR in Biomedicine
|August 27, 2009
Summary
This study introduces a new method for combining magnetic resonance spectroscopy (MRS) signals from multiple receiver coils, enhancing signal-to-noise ratio (SNR) even with correlated noise. The technique improves spectral quality in MRS imaging.
Area of Science:
- Biomedical Engineering
- Magnetic Resonance Imaging (MRI)
- Spectroscopy
Background:
- Multiple receiver coils in magnetic resonance spectroscopy (MRS) offer improved signal-to-noise ratio (SNR) or reduced scan times.
- Phased array coils in MRS necessitate advanced data processing for optimal sensitivity exploitation.
- Existing MRS data combination methods often overlook correlated noise between acquisition channels.
Purpose of the Study:
- To present a novel method for combining MRS signals acquired using phased array coils.
- To address and overcome the challenge of correlated noise in multi-channel MRS data.
- To maximize the SNR of combined spectra by incorporating a noise decorrelation stage.
Main Methods:
- A novel data combination technique for phased array coil MRS signals is described.
- Principal Component Analysis (PCA) is employed for noise decorrelation.
- Acquired spectra are projected into a subspace where noise vectors are orthogonal, followed by SNR weighting.
Main Results:
- The proposed method effectively combines MRS signals, even with correlated noise.
- Simulations and phantom experiments demonstrate the method's performance on (1)H-MRS data.
- A modest SNR gain of approximately 0.5% was observed in the center of the field of view (FOV), with greater improvements in peripheral regions.
Conclusions:
- The developed noise decorrelation and SNR weighting approach optimizes combined spectra from phased array coils.
- This method enhances spectral quality in MRS by effectively managing correlated noise.
- The technique shows promise for improving MRS data acquisition and analysis in various applications.
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