Preliminary development of a Magnetic Resonance Fingerprinting framework for fast multiparametric low-field NMR relaxometry
Giovanni Vito Spinelli1, Leonardo Brizi1,2, Marco Barbieri3, Fabiana Zama4, Germana Landi4, Villiam Bortolotti5, Daniel Remondini1,2, and Claudia Testa1,2
1Department of Physics and Astronomy "Augusto Righi", University of Bologna, Bologna, Italy, 2INFN, Istituto Nazionale di Fisica Nucleare, Sezione di Bologna, Bologna, Italy, 3Department of Radiology, Stanford University, Stanford, CA, United States, 4Department of Mathematics, University of Bologna, Bologna, Italy, 5Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, Bologna, Italy
A novel application of Magnetic Resonance Fingerprinting (MRF) on low-field NMR is presented. To successfully implement MRF, the correlation between the static and the radio-frequency fields has to be measured, because the evolution of the signal cannot be analyzed without accounting for magnetic fields inhomogeneities. Experimental results have been validated by simulations and compared using the RMSE. This preparatory evaluation allows the use of MRF for NMR parameters quantification. Then, an artificial intelligence approach for parameters reconstruction has been used to overcome the limitation of the standard dictionary approaches when several parameters have to be estimated.
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