Abstract #2189

Fast Deep Learning models for Magnetic Resonance Fingerprinting

Raffaella Fiamma Cabini^1,2, Davide Cicolari^2,3, Leonardo Barzaghi^1,4, Paolo Arosio^5,6, Stefano Carrazza^5,6, Silvia Figini^2,7, Marta Filibian^2,8, Marco Peviani⁹, Anna Pichiecchio^4,10, and Alessandro Lascialfari^2,3

¹Mathematics, University of Pavia, Pavia, Italy, ²INFN, Istituto Nazionale di Fisica Nucleare, Pavia, Italy, ³Physics, University of Pavia, Pavia, Italy, ⁴Advanced Imaging and Radiomics, Department of Neuroradiology, IRCCS Mondino Foundation, Pavia, Italy, ⁵Physics, University of Milano, Milano, Italy, ⁶INFN, Istituto Nazionale di Fisica Nucleare, Milano, Italy, ⁷Department of Social and Political Science, University of Pavia, Pavia, Italy, ⁸Centro Grandi Strumenti, University of Pavia, Pavia, Italy, ⁹University of Pavia, Pavia, Italy, ¹⁰Department of Brain and Behavioural Sciences, University of Pavia, Pavia, Italy

Synopsis

Keywords: MR Fingerprinting/Synthetic MR, Data ProcessingWe proposed a DL method and an automatic hyperparameters optimization strategy to reconstruct T₁ and T₂ maps acquired with two Magnetic Resonance Fingerprinting (MRF) sequences. The model was trained and validated on a preclinical MRF dataset and tested on an independent test set. Through a lower number of MRF images and a lower k-space sampling percentage than the standard post-processing, the DL-based method and the automatic hyperparameters optimization strategy deliver parametric maps with similar accuracy as the dictionary-based methodology.

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