Received: 19 January 2009, Revised: 12 May 2009, Accepted: 20 June 2009, Published online in Wiley InterScience: 25 August 2009 Noise correlations and SNR in phased-array MRS N. Martini a,b * , M. F. Santarelli b , G. Giovannetti b , M. Milanesi b , D. De Marchi b , V. Positano b and L. Landini b,c The acquisition of magnetic resonance spectroscopy (MRS) signals by multiple receiver coils can improve the signal-to-noise ratio (SNR) or alternatively can reduce the scan time maintaining a reliable SNR. However, using phased array coils in MRS studies requires efficient data processing and data combination techniques in order to exploit the sensitivity improvement of the phased array coil acquisition method. This paper describes a novel method for the combination of MRS signals acquired by phased array coils, even in presence of correlated noise between the acquisition channels. In fact, although it has been shown that electric and magnetic coupling mechanisms produce correlated noise in the coils, previous algorithms developed for MRS data combination have ignored this effect. The proposed approach takes advantage of a noise decorrelation stage to maximize the SNR of the combined spectra. In particular Principal Component Analysis (PCA) was exploited to project the acquired spectra in a subspace where the noise vectors are orthogonal. In this subspace the SNR weighting method will provide the optimal overall SNR. Performance evaluation of the proposed method is carried out on simulated 1 H-MRS signals and experimental results are obtained on phantom 1 H-MR spectra using a commercially available 8-element phased array coil. Noise correlations between elements were generally low due to the optimal coil design, leading to a fair SNR gain (about 0.5%) in the center of the field of view (FOV). A greater SNR improvement was found in the peripheral FOV regions. Copyright ß 2009 John Wiley & Sons, Ltd. Keywords: magnetic resonance spectroscopy; phased array coils; noise correlation; signal-to-noise ratio; signal combination INTRODUCTION It is well known that phased array coils provide an improvement of signal-to-noise-ratio (SNR) or speed, when associated with parallel imaging techniques, in magnetic resonance imaging (MRI), magnetic resonance spectroscopy (MRS), and magnetic resonance spectroscopy imaging (MRSI). The proper combination of the data acquired using phased array coils is a valid strategy for SNR improvement (1–3). In order to obtain an effective combination of the multichannel data, two aspects are of primary importance: a phase alignment and a proper weighted summation of the signals coming from the individual channels. Each coil element is located in a different position around the patient. Since the axis of each element is at different distance and angle with respect to the excitation volume, a simple weighted addition of signals from the channels would combine them incoherently. For these reasons, an adequate phase alignment of the signals is necessary prior to channel combination. The optimal weighted summation of the signal from each coil element must take into account both the coil sensitivity and the correlated background noise (1). In fact, the individual elements of a phased-array coil generally have different sensitivities, that can be observed as a difference in the background noise associated with the coil elements. Coil sensitivity correction can be performed by using weighting factors derived from the background noise measured for each element (2). Noise factors depend on both hardware aspects (e.g. coil sensitivity, preamplifier noise figure, mutual inductance between coils) and acquisition sequence parameters (e.g. receiver bandwidth). Several approaches have been proposed for accurate calculation of weighting factors (2–7) and phase alignment (2–3,7–8). As far as phase alignment is concerned, it can be obtained by evaluating spectra in time (2) or frequency domains (3,7–8). In particular, in (3) a method is proposed evaluating repeatedly the total area under the real component of the spectrum, in a selected frequency range, while changing the phase of the spectrum by a predefined Df. The final phase shift is the one that maximizes the integrated area. Likewise, weighting factors can be obtained by direct spectral peaks evaluation (4,6) or by frequency domain fittings (8), or by evaluation of time domain signals (2,5). However, weighting factor evaluation by SNR rather than by a peak height (3,7), should be preferred. In (www.interscience.wiley.com) DOI:10.1002/nbm.1429 Research Article * Correspondence to: N. Martini, ‘G. Monasterio’ Foundation, CNR Research Area, Via Moruzzi 1, 56124 Pisa, Italy. E-mail: nicola.martini@ifc.cnr.it a N. Martini Interdepartmental Research Center ‘E. Piaggio’, University of Pisa, Pisa, Italy b N. Martini, M. F. Santarelli, G. Giovannetti, M. Milanesi, D. De Marchi, V. Positano, L. Landini MRI Laboratory, ‘G. Monasterio’ Foundation and Institute of Clinical Physiology, CNR Pisa, Italy c L. Landini Department of Information Engineering, University of Pisa, Pisa, Italy Abbreviations used: SNR, signal-to-noise ratio; SV, single voxel; FID, free induction decay; RF, radio-frequency. NMR Biomed. 2010; 23: 66–73 Copyright ß 2009 John Wiley & Sons, Ltd. 66