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Abstract #4246

Windowed whitened singular value decomposition (wWSVD): An improved data-driven strategy for combining MR spectra from multi-channel phased array coils

Ren Geryak1, Kelsey Li2, Maame Owusu-Ansah1, Xiaodong Zhong3, Hui Mao1, and Candace C Fleischer1

1Department of Radiology & Imaging Sciences, Emory University School of Medicine, Atlanta, GA, United States, 2Department of Biomedical Engineering, Duke University, Durham, NC, United States, 3MR R&D Collaborations, Siemens Healthineers, Los Angeles, CA, United States

Data driven methods to combine multi-channel phased array data such as singular value decomposition (SVD) can greatly improve SNR in MR spectra. Current SVD implementation has two primary limitations: 1) the assumption that noise is independent between coil channels; and 2) utilization of the entire spectrum to calculate the optimized coil combination. Here, we present a method using a whitened SVD (WSVD) matrix to decorrelate noise from individual coil elements, followed by an optimized and iterative windowing approach termed windowed whitened SVD (wWSVD) to determine the optimal subset of the spectrum for SVD analysis that is then used to combine multi-channel spectral data. We report a significant in vivo improvement in spectral SNR using wWSVD over WSVD.

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