Abas Abdoli1, Radka Stoyanova2, Andrew A Maudsley3. 1. Department of Radiology, University of Miami School of Medicine, 1150 NW 14th St, Suite 713, Miami, FL, 33136, USA. 2. Department Radiation Oncology, University of Miami School of Medicine, Miami, FL, USA. 3. Department of Radiology, University of Miami School of Medicine, 1150 NW 14th St, Suite 713, Miami, FL, 33136, USA. amaudsley@miami.edu.
Abstract
OBJECTIVES: To evaluate a new denoising method for MR spectroscopic imaging (MRSI) data based on selection of signal-related principal components (SSPCs) from principal components analysis (PCA). MATERIALS AND METHODS: A PCA-based method was implemented for selection of signal-related PCs and denoising achieved by reconstructing the original data set utilizing only these PCs. Performance was evaluated using simulated MRSI data and two volumetric in vivo MRSIs of human brain, from a normal subject and a patient with a brain tumor, using variable signal-to-noise ratios (SNRs), metabolite peak areas, Cramer-Rao bounds (CRBs) of fitted metabolite peak areas and metabolite linewidth. RESULTS: In simulated data, SSPC determined the correct number of signal-related PCs. For in vivo studies, the SSPC denoising resulted in improved SNRs and reduced metabolite quantification uncertainty compared to the original data and two other methods for denoising. The method also performed very well in preserving the spectral linewidth and peak areas. However, this method performs better for regions that have larger numbers of similar spectra. CONCLUSION: The proposed SSPC denoising improved the SNR and metabolite quantification uncertainty in MRSI, with minimal compromise of the spectral information, and can result in increased accuracy.
OBJECTIVES: To evaluate a new denoising method for MR spectroscopic imaging (MRSI) data based on selection of signal-related principal components (SSPCs) from principal components analysis (PCA). MATERIALS AND METHODS: A PCA-based method was implemented for selection of signal-related PCs and denoising achieved by reconstructing the original data set utilizing only these PCs. Performance was evaluated using simulated MRSI data and two volumetric in vivo MRSIs of human brain, from a normal subject and a patient with a brain tumor, using variable signal-to-noise ratios (SNRs), metabolite peak areas, Cramer-Rao bounds (CRBs) of fitted metabolite peak areas and metabolite linewidth. RESULTS: In simulated data, SSPC determined the correct number of signal-related PCs. For in vivo studies, the SSPC denoising resulted in improved SNRs and reduced metabolite quantification uncertainty compared to the original data and two other methods for denoising. The method also performed very well in preserving the spectral linewidth and peak areas. However, this method performs better for regions that have larger numbers of similar spectra. CONCLUSION: The proposed SSPC denoising improved the SNR and metabolite quantification uncertainty in MRSI, with minimal compromise of the spectral information, and can result in increased accuracy.
Authors: Xiao-Ping Zhu; An-Tao Du; Geon-Ho Jahng; Brian J Soher; Andrew A Maudsley; Michael W Weiner; Norbert Schuff Journal: Magn Reson Med Date: 2003-09 Impact factor: 4.668
Authors: Nestor Andres Parra; Alan Pollack; Felix M Chinea; Matthew C Abramowitz; Brian Marples; Felipe Munera; Rosa Castillo; Oleksandr N Kryvenko; Sanoj Punnen; Radka Stoyanova Journal: Front Oncol Date: 2017-11-10 Impact factor: 6.244
Authors: Andrew A Maudsley; Ovidiu C Andronesi; Peter B Barker; Alberto Bizzi; Wolfgang Bogner; Anke Henning; Sarah J Nelson; Stefan Posse; Dikoma C Shungu; Brian J Soher Journal: NMR Biomed Date: 2020-04-29 Impact factor: 4.044