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Abstract #2902

MP-PCA and Low-rank noise-reduction in 1H-FID-MRSI data in the rat brain at 14.1T

Brayan Alves1,2, Dunja Simicic1,2,3, Jessie Mosso1,2,3, Ileana Jelescu4, Cristina Cudalbu1,2, and Antoine Klauser1,5
1CIBM Center for Biomedical Imaging, Lausanne, Switzerland, 2Animal Imaging and Technology, EPFL, Lausanne, Switzerland, 3LIFMET, EPFL, Lausanne, Switzerland, 4Service de radiodiagnostic et radiologie interventionnelle, Lausanne University Hospital CHUV, Lausanne, Switzerland, 5Department of Radiology and Medical, Informatics, University of Geneva, Geneva, Switzerland

Synopsis

1H-MRSI is highly challenging and the constant appetite for higher spatial resolution leads to increased search for post-processing methods aiming to reduce the noise variance in 1H-MRSI. The aim of the present study was to implement and test the feasibility of two noise-reduction techniques on preclinical 14.1T fast 1H-FID-MRSI datasets: the MP-PCA based denoising and the low-rank TGV reconstruction. Our results are promising showing an enormous potential of the two noise-reduction techniques towards novel and fast MRSI developments. Further studies will be performed to evaluate if the “apparent” increase in spectral SNR translates in true lower uncertainty in metabolite concentrations.

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