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Abstract #2002

The impact of Marchencko-Pasteur principal component analysis denoising on high-resolution MR spectroscopic imaging in the rat brain at 9.4T.

Dunja Simicic1,2,3, Jessie Julie Mosso1,2,3, Thanh Phong LĂȘ3,4, Ruud B. van Heeswijk5, Ileana Ozana Jelescu1,2, and Cristina Cudalbu1,2
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 2Animal Imaging and Technology, EPFL, Lausanne, Switzerland, 3Laboratory of Functional and Metabolic Imaging, EPFL, Lausanne, Switzerland, 4Geneva School of Health Sciences, HES-SO University of Applied Sciences and Arts Western Switzerland, Geneva, Switzerland, 5Department of Radiology, Lausanne University Hospital (CHUV) and University of Lausanne (UNIL), Lausanne, Switzerland

MRSI is a powerful tool for the non-invasive simultaneous mapping of metabolic profiles at multiple spatial positions. This method is highly challenging due to low concentration of metabolites, long measurement times, low SNR, hardware limitations and need for advanced pulse sequences. Denoising based on singular value decomposition has been previously used, but determination of the appropriate thresholds that separate the noise from the signal is problematic leading to possible loss of spatial resolution. Aim of the present study was to implement an improved denoising technique (Marchenko-Pastur principal component analysis) on high resolution MRSI data acquired at 9.4T in the rat-brain.

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