Abas Abdoli1, Andrew A Maudsley1. 1. Department of Radiology, University of Miami School of Medicine, Miami, Florida, USA.
Abstract
PURPOSE: To evaluate methods for multichannel combination of three-dimensional MR spectroscopic imaging (MRSI) data with a focus on using information from a water-reference spectroscopic image. METHODS: Volumetric MRSI data were acquired for a phantom and for human brain using 8- and 32-channel detection. Acquisition included a water-reference dataset that was used to determine the weights for several multichannel combination methods. Results were compared using the signal-to-noise ratio (SNR) of the N-acetylaspartate resonance. RESULTS: Performance of all methods was very similar for the phantom study, with the whitened singular value decomposition (WSVD) and signal magnitude (S) weighting combination having a small advantage. For in vivo studies, the S weighting, SNR weighting and signal to noise squared (S/N(2) ) weighting were the three best methods and performed similarly. Example spectra and SNR maps indicated that the SVD and WSVD methods tend to fail for voxels at the outer edges of the brain that include strong lipid signal contributions. CONCLUSION: For data combination of MRSI data using water-reference information, the S/N(2) weighting, SNR and S weighting were the best methods in terms of spectral quality SNR. These methods are also computationally efficient and easy to implement. Magn Reson Med 76:733-741, 2016.
PURPOSE: To evaluate methods for multichannel combination of three-dimensional MR spectroscopic imaging (MRSI) data with a focus on using information from a water-reference spectroscopic image. METHODS: Volumetric MRSI data were acquired for a phantom and for human brain using 8- and 32-channel detection. Acquisition included a water-reference dataset that was used to determine the weights for several multichannel combination methods. Results were compared using the signal-to-noise ratio (SNR) of the N-acetylaspartate resonance. RESULTS: Performance of all methods was very similar for the phantom study, with the whitened singular value decomposition (WSVD) and signal magnitude (S) weighting combination having a small advantage. For in vivo studies, the S weighting, SNR weighting and signal to noise squared (S/N(2) ) weighting were the three best methods and performed similarly. Example spectra and SNR maps indicated that the SVD and WSVD methods tend to fail for voxels at the outer edges of the brain that include strong lipid signal contributions. CONCLUSION: For data combination of MRSI data using water-reference information, the S/N(2) weighting, SNR and S weighting were the best methods in terms of spectral quality SNR. These methods are also computationally efficient and easy to implement. Magn Reson Med 76:733-741, 2016.
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