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Abstract #0395

MP-PCA denoising dramatically improves SNR in large-sized MRS data: an illustration in diffusion-weighted MRS

Ileana Ozana Jelescu1, Jelle Veraart2, and Cristina Cudalbu1
1Center for Biomedical Imaging, Ecole Polytechnique Fédérale de Lausanne, Lausanne, Switzerland, 2Dept. of Radiology, New York University School of Medicine, New York, NY, United States

MRS is an inherently low signal-to-noise technique resulting in substantial spectral averaging and large voxel volumes. The problem is further amplified for diffusion-weighted MRS. Here we test the performance of denoising using principal component analysis coupled with Marchenko-Pastur’s random matrix theory in the context of DW-MRS. We report 50 – 100% increase in SNR, reduction in Cramer-Rao bounds and a potential eight-fold reduction in scan time. This technique is expected to also bring significant improvements in the context of fMRS, X-nuclei MRS and CSI.

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