Principal Component selections and filtering by spatial information criteria for multi-acquisition CEST MRI denoising
Stefano Casagranda1, Christos Papageorgakis1, Feriel Romdhane2, Eleni Firippi1, Timothé Boutelier1, Laura Mancini3,4, Moritz Zaiss5, Sotirios Bisdas3,4, and Dario Livio Longo2
1Department of Research & Innovation, Olea Medical, La Ciotat, France, 2Institute of Biostructures and Bioimaging (IBB), National Research Council of Italy (CNR), Torino, Italy, 3Lysholm Department of Neuroradiology,, University College of London Hospitals NHS Foundation Trust, London, United Kingdom, 4Institute of Neurology UCL, London, United Kingdom, 5Department of Neuroradiology, University Clinic Erlangen, Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), Erlangen, Germany
This work provides a new denoising methodology for multi-acquisition magnetic resonance images (MRI) based on principal components analysis (PCA). We are proposing a new principal component selection criterion that identifies spatial information in the extracted component coefficients, leading to a better preservation of anatomical structures and pathological information. In addition, our adaptive filtering step allows us to further denoise the MRI data, rejecting persistent spatial noise from the extracted component coefficients. In our investigations the proposed method outperformed the eigenvalue based selection criteria on Amide Proton Transfer weighted CEST data.
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