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 Simicic^1,2,3, Jessie Julie Mosso^1,2,3, Thanh Phong Lê^3,4, Ruud B. van Heeswijk⁵, Ileana Ozana Jelescu^1,2, and Cristina Cudalbu^1,2

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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